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Fake Account Detection using AI in Facebook.  Ashesh AnandNov 04, 2022

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Fake Account Detection using AI in Facebook.  Ashesh AnandNov 04, 2022

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63104269 Thanakhan Suwanta
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Facebook serves as a platform for fostering community growth and interconnection. People here form friendships and discuss their interests. But what if some fake individuals pose as pals in these communities? That would increase their vulnerability.

One of the most difficult problems Facebook faces is fake accounts. The social media behemoth unveiled a new Artificial Intelligence (AI) technology in 2017-18 to categorize and remove millions of bogus accounts. The Securities and Exchange Commission (SEC) filings and the Community Standards Enforcement Report (CSER) frequently cast doubt on Facebook's fake account identification process.

But before delving into the process, it's critical to comprehend why fake accounts are viewed as a threat.
Also Read | How Facebook uses Artificial Intelligence?
Why are Fake Accounts viewed as a Danger to users?

On the Facebook platform, both humans and bots attempt to establish or create hundreds of thousands of fake accounts each day. These accounts are credible technological spoofs that are made to look more real by using fabricated email addresses, artificial images, and highly accurate data.

These accounts typically pose a serious threat to the site and its users. These con artists frequently prey on unsuspecting users, making these accounts a simple way to make money. These accounts are used by scammers to spread phishing links, launch spam campaigns, encourage violence, support terrorism, and more.

One such well-known instance of the dangers posed by false accounts was the impact of Russian agencies on the outcomes of the US midterm elections. Experts predicted that Facebook's fake accounts would also be crucial in the forthcoming US elections using tools like Deepfakes. Facebook is also taking every precaution to avoid any meddling.
Also Read | Detection of Fake and False News (Text Analysis)
Effects of Fake Accounts on Facebook:

The majority of Facebook's income comes from adverts. The revenue growth may remain steady as long as the advertisements are viewed, clicked, shared, or loved. Facebook does not grant access to third parties to determine the effectiveness of its adverts. Investors are unsure if the advertisement is reaching the intended target because of the threat of fraudulent accounts.

False accounts may go dormant for long periods, producing insufficient data to run generic consumer adverts on the website. Additionally, neither Facebook nor advertisers may use the false profiles created by bots. These elements have all had a variety of effects on revenue generation. As long as they see a positive return on their investment, marketers will continue to use Facebook and its affiliated products for advertising.

False accounts might be a major worry moving forward for Facebook and marketers alike. In 2018, Facebook announced that no new users from the US or Canada signed up between January and July and that the number of new unique users from Europe decreased by one million. Although there was no income loss, the market value of its shares decreased by $120 billion. Users can have this kind of impact on social media platforms and their businesses.
Also Read | Applications of Big Data in Social Media
How does Facebook identify these accounts?

Facebook uses both machine learning and human intelligence to identify bogus accounts while adhering to the CSER's set of criteria. To assess whether accounts are authentic or not, it employs machine learning algorithms and hand-coded criteria.

Once identified, fake accounts are blocked either as they are being created or even before they go live on the network. However, the true issue appears when these fake accounts manage to evade all of these safeguards and start using them. When these accounts' nefarious behaviors are picked up by Facebook's detection system, such accounts are detected using cutting-edge AI techniques. Active users occasionally also report these accounts.

Facebook's AI Technology in Action:

Since solitary accounts are challenging to categorize, DEC examines both an account's behavior as well as its interactions with other entities. Attackers may find it simple to maintain their activities, but it is more difficult for them to function normally and connect with other accounts, sites, or groups.

Facebook technology uses direct, fanout, and aggregated features to investigate the social graph of suspect accounts on several levels. If it's a personal account, direct features include things like age, gender, and the number of friends. DEC considers the fan (or member) counts and categories for pages (or organizations). In the meanwhile, fanout types include the number of administrators, postings or status updates, and pages that are administrated.

Additionally, these two categories of features are combined using mathematical operations to offer a more thorough analysis. DEC analyses account using thousands of these variables in its decision-making process, which allows it to identify fake accounts more precisely than ever before.
Also Read | New Technologies in Facebook
What equipment is used throughout the process?

Knowing that Facebook alone eliminated 5.4 billion fake profiles in its Q1, Q2, and Q3 2019 transparency report provides an easy way to gauge the severity of the issue. There are two types of fake accounts:

Some are user-created accounts for people's kids, pets, businesses, hobbies, etc. rather than actual pages. These accounts are labeled as "misclassified accounts" by Facebook, which then turns them into pages.

Scammers also create accounts, which are the others. Facebook has identified these accounts and removed them. However, blocking users' access to the platform by disabling their accounts goes directly against Facebook's rules. Therefore, how may weeding be done safely? The social media giant developed multiple layers of research to identify problematic accounts while keeping all of these in mind. Deep Entity Classification is the tool it employs for this (DEC).
Facebook uses three distinct processes for account screening:
Preventing fake accounts from ever entering Facebook is the best approach to combat them. Detection technology that can identify and block accounts even before they are formed has been developed for this reason. Their algorithms scan for a variety of signals that show if accounts are being created in bulk from a single location.

With the aid of specialized detection equipment, fake accounts can be identified as they are being generated. AI prevents the creation of these accounts right away. It works with massive amounts of data and acts quickly. For instance, the system can identify and report on many accounts that were made using the same IP address. These accounts are then prevented from accessing the social networking site in the future. AI deletes these accounts very away, protecting active users from danger.

After these accounts register, the machine learning algorithms search for any suspicious behaviors that can trigger an alarm. To identify such accounts, these algorithms take advantage of the information learned from the elimination of earlier fraudulent accounts.

For instance, DeepFace technology detects Deepfakes and alerts the system. Before the user even appears on the platform, AI bans their account by tracking signals linked to questionable email addresses or accounts that have already been disabled.
Also Read | What is Deepfake Technology?
Deep Entity Classification is quite important in locating problematic Facebook accounts. It outperforms individuals who have already mastered the art of manipulating identifying characteristics like age, geography, and friend counts. After carefully scrutinizing them, DEC's AI detects these accounts and, if defective, blocks them.
The image depicts the whole process of Fake Account Detection
Facebook utilizes a straightforward system to categorize suspect accounts: misclassified accounts come in first, followed by breaching accounts.

Accounts that users have incorrectly classified are known as misclassified accounts. One such narrative is called "Bruno The Dog." Technically speaking, this is not a fake, and everything about it is perfect besides the fact that they are not properly categorized. These are transformed into what would have been pages.

Accounts that violate the law are those that may be made up or used as a platform for extensive spamming, phishing, and other criminal actions. These may also be accounts that transgress the terms and conditions of the business.

Facebook employs a combination of regulations and machine learning algorithms to stop malicious activity from bogus accounts (preventing the creation of a fake account or preventing its activation). When a fake account goes live and is prepared to cause trouble, a problem occurs. The machine learning tool DEC enters the picture at this time.
Also Read | Applications of Machine Learning in Social Media
Since its introduction, Deep Entity Classification has assisted in the removal of countless plausible false accounts. Instead of only looking at the surface, the AI examines in-depth account data like the specifics of the pages, groups, friends, and other users that the account interacts with.

The algorithms check all of the following: the age of their friends, the gender distribution of their friends, the age groups they connect with, page administrators, group members, etc. The technology takes up to 20,000 deep features into account. These features are inaccessible to the user, and as a result, a clear picture of how a profile will act will be revealed based on the AI's choices.

To determine the veracity of the narrative, it uses a variety of machine learning models built on sophisticated algorithms. For a better level of accuracy, these models are trained on a huge collection of simulated and real-time data. High-precision human tags created by security professionals are then used to examine the separate accounts.

Additionally, bogus accounts are detected using language-neutral AI that was trained on 93 different language varieties and 30 dialect patterns. The algorithms then label accounts as phony if they frequently send messages that generate revenue, are real-user accounts that have been hacked, are wrong accounts that don't correspond to characters claimed, or are accounts used by con artists to manipulate their victims. The final step of AI's investigation is to block these with a 95% accuracy rate.

On its Transparency page, Facebook asserts that DEC has stopped the creation of millions of false accounts every day in 2019.

End Note: Is the issue resolved?

No, spammers' intellectual capacity is evolving alongside the development of AI. Facebook estimates that 5% (or about 125 million) of monthly active users are still false accounts, despite all of its efforts. Therefore, it wouldn't be incorrect to conclude that both sides are engaged in a never-ending race for technological superiority. And to stay on top, Facebook must continue to develop defense systems that combine AI, ML, and human intelligence.

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why are fake accounts viewed as a danger to users?

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how does facebook identify these accounts?

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what equipment is used throughout the process?

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the process of fake account detection using ai

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Fake Account Detection using AI in Facebook | Analytics Steps

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Facebook utilizes a clear system to categorize suspected and Fake accounts. Here's all you need to know about the whole process that how AI detects fake accounts.

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8 Applications of Data Mining in Retail (Soumalya Bhattacharyya,Jul 29, 2022)

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8 Applications of Data Mining in Retail (Soumalya Bhattacharyya,Jul 29, 2022)

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63104111 Tanapon Rattanamano (Resubmit)
PASS

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The retail sector gathers a lot of information on sales and client purchasing patterns. The amount of data gathered keeps growing quickly, in part because online or e-commerce business is becoming more popular, accessible, and easy to perform.

The retail sector offers a wealth of data for data mining. Retail data mining may be used to track consumer activity, find purchasing patterns and trends, enhance customer service, increase customer happiness and retention, boost product consumption rates, create more efficient product transportation and distribution plans, and save costs for businesses.

Due to the enormous volumes of data it gathers on sales, consumer purchasing habits, the transportation of products, consumption, and services, the retail sector is a key application area for data mining. The amount of information gathered keeps growing quickly, especially in light of how simple, widely available, and well-liked online commerce has become.

Today, a number of retailers also have websites where customers may make purchases. Some companies, like Amazon, only operate online and don't have any brick-and-mortar (i.e., physical) storefronts. Retail data provides a wealth of information for data mining.
Also Read | Benefits of Data Mining in Healthcare
Retail data mining may assist in identifying user purchasing patterns and trends, enhancing user service quality, enhancing user retention and satisfaction, increasing product consumption ratios, designing more efficient product transportation and distribution strategies, and lowering company costs.

The act of evaluating data from many angles and distilling it into valuable information, information that may be utilized to enhance income, decrease costs, or both, is known as data mining (also known as data or knowledge discovery). One of the analytical techniques for examining data is data mining software.

Users are able to classify the data, summarize the correlations found, and evaluate it from a variety of various dimensions or viewpoints. In big relational databases, data mining technically refers to the act of identifying correlations or patterns among several fields.

The word "data mining" may be new, but the technology is not. Powerful computers have been used by businesses for years to sift through mountains of data from supermarket scanners and examine market research studies.

But as computer processing speed, disc storage, and statistical software continue to advance, analysis accuracy is rising sharply while costs are falling.

The retail sector is discovering that data mining may provide them a competitive advantage. Similar to the banking sector, retailers have been gathering vast volumes of data over the years, and they now have the tools necessary to go through this data and uncover relevant pieces of information.

In order to make proactive decisions, merchants can utilise data mining to offer information on product sales trends, customer purchasing patterns and preferences, supplier lead times and delivery performance, seasonal changes, consumer peak traffic hours, and similar predictive data.

Applications of Data Mining in Retail

By evaluating their data, almost any retailer might benefit in some way. The amount of possible benefits and return on investment (ROI) in relation to the expense of data collection, storage, and analysis will be the primary determinant of whether or not to proceed.

A specialized shop with few customers and items may thus find that their expertise of their industry is sufficient and that data mining does not add much to it. Here are a few instances of how the retail sector has successfully applied data mining:
Applications of Data Mining in Retail
CRM focuses on attracting and keeping consumers, enhancing customer loyalty, gathering customer intelligence, and putting customer-centered initiatives into practice. Your business may achieve operational excellence, fresh growth, and competitive agility by adopting a truly customer-centric business model.

A business theory known as customer relationship management (CRM) involves recognising, comprehending, and better serving your customers while developing a connection with each one in order to increase customer happiness and optimize profitability. It involves recognising, foreseeing, and meeting client demands.

A company has to gather the relevant data about its customers and arrange that data for effective analysis and action in order to manage the connection with the client. It must maintain that data current, make it available to staff members, and provide staff members the skills they need to turn that data into goods that are better suited to consumers' demands.

The key to a successful CRM package lies not just in the data that is gathered, but also in its organization and analysis. Of course, computers can't change the way you interact with your customers.

That does require a corporate, cross-departmental, top-to-bottom drive to improve connections. However, using computers and a competent computer-based CRM solution may boost sales by up to 40-50%.

It is employed to investigate natural affinities between various items. The beer-diaper affinity, which argues that males who buy diapers are likely to also buy beer, is one of the most well-known instances of market basket analysis.

This is a prime illustration of "two-product affinity." However, in practice, market basket analysis can get very complicated and reveal previously unrecognized affinities between a variety of goods. The retail company can use this analysis in a variety of ways.

In-store product placement is one highly popular application. Product bundling, or the act of combining items to be marketed as a single package deal, is an additional common application. Designing the company's online store and product catalogs are further uses.

In the retail sector, data mining may also aid in attracting and keeping customers. Data mining can help the retail sector, which faces intense rivalry, better understand the demands of its customers. Retailers may target customers with the right promos and incentives by researching their historical shopping habits.

Additionally, data mining may be used to examine historical purchase patterns of consumers who have left a business and gone to rivals, and use this knowledge to discourage other customers from doing the same.

There are a number of ways to create a data warehouse for the retail sector due to the large range of topics covered by retail data (such as sales, customers, staff, products transit, consumption, and services).

The amount of detail to be included might likewise vary greatly. The outcomes of first data mining activities may be utilized as a reference for designing and creating a data warehouse architecture. Making decisions on the dimensions, levels, and pre-processing to use will enable efficient data mining.

The retail industry required up-to-date information about consumer needs, product sales, trends, and styles, as well as the quality, price, profit, and level of service of commodities.

The provision of dynamic tools for multidimensional analysis and visualization, such as the creation of complex data cubes in accordance with the needs of data analysis, is crucial.

Advertising, vouchers, and a variety of discounts and incentives are used in retail market sales campaigns to promote goods and draw customers. Analyzing sales campaign effectiveness carefully may help increase business revenues.

By comparing the volume of sales and various transactions involving the sales products during the sales period to those involving the same things prior to or following the sales campaign, multidimensional analysis may be utilized to achieve these aims.

In addition, association analysis can reveal which products are more likely to be purchased along with those that are on sale, especially when compared to purchases made before or after the campaign.

One role of data mining in retail is risk management in the retail sector. In contrast to the other sections of the retail industry, this one has the least amount of study. Retail businesses utilize data mining techniques to identify which items are susceptible to market risks or shifting consumer purchasing patterns.

Customers' past purchasing behavior is taken into account when assessing their brand loyalty. Retailers may adjust their tactics to remain competitive in the market and lower their risk of loss by using data mining techniques to understand what their consumers are doing.

Retailers may use data mining techniques to target customers who are more inclined to buy items from a certain brand and decide when and where to advertise things as needed.

In order to maintain a successful firm, fraud detection is crucial in the retail sector. Retailers are often concerned about fraud occurring at the point-of-sale, but data mining can help to prevent this.

Retailers work very hard to uncover dishonest staff as a result. In addition to point-of-sale data mining, several supermarkets deploy CCTV camera systems. The shop manager can avoid having to gather proof to persuade an employee of theft by using these strategies.

The data mining tools may be used to extract the alleged transaction, and CCTV video can be used to determine exactly what transpired during the transaction. They don't need to be physically present in the store to do this; they may do it all from the office.

Data mining is extremely useful in the retail sector since it gathers a tonne of information on sales, client purchase patterns, the movement of products, consumption, and services. Because of the web's growing accessibility, availability, and popularity, it is only inevitable that the amount of data collected will keep growing quickly.

In the retail sector, data mining aids in detecting client purchasing patterns and trends that boost customer service quality, increase customer retention, and boost customer happiness

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data mining applications in retail and telecom industry

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what is the most common application of data mining

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8 Applications of Data Mining in Retail | Analytics Steps

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In the case of Retail, there are several applications of Data Mining. It can be used to spot network issues, detect marketing fraud, and enhance marketing efficiency.

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AI Enable Wearable Devices: The Next Layer of IoT and Machine Learning

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AI Enable Wearable Devices: The Next Layer of IoT and Machine Learning

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63117949 Palita Pikunkan
(RESUBMIT)
PASS

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Wearable devices are gadgets that can comfortably be worn on a body. These devices have intelligent sensors connected to the internet for collecting data. The growing partnership and collaboration for bringing AI functionalities to wearable devices provide new developments in AI wearable devices in the coming years.

In today's busy schedule, people want to improve their lives by tracking daily activities. AI enables wearable devices to significantly change people's lives by tracking and maintaining data of your diet and body movements; monitoring blood pressure; providing a trendy, safe, and secure lifestyle with IoT and Machine Learning technologies.

The global Wearable Fitness Technology market is expected to witness progressive growth in the coming years due to the increasing consumer preferences regarding Wearable Fitness Technology. This wearable fitness helps the consumer track their footsteps and displays the smartphone's notification. In addition, the growing awareness of chronic diseases and obesity has led to the adoption of wearable devices that can give real-time information about the body's overall health. These wearables can provide information like cholesterol levels, blood pressure, calorie intake, and other information required by the body for day-to-day activities.

Challenges: Security may cause issues due to the devices' physical hardware and battery life.

Opportunities with data generated by wearable gadgets: Data analytics, Event Triggers, and Predictive analysis

The collaboration between the companies will increase the development of new products with AI-integrated and intelligent devices for the customers. Further, all these AI-enabled devices will significantly change wearable fitness accessories. This, in turn, supports the growth of the global wearable AI market in the foreseeable future.

The wearable AI market will likely witness significant growth owing to rising demand for smartwatches with high quality and more prominent displays. The increasing awareness among customers regarding the benefits associated with OLED technology with a better angle and excellent contrast is one of the key factors stimulating the growth of OLED displays. This, in turn, boosted the growth of the AI-enabled wearable device market. Furthermore, the rising adoption of VR/AR-based head-mounted devices in several verticals is propelling the growth of the global wearable AI market.

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AI Enable Wearable Devices: The Next Layer of IoT and Machine Learning - DataScienceCentral.com

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Learn more about wearable consumer devices that are powered by AI, and the potential markets this technology amplifies.

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6 Benefits of using Data Mining in Healthcare (Ashesh Anand  , Mar 12, 2022)

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6 Benefits of using Data Mining in Healthcare (Ashesh Anand  , Mar 12, 2022)

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63104111 Tanapon Rattanamano (Resubmit)
FAIL: ซ้ำกับ 63104962 Nattida Tamad --หาใหม่แล้ว resubmit อีกรอบ

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How is Myntra Using AI? (Vanshika Kaushik , May 25, 2022)

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How is Myntra Using AI? (Vanshika Kaushik , May 25, 2022)

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63108518 Methawi Tantiyavut (RESUBMIT)
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What if I told you that the T-shirt you are wearing is machine made, and its AI and not the designers that have carefully crafted the design; this might be unbelievable for some but Myntra has made this a reality.

Yes you heard it right, Myntra is making use of AI, to design T-shirts, kurtis and jeans. Fascinating, intriguing and a whole new concept right. Now you might want to know how this is possible and if you are geeky; you might want to know more about the fusion of Tech and Fashion so let's understand step by step how Myntra is making use of AI for designing apparels.
The Fashion GameChanger- Myntra

F in Fashion stands for Fast and Myntra is embracing this Fast Fashion concept in a whole new way, the biggest game changer in the Fashion realm "Myntra" was founded in 2007 by Mukesh Bansal, Ashutosh Lawania and Vineet Saxena.

What started as a personalized gift shopping hub in 2007 transitioned into the Fashion and Lifestyle market in 2011. In 2012 Myntra offered products from over 350 international brands and Indian brands presently it follows a Business-to -Customer (B2C) model.

Fast forward to 2021 this e-commerce brand currently offers more than 1,50,000 products from 1000 brands and is making a profit of Rs. 1,719 crores (as on November 22, 2021).

Myntra has brands like Mango, Vero Moda, and Van Heusen and well now with machine made designs from Myntra's very own Moda Rapido and Here and Now, the company is set to hit its profit targets in the financial year 2022.
How Myntra is using Artificial Intelligence
How Myntra use AI to Design and Customize Apparels

Myntra has embraced the AI revolution and is putting this new age Technology in use in a variety of ways. Along with AI the company is also making use of Augmented Reality to design "Talking T-shirts".

The futuristic technologies like AI and data science and augmented reality has enabled Myntra to level up the competition.

Following are the 4 ways in which Myntra is making use of AI:-

Moda Rapido is the "computer generated" brand at Myntra. Moda Rapido is an entirely automated brand. For designing purposes the AI models are trained by feeding the data.

Myntra has access to data from websites like Flipkart and Jabong. With a huge amount of data from Myntra's customer records, bestsellers, social media popularity, the computer creates a mix of multiple combinations and further analyzes on its own which design can be the "next bestseller".

That means the computer is scanning data, creating designs, and even finalizing the best combination all on its own.

"No Humans Involved in the Designing Process" is how it is going. Previously Myntra was making use of AI to shortlist the "best selling designs", meanwhile Fashion designers used to design the latest T-shirts, kurtas by mixing the best selling designs with new ones.

But at Moda Rapido everything is automated and even people are excited to hop on this new trend. The computer generated brand made 50 crores that too within the first few months of its release. With the latest advancements in AI Moda Rapido is sure to hit new highs in the upcoming years.

In simple words Generative Adversarial Methods refer to a machine learning model, where two neural networks compete with each other to analyze the variations in a particular dataset. GNA's are helpful for multimodal query. With a large amount of data pouring in the e-commerce websites at times it is difficult to get the desired product results, and this is when GAN's come into play.

Conditional GAN related methods are used to show the accurate search results. So next time when you type "pink socks with stripes" and use image plus text search for getting the desired results on Myntra know that this search operation has been made possible by GANs.

For training the GAN the machine learning model is fed with different types of data, this data helps the ML model to differentiate between similar products. The GAN's are trained to learn the differences, for example the GAN model is fed with data to understand "black T-shirt with white stripes" and " black T-shirt with cross check" are two different product categories.

So next time when you wonder how the Myntra application shows the right product category you know Generative Adversarial Networks are scaling up the game.

The fashion world is extremely dynamic so what is in trend today might be a thing of the past tomorrow. Returns are hence an indispensable part of the fashion industry with different requirements in terms of size, design, colours customers often tend to return apparels more frequently in the fashion industry.

Myntra follows a 30 day return policy, so if the product is not the right fit for you, you can exchange it within 30 days.

Checking out the real and genuine exchange cases is a big deal. For this purpose Myntra use Sabre, an AI based return system. This return system is fed with the customer's shopping history and return details. It analyzes the data to make a list of customers that are very frequently returning products with no genuine reasons.

The list is further assessed by humans and the customers that are too frequently returning orders with no genuine reasons are further contacted to inquire about the reasons of return.

In a collaboration with Google researchers Myntra has also developed an AI based system that can "predict whether the customer will return the product or will keep it". The uniqueness of the machine learning model lies in the fact that even before a consumer actually buys the product, the model will predict whether it will be returned or kept.

Myntra's product return rates varies from 12 to 14% it classifies the returns in different categories based on its AI return system Sabre.

Collaborative Filtering technique is a way to understand the user preferences based on the collaborative choices (or preferences of the past users). In simple terms the review recommender system suggests the products that have good reviews and are among the best selling categories.

For the fashion industry what looks best is always preferred fast and at times consumers come up to shop for a different T-shirt/ kurta/ dress and end up buying something else. Myntra recommends similar products to the consumers that lead to quick conversions.

The most boring task of all is to choose what to buy. Consumers often tend to get bored of choosing the right fit for them.

Myntra's recommender systems suggest similar types of products so let's say if you come up for "purchasing a black cardigan " its recommender system will also display the results of "brown" and "blue" cardigans alongwith the black ones.

Collaborative Filtering is extensively used to suggest similar products in terms of slightly different colour combinations and slightly different designs. Myntra also use content based filtering and historical product attributes like fabric, sleeve type, designs, colours, patterns for personalized suggestions.

Myntra offers personalized recommendations based on users historical data. The recommender system filters the data and further analyzes it to provide personalized product recommendations.

The bigger goal for us is to build an AI-based fashion platform which continuously produces insights and churns out customer-loving products in real time. Consumption is moving from plan and buy to on-demand across industries. Seeing the mobile revolution kicking in, the pace of change is faster than ever before. The customer today is a curator and creator. By using data science and AI, we are able to give real time responses."- Ganesh Subramanian, Head of New Initiatives - Myntra

The e-commerce market in India is expected to reach US $ 111.40 by 2025. With some big players like Amazon, Flipkart and Nykaa the e-commerce industry is set to hit some big records in the upcoming years with futuristic technologies like AI, data science and machine learning coming into play; the future of e- commerce industry is surely bright.
(Related Reading:- 11 Top E- Commerce Startups)
Myntra is using the new age ways to maximize profits and it is also setting some high standards for other Fashion based companies. With technology setting in the Fashion domain, the fusion of Fashion and Tech will indeed rule the future.

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How is Myntra Using AI? | Analytics Steps

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Myntra the top fashion company is making use of AI for multiple purposes. Take a look at how its making use of AI to filter content and to customize apparels.

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Applications of AI in Biotechnology(Soumalya Bhattacharyya, Jun 03, 2022)

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Applications of AI in Biotechnology(Soumalya Bhattacharyya, Jun 03, 2022)

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63110951 sanita jitturat(RESUBMIT)
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63117949 Palita Pikunkan
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India is a land of agriculture. With three cropping seasons, agriculture contributes about 20% of India's GDP growth. This makes it necessary to evolve and upgrade this sector on a regular basis. This is where Artificial Intelligence (AI) comes in. AI has enough potential to transform the agriculture sector. It can take the traditional farming approach into innovative and modern farming ways.

In this blog, we will study the advantages and limitations of AI in the agriculture sector and the top trends of AI in this sector.
Also Read | Technologies Improving Agriculture Analytics
AI is defined as an embedded brain into machines and equipment. While agriculture is the practice of growing crops on large areas for meeting the needs of the population.

AI when applied to agriculture known as precision agriculture. In the agriculture sector AI is used for field harvesting, health monitoring, pest control, and deficiency detection. The benefits of implementing artificial intelligence in agriculture are as follows:
Also Read | What is Prescriptive Analysis?
Advantages of AI in Agriculture

Both machine learning and AI are replacing the outdated methods of forecasting and intelligence in the agricultural sector. AI helps the agricultural sector be more adaptable by applying the latest technologies to the field.

Here is the list of different ways by which AI is easing the food supply chain. And making the agriculture sector essential and affordable for both the producers and consumers:
Merits of AI in Agriculture
Weeds are the wild plants that are grown automatically in the field and stunt the growth of the main food crop. These steal sunlights and nutrients from the crop. Increasing the vulnerability of main crops, weeds make the main plant more targeted for pest attacks.

In traditional methods, farmers used manpower to get these weeds removed. But the advent of AI has opened one more way for the farmers, known as automatic weeding. Automatic weeding employs robots and machines to remove the weeds from the field. This is done by the means of laser lights.

Robots and AI machines survey the entire field. They are equipped with cameras. These cameras are further attached to software identifying the weeds. One identified, the AI commands its high-energy lasers to eradicate these weeds using thermal energy.

Harvesting is the process of gathering important parts of crops for the personal and business benefits. Traditional methods of harvesting involve handpicking and manual labor. But AI employs machines and software for this purpose.

The software equips the camera to recognize the harvesting products. Harvesting products include fruits or vegetables. After analyzing the targeted products, the machine commands the AI to pick them and collect them. In recent years, these machines have been employed for the collection of peppers, lettuce, tomatoes, apples and other fruits.

One more astonishing fact is that these machines choose and pick only ripe fruits and vegetables. The concept beyond this is predictive analysis. The machine learning algorithms use the historical and fed data to make decisions and detect objects.

One of the other advantages of AI is that it helps in identifying the diseases of the plants and the crops. There are many such AI applications present online. These applications help in detecting plant health problems. After analyzing the problems, by means of the internet these applications also find the solutions for them.

AI follows the technique of image recognition to implement the intelligent farming solution. The plant image captured by the camera is run through an image recognition software. The software further generates the disease information, method of control and treatment.

One such application is Plantix. It is one of the growing apps. It has a database that consists of information related to more than 100,000 photos of the sick plants. Also, it can identify more than 60 plant diseases. For consideration, it can give information related to every disease that affects tomatoes, peppers, melons, squash, and lettuce.

Check out this video to know how Plantix detects plant diseases :

Soil is the basic necessity of farming. Soil helps the plants and crops to grow. A healthy nutrient soil enables the efficient plants growth and more profits. Hence it is equivalently important to detect the deficiencies of the soil to prevent the net yield loss.

The traditional analysis techniques are not efficient enough to deliver the desirable results. But its drawbacks are covered by the AI software. AI analysis equipment is taken by taking a pinch of soil samples. The sample is analyzed in the lab by different machines and procedures.

The data derived after analysis is then compared with hundreds of samples present in different online databases. Later, the final predictions are made indicating the moisture level, nutritional deficiencies, pH level and microbes presence of the soil. And also, give recommendations and solutions to improve the soil quality.

Irrigation is the other most required thing for agriculture. Different crops have different water requirements. The excessive water is harmful for the crops so does the inadequate lesser amounts. Around 70% of total water use counts under irrigation.

Thus, it is essential to use this precious resource in an effective and optimal manner using technologies and development to ensure minimum wastage. AI resolves this problem too. The drones are equipped with software that uses advanced analytics.

AI tools collect the high-resolution aerial images and collect the information about irrigation systems required for the fields. AI also helps in revealing the soil problems such as clogs and leaks. It evaluates the ill conditions of soil and rates its conditions.
Also Read | AI in Manufacturing
AI helps in enhancing the productivity of the farm. The automated and autonomous farming operations, AI enabled productions and yield management improve the net output from the field. AI-enhanced picking, packing and sorting enhance the food production, packing and sorting.

The robots and drones developed by the AI assist the farmers in cultivation and harvesting of the crops. Target irrigation, weed control, and crop management helps in improving the overall efficiency of the field.

Predictive analysis helps in detecting the early problems of the field. The early detection helps the farmers and organizations to identify the issue and prevent huge loss or damage to the crops. In coming times, AI can also be used to detect flood or drought conditions before they occur.

By studying the characteristics of the field. AI helps in analyzing the requirements of weedicides and pesticides required as per the field. AI can even trace a small change in the field.

AI software helps in identifying pest attacks and plant health problems. It also improves the soil fertility maintenance and saves the excess use of pesticide and herbicide as per as the specific field.

And not only the monitoring of the crops, AI also helps in spraying the pesticides and weedicides in the field. The spray of chemicals by drones not only makes it effective, but also reduces human efforts and load on manpower.

Challenges of AI in Agriculture

Despite so many advantages and benefits, the government and individuals face some challenges while implementing AI in agriculture. These are as follows:

AI sometimes fails to explain the rules for the maintenance and implementation.

High-cost equipment and infrastructure.

Also Read | AI in Cybercrime
Though, there are challenges, but still there is no doubt in this, that implementing AI technologies and algorithms in agriculture is an effective as well as essential need to meet the growing demand of food.

AI has the power to improve the crucial issues of farming like overuse of chemicals, manual labor and process efficiency. AI is making the agricultural businesses more efficient, quick and progressive.

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benefits and limitations of ai in agriculture

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7 Advantages of AI in Agriculture | Analytics Steps

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From hybrid seeds production to analyzing the market requirement and depends, check out 7 advantages of AI in the agriculture sector.

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FINDING LOOPHOLES WITH MACHINE LEARNING TECHNIQUES by KEREM GULEN October 12,2022

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FINDING LOOPHOLES WITH MACHINE LEARNING TECHNIQUES by KEREM GULEN October 12,2022

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63104269 Thanakhan suwanta
PASS แต่หัวข้อจะลงเชิงเทคนิค-แนะนำให้หาหัวข้อที่เป็นแนวประยุกต์ใช้งาน -ถ้าจะเปลี่ยนหัวข้อให้ RESUBMIT อีกรอบ

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Finding loopholes with machine learning techniques

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One of the most popular applications of machine learning is anomaly detection. Outliers can be found and identified to help

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The Impact of AI on Sports Betting and Its Software(July 17, 2022)

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The Impact of AI on Sports Betting and Its Software(July 17, 2022)

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63118343 Jirawat chatree
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Unless you have been living under a rock, you have heard of artificial intelligence, a technology that allows machines to mimic human intelligence. It is a broad topic that includes tools and technologies such as knowledge representation, machine learning, natural language processing (NLP), database management, etc. Unsurprisingly, AI has found countless applications across various sectors, including the sports betting industry.

You would agree since its inception, artificial intelligence (AI) has helped improve efficiency and productivity across various industries. It is changing entirely because of AI - design, transportation, healthcare, finance & sports. Sports technology is often less discussed in the tech world since it is a relatively new concept. Nevertheless, AI has changed how team coaches, managers, players, and fans think and talk while playing sports. And going by its popularity, it is set to become the norm for sports organizations & leagues across the world.

This technology has found particularly impressive applications in this industry, delivering a world of benefits such as the ones listed below:

1. Data collection: One of the leading uses of artificial intelligence in the betting industry is smart data collection, i.e., gathering data from folks who place bets. This data can then be leveraged for myriad uses, including personalized service delivery. And since data is crucial to such endeavors, AI-driven solutions help ensure that the data collected is authentic and of high quality.

2. Predictive analysis: Yet another compelling benefit of artificial intelligence in betting is that it can also be used to run predictive analyses based on historical data about patterns, trends, and other relevant factors. Furthermore, such software can help companies better understand different teams' strengths and weaknesses and how they would fare when pitted against each other. This, in turn, helps companies understand the type of strategy the teams are likely to use during a game.

3. Identify and prevent fraud: As unfortunate as it may be, the truth remains that cheating and fraud are very likely to happen during the betting process. Cheating is even more challenging in this context because it can happen in many ways, such as rigged slots, match-fixing, etc. Some players, too, can cheat by studying the game's patterns to turn the outcomes in their favor. However, with a solution fortified with artificial intelligence, one can run extensive analysis, study patterns, etc. to identify fraud, often before it even happens.

4. Game monitoring: An AI-driven betting solution can also be used to keep an eye on all the games, but to what end? To identify any inefficiencies in the betting processes and the market in general. Once that is done, the software can then automate the betting process based on any suitable opportunities that may have been found. This offers a huge leg-up to players compared to those who rely on manual analysis to place their bets.

There is no denying that artificial intelligence has changed the sports betting industry for good: advanced technology has enabled this sector to enter a new age, making the concept much more popular, accessible, and safer than ever. Additionally, artificial intelligence has helped create new jobs, increased revenue for companies, and ensured fairness in the processes. No wonder more and more companies are foraying into the world of sports betting these days, huh? So, suppose you, too, are looking to foray into the sports betting sector. In that case, we highly recommend that you fortify your betting software development project with artificial intelligence and other leading technologies.

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The Impact of AI on Sports Betting and Its Software - DataScienceCentral.com

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Unless you have been living under a rock, you have heard of artificial intelligence, a technology that allows machines to mimic human intelligence. It is a broad topic that includes tools and technologies such as knowledge representation, machine learning, natural language processing (NLP), database management, etc.

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6 Benefits of using Data Mining in Healthcare(Ashesh Anand Mar 12, 2022

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6 Benefits of using Data Mining in Healthcare(Ashesh Anand Mar 12, 2022

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63104962 Nattida Tamad
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Copyright © Analytics Steps Infomedia LLP 2020-22. All Rights Reserved.

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What the Terminator Got Wrong: Why Humans & AI are a Harmonious Pairing (Scott Barker 22 AUGUST 2022)

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What the Terminator Got Wrong: Why Humans & AI are a Harmonious Pairing (Scott Barker 22 AUGUST 2022)

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63104020 Tanachoad Aree
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The Data Science Talent Crisis

Whether you're a data scientist thinking about quitting your job or an exec grappling with high turnover rates-there's no denying that we're in the midst of a data science talent crisis. Here's how you can put a stop to it.

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Why Humans & AI are a Harmonious Pairing | RapidMiner

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It's time to stop thinking about humans vs AI and start thinking about humans and AI-and all the opportunities this partnership can and will create.

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How to Win an Election Using Big Data(Bill Schmarzo, September 11, 2022)

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How to Win an Election Using Big Data(Bill Schmarzo, September 11, 2022)

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63103949 Teepakorn Sinhon
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# data[].attributes.attachment_link.title
How to Win an Election Using Big Data - DataScienceCentral.com

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Learn how political campaigns are embracing data science best practices (nanoeconomics, analytic scores, analytic profiles, thinking like a data scientist methodology) used by leading commercial organizations to drive more meaningful business and operational outcomes.

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How AI Is reshaping Manufacturing (Nikita Godse ,May 8, 2022 at 1:49 pm)

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How AI Is reshaping Manufacturing (Nikita Godse ,May 8, 2022 at 1:49 pm)

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How AI Is reshaping Manufacturing - DataScienceCentral.com

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Last few years ago, the industrial revolution is the most popular evolution ever faced by the industrial sector. It encompasses all the latest technology trends which affecting industries over the world. Autonomous cars, smart connected devices, sensors, computer chips, and many other technologies r…

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10 Uses of Machine Learning in Healthcare (Soumyaa Rawat, Jul 10, 2022)

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10 Uses of Machine Learning in Healthcare (Soumyaa Rawat, Jul 10, 2022)

# data[].attributes.body
63123996 Bisminla Bilreem
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Healthcare is a vital domain that the world can't do without. With an ever-increasing demand amidst a global pandemic to boost healthcare facilities and raise the bar of excellence in the field, healthcare professionals are digging into the role of technology that can possibly contribute to this cause in an efficient way.

When it comes to healthcare technology, a lot can go into exploring its whereabouts and finding the true applications of technology in healthcare. Ever since computers became a part of our world, the world of healthcare expanded in terms of data storage and efficient record-keeping.

That said, the role of technology is not only restricted to record-keeping but it is certainly evolving at a rapid pace to unfold the rising use of advanced technologies like machine learning, artificial intelligence, and neural networks.

While it all sums down to efficiency and rapid data evolution, technology can traverse through various horizons in order to amalgamate nascent prospects of ML and AI. This, in turn, can unveil the world of healthcare and medicine to the masses with more transparency and educated decision-making power.

Discussing the role of machine learning, in particular, one needs to take into consideration a lot of factors before processing the true scope of the technology in the domain. The following segment throws light on the top 10 machine learning applications in healthcare to help you understand its role in a better way.

These applications range from predictive analytics in a post-pandemic world to automated procedures that narrow down the manual labor and resources supposed to be invested.

Uses of Machine Learning in Healthcare

Suppose you have a magic wand that can do anything for you. What would you wish for? While some would say a day full of treats and delicacies, others would certainly look at the bigger picture and aim to improve the world of healthcare.

The only difference is that magic lies in our hands now that we have the power to combine machine learning models with health and medicine. Let us discuss in brief the uses of machine learning in healthcare.
Uses of Machine Learning in Healthcare
One of the topmost applications of ML in healthcare is that it can help in disease classification. Using machine learning algorithms and cluster sampling, diseases can be categorized on the basis of their symptoms and clubbed into different categories using machine learning.

This is a fairly simple task and requires customized algorithms empowered by AI and ML to help doctors and researchers place diseases in various categories.

This is also related to the application of disease diagnosis using the classification algorithms to help identify tough diseases or the ones that might be hard to catch.

"Machine learning algorithms can help to determine and label the kind of disease or medical case you're dealing with."
- Disease Classification using ML

The second use of ML in healthcare is that it helps in predicting phenomena, health risks, plausible pandemics or outbreaks by extracting useful insights from the past data available.

Using predictive analytics, such forecasts can be made beforehand. With the help of machine learning tools and software applications that are capable of determining future possible outcomes or phenomena, this is a very helpful use of ML in this field.

In addition, such a purpose can also indicate the plausible symptoms of a disease that is already on the rise.

For instance, when the Covid-19 pandemic broke out, predictive analytics was not very useful as the world had seen such an outbreak of one of a kind. However, as time went by, data reports and patient records were analyzed in order to extract future symptoms or future effects of the pandemic on those affected.

This was a very useful application of technology that helped minimize and contain the disease manifold.

Another powerful application of ML in healthcare lies in the hands of operative procedures that were traditionally performed by doctors and a team of nurses, anesthesiologists, and other skilled professionals.

Automated procedures are easy operative procedures that can be performed by machines powered by ML and AI algorithms. With the help of customized technology and trained equipment, automated procedures require less manual labor and resources and result in equally good results.

This way, hospitals need to deploy less staff for such procedures and invest more in such machines that have a long shelf-life supported by regular maintenance.

While machines can skillfully perform easy operations and less technical procedures, there is more to the machines and their ability than just doing what they are trained to do.

This brings in the use of machine learning in healthcare to detect abnormalities or irregular outcomes. From operations to research protocols, machines induced with the technology of ML can react to anomalies as soon as they are made available to do so.

This is a boon for the healthcare industry as not only does it detect anomalies and irregular patterns but it simultaneously keeps a record of everything. This is a useful application as many of the procedures performed in the industry across various horizons can go unnoticed or even irregular sometimes.

The very first thing that comes to our mind when we hear about medicine is drugs. Medicines are as important as the entire industry is and it goes without saying that drug development also requires technology at its forefront.

From research and development (R&D) to trials, machine learning can change the face of drug development and equally alter the course of its evolution. By employing big data analytics and machine learning tools, drug development can go a long way with respect to drug development.

"This also includes R&D technologies such as next-generation sequencing and precision medicine which can help in finding alternative paths for therapy of multifactorial diseases. "
- Drug development in Medicine

What's more, the use of ML in medicine can also indicate the development of healthcare applications and the bot system. Healthcare applications, among other software apps on mobile and desktop, have been a magic wand for the masses during the times of the pandemic.

Using the bot system, healthcare facilities like hospitals and medical centers can deploy chatbots at the forefront to communicate with patients and care-seekers and process them further for one-to-one conversation.

The bot system is a comparatively easier and more efficient use of ML as it helps to reduce staff requirements at the consultation levels.

The world of medicine receives a lot of unidentified patients and care seekers who have no way of going back to their families. The use of image identification using ML helps in identifying lost patients and tracking their unknown relatives and families.

In the tough times of calamities and outbreaks, image identification also helps in an efficient way of keeping records of patients by registering them with their names and images for better recognition later on.

This application is also used when it comes to clinical research and trials in order to segregate candidates as per their health charts and records. Even though it may be minimally impactful, this particular use goes a long way in the field of healthcare.

While the field of healthcare is already an organized setting of patient records and operations performed, there is a need and a dire demand for more streamlined processes from top to bottom.

This means that by using ML, medicine can be made more streamlined and organized by using customized portals and interfaces for doctors and nurses to maintain patient records and visits on a regular basis.

From the very first consultation to the discharge papers, patient records can be streamlined in a way that helps the hospital to keep a track of activity in each and every case.

One of the biggest advantages of machine learning in healthcare revolves around rapid research procedures. In order to find cures for diseases, research has to be performed across various horizontals.

Moreover, research procedures also involve past data analysis with possible outcomes in the future. All these requirements can be simply solved with the help of one technology - machine learning.

Research procedures can be made easier to conduct with the help of machine learning tools in order to strategically proceed from one end to the other. As machine learning becomes more and more advanced, such procedures will be easier to perform with the help of advanced technology and related equipment.

Lastly, the use of ML and the entire technology field in the healthcare industry is centric to record keeping. By using categorization and ML classification, record keeping has become far more organized and computerized at the same time.

From the very first time the patient enters into the hospital till the time s/he steps out of it, hospital staff can maintain all types of records - medical, administrative, and financial, to keep up with increasing patients day by day.

This goes with the task of presenting information and performance evaluation at the time of audits to make sure that all records are in place.

Future of Healthcare

The future of healthcare, when combined with that of rapidly evolving technology seems bright. As long as the technology sector is benefitting the field of medicine in and out, the future holds a lot many unexplored surprises for the world. Who knew that one-day robots could enter the operating rooms and operate on patients?

Well, with the changing face of technology, the field of medicine has also kept up with the course. To sum up, machine learning is a major contributor to the growth of the healthcare industry.

Had it been distant from this world, hospitals would still have maintained paper records, research centers would still have invested years in conducting clinical trials, and the world of medicine would have been no faster than it used to be.

However, thanks to machine learning and other technologies like AI, understanding medicine has become rapid, responsive, and ravishing!

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advantages of machine learning in healthcare

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benefits of machine learning in healthcare

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application of machine learning in healthcare

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10 Uses of Machine Learning in Healthcare | Analytics Steps

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Healthcare is a vital domain that the world can't do without. With an ever-increasing demand amidst a global pandemic to boost healthcare facilities and raise the bar of excellence in the field, healthcare professionals are digging into the role of technology that can possibly contribute to this cause in an efficient way.

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 This World Cup is wired and fueled by AI (Pranshu Verma, November 21, 2022)

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 This World Cup is wired and fueled by AI (Pranshu Verma, November 21, 2022)

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62115340 Surin Hawae
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Soccer fans pose for photos next to a giant replica on the World Cup trophy in front of Stadium 974 in Doha, Qatar. Artificial intelligence will be a major part of this year's tournament. (David Gannon/AFP/Getty Images)

The global sports bonanza has started. Millions are tuning in at home, and others are braving the heat to watch the 2022 FIFA World Cup in Qatar in person.

A host of worries come with the attention on Doha: Fans will probably complain about botched calls. Stadium officials hope to minimize crowds. There are worries of overheating. Government officials will have public safety at the top of their minds. Scandals and human rights abuses abound.
Tech is not your friend. We are. Sign up for The Tech Friend newsletter.
Qatar, in reversal, bans alcohol sales at World Cup stadiums

Technology can't solve it all, but will be part of the answer. Officials are relying on sophisticated tools to control almost every aspect of the games: from the soccer balls being kicked around to the thousands of cameras tracking fans' and players' nearly every move, bringing intrigue and concern.

Here's a look at the innovations being used.

Sensor soccer ball

The official match ball, made by Adidas, will have motion sensors inside. The sensor will report precise location data on the ball 500 times per second, according to the company, helping referees make more precise calls.

The sensor-filled ball was road-tested at several soccer tournaments leading up to the main event, including the 2021 FIFA Club World Cup, and did not affect player performance, Adidas said.

The ball will be used in all of the tournament's 64 matches, and will feed information back to a data nerve center, which officials can use to track statistics and monitor game play.

Fox's World Cup coverage of Qatar has a notable sponsor: Qatar

Video Assistant Referees

A staple of watching any soccer match is complaining about the calls.

But in this tournament, officials will try to minimize the controversy by using video assistant referees, which use algorithms and data points to help on-field referees make accurate calls, FIFA officials said.

The technology was tested in the 2018 World Cup, and has gotten enhancements for this year's games.

The system will rely on tracking cameras mounted underneath stadium roofs to track the sensor-filled ball and up to 29 data points on each player's body, at 50 times per second, FIFA officials added.

The data points tracking players' limbs and ball location will be fed into an artificial intelligence system, helping referees make accurate calls on penalties, such as who is offside.

An automated alert will ping match officials inside a video operation room, who will then validate the decision before informing the referee, they said.

Stadium cooling

The heat was always going to be an issue. Though not scorching summer temperatures, temperatures in Qatar could get stiflingly hot during the next month.

Officials are relying on an advanced cooling system. According to FIFA, it is designed by a Qatari professor, Saud Abdulaziz Abdul Ghani, who is often called "Dr. Cool." Air is drawn into pipes and vents in the stadium, cooled, filtered and pushed out again. It will create a cool bubble inside the stadium, where sensors will help regulate temperatures, game officials told news outlets.

Using insulation and a tech-fueled method called "spot cooling" - which allows cooling to take place only where people are - stadiums will be kept between 64 and 75 degrees Fahrenheit.

The making of a World Cup kit

Cameras and algorithms

Command and control centers in Qatar will rely on more than 15,000 cameras to track people's movements throughout the games, Qatari officials told Agence France-Presse in August.

The cameras will be spread among all eight stadiums. In Lusail Stadium, which holds more than 80,000 people and where the final match will be held, facial recognition technology will be used to track fans, according to Al Jazeera, which has generated concerns over privacy.

Tim Ream, the USMNT's 'grandpa' at 35, never gave up on his World Cup dream

In addition, algorithms will be used to try to prevent stampedes in the stadium, such as the one at a soccer match in Indonesia last month that killed more than 130 people, news reports indicate.

The command-and-control team, news reports said, will be able to forecast crowd patterns using algorithms that rely on several data points, including ticket sales, and places where people enter.

Who will win?

The Alan Turing Institute in Britain has created an algorithm to predict which team is most likely to win the World Cup.

Their algorithm is based on a previous one they used called AIrsenal, which they developed in 2018 to play Fantasy Premier League, institute officials said.

They relied on a data set from GitHub, a website for sharing and collaborating on computer code, which tracked the results of every international soccer match since 1872, they said. Their model gave more weight to World Cup matches and games played recently.

They ran the model 100,000 times.

The results, according to the institute: Brazil won the tournament in roughly 25 percent of the simulations; Belgium came out the victor roughly 18 percent of the time; Argentina came out on top at a little under 15 percent.
View 3 more stories

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This World Cup is wired and fueled by AI

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The global sports bonanza has started. Millions are tuning in at home, and others are braving the heat to watch the 2022 FIFA World Cup in Qatar in person. A host of worries come with the attention on Doha: Fans will probably complain about botched calls. Stadium officials hope to minimize crowds.

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5 Powerful Use Cases for AI in Finance14 (Ebby Bowles , JULY 14 , 2022 )

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5 Powerful Use Cases for AI in Finance14 (Ebby Bowles , JULY 14 , 2022 )

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63113203 Tunpisit Somket
PASS

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The Data Science Talent Crisis

Whether you're a data scientist thinking about quitting your job or an exec grappling with high turnover rates-there's no denying that we're in the midst of a data science talent crisis. Here's how you can put a stop to it.

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Artificial intelligence helps financial organizations increase efficiency and drive results in dozens of ways. Here are some of the most powerful applications of AI in finance.

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AI in Telecommunications: Qualities and ApplicationsSoumyaa (Rawat Jun 20, 2022)

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AI in Telecommunications: Qualities and ApplicationsSoumyaa (Rawat Jun 20, 2022)

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Copyright © Analytics Steps Infomedia LLP 2020-22. All Rights Reserved.

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10 Applications of Machine Learning in Social Media (Soumyaa RawatJul 29, 2022)

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10 Applications of Machine Learning in Social Media (Soumyaa RawatJul 29, 2022)

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63110282 Sakda Iamphongsa
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Copyright © Analytics Steps Infomedia LLP 2020-22. All Rights Reserved.

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How Automatic Outbound Calling is Important for Small Businesses (Aileen Scott , October 10, 2022 )

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How Automatic Outbound Calling is Important for Small Businesses (Aileen Scott , October 10, 2022 )

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How Automatic Outbound Calling is Important for Small Sized Businesses?

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Automated outbound calls are the preferred method of interaction with clients, especially for small businesses.

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Declarative Machine Learning Alone isn’t Enough for the Data Science Community (Jorge TorresOctober 11, 2022)

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Declarative Machine Learning Alone isn’t Enough for the Data Science Community (Jorge TorresOctober 11, 2022)

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63112379 Alongkod  Thurawong
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Declarative Machine Learning Alone isn’t Enough for the Data Science Community - DataScienceCentral.com

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Declarative ML and AutoML are useful for data gathering, but neither can satisfy an organization’s need for near real-time intelligence.

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Data Normalization: Meaning, Forms and Advantages(Sangita Kalita ,Aug 06, 2022)

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Data Normalization: Meaning, Forms and Advantages(Sangita Kalita ,Aug 06, 2022)

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63110951 sanita jitturat
FAIL: เนื้อหาไม่ใช่การประยุกต์ใช้
ให้ post ใหม่ โดยไม่ต้องลบ post เก่า (ให้วงเล็บด้วยว่า RESUBMIT)

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SCM and Role of AI (ProlayGhosh,August 9, 2022)

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SCM and Role of AI (ProlayGhosh,August 9, 2022)

# data[].attributes.body
63110068 Sarunpat Promthong
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SCM and Role of AI - DataScienceCentral.com

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Introduction: Business organizations exist to make a profit out of their business. If there is no profit, then there is no meaning in running a business. Supply Chain Management (SCM)’s main objective is to create a profitable SCM. The higher the SCM profitability, the more successful the SCM is. In…

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What is AI Marketing and why is it needed? (Ashesh Anand, Jan 22, 2022)

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What is AI Marketing and why is it needed? (Ashesh Anand, Jan 22, 2022)

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What is Ambient Intelligence? Factors and Applications(Vrinda Mathur  , 17 Jun 2022)

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How Artificial Intelligence Can Combat Cybercrime? (Tanesh Balodi, November 07, 2021)

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How Artificial Intelligence Can Combat Cybercrime? (Tanesh Balodi, November 07, 2021)

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How IoT Uses Machine Learning To Change The World (Avani Trivedi, April 12, 2022)

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How IoT Uses Machine Learning To Change The World (Avani Trivedi, April 12, 2022)

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Machine Learning Makes IoT Adaptable.

IoT and Machine Learning are the most advanced and evolving technologies that continue to rise in today's modern world, simplifying human efforts and making lives easier. These technologies have proved to streamline operations and workflows for various industries and provide more robust and scalable applications that allow users to make things done seamlessly.

In recent years, the popularity of the Internet of Things (IoT) and Machine Learning (ML) has greatly increased, especially in finding new ways to uncover business opportunities. The way people used to live before has revolutionized with the power of ML and IoT, making their lives simpler and easier.

From retail & eCommerce, sports & entertainment, and medical & healthcare to oil & gas and software industries, ML and IoT are widely adopted for building modern and innovative mobile applications that tailor their unique business needs. These technologies have absolutely transformed the way people communicate, live, travel, work, and other daily core tasks and activities.

Well, in this article, you will learn the impact of IoT and ML and how these technologies are changing everyone's lives. Let's get started.

How IoT and Machine Learning Are Changing Everyone's Lives

Let's take a closer look at the most important aspects that have been changing exponentially with the use of Machine Learning and the Internet of things.

Communication

When IoT and ML enter the communication industry, they can not only transform how we communicate with each other but can also make millions of lives easier by letting them connect with various devices they use on a daily basis. Think of how we communicated in the past, on landlines, then on mobile phones, then video calls became the norm.

Voice assistants are the best examples that facilitate day-to-day tasks, including playing music, knowing the weather, sending messages, calling your loved ones, setting the alarm, finding info, searching for local service providers, etc.

User Experience

Machine Learning has the capability to generate patterns based on pre-fed data and provide more accurate predictions and data analytics. Businesses looking to access their user data can make use of Machine Learning to quickly analyze the data to create more personalized customer experiences no matter if it's a web or mobile app.

The feature is widely used by retail & e-commerce businesses for online shopping platforms so they can provide recommendations based on the past shopping experience and history.

Smart Homes

Having an Internet of Things-enabled smart home is possible; the electronic devices in your house can be connected to a central control system and can interact with each other. IoT can even decide what is best for you, like the temperature in the air conditioner, the food that should be stored in the refrigerator, and the list goes on.

Think of how cool it would be to be able to control all devices and home appliances right from your smartphone, such as your air conditioner, ventless portable air conditioners, lights, refrigerator, television, etc.

Businesses

If you are running an online business, you can take competitive advantage of Machine Learning and the Internet of Things, to grow your business more efficiently. Many businesses make use of the Internet of Things and Machine Learning (ML), to create more interactive and unique experiences for their customers.

These technologies help businesses across multiple segments to revamp their customer service. Netflix and Amazon Prime Video are the best examples of using these technologies, they offer users the most suggested movies or web series based on their past experience, providing a more personalized user experience on the platform.

Voice-enabled Services

The most popular voice assistants like Apple Siri, Google, Amazon Alexa, etc., are most loved by almost everyone and have gained more fame, answering all your questions in no time. You can now make all your home tasks easier by using voice-enabled devices, allowing you to control everything by speaking. Even businesses are moving toward bot development for better service delivery.

Healthcare

Machine Learning has played the most significant role in the healthcare sector through a wide range of applications and technologies that have proved useful to patients as well as healthcare professionals. Machine Learning has several significant benefits in the healthcare industry, including the ability to diagnose patients quickly, predict diseases based on their medical history, and assess symptoms online.

These technologies are widely used in hospitals to carry out big surgeries more perfectly, early diagnosis of deadly diseases, detect tumors and their growth with accuracy, etc.

Education Sector

The education industry can also take numerous benefits from the Internet of Things and Machine Learning (ML) by implementing various features in the system or application. It helps to eliminate the repetitive tasks of tutors, educators, and counselors and allows them to focus more on core subjects.

Using IoT and ML in the educational system can facilitate the creation of automatic study plans, schedules, syllabus, marking sheets, and customized programs for students.

Legal Firms

A significant amount of data is generated in the legal sector, and this data must be analyzed to move forward. Law firms are also using Machine Learning applications that allow them to process data faster and precisely compared to humans with the use of these technologies, reducing time and effort and providing more satisfying results.

By utilizing pre-fed human logic and ensuring that all the rules and regulations mentioned in the constitution are taken into account, Machine Learning can assist the courts in making their decisions more accurately.

Better Economic Management

The use of IoT and Machine Learning devices in factories and companies can reduce the need for workers, increase efficiency, and lower costs over time. The use of IoT and Machine Learning devices in factories and companies can reduce the need for workers, increase efficiency, and lower costs over time.

Companies can install machines with sensors that can notify them if there is a malfunction in the machine's operation, allowing them to free up the testing contingent. A variety of devices are available that work in company environments in order to detect the presence of fires, gas leaks, etc. through notifications whenever an abnormal temperature or pressure is detected.

Transportation

The Internet of Things and Machine Learning in transportation, in a single line. You may not drive home in the future if machines learn to drive for you. People are increasingly interested in autonomous vehicles, and many people are making the transition to them.

In addition to this, the transportation sector is gradually adopting IoT and Machine Learning to make their process as smooth and easy as possible. A variety of new vehicles are undergoing testing in the world today including driverless public buses, autonomous ships, autonomous cars, etc.

Enhanced security protocols

AI is an excellent tool for automating the scanning of security footage, and IoT can trigger gates to open and close when an intruder is detected. AI can enhance workplace security by automating both processes.

Enterprises can now easily detect possible security threats and automate responses to swiftly and efficiently recover from them thanks to these advantageous technologies, which are great both individually and when combined provide a robust security layer.

Time to Adopt the IoT and Machine Learning

It is impossible for businesses to reach their potential without the Internet of Things and Machine Learning. As these technologies are growing continually, they will only become more effective, enabling companies to save money otherwise not possible.

As the Internet of Things and Machine Learning reshape the business landscape, businesses that don't take advantage of these transformative technologies will likely be left behind.

Final Thoughts

Thousands of companies have adopted one of the major cloud platforms, and technology giants like Amazon, Google, and IBM are providing a variety of data collection services for organizations. Until you know what your competition is up to from IoT data, you are unable to launch a successful project, contends Forrester's Bieler.

Small businesses find it difficult to compete with the big players when the Machine Learning models rely on good-quality data. In order to estimate the potential market size, it's required for all potential marketers to identify and analyze the latest trends.

Take advantage of AI and ML development services for your existing business if you are looking for a development partner to implement IoT and ML.

As per the experts, Machine Learning and the Internet of Things is the next big thing that will revolutionize the industries, the way people live and bring about a digital revolution. Companies have already begun incorporating these technologies into their products. It is also possible for your company to take advantage of this new technology.

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How IoT Uses Machine Learning To Change The World - DataScienceCentral.com

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IoT and Machine Learning are the most advanced and evolving technologies that continue to rise in today's modern world, simplifying human efforts and making lives easier. These technologies have proved to streamline operations and workflows for various industries and provide more robust and scalable applications that allow users to make things done seamlessly.

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HOW CLOSE ARE WE TO AI WITH HUMAN CAPABILITIES?(KEREM GÜLEN · OCTOBER 6, 2022)

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HOW CLOSE ARE WE TO AI WITH HUMAN CAPABILITIES?(KEREM GÜLEN · OCTOBER 6, 2022)

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There is a disagreement among professionals over artificial general intelligence definition. Artificial general intelligence (AGI) is the capacity for machines to perceive, learn, and carry out intellectual tasks in a manner similar to that of humans.

AGI allows machines to mimic human behavior and thought processes in order to tackle any kind of complex problem. The performance of these machines is identical to that of humans due to their design, which includes comprehensive knowledge and cognitive computing skills.

Artificial general intelligence definition

Artificial general intelligence (AGI) is the software representation of generalized human cognitive capabilities so that the AGI system can come up with a solution when presented with a challenging issue. AGI systems are designed to carry out any task that humans are capable of.

What is artificial general intelligence?

Because specialists in various domains interpret human intelligence differently, there are many possible definitions of AGI. Human intelligence is frequently described by computer scientists in terms of the capacity to do tasks. On the other hand, psychologists frequently describe general intelligence in terms of survival or adaptability.

Artificial general intelligence definition: What is another term for general AI?

The terms "strong AI," "full AI," and "general intelligent action" are also used to refer to AGI. However, some academic sources reserve the phrase "strong AI" for computer systems that have sensibility or consciousness.

Artificial general intelligence examples

True AGI systems are not yet commercially available. However, there are instances of limited artificial intelligence systems that come close to or even match human capabilities in some circumstances. These systems and the potential applications of AGI in the future are the subjects of current research in artificial intelligence.

Here are some examples of those systems:

Autonomous cars: These are capable of identifying other cars, pedestrians, and other things on the road, and they follow all driving laws and regulations.

ROSS Intelligence: The "AI attorney" is another name for ROSS, a system that provides legal expertise. It can extract data from approximately 1 billion text documents, analyze the data, and deliver accurate answers to challenging queries in under three seconds.

IBM Watson: Calculations that an average computer couldn't do can be handled by Watson and other supercomputers. They combine their enormous computer capacity with AI to complete previously unachievable engineering and science tasks, such as simulating the Big Bang theory of the universe's origin or the human brain.

AlphaGo: Another instance of a restricted intelligence that excels at a particular kind of problem-solving is this one. A computer software called AlphaGo is capable of playing the Go board game. Go is a challenging game that is challenging for humans to learn. In a five-game battle in 2016, AlphaGo defeated Lee Sedol, the reigning world champion.

Expert systems: These AI-powered devices imitate human judgment. They can forecast molecular structure and make medication recommendations based on patient data.

Generative Pre-trained Transformer 3: GPT-3 is a tool that can produce human language on demand. Sometimes it is impossible for someone to distinguish the outcome from human production, but the output is frequently deficient. General human intellect can continuously be mimicked by technology.

Music AIs: An AI program called Dadabots can create a stream of music that is an approximation of a body of preexisting music when given that body of music.

Some of the examples from above might function more effectively if AGI were used in them. For instance, a human must be present when self-driving automobiles are used to handle decision-making in confusing circumstances. The same holds true for language models, judicial systems, and music-making algorithms. These fields cover both automatable jobs and those that demand human intelligence and a higher level of abstraction.
Artificial microswimmers can navigate similarly to natural microorganisms, thanks to AI
What are the 3 types of artificial intelligence?

Now you have learned artificial general intelligence definition. It's time to make it clearer with some examples. The ability of AI technologies to replicate human qualities, the technology they employ to do so, the applications they have in the actual world, and the theory of mind-which we'll cover in more detail below-are used to classify them.

All hypothetical and practical artificial intelligence systems can be classified into one of three categories using these traits as a guide:

Artificial narrow intelligence (ANI): It offers a narrow range of abilities.

Artificial general intelligence (AGI): It aims to offer human-like abilities.

Artificial superintelligence (ASI): It aims to offer more capabilities than a human.

What is artificial narrow intelligence (ANI)?

In the 1950s and 1960s, the pioneers of the area of artificial intelligence were primarily focused on developing hardware or software that could simulate human-like general intelligence. Since then, the field has shifted its attention primarily to the pursuit of distinct abilities or particular usable activities. Although this strategy has produced a number of intriguing technology and theoretical findings, it has so far been largely ineffective in achieving the field's initial primary objectives. In order to distinguish the pursuit of general intelligence from more narrowly focused associated activities, several academics now use the name and idea of "AGI."

Sometimes a distinction is made between AGI and "narrow AI." For instance, Kurzweil compared "strong AI" with "narrow AI," using the latter to essentially refer to what is now known as AGI. The former refers to the development of systems that carry out specified "intelligent" actions in specific settings.

For a restricted AI system, some degree of human retraining or reconfiguration is typically required to enable the system to preserve its level of intelligence if the context or the behavior specification is even slightly altered. In terms of quality, this appears to be very different from naturally intelligent systems like humans, who have the capacity to adapt to changes in their goals or environment by engaging in "transfer learning," which is the process of generalizing knowledge from one purpose or context to another.

One of the research topics in the realm of AGI is the accurate description or definition of AGI. It is widely acknowledged that humans do not possess infinite generality of intellect given realistic space and time resource limits and that no real-world system will possess infinite generality for similar reasons. Real-world AGI systems will combine generality and specificity in their own unique ways, just as human intelligence does. Human intelligence combines a certain degree of generality of scope with a variety of highly technical aspects aimed at providing efficient processing of pragmatically important problem types.
NovelAI now offers NovelAIDiffusion, a text-to-image AI tool
What is artificial superintelligence (ASI)?

A software-based system called artificial superintelligence (ASI) has cognitive abilities that are greater than those of humans in a nearly infinite number of categories and fields of study.

Artificial intelligence (AI) is the simulation of human intellectual capacities such as learning (the acquisition of knowledge and rules for applying it), reasoning (using the knowledge to arrive at approximations or firm conclusions), and self-correction using computer software.

In systems like virtual assistants, expert systems, and self-driving cars, AI is becoming more prevalent in our daily lives. But the technology is still in its early stages of development. All of the AI systems in use today are weak AI systems, which are applications of artificial intelligence technology to create high-functioning computers that imitate and possibly even outperform human intelligence for a specific purpose.

Super AI definition

Despite this unpredictability, there are some fierce proponents of AGI in the foreseeable future. The most well-known of these is perhaps Ray Kurzweil, Google's director of engineering, who believes an AGI that can pass the Turing Test will exist by 2029 and that by 2040, inexpensive computers will be able to process as many computations per second as the whole human race put together.

According to Kurzweil, once an AGI is created, it will swiftly evolve to the point where its intellect is beyond human comprehension. This improvement upon itself will happen at an exponential rate. The singularity, as he calls it, will happen in 2045, at which time there will be an AI "one billion times more powerful than all human intelligence today," according to him.

Some of the most well-known scientists and technologists in the world have expressed concern about the grave dangers posed by AGI in response to the prospect of a near-future superintelligence. Elon Musk, the founder of SpaceX and Tesla, refers to AGI as the "biggest existential threat" to humanity, while Stephen Hawking, a renowned physicist, and professor at Cambridge University, warned the BBC that "the development of full artificial intelligence could spell the end of the human race."

The AI community was urged to conduct "research on how to make AI systems robust and beneficial" in an open letter that they both signed.

The philosopher and director of Oxford University's Future of Humanity Institute, Nick Bostrom, has issued a warning about what might occur if superintelligence is attained.

He believes that superintelligent agents may constitute a threat to humans, who may be "in its way," and compares superintelligence to a bomb that is just waiting to go off due to irresponsible research.

Bostrom warned that if the robot gains enough strength, it "might seize control to gain rewards."

Is artificial general intelligence possible?

The issue with talking about AGI and superintelligence is that the majority of people working in the field of AI emphasize that AGI is currently science fiction and may stay that way for a very long time.

Debates concerning the emergence of artificial general intelligence, according to Chris Bishop, laboratory director at Microsoft Research Cambridge, are "utter nonsense" and "at best, such discussions are decades away."

Even worse, according to other AI researchers, such talks are deflecting attention away from the concerns that today's limited AI poses in the near future.

A well-known name in the deep learning community, Andrew Ng formerly worked on the "Google Brain" project and was the head scientist for the huge Chinese search engine Baidu. He urged individuals discussing AI's ethical implications to "cut out the AGI nonsense" and instead concentrate on how current technology is causing or would cause issues like "job loss/stagnant wages, undermining democracy, discrimination/bias, wealth disparity" to become worse.

Even emphasizing the advantages of AGI could harm public impressions of AI by fostering dissatisfaction with the relatively restricted capabilities of current machine-learning systems and their specific, one-note skill set, such as text translation or facial recognition.

What is the difference between AI and general AI?

Theoretically, AGI should be able to carry out every work that a person can and display a variety of intelligence in various domains. In most areas of intelligence, its performance ought to be on par with or superior to that of humans when it comes to problem-solving.

Narrow AI, on the other hand, excels in solving particular problems or tasks. For self-improvement and problem-solving, many current AI systems combine machine learning, deep learning, reinforcement learning, and natural language processing. These technologies, however, fall well short of the combined power of the human brain.

While AI is employed in numerous areas, AGI is not yet a reality. These are some instances of AI:

Chatbots

Face recognition software

Voice assistants such as Siri, Alexa, and Google Assistant

Recommendation engines that Spotify, Netflix, Google, etc. utilize.

AGI technology: Approaches

The intelligence of machines that enables them to comprehend, learn, and carry out intellectual tasks, much like humans, is known as artificial general intelligence (AGI).

AGI enables machines to mimic human thought and behavior in order to tackle any challenging task. The performance of these robots is identical to human performance because of their design for extensive knowledge and cognitive computing.

The core idea behind mind-level AI is to teach machines how to mimic human behavior and comprehend the essential components of consciousness. AGI can plan, gain cognitive abilities, make judgments, handle uncertain situations, use prior knowledge in decision-making, or increase accuracy with such a solid AI base. AGI can make it possible for machines to carry out creative, imaginative, and innovative jobs. Achieving strong AI has significant challenges. Multiple approaches have been tried and tested to achieve human-like intelligence. Listed below are some of the core approaches to AGI.

The symbolic approach

The term "symbolic approach" refers to the use of symbols and logic networks (i.e., if-then statements) to study and build a broad body of information. By manipulating these symbols that reflect the fundamental elements of the physical world, this knowledge base is expanded even further. The method imitates the more sophisticated levels of human thought.

The symbolic approach is capable of higher-level logic and thought, but it falls short when it comes to teaching lower-level skills like perception. The CYC project, founded by Cycorp's Douglas Lenat in the 1980s to promote AI research, is a good illustration of the symbolic approach. CYC has a substantial body of knowledge, a sound logic framework, and a potent representational language.

The connectionist approach

The connectionist approach, a sub-symbolic technique, builds general intelligence using designs that resemble the human brain (such as neural nets). The method anticipates that lower-level sub-symbolic systems, such as neural nets, will eventually develop into higher-level intelligence, although this has not yet occurred. Convolutional neural networks and deep learning systems, like DeepMind's AlphaGo, are excellent instances of the connectionist methodology.

The hybrid approach

The symbolic and connectionist systems are combined in the hybrid approach. The hybrid strategy is frequently used by the designs that are at the forefront of the AGI race, such as the CogPrime architecture. It uses a single knowledge representation known as AtomSpace to represent both symbolic and sub-symbolic knowledge. With the use of neural architecture, CogPrime, Hanson Robotics, and OpenCog developed the well-known social humanoid robot, Sophia.

Whole-organism architecture

According to experts, a general artificial intelligence system should have a physical body and gain knowledge via interacting with people physically. While such systems do not yet exist, Sophia, a humanoid robot that mimics human motions and facial expressions and engages in dialogues about predetermined themes, comes the closest.

Reaching to artificial general intelligence: Challenges

Although AGI has not yet been achieved, it opens up a wide range of opportunities that have the potential to change the AI industry completely. The development of artificial general intelligence is now hampered by significant obstacles and difficulties.

These are the key challenges of reaching the ultimate AGI stage.

Learning human-like skills

AGI must learn several human-like skills in order to reach full human-level intelligence, including:

Sensory perception: Though computer vision has seen considerable success with deep learning systems, human-like sensory perception is still lacking in AI systems. Deep learning systems that have been trained, for instance, still have poor color vision. This is demonstrated by the ease with which small bits of black tape or stickers on a red stop sign may deceive self-driving automobiles. Sound perception is seen in a similar situation. AI algorithms today are unable to recognize and mimic distinct human hearing.

Motor skills: Due to our fine motor abilities, humans can effortlessly recover anything from their pockets. Recently, reinforcement learning was used to teach a robotic hand how to solve a Rubik's cube. The experiment, while noteworthy, highlights the difficulties of programming robot fingers on a single hand to manipulate insignificant objects like keys.

Natural language understanding: Through books, articles, blog entries, and videos, people share knowledge. Because humans tend to assume that their readers are familiar with certain concepts, a lot of information is left out of written communication. The current AI must first consume enormous volumes of data from all sources of knowledge, which is a crucial duty. It will be challenging for these systems to comprehend circumstances and function in the actual world if AI lacks the foundation of common sense.

Problem-solving: Consider a scenario in which a home robot must detect that an LED light bulb is burnt out and either inform someone or replace it with a new one. The robot must possess common sense, as was mentioned before, or be able to simulate all the permutations and combinations that define possibilities, plausibility, and probabilities in order to do this task. Both common sense and simulation capabilities are lacking in modern AI.

Human-level creativity: If AI systems can comprehend the enormous quantities of code that humans have written, find new techniques that can be made better, and then rewrite the recognized code, they will automatically become smarter. Although AI-based computers have created music and drawn drawings, further AI development is required to demonstrate human-level creativity for self-improvement.

Social & emotional connect: Human interaction is necessary for AI-enabled robots to function in the real world. Given the difficulties with perception outlined above, AI systems that are able to feel an emotional connection and demonstrate empathy currently appear like a far-off dream.

The absence of a working protocol

A functional protocol that enables artificial intelligence or machine learning networking systems to interact together is lacking in current AI systems. When deploying a full AGI system, this creates a serious technological weakness. Thus, the systems are compelled to operate as independent models in secure contexts. AGI systems require a complex and highly social human environment, which is not compatible with such a style of functioning.

Communication gaps

AI systems currently confront a significant communication barrier. Lack of communication between various AI systems makes it difficult to share data in an efficient manner. Machine learning models are no longer able to learn from one another. AI may not be able to maximize the assigned tasks due to the impact on inter-learning. This eventually decreases the general AI system's universality.

The absence of business alignment

Business executives must adopt a strategic approach by establishing goals, choosing KPIs, and monitoring ROI in order to integrate AI effectively. Otherwise, it could be challenging to evaluate and compare the outcomes of AI investments in order to determine their success (or failure).

It takes a lot of work to integrate AI into current systems. It is necessary to take into account a number of factors, including the need for data infrastructure, data storage, data labeling, feeding the data into the system, and others. Concerned stakeholders appear to now be unaware of all these AI operational factors. This impedes the overall growth and accomplishment of business objectives.

The absence of AGI direction

Businesses are frequently unable to grasp the basics of the AGI system, which forces them to recruit a specialized team of AI experts, which can be an expensive endeavor. In addition, businesses lack a clear plan and direction based on AI for how to run their operations. Because of this, implementing AI systems is expensive and difficult. These issues have a substantial impact and serve as obstacles to the development of a complete AGI system.

Artificial general intelligence: The latest advancements

The advancement of human-like intelligence has been advancing more quickly than ever before as AI developments take center stage amid the COVID-19 epidemic. A complete AGI system does not yet exist, but recent developments in AI may push the limits of AGI and hasten its development.

The top 10 AI trends that could advance AGI are listed below.

NLP improvement

Artificial intelligence (AI) technology called natural language processing (NLP) greatly decreases the need to interact with screens by understanding human language. AI-enabled gadgets can translate human languages into computer codes that are then utilized to operate programs and applications.

The most recent iteration of NLP, GPT-3, was just released by OpenAI. GPT-3 processes languages using more than 175 billion parameters. Additionally, OpenAI is also focusing on GPT-4, which has the potential to handle 100 trillion parameters for thorough language processing. A machine that can interact and engage with humans in a way that is as good as real is definitely possible thanks to such AI developments.

Metaverse

As businesses and individuals experiment with immersive technologies to work and engage in this virtual world, Metaverse has been thriving. Users spent almost $106 million to purchase virtual goods in the Metaverse in November 2021, with a focus on digital land, opulent yachts, and other assets, according to data from DappRadar.

In light of this development, AI and ML are predicted to advance the Metaverse by creating a virtual environment with AI chatbots that make users feel at home.

Hyper automation

Robotic process automation (RPA) and intelligent business process management are only two examples of the procedures that various sectors use AI and ML technology to automate. As it extends the opportunities for automation for enterprises, hyper-automation adds a further layer to sophisticated automation capabilities. Gartner predicted in April 2021 that the hyper-automation market would reach $600 billion by 2022.

Increased diversity on governance jobs

The absence of model governance can lead to algorithmic bias. Here, AI specialists should pay more attention and make sure that AI/ML models don't form biases or make poor judgments. Twitter recently acknowledged, in October 2021, that its algorithm has established a bias favoring right-wing politicians and news sources.

In a related event, Amazon identified prejudice in its employment algorithm against women in 2015. This was due to the algorithm's scrutiny of resumes over the previous ten years, the majority of which were written by men, which caused it to be prejudiced against women.

Examples like these will open the door for a surge in jobs like chief AI officer, chief AI compliance officer, and other similar posts. Such occurrences are anticipated to increase in the near future due to the fast use of AI and ML.

The rise in low-code or no-code AI

Today, there is a big need for qualified AI engineers. Companies are constantly looking for engineers that can create AI tools and algorithms to support their company processes. This problem can be solved by low-code and no-code AI solutions by providing user-friendly interfaces that facilitate the development of sophisticated systems.

Drag-and-drop possibilities are typically offered by low-code solutions, which streamlines the development of applications. Moreover, voice-based directions for completing difficult activities can be provided using NLP and language modeling technology.

Workforce augmentation

There has been concern about AI taking over human occupations for a while now. In reality, corporations appear to be utilizing AI/ML models to collect, evaluate, and generate insights from data that aid in corporate decision-making. In such a situation, businesses need both human workers and AI machines operating together.

Sales, marketing, and customer support are just a few of the areas that already use AI/ML technology to improve operations. This hasn't, however, lessened the potential reliance on humans. In actuality, it has only improved these departments' efficiency. From here on, this trend is only anticipated to increase.

AI chatbots

AI-enabled virtual assistants are referred to as conversational chatbots. They engage in normal discussions and perform some rule-based tasks, such as answering questions or changing passwords. These chatbots have taken the position of customer service representatives, significantly lowering operational expenses for enterprises. Conversational AI chatbots may transform the field of AGI in the future due to the changing NLP environment.
NVIDIA announced the new NeMo and BioNemo large language models at GTC 2022
Ethic AI

In recent years, the number of AI application cases has increased considerably across all industry sectors. Despite the advantages of AI technology, it is impossible to disregard their potential downsides. As a result, in the years to come, there will be a greater emphasis on AI ethics because if these technologies are not employed for good, the world may come to an end.

AI-based hiring process

Companies are now anticipated to utilize more AI/ML-based solutions as the virtual world replaces the traditional physical reality, as the epidemic has already hurt the hiring process. Moreover, companies are anticipated to use AI-powered solutions to handle the employment process as language modeling techniques advance, and conversational AI chatbot competence rises.
The AI Dungeon's never-ending adventures are now more striking with images created by Stable Diffusion
Quantum AI

Despite the fact that the science of AI has made significant advancements recently, quantum computing has the potential to speed up machine learning algorithms and produce results more quickly. Quantum AI has the potential to remove barriers to AGI since it can quickly analyze vast amounts of material from books, papers, blogs, and other similar sources to build a solid knowledge base.

Conclusion

The current decade will be incredibly important for the creation of AGI systems. By 2030, there is a 25% probability, according to experts, that human-level AI will have been created. Additionally, the current data explosion and technological breakthroughs, along with the growing preference for robotic processes and machine algorithms, will provide a favorable environment for the spread of human-level AI platforms. In this highly technical society, AGI systems will eventually become commonplace. We hope you were able to learn artificial general intelligence definition.

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How close are we to AI with human capabilities?

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There is a disagreement among professionals over artificial general intelligence definition. Artificial general intelligence (AGI) is the capacity for

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Pros And Cons of AI In Manufacturing (Manoj Kumar, May 25, 2022)

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Pros And Cons of AI In Manufacturing (Manoj Kumar, May 25, 2022)

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63124101 Natchanon Jeanmas
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Smart industry robot arms for digital factory production technology showing an automation manufacturing process of the Industry 4.0 or 4th industrial revolution and IOT software to control operation.

The fourth industrial revolution has been a game-changer, with the global economy's expansion driving the adoption of new technologies across sectors. Manufacturers are using AI software in product design, production, supply chain, and logistics. AI analytics and data are helping in improving product quality and efficiency. Advances in machine learning, artificial intelligence (AI), and Big Data have initiated an algorithm-based era. Today companies are able to automate multiple tasks, cutting down on errors as well as downtime and expenditures associated with them using AI. Not only does AI provide self-generated decision-making capabilities but it gathers data, identifies patterns in data, and provides recommendations. AI enables machines to make decisions without the need for human intervention. Although humans are not perfect but are much more efficient at performing certain tasks.

Humans are not equipped with the skills to process data and the complex patterns that appear within large datasets. AI easily sorts data from a manufacturing machine and identifies outliers in the data that clearly indicate if the machine requires maintenance in the coming weeks. AI can complete this task in a fraction of the time that a human would spend analyzing the data. Like many other sectors, the manufacturing industry is also using AI in different ways like predicting maintenance, generative design, and market predictions. Since everything has its advantages and disadvantages, AI being such an asset has some pros and cons as well.

Pros of AI in Manufacturing

1. Predictive analytics for increased production output

A predictive setup allows companies to easily apply a predict-and-fix maintenance model. AI finds anomalies faster and suggests tools and solutions to correct the problem. This helps manufacturers to schedule production which will reduce downtime and enhance production output. A report from Mckinsey says that an AI predictive maintenance model can increase productivity by 20% and can decrease maintenance costs by 10Having prior knowledge of downtime can prevent a sudden halt in production when a machine part or system fails, as one will be able to solve the problem before it occurs. Using AI can avoid the expense that arises from turning machines off. The manufacturing sector has a large volume of data. AI algorithms can assess any malfunction in machines. Equipment disruptions are analyzed or monitored by the data kept in the cloud.

2. AI enables higher quality

Manufacturing companies are finding it difficult to maintain high levels of quality while also adhering to quality regulations and standards. Increasing production without addressing quality can lead to defects that can seriously damage a company's brand in addition to being a costly expense. AI ensures that the best quality should be maintained throughout the manufacturing process.

AI algorithms can notify workers of production faults that could potentially create quality issues in the future. By working on those issues manufacturers can maintain quality levels. AI enables a more innovative production process and better product quality. It also reduces process downtime and increases productivity. For instance, tracking sensors could be used in logistics and haulage, it will help to monitor location, take stock, freight charges, and more. According to various published reports, automation of inventory improves services by 16%. At the same time, inventory turnovers are likely to be increased by 25%.

3. Ever-changing market adaptability

Apart from production, there are other significant aspects of manufacturing where AI plays a key role. These include supply chains, monitoring, customer behavior, change patterns, and predicting possible market changes. All this leads to strategizing toward better products and other cost management processes. Additionally, manufacturers can use AI algorithms to judge market demands. These estimates are possible because AI uses the information gathered through consumer behavior, inventory of raw materials, and other manufacturing processes.

4. AI can enhance production designs

AI is redefining the way products are developed and designed. For instance, one method for designers and engineers is to integrate the AI algorithm in the process. The summary data would include things like restrictions and parameters for types of materials, available production methods, time constraints, and budget limitations.

Cons of AI in Manufacturing
1. AI can be expensive
Adopting AI in the manufacturing sector can cut labor costs but the initial implementation of AI can be pretty costly, especially in startups and small companies. Initially, there will be ongoing maintenance costs as well as expenses to protect systems from cyberattacks as ensuring cybersecurity is also important.
2. You need skillful experts
AI is an evolving field, and therefore AI experts with the requisite skills are few. Since this toolset needs regular sophisticated programming, it's essential to consider expert availability. And also, because they are in high demand, the cost of employing them is also high.
3. AI is open to vulnerabilities
AI is vulnerable to cyberattacks, and as AI becomes more sophisticated and widespread, cybercriminals will try to come up with new hacking methods. If there is even a small gap, it can disrupt the production line. In fact, a small breach can potentially shut down an entire manufacturing business. So one should always be up-to-date with security measures and be aware of the possible cyberattack, which would be costly.

Conclusion

AI will change the Face of Manufacturing. Predictive analyses help manufacturers make more informed decisions. From product design down to customer management, there're several advantages of artificial intelligence. All of this contributes to an increase in process quality, an optimized supply chain, adaptability, and so on. Even so, AI technology is not without drawbacks, such as high budgets and susceptibility to cyber-attacks. Since everything has its advantages and disadvantages, AI being such an asset has some pros and cons as well. However, the benefits of AI outweigh the drawbacks. Many companies are enhancing the manufacturing sector using AI, such as Aeologic Technologies.

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Pros And Cons of AI In Manufacturing DataScienceCentral.com

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AI will change the Face of Manufacturing. Predictive analyses help manufacturers make more informed decisions.

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10 Applications of Big Data in Social Media (Soumyaa Rawat, Jun 28, 2022) (RESUBMIT เพิ่มชื่อ)

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10 Applications of Big Data in Social Media (Soumyaa Rawat, Jun 28, 2022) (RESUBMIT เพิ่มชื่อ)

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63122980 Saharat Suwannapapond
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- Jon Ronson

Social media is a powerful tool that has traversed all kinds of boundaries to connect people and bring them closer to one another. With the foundation of the internet fuelling the engine of social media, netizens have become social butterflies to engage in communication and connection.

Unlike the conventional times when people had to set free their pigeons in order to send messages to their lovers, the contemporary era requires you to send a message that gets delivered in a moment of time.

The unbreakable bonds that form the chain of the internet are so powerful that before anyone wishes to unsend the message that they have just sent, the other person already receives a notification.

Powerful social media platforms like Instagram, Facebook, and Twitter are literally the drivers of the journey that you and I embark upon every day. From watching memes to sharing news, we all use social media and some even benefit from its mere existence.

Today, it is essential to acknowledge the fact that social media has quite well spread like a wildfire that cannot be extinguished easily. From leisure time interests to full-time careers, the masses have extracted many benefits from social media sites that only seem to expand as time goes by.

However, social media is not alone in the journey of technological advancement that happens to have developed so much. The increasing use of artificial intelligence and big data in the social media environment has advanced very well ahead of its expectations and thus, its scope has also widened.

Applications of Big Data in Social Media

The use of big data in various fields and industries is a closely debated and observed topic that social media experts are actively engaged in.

With such a wide scope and an expansive area of work, social media can not only use big data to explore the unfound facts and figures but also develop new areas of work in social media analytics and other related tasks.

As the world of social media has developed so much, big data has a major role to play in its development. Given below are the top 10 applications of big data in social media.
Applications of Big Data in Social Media
As we begin to understand the importance of big data in the realm of social media, it is necessary that we grasp the significance of big data in itself. Big data in its entirety, is a concept that handles huge amounts of data and organizes them to extract insights from it.

These insights can be related to the audience of a certain product, their feedback for the same, or anything else. When it comes to social media, big data is used to extract target audiences and a real-time market segmentation data report in order to help various companies promoting their products on Instagram or Facebook hit the right spot.

This means that big data accumulates information on the audiences and distributes it to influencer accounts. Aiming for the right target in marketing and promotion is all about spreading the right information to the right people.

Thus, big data helps companies and businesses in achieving their target audience and reaching them without any delays.

"Like any other business initiatives, social media marketing is aimed at increasing revenue, but it is no use offering meat to a vegan. So knowing your targeted audience means everything."
- Big Data in Social Media Revenue

Since big data can also be called the mine of information, data is always present at the disposal of those who want to gain insights from it. The role of big data can also be perceived as that of indicating future outcomes and preparing businesses and companies for the same.

Predictive Analytics forecasts possible outcomes on the basis of the past records of the company's performance or pertaining to a certain phenomenon. With the help of predictive analytics, social media users can very well gain insights into what can happen in the future and prepare themselves for that.

Using humongous information that big data stores and studies through, social media can be predicted and forecasted. This helps to avoid numerous social media crises.

The third use of big data in social media is of privacy concerns. Even though big data is the pro and con of privacy concerns in social media, it certainly helps to protect the privacy of millions of users.

On one hand, big data helps data vendors to provide useful information to social media businesses to reach their audiences.

On the other hand, big data solutions help social media platforms to restrict information leakage and install various checkpoints to verify whether the person who is trying to barge into someone's information is himself or someone else. This can include facial recognition, question-answer keys, or even a passcode.

One of the biggest advantages of big data in social media lies in restricting the spread of fake news. Social media is flooded with fake news that intends to trigger violence and hate speech against one another.

However, the origin of fake news and the dire need for fake news detection also lies among its users. With the help of big data, social media operators can easily identify real news and fake news and pull down the latter to maintain social harmony and peace.

With the help of big data, such platforms can also identify fake social media accounts to declutter the social media space and regulate it for the welfare of all users.

It is an unsaid fact that social media goes without competition. Almost every social media business is competing with each other in order to reach most users and establish brand loyalty. However, that is not possible for all users. While some flare and maintain their brand reputation, others lag behind wondering what went wrong.

This is where big data can be of big help and help businesses evaluate their presence and performance on social media. By scanning through huge amounts of data, the performance of various social media accounts can be evaluated on a variety of criteria.

These criteria include account engagement (likes, comments, and shares), followers, and other simple yet significant grounds for identifying whether your social media account is going in the right direction or not.

The sixth application of big data analytics in social media refers to product demand. As netizens surf through social media all day long, they search for various items or products that they might need or desire.

As soon as a user looks for a product or a social media account for the same, big data analytics adds this search to its report of product demand and product development.

This helps businesses to gain insights into what is trending among the audiences and thus, they can better develop their product with the same features.

As mentioned before, big data is a wealth of information that can provide useful insights to marketing agencies and professionals. A lot of time, marketing stints and strategies fail simply because the audiences do not like them.

This can be solved by digging useful information from big data analytics to evaluate the most popular and effective marketing strategies. Since these strategies play a very significant role in promoting a product or a service, they must be very effective in reaching out to the audiences.

By using big data and its tools, marketing professionals can choose from the most responded to strategies and employ them to generate more sales and popularity of the product.

Related to the use of marketing strategies, the application of big data can also help businesses to deal with the promotion. Social media promotion is a lot different than offline promotion.

With the help of influencer marketing and promotion using sponsor advertisements, social media businesses can easily generate an organic following on their account and simultaneously transform advertising.

While this is not enough to sell their product, they can certainly promote content related to that product. For instance, shopping brands can promote content based on fashion, looks, and accessories related to clothing.

By surveying the data available, businesses can develop promotion ideas and work on them to generate support and sales from the audiences in a short span of time.

Moving towards the end, the use of big data in social media is also witnessed in the field of mass communication. Originally a medium of mass communication, social media helps people connect and communicate with each other from one corner of the world to the other.

With the help of big data reports, social media platforms can mass communicate messages and address the users related to some issues. All it needs is to extract their personal details, reach out to them, and contact them for the same purpose.

This is similar to mass emailing or mass messaging which helps social media platforms to propagate or promote a certain type of issue.

Last but not the least, big data leads to data analysis that is very helpful for social media. As social media is all about popular trends and content material, data analysis using big data tools aids social media professionals to analyze what is actually being liked.

Other than that, social media accounts can be analyzed on the basis of their activity or liking. With so much data to evaluate and filter, big data tools are the most effective in performing such tasks with minimal errors and maximum efficiency.

"Big data allows marketers to identify social media trends and gain insights, which can be used to make engagement decisions like which users to communicate with, which group of users should receive marketing emails., etc."
- Identifying Social Media trends using Big Data

To sum up, social media has paved the way for so many people to become influencers. Apart from being the center of the stage, social media has also brought along a wave of new employment opportunities from graphic designers to brand managers in the digital domain.

The future of social media analytics holds a promising scope as is prevalent in the current scenario. With the increasing activity on social media platforms, people have begun to flood the digital space. Similar to a convergent medium such as the internet, social media has become the temple of advertising, communicating, advocating, and mobilizing issues.

Owing to the significant impact of big data on social media, social media has achieved insights, audience interests, and intelligence that will only benefit these platforms in the long run.

From identifying product demand to promotion strategies, social media holds a bright future where scope and skills are all about big data. Using big data analytics and other AI technologies, social media can escape all obstacles and converge with the metaverse to allow people to socialize with their virtual avatars in a parallel world.

Lastly, the power of social media can be best described by the data of its millions of users which is what big data does the best!

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applications of big data in social media

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10 Applications of Big Data in Social Media | Analytics Steps

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The role of big data in social media varies from mass-emailing to finding the right target audience in order to promote a business more effectively. Learn more about applications of big data in social media.

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Association Rule Mining: Importance and Steps (Vrinda MathurSep 26, 2022)

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Association Rule Mining: Importance and Steps (Vrinda MathurSep 26, 2022)

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63104111 Tanapon Rattanamano
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As the name implies, association rules are simple If/Then statements that aid in the discovery of relationships between seemingly independent relational databases or other data repositories.

Because most machine learning algorithms work with numerical datasets, they are mathematical in nature. Association rule mining, on the other hand, is appropriate for non-numeric, categorical data and requires a little more than simple counting.

Association rule mining is a procedure that seeks out frequently occurring patterns, correlations, or associations in datasets from various types of databases, including relational databases, transactional databases, and other types of repositories.
Also Read | Introduction to Database Management System
Association rule mining is a procedure used to discover frequent patterns, correlations, associations, or causal structures in data sets stored in various types of databases such as relational databases, transactional databases, and other types of data repositories.

The goal of association rule mining, given a set of transactions, is to find the rules that allow us to predict the occurrence of a specific item based on the occurrences of the other items in the transaction. The data mining process of discovering the rules that govern associations and causal objects between sets of items is known as association rule mining.

So, in a given transaction involving multiple items, it attempts to identify the rules that govern how or why such items are frequently purchased together. For example, peanut butter and jelly are frequently purchased together because many people enjoy making PB&J sandwiches.

Association Rule Mining is a Data Mining technique for discovering patterns in data. Association Rule Mining patterns represent relationships between items. When combined with sales data, this is known as Market Basket Analysis.

Fast-food restaurants, for example, discovered early on that people who eat fast food tend to be thirsty due to the high salt content and end up buying Coke. They took advantage of this by creating combo meals that combine food that is sure to make you thirsty with Coke as part of the meal.

Maybe these chains didn't use data mining to make this business decision, but maybe they did. In any case, it has contributed to their increased profits. The purpose of this example is to demonstrate that Association Rules represent relationships, which must be interpreted before they can be used in strategies.

Association rules are used in data science to discover correlations and co-occurrences between data sets. They are best suited for explaining patterns in data from seemingly unrelated information repositories, such as relational and transactional databases. The use of association rules is sometimes referred to as "association rule usage."

Importance of Association Rule Mining

Here are some of the reasons why Association Rule Mining is important and such an effective business tool.
Importance of Association Rule Mining
The ultimate goal of any business is to become profitable. This entails attracting more customers and increasing sales. They can develop better strategies by identifying products that sell better together. For example, knowing that people who buy fries almost always buy Coke can be used to boost sales.

Attracting customers is a critical component of any business. Understanding which products sell well together and which do not is essential when developing marketing strategies.

This includes sales and advertisement planning, as well as targeted marketing. For example, knowing that some ornaments do not sell as well as others during the holiday season may enable the manager to offer a discount on the infrequent ornaments.

Knowledge of association rules can help store managers plan their inventory and avoid losing money by overstocking low-selling perishables.

For example, if olives aren't selling well, the manager won't stock up on them. However, he wishes to ensure that the existing stock is sold before the expiration date. Given that people who buy pizza dough also buy olives, the olives can be sold at a lower price when purchased with the pizza dough.

Products that have been shown to increase the sales of other products can be moved closer together in the store. For example, if butter sales are driven by bread sales, they can be moved to the same aisle in the store.

Association Rule Mining is also used in media recommendations (movies, music, etc.), webpage analysis (people who visit website A are more likely to visit website B), and so on.
Also Read | Applications of Data Mining in Retail
Steps in Association Rule Mining

Association Rules are based on if/then statements. These statements aid in the discovery of associations between independent data in a database, relational database, or other data repository. These rules are used to determine the relationships between objects that are commonly used together.

Support and confidence are the two primary patterns used by association rules. The method searches for similarities and rules formed by decomposing data for commonly used if/then patterns. Association rules are typically used to simultaneously satisfy user-specified minimum support and a user-specified minimum resolution. To implement association rule learning, various algorithms are used.

Association Rule Mining can be described as a two-step process.

Step 1: Locate all frequently occurring itemsets

An itemset is a collection of items found in a shopping basket. It can include many products. For example, [bread, butter, eggs] is a supermarket database itemset.

A frequently occurring item set is one that frequently appears in a database. This raises the issue of how frequency is defined. This is where your support comes into play. The frequency of an item in the dataset is used to calculate its support count.

The number of supporters can only speak to the frequency of an item set. It does not consider the relative frequency, or the frequency in relation to the number of transactions. This is referred to as an itemset's support. The frequency of an item set in relation to the number of transactions is referred to as its support.

Step 2: Create strong association rules using the frequently used itemsets

Association rules are created by constructing associations from the frequent itemsets created in step 1. To find strong associations, this employs a metric known as confidence.

The Apriori algorithm is one of the most fundamental Association Rule Mining algorithms. It is based on the idea that "having prior knowledge of frequent itemsets can generate strong association rules." The term Apriori refers to prior knowledge.

Apriori discovers frequent itemsets through a process known as candidate itemset generation. This is an iterative approach that uses k-itemsets to explore (k+1)-itemsets. The set of frequent 1-itemsets is found first, followed by the set of frequent 2-itemsets, and so on until no more frequent k-itemsets can be found.

An important property known as the Apriori property is used to reduce the search space to improve the efficiency of the level-wise generation of frequent itemsets. According to the Apriori Property, "all non-empty subsets of a frequent itemset must also be frequent."

This means that if an item is frequent, its subsets will also be frequent. For example, if [Bread, Butter] is a frequent item set, [Bread] and [Butter] must be frequent individually as well.

Apriori Algorithm for Mining Association

Apriori is one of several statistical algorithms that have been developed to implement association rule mining. In this article, we will look at the theory behind the Apriori algorithm and then implement it in Python.

The Apriori algorithm is made up of three major components:

With the help of an example, we will explain these three concepts.

Assume we have a database of 1,000 customer transactions and want to find the Support, Confidence, and Lift for two items, such as burgers and ketchup. One hundred transactions contain ketchup, while 150 contain a burger. In 50 of the 150 transactions where a burger is purchased, ketchup is also included.

Support refers to an item's default popularity and can be calculated by dividing the number of transactions containing a specific item by the total number of transactions. Assume we want to find help for item B. This can be calculated as follows:
Support(B) = (Transactions with (B))/ (Total Transactions)
If item A is purchased, confidence refers to the likelihood that item B will be purchased as well. It can be calculated by dividing the number of transactions in which A and B are purchased together by the total number of transactions in which A is purchased. It can be expressed mathematically as:

Confidence(AB) = (Transactions with both (A and B))/ (Transactions containing A)
Lift(A -> B) denotes the increase in the sale ratio of B when A is sold. Lift(A -> B) is computed by dividing Confidence(A -> B) by Support (B). It can be expressed mathematically as:
Lift(AB) = (Ab) Confidence/(B) Support Also Read | Top Data Mining Algorithms
The strength or dependability of association rule mining is critical to consider. As association rule mining uncovers interesting associations and relationships among large sets of data items, it reveals how frequently a particular item set appears in a transaction. Association rules are useful in data mining for analyzing and forecasting customer behavior.

However, the main disadvantage of association rule algorithms is that they produce boring rules, have many discovered rules, and perform poorly. The employed algorithms have too many parameters for someone who is not a data mining expert, and the produced rules are too many, with most of them being uninteresting and having low comprehensibility.

With the rapid growth of e-commerce websites and the general trend in industries (particularly retail) to turn to data for answers, every organization is looking for more opportunities for best product bundles to run discounts and promotions on.

In exchange for these decisions, it is expected that sales will increase and inventory levels will decrease. Analyzing "what is bought together" can often produce very interesting results.

A rule-based method for discovering relationships between variables in large datasets is association rule learning. The retail products will be our variables in the case of retail POS (point-of-sale) transactions analytics. It basically finds strong associations (rules) with some "strength" level represented by several parameters.

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why is association rule mining important

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benefits and drawbacks of association rule mining

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Association Rule Mining: Importance and Steps | Analytics Steps

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Association rule mining is a technique for determining underlying relationships between various items. Learn more about association rule mining importance and steps.

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8 Applications of AI in the Aerospace Industry (Hrithik SainiMar 21, 2022)

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8 Applications of AI in the Aerospace Industry (Hrithik SainiMar 21, 2022)

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63102156 Chainarong Kaewprasert
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5 Benefits of using AI in the Cryptocurrency Market(Ashesh Anand, Mar 23, 2022)

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5 Benefits of using AI in the Cryptocurrency Market(Ashesh Anand, Mar 23, 2022)

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Example 1: How is AI transforming Business Intelligence (BI)? (Ashesh Anand, Jun 06, 2022)

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Example 1: How is AI transforming Business Intelligence (BI)? (Ashesh Anand, Jun 06, 2022)

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63xxxxxx Nichnan Kittiphattanabawon
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AI is strengthening its claws everywhere, leaving lasting notes on the world, from deep learning-enabled software to autonomous cars, smart robotics to IoT apps. In the Gartner Hype Cycle for Emerging Technologies, AI-enabled technologies have distinct ratings, indicating that AI is invincible in the technology world.

We live in a time when everything is changing. Industries are undergoing changes. Small businesses are gradually gaining market share and developing a brand. In business, the term monopoly is slowly receding, and anyone with the appropriate strategy and new products can share revenue.

Businesses, on the other hand, are attracted to AI business intelligence and analytics skills since the notion increases income streams and provides useful insights. In fact, the growing volume and complexity of data within firms is encouraging the use of AI to improve operations.

There are a wide range of applications of AI in the business world. In reality, most of us engage with AI on a regular basis in some form or another. AI is already affecting practically every business activity in every industry, from the commonplace to the spectacular.

As AI technologies become more widely used, they are becoming increasingly important for businesses to maintain a competitive advantage. So, how does artificial intelligence (AI) operate in the corporate world? Let us try to grasp what artificial intelligence is and why it is so vital for today's commercial corporations through this blog.
Also Read | Top 5 SaaS Tools Every Business Should Use
Business Intelligence and Artificial Intelligence

For those unfamiliar with the term, business intelligence (BI) refers to systems that automate the collecting, processing, and analysis of large amounts of data. Organizations may use the massive amounts of data they collect to improve their business processes and make better decisions.

As important as BI is, it has inherent flaws that limit the value it provides to businesses. For example, BI capability has a limit, and the sheer volume of data being collected is approaching that limit. The use of AI to BI will be extremely beneficial because the latter complements the former.

Artificial intelligence, or AI, is a phrase used to describe the process of teaching computer systems human intelligence features like learning, problem solving, and decision making. AI-powered machine learning (ML) technology has largely enabled AI applications in business, which can be utilized to accomplish specialized activities such as:

Read and understand written text, such as criticism or suggestions from users.

Identify and categorize visual pictures and photos.

Recognize face traits and objects, allowing for product recommendations and facial identification.

The enhanced process automation capabilities that AI technology delivers are one of the most significant ways that AI technology is revolutionizing modern enterprises. Automation in all forms is a welcomed business element as firms emphasize their attention on developing their business, lowering overhead expenses, and improving their customer experience.

While AI-driven technologies can work as stand-alone business solutions, in many cases, the AI components are employed to improve and streamline existing systems and processes.

AI is now being utilized to help web developers create highly functional and aesthetically beautiful website layouts, for example. Machine learning algorithms can be used to suggest layout changes and font styles based on the type of business or target market.

The landscape of fleet management is another area where AI has caused major disruption. Thanks to automakers like Tesla, autonomous car operating has been well-known for a long time. That same technology, on the other hand, is now being leveraged to create new solutions for commercial freight businesses.

Using telematics and on-vehicle sensors, AI-powered solutions can follow mobile assets like self-driving semi-trucks or courier vans in near real-time, giving considerable safety and cost-saving benefits.

Take, for instance, the consumer goods industry. The firm has no idea how well their trade promotions are performing and wants to know how their data is performing in various sectors.

The only way to address this problem is to use artificial intelligence in business. Big data and AI technologies make it easier to combine chaotic and irregular data when performing text analysis.

AI algorithms have enabled the integration of a wide range of data sources into a dependable and consistent business. Furthermore, AI assists users in retrieving the information and insights they require.

BI inside AI may be more consistent when the user interacts with and works on these insights. When AI is paired with BI tools, it can advise the design team on what to cut and what to keep in the event of a new promotion. The sales rep will have all of the information they need on their smartphone.

How can AI assist us in putting data to use?

Following the real-time collection of data. Artificial intelligence enables users to compete with other data sources by utilizing their own data. Users will be able to put AI to the test by detecting new patterns and developing actionable recommendations based on those new trends. Finally, it ensures that the user receives the data insights when and where they are required.

Despite this, many companies are still trailing behind when it comes to adopting AI into their analytics. Companies that employ artificial intelligence outperform their competitors in terms of generating revenue and overall business performance.

Breaking down a big volume of data into granular insights is where business intelligence's true value may be seen. It allows businesses to grasp the finer points of a larger picture. BI applications' capacity and functionality are also enhanced by AI. Real-time insight, on the other hand, can be a serious problem.

In fact, the primary function of BI is to analyze and visualize data. But! This data result cannot be generated by BI in real-time, nor can it be predicted by it. AI, combined with cutting-edge technology like machine learning (ML), gives it the ability to generate real-time insights and trends. Increasing the value of BI to the organization is a good thing.
Also Read | Advantages of Business Intelligence (BI) in Finance
What role does AI play in Business Intelligence Applications?

Business intelligence applications benefit from artificial intelligence. Business intelligence is better positioned with AI to break down vast volumes of big data into granular insights. AI helps businesses to better comprehend the value of tiny components within a broader picture.

Then there's the problem of real-time insights, given that BI can analyze and visualize huge data but not foresee trends or provide real-time insights in its current iteration. AI, on the other hand, makes use of cutting-edge technology such as machine learning to provide real-time predictions regarding future trends.

As a result, business intelligence systems' capabilities will be expanded, and their value to businesses will be enhanced.

AI-enabled Business intelligence (BI) enables organizations to gain vital insights from previously unexplored data. AI-powered business intelligence apps may analyze new data and spot any significant trends for the company.

AI also enables BI to take advantage of cutting-edge technologies such as predictive analytics, machine learning, and natural language processing in order to broaden the scope of the information displayed.

Organizations want solutions that bridge the gap between visual representation and actionable insights, rather than relying on a visual dashboard of big data trends. This is where AI-powered business intelligence can help.

Even with business intelligence technologies, surveying massive data can be a difficult operation. To gather the necessary information, professional data analysts must examine hundreds of charts and dashboards. AI, on the other hand, can make things easier.

Because AI-based technologies such as natural language processing and machine learning are bridging the gap between machine and human communication, simplification occurs. AI enables robots to better understand human language and vice versa, making it easier for data analysts to discover correlations and insights.

AI-powered BI enables businesses to analyze a wider range of data, including both structured and unstructured data, in order to obtain more detailed and comprehensive insights.

When data comes in from various sources, it's difficult for dashboards to present the data in an easy-to-read style. With AI, however, the data may be specified at a large scale, making it easier to acquire meaningful insights. Then there's the question of talent.

Data analysts are in limited supply as of 2019. By doing some of the responsibilities traditionally assigned to a data analyst, the correct processing software can assist ease some of the challenges caused by a skills shortage.

Employees in the UK waste millions of days every year on time-consuming jobs, according to Entrepreneur Magazine.

Similarly, according to a workplace research conducted by AtTask and Harris Interactive, employees in the United States spend only 45 percent of their time on their principal responsibilities. Employers are essentially compensating their employees for not working as much as they should.

With such revelations of employee irresponsibility, any method to address this waste of company resources is a welcome development for executives.

Automating routine tasks is possible. Employers will no longer have to be concerned about their employees failing to complete their tasks. Machines that have been programmed will carry out tasks in accordance with the criteria and norms that have been established.
Also Read | Business Intelligence in Retail Industry
What Are the Industry-Wide Benefits of AI?

The advent of artificial intelligence (AI) into the corporate sphere has resulted in a slew of automation advantages for developing companies. This is especially true for data scientists who use machine learning to improve the accuracy and efficiency of very complicated and technical business analysis. AI's importance isn't confined to tech-based businesses, however.

There are many applications of AI in business like human resources, accounting, and even healthcare, where enormous volumes of client and customer data are accessible and maintained.

By processing vast volumes of data faster and more efficiently than humans, AI-driven systems can help medical practitioners diagnose and treat their patients faster and more accurately. Meanwhile, AI technologies in HR and accounting departments aid in the automation of redundant and repetitive data input and reporting chores, as well as increasing productivity and output.

Small businesses can now use tried and true strategies to attain higher business goals thanks to the introduction of AI into the market. Whether pleasant or rough, the future is rapidly approaching, and artificial intelligence will undoubtedly play a role.

As this technology advances, the world will witness new businesses, consumer applications, and employment displacement, as well as the creation of whole new jobs.

AI boosts competitive advantage for businesses, while established corporations offer the infrastructure on which to construct creative solutions. AI has become a necessary component of reality, from functioning as a robot in a manufacturing plant to self-driving cars and voice-activated resources in complex medical procedures.

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what is artificial intelligence (ai) and why does it matter in business?

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applications of ai in business intelligence

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how can artificial intelligence (ai) assist us in putting data to use?what role does ai play in bi applications?

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How is AI transforming Business Intelligence (BI)? | Analytics Steps

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How does AI operate in the corporate businessworld? Let us try to grasp what artificial intelligence is and how it is transforming business intelligence.

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Example 2: New Books and Resources for DSC Members (Vincent Granville, November 18, 2018)

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Example 2: New Books and Resources for DSC Members (Vincent Granville, November 18, 2018)

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63xxxxxx Nichnan Kittiphattanabawon
FAIL เป็นการแนะนำหนังสือไม่ใช่การประยุกต์ใช้

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We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. We invite you to sign up here to not miss these free books.

Currently, the following content is available:

By Vincent Granville. This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from applications to theory), in contrast to the traditional top-down approach. The material is accessible to practitioners with a one-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications with numerous illustrations, is aimed at practitioners, researchers, and executives in various quantitative fields.

The table of content is available here. The book can be accessed here (members only.)

By Dan Howarth and Ajit Jaokar, October 2019. 42 pages. Part 1 will introduce the core concepts of Deep Learning. We will also start coding straightaway with Tensorflow 2.0. In part 2, we use another dataset - the mnist dataset - to build on our knowledge. In particular, we will:

Introduce Computer Vision

Introduce convolutional layers into our models

Introduce the concept of regularisation

Introduce the validation set in training our model

Introduce how to save and reuse our model

The table of content is available here. The book can be accessed here (member sonly.)

3. Getting Started with TensorFlow 2.0

By Amita Kapoor and Ajit Jaokar. In this book, we introduce coding with tensorflow 2.0. We show how to develop with tensorflow 1.0 and contrast how the same code can be developed in tensorflow 2.0. The book emphasizes the unique features of tensorflow 2.0. Earlier this year, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. The key differences are:

Ease of use

Eager Execution

Model Building and deploying made easy

The Data pipeline simplified

The table of content is available here. The book can be accessed here (members (members only.)

This tutorial began as a series of weekend workshops created by Ajit Jaokar and Dan Howarth. The idea was to work with a specific (longish) program such that we explore as much of it as possible in one weekend. This book is an attempt to take this idea online. The best way to use this book is to work with the Python code as much as you can. The code has comments. But you can extend the comments by the concepts explained here.

The table of contents is available here. The book can be accessed here (members only.)

This online book is intended for beginners, college students and professionals confronted with statistical analyses. It is also a refresher for professional statisticians. The book covers over 600 concepts, chosen out of more than 1,500 for their popularity. Entries are listed in alphabetical order, and broken down into 18 parts. In addition to numerous illustrations, we have added 100 topics not covered in our online series Statistical Concepts Explained in Simple English. We also included a number of visualizations from our series Statistical Concepts Explained in One Picture.

The table of content is available here. The book can be accessed here (members only.)

This book by Ajit Jaokar and Ayse Mutlu is the second book in the 'in a weekend' series - after Classification and Regression in a weekend. The idea of the 'in a weekend' series of books is to study one complex section of code in a weekend to master the concept. Cloud computing changes the development paradigm. Specifically, it combines development and deployment (the DevOps approach). In complex environments, the developer has to know more than the coding. Rather, she has to be familiar with both the data engineering and the DevOps. This book helps you to get started with coding a complex AI application for the Cloud(Azure).

The table of content is available here. The book can be accessed here (members only.)
7. Book: Applied Stochastic Processes
Full title: Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems. Published June 2, 2018. Author: Vincent Granville, PhD. (104 pages, 16 chapters.)

This book is intended to professionals in data science, computer science, operations research, statistics, machine learning, big data, and mathematics. In 100 pages, it covers many new topics, offering a fresh perspective on the subject. It is accessible to practitioners with a two-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications (Blockchain, quantum algorithms, HPC, random number generation, cryptography, Fintech, web crawling, statistical testing) with numerous illustrations, is aimed at practitioners, researchers and executives in various quantitative fields.

New ideas, advanced topics, and state-of-the-art research are discussed in simple English, without using jargon or arcane theory. It unifies topics that are usually part of different fields (data science, operations research, dynamical systems, computer science, number theory, probability) broadening the knowledge and interest of the reader in ways that are not found in any other book. This short book contains a large amount of condensed material that would typically be covered in 500 pages in traditional publications. Thanks to cross-references and redundancy, the chapters can be read independently, in random order.

The table of content is available here. The book (PDF) can be accessed here (members only.)

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New Books and Resources for DSC Members - DataScienceCentral.com

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We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. We invite you to sign up here to not miss these free books.

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Example 3: Covid or just a Cough? AI for detecting COVID-19 from Cough Sounds

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Example 3: Covid or just a Cough? AI for detecting COVID-19 from Cough Sounds

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FAIL ไม่มีชื่อผู้เขียนและวันที่

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By Gowtham Ramesh, researcher at Bosch Centre for Data Science and AI at IIT Madras and Dr. Sundeep Teki, a leader in AI and Neuroscience.
Can you spot the Covid-19 cough? Click here to watch the video.
Coughing and sneezing were believed to be symptoms of the bubonic plague pandemic that ravaged Rome in the late sixth century. The origins of the benevolent phrase, "God bless you," after a person coughs or sneezes is often attributed to Pope Gregory I, who hoped that this prayer would offer protection from certain death. The flu-like symptoms associated with the plague co-occur during the current Covid-19 pandemic as well, to the extent where "normal" coughs draw immediate alarm and concern. However, in the present technologically advanced times, we need not resort only to prayers. We can now build sophisticated AI models that learn complex acoustic features to distinguish between cough sounds from Covid-19 positive and otherwise healthy patients.

Since the start of the Covid-19 pandemic, multiple AI research teams have been working towards leveraging AI to improve screening, contact tracing, and diagnosis. Most of the preliminary work involved CT or X-ray scans [1,2,3,4] to diagnose Covid-19 faster and, in some cases, with better accuracy than the RT-PCR test. Recently, AI researchers have started testing cough sounds for preliminary diagnosis or a prescreening technique for Covid-19 detection in asymptomatic individuals. This is beneficial because, while someone may not have noticeable symptoms, the virus may still cause subtle changes in their body that may be detected by specific algorithms combining audio signal processing and machine learning. Cough-based audio diagnosis is non-invasive, cost-effective, scalable, and, if approved, could be a potential game-changer in our fight against Covid-19. This technology might also prove to have better efficacy than the standard strategy of prescreening for Covid-19 on the basis of temperature, especially for asymptomatic patients.

The intuition behind using cough sounds
Figures 1a and 1b: Four types of cough sounds with the original amplitude, Fast Fourier transform output, and 1D spectrogram (source).
Cough, along with fever and fatigue, is one of the key symptoms of Covid-19 [5]. Studies have shown that cough from different respiratory ailments has unique characteristics due to the different nature and location of the underlying irritants [6]. Though a human ear cannot differentiate these features, AI models can be trained to learn these features and discriminate between a cough from a Covid-19 positive and negative patient.

One of the significant challenges is the availability of the right quantity and quality of data to build an AI model that can make robust predictions about the underlying medical ailment based on cough sounds. Cough is, unfortunately, a common symptom of many respiratory and non-respiratory diseases (see Figure 2). Hence, an AI model must also learn to distinguish coughs related to Covid-19 from coughs caused by other respiratory ailments. The prediction of such AI models could be considered as such or be further substantiated by other clinical tests, for instance, an RT-PCR screening test.
Figure 2: Non-Covid-19 infections that can cause cough (source). Figure 3: Overview of AI-based prescreening app by 'Cough against Covid-19' project (source).
Since spring 2020, AI researchers have collected cough sound data from the general public via mobile apps and websites and developed AI solutions for cough-based prescreening tools. Some of these works include - AI4Covid-19 [6] from the University of Oklahoma, Covid-19 sounds [7] from the University of Cambridge, Coswara [8] from IISC Bangalore, Cough against Covid-19 [9] from Wadhwani AI, Covid-19 Voice detector [10] from CMU, COUGHVID from EPFL [11], Opensigma from MIT [12], Saama AI research [13] and UK startup Novoic amongst others.
Figure 4: Interface of Covid-19 sounds app from a University of Cambridge team (source).
While the cough data in the AI4Covid-19, Cough against Covid-19, and Saama AI research projects are collected in a controlled setting or collected from hospitals under clinical supervision, Coswara, Covid-19 sounds, and COUGHVID, MIT's project, use crowdsourced and uncontrolled data collected through their websites or app. The website/app records forced coughs (Coswara also collects more audio - breathings sounds, vowel pronunciations, counting numbers from one to twenty) and gather useful metadata like age, gender, ethnicity, and health status information, like details of a recent Covid-19 test, current symptoms, and health status, like the occurrence of diabetes, asthma, heart disease, amongst others.

The AI4Covid-19, Covid-19 sounds, and Saama AI research projects also train models to differentiate Covid-19 cough sounds from non-Covid-19 infection coughs like pertussis, asthma, and bronchitis. MIT researchers used features from their previous work to detect Alzheimer's from cough sounds [14] and fine-tuned their AI model to detect Covid-19 from a healthy person's cough. The connection between Covid-19 and the brain with recently reported symptoms of neurological impairments in Covid-19 patients led authors to test the same biomarkers - vocal cord strength, sentiment, lung performance, and muscular degradation for detecting Covid-19 coughs. " Our research uncovers a striking similarity between Alzheimer's and Covid-19 discrimination. The exact same biomarkers can be used as a discrimination tool for both, suggesting that perhaps, in addition to temperature, pressure, or pulse, there are some higher-level biomarkers that can sufficiently diagnose conditions across specialties once thought mostly disconnected. " [11]
Figure 5: Overview architecture of AI4Covid-19 COVID-19 discriminator with cough recordings as input (source).
Once an AI model is trained, it can be incorporated into a user-friendly app where users can log in and submit their cough sounds via their phones to get instant results. The model prediction can be used to ascertain whether a user might be infected and follow-up to confirm with a formal test like RT-PCR. Figure 5 shows an overview of the architecture developed by the AI4covid-19 team. It includes a cough detection model to check the quality of the cough sound and prompts the user to re-record in case of noisy recording or non-cough sound. The detected cough is then sent to Covid-19 diagnosis model(s) to discriminate between a cough from a Covid-19 positive and negative patient.

The preliminary results of most of the teams look promising and confirm the hypothesis that cough sounds contain unique information and latent features to aid diagnosis and prescreening for Covid-19. The MIT lab has collected around 70,000 audio samples of different coughs with 2,500 coughs from confirmed Covid-19 positive patients. The trained model correctly identified 98.5% of people with Covid-19 and correctly ruled out Covid-19 in 94.2% of people without the disease. For asymptomatic patients, the model correctly identified 100% of people with Covid-19, and correctly ruled out Covid-19 in 83.2% of people without the disease. Cambridge's Covid-19 sounds project reported an 80% success rate in July 2020.

In spite of the similar acoustic modeling pipeline and deep learning approaches, it is difficult to compare these preliminary results across these projects as each AI model is trained using distinct datasets (owing to the scarcity of publicly available datasets to different benchmark works). Since cough also covaries with age and gender, it is important to collect diverse data to make any AI solution generalize across patient populations around the world and accepted as a standard non-invasive prescreening tool for Covid-19. The data collection for most of the projects is still ongoing, and readers are suggested to check out these websites, donate coughs, and help save lives: Covid-19 sounds, Coswara, Cough against Covid-19, Covid-19 Voice detector, COUGHVID, Opensigma, Novoic, and AI4COVID-19.

References:

[1] L. Wang, A. Wong ''Covid-19-Net: a tailored deep convolutional neural network design for detection of Covid-19 cases from chest radiography images,'' (2020) arXiv preprint arXiv:2003.09871vol. 1

[2] Zhang I, Xie Y, Li Y, Shen C, Xia Y. ''Covid-19 screening on chest X-ray images using deep learning based anomaly detection,''. 2020. arXiv preprint arXiv: 2003.12338.

[3] Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, et al. ''Artificial intelligence distinguishes Covid-19 from community acquired pneumonia on chest ct. '' Radiology; 2020. 200905

[4] Zhao W, Zhong Z, Xie X, Yu Q, Liu J. ''Relation between chest ct findings and clinical conditions of coronavirus disease (Covid-19) pneumonia: a multicenter study. ' American Journal of Roentgenology 2020:1-6.

[5] WHO. 2020b. Q&A on coronaviruses (COVID19). https://www.who.int/emergencies/diseases/novelcoronavirus-2019/question-and-answers-hub/q-a-detail/qa-coronaviruses. Accessed: 2020-11-17.

[6] Imran, Ali, et al. "AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app." (2020) arXiv preprint arXiv:2004.01275

[7] Brown, Chloë, et al. "Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data." (2020) arXiv preprint arXiv:2006.05919

[8] Sharma, Neeraj, et al. "Coswara--A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis." (2020) arXiv preprint arXiv:2005.10548

[9] Bagad, Piyush, et al. "Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds." arXiv preprint arXiv:2009.08790 (2020).

[10] Deshmukh, Soham, Mahmoud Al Ismail, and Rita Singh. "Interpreting glottal flow dynamics for detecting COVID-19 from voice." arXiv preprint arXiv:2010.16318 (2020).

[11] Orlandic, Lara, Tomas Teijeiro, and David Atienza. "The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms." arXiv preprint arXiv:2009.11644 (2020).

[12] Laguarta, Jordi, Ferran Hueto, and Brian Subirana. "COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings." IEEE Open Journal of Engineering in Medicine and Biology (2020).

[13] Pal, Ankit, and Malaikannan Sankarasubbu. "Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing." arXiv preprint arXiv:2010.02417 (2020).

[14] J. Laguarta, F. Hueto, P. Rajasekaran, S. Sarma, and B. Subirana, "Longitudinal speech biomarkers for automated alzheimer's detection," Cognitive Neuroscience, Preprint, pp. 1-10, 2020. https://www.researchsquare.com/article/rs-56078/latest.pdf

Bios: Gowtham Ramesh is a Post Baccalaureate fellow and researcher at Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI) at IIT Madras. He was previously a Senior Machine Learning Engineer at Quantiphi, where he worked on low resource speech to text, handwritten OCR, and Conversational Question answering, amongst others.

Dr. Sundeep Teki is the Founder of a new EdTech startup (stealth) focused on AI reskilling to create a talent pipeline at scale that will build AI for India. He is a leader in AI and neuroscience with professional experience in BigTech (Conversational AI at Amazon Alexa AI, Seattle), unicorn startup (Applied AI at Swiggy, Bangalore), and academia (Cognitive Neuroscience at Oxford University and University College London). He has published 40+ papers on Neuroscience and AI (~1800 citations), secured £500k+ in research funding, and is recognised by the Royal Society, UK as an 'Emerging Leader in Neuroscience.'
Related:

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Covid or just a Cough? AI for detecting COVID-19 from Cough Sounds - KDnuggets

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Increased capabilities in screening and early testing for a disease can significantly support quelling its spread and impact. Recent progress in developing deep learning AI models to classify cough sounds as a prescreening tool for COVID-19 has demonstrated promising early success. Cough-based diagnosis is non-invasive, cost-effective, scalable, and, if approved,...

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Example 4: Role of Artificial Intelligence in Healthcare (Riya Kumari, Mar 05, 2021)

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Example 4: Role of Artificial Intelligence in Healthcare (Riya Kumari, Mar 05, 2021)

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63xxxxxx Nichnan Kittiphattanabawon (RESUBMIT)
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Artificial Intelligence or AI technology is gaining popularity worldwide. It is being used in big organizations as well as our daily lives. Now, taking one step forward, AI is developed to prove useful in the healthcare industry. In simple terms, all the data from diagnosis will be collected and then used for understanding the diseases, so that they can be treated with more success rate.

Also, it will ease the life of patients. They will have a digital record that will be accessible to doctors. This way, they can get healthcare advice by sitting at home. Doctors who access the records might want to change them. So, they can do it easily. Also, it will provide security to the personal data of people.

So, let's read in detail and learn how AI will assist healthcare in advancement.

AI gives an enormous number of benefits than traditional techniques. So, let's have a glance at some points that clearly portray some benefits of AI in healthcare.

It helps in accurate decision making - There are many times when a patient requires to be operated on or given medications instantly. At such times, AI will prove to be life-saving. Where the doctors need to go through previous records of the patient, AI can quickly analyze the records and suggest actions instantly. This will consume less time and will also be more effective when it comes to decision making.

Recommended blog - IoT in healthcare

The perks of Robot Process Automation (RPA) - As per Business Insider Intelligence, 30% of healthcare costs are associated with administrative tasks. Robot Process Automation (RPA) can aid in a massive range of healthcare tasks that include managing the appointment requests of patients, patient registration, reviewing claims and payment integrity complaints, in denials and appeals, in data entry, care coordination, provider data management, and credentialing and also in finance and accounting as well as billing tasks.

Related blog - IoT examples
You might at times wonder if nurses will ever be replaced by robots? The answer is debatable, posing several dilemmas. Robots can actually play a positive role in the lives of nurses.

The Robots are made to execute nursing functions such as collecting vitals signs, providing ambulation support, administering medications, and learning protocol for infectious diseases. With all these perks emerging as a reality and robots gradually getting integrated inside healthcare settings, the conventional role held by nurses will possibly undergo a change.

As per Research, eight to sixteen percent of the time nurses spend on the job is assigned for duties that are actually not specifically nursing duties and can be delegated over to another staff or member. With the support of robots now shadowing them, the nurses will have more time to invest in taking care of their patients.

Technologies are progressing day by day and if we take the example of a robot who matched the human level then Sophia is the best example of it. Sophia is a widely known social robot who was developed to serve as a companion for senior citizens. This robot symbolizes the potential technology holds in enhancing how robots can operate on a human-like level.
Recommended blog - AI myths
Thus, Artificial Intelligence is still at the infant level in the healthcare sector and robots will take time to match the level of human nurses.

Future of AI in Healthcare

Now, coming towards the future of AI in healthcare, are you thinking about what the future of the healthcare sector with AI looks like? So, the answer is the future of Artificial Intelligence is pretty promising.

(You can also check out our blog on Big Data in Healthcare)

As indicated by a report by Tractica, the utilization of 22 healthcare AI apparatuses will produce a yearly income of $8.6 billion by 2025, in view of the current use patterns.

The future of healthcare and the future of machine learning and AI are enormously tiered.

Artificial Intelligence in the healthcare market is discharged to rise at a CAGR of 44.9% from 2020 to 2026.

By 2030, AI will permit numerous citations of data to disclose structures in disorder and assist medicine and supervision.

AI has a lot of scope in healthcare services in the coming years. The hybrid models will help doctors in the diagnosis of patients, identifying diseases, etc.

However, this will just reduce the doctor's tasks. The main responsibility still lies in the hands of the doctor. In this way, the treatment process will be faster and more effective.

"The promise of artificial intelligence and computer science generally vastly outweighs the impact it could have on some jobs in the same way that, while the invention of the aeroplane negatively affected the railroad industry, it opened a much wider door to human progress." -Paul Allen, Co-Founder of Microsoft

So, in conclusion, we can say that Artificial Intelligence is striving its best to expand the healthcare sector. AI in healthcare is assisting doctors, nurses, and even patients health and encouraging them in recovery even faster. AI will help patients in cost dropping with its developed and better credible diagnoses. AI provides doctors time to concentrate slightly on desk work and more on understanding patients and caring for them.
Suggested blog - Healthcare data analytics
In simple words, the objective of AI is to make computers extra beneficial in understanding complicated healthcare challenges. Accordingly, in the coming days, we will discern more advancement in the healthcare sector because of artificial intelligence.

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what is the role of ai in healthcare industry

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the role of ai in the future of healthcare

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what is the role of artificial intelligence in healthcare

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Role of Artificial Intelligence in Healthcare | Analytics Steps

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Artificial Intelligence or AI technology is gaining popularity worldwide. It is being used in big organizations as well as our daily lives. Now, taking one step forward, AI is developed to prove useful in the healthcare industry.