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Face Swap

Main idea:
An explanation of how face-swaps work, and what it provides.
Arguments/facts:
- The way it works is that the computer scans all the key points of both people's faces, and "layer" it onto the corresponding parts.
- It's sometimes a very complicated and long process.
-It may take up a lot of computing power, depending on the face.

Dialogue:
This could be very dangerous. If it gets too advanced, people may use it for the wrong reasons, like making a public figure look bad.

Summary:
Face swapping is still a bit early, as if you try to do it yourself, you can notice in some places that it looks very weird. However, technology is still improving, and so it may advance into something a lot more in the future.
APA

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June 23, 2022

#AI #face swap

Currently, digital systems for analyzing faces and working with them are widely used in various systems, for example, identification of individuals by photo or signature of documents. One branch of face analysis is face replacement systems. In this post, we will talk about this technology.

To begin with, it is worth answering the question: what does face replacement provide? Face swap is a system that, for the selected existing photo/video stream, replaces the real existing face in the original image with the selected one from another. Where can it be applied? These systems are already in operation for entertainment purposes, such as celebrity masks in popular social services, but also supporting software for video recording studios, video editing, etc. Each of the face swapping apps or systems has its own implementation, and the question arises of how to make the system cheaper and less costly to implement by automating the approach as much as possible.

How do these systems work, and what do you need to swap your photos and make a new realistic photo or video?

Face swap by mesh

We begin our discussion with a method based on affine transformations of a triangular face mesh. On both images, key points are searched, the next step is the designation of a grid in a shape of a triangle that will make up the complete picture of each photo, and the final step is the transformation of the corresponding triangles from the original image into a given image according to their position. You can witness this process real-time. Check how face swapping looks in action.

Matt Damon and Mark Wahlberg - face swap by mesh

Example

The advantage of this method is the speed of work, in fact, the most energy-intensive process is a neural network that will search for key points of faces; the rest of the transformations do not require large computing power, so we can use this face swap solution in many real-time devices without using cloud computing, unlike the solutions below.

Face swap systems: Encoder - Decoder

This approach is implemented using a relatively simple idea to train an autoencoder neural network consisting of 2 parts, an encoder and a decoder , the encoder is used to compress the input image and obtain a hidden representation of the image, and the decoder to reconstruct the original image, a feature of this network is that we will use a common encoder, and everyone will have their own decoder.

The operation of this algorithm is as follows, suppose we want to replace the first person's face with the face of a person from the second image, we run the face of the second person through the encoder and run the resulting latent representation through the decoder of the first. Essentially, the purpose of the encoder is to get a hidden representation that describes the facial expressions, the direction of the gaze, the turn of the face, skin tones, and so on., and the decoder, in turn, having learned a specific face, applies these features to reconstruct the original image.

DeepFaceLab

It is one of the most effective face swapping systems in terms of the quality of work, the main drawback of which in certain tasks can be a decisive factor is manual tuning and large computing power. If you want to use this face swap system, an internet connection is required. This algorithm is similar to the solution of auto encoders, and is equipped with additional features such as manual frame correction on processed images. It should be noted that the high-quality work of the algorithm requires numerous source images for each of the transferred personalities, as well as the heterogeneity of image data such as the angle of rotation of the face, lightness, and even moving mouth or eyes. Simply choose anything from your camera's video feed, and let the fun begin!

In general, this face swapping system is a good option for those for whom the quality of work is a determining factor in other cases, it is necessary to use alternative solutions.

First Order Motion Model / Motion-supervised Co-Part Segmentation

In the previous approach, we considered a model that requires significant intervention in the operation of the algorithm by a specialist, but what if we want a fully automated process? For such a task, there is a solution using only one image: First Order Motion Model. The project has an open-source code on GitHub, the idea of ​​this solution is not to generate new frames but by studying the position of key points, apply affine transformations to the target image.

Example of the algorithm

This face swap model has a few significant drawbacks, and the main one is poor quality when working with face rotations; the problem is that the hidden parts of the body at the zero point are difficult to reconstruct by the generator, as well as the lack of image adaptation to the target skin color.

Conclusion

We have made an overview of different ways to solve the face swapping problem based on neural networks, from the simplest to SOTA solutions that currently exist. Of course, there are a lot of face swap apps available on the market, but they all rely on the above or similar technologies.

We hope that you have learned more about face swapping systems and how they differ. A better understanding of them can help you with efficient use. Start swapping faces from static photos and make your own unique pictures!

As you can read, there are a few face swap systems based on similar technologies. Nevertheless, they differ a lot. Hopefully, this piece will be a good start to expanding your knowledge, learning how to manage this kind of technology and choosing the best face swapping app.

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Codahead desings and develops custom software, Internet of things (IoT), Artificial Intelligence (ML) and blockchain products. Have expertise in web and mobile technologies, data science, R&D and product design. Since 2008, Codahead delivered a variety of products for companies of all sizes.

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AI Could Displace 800 Million Jobs by 2030

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AI Could Displace 800 Million Jobs by 2030

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Last updated 15th Dec 2022

Artificial intelligence (AI) could displace 800 million jobs (30% of the global workforce) by 2030. That's according to a new BanklessTimes.com report. The site holds that increased AI adoption will greatly impact people's lives and their ability to make a living in the years ahead.

According to BanklessTimes CEO Jonathan Merry,

AI is gaining traction in many industries as automation can simplify mundane tasks and help companies become more efficient. But it also threatens jobs traditionally done by humans. Machines can analyze data faster, making them ideal for repetitive jobs involving crunching numbers or sorting items. For instance, some businesses are using bots as customer service agents.

BanklessTimes CEO Jonathan Merry

Merry holds that as AI capabilities increase and become more widespread, there's an urgent need to assess its effects on the global workforce. Further, we must consider steps to prevent displacement due to automation. He concludes that the impact of AI on employment will vary depending on location, sector, and job type.

So What Are the Negative Impacts of AI?

As already stated, AI-driven automation will likely result in significant job losses across sectors. Soon, bots and AI algorithms could render many workers redundant. This could have a severe economic impact on the affected individuals, communities, and entire countries.

Massive unemployment could lead to increased poverty and wide income inequalities in the affected areas. And in turn it could trigger social unrest and accompanying vices like crime.

Again the rising demand for AI-powered solutions will lead firms to shift their focus from investing in human resources toward automation. This trend will likely continue, creating a massive gap between available job opportunities and those who can fill them.

Furthermore, automation might reduce incentives for companies to invest in training and upskilling efforts for existing employees. That will leave them ill-equipped for future technological challenges or labor market adjustments. Additionally, individuals still in employment that need re-training could prove dear.

What Are the Upsides of AI?

The potential impact of AI on employment extends beyond job displacement. By automating tasks previously done by humans, companies can increase efficiency and productivity while reducing costs. That's an attractive prospect for any business looking to remain competitive in today's market.

It could also lead to changes in working conditions for those in employment. For instance, bots may replace human labor for specific roles or tasks deemed too dangerous. Such situations may force changes in workplace dynamics between employees and employers.

Moreover, new job opportunities could emerge due to AI-driven automation. Some experts anticipate a growth in tech-related roles, such as software engineers or robotics technicians. Additionally, AI algorithms could enable more accurate decision-making within organizations.

What Should We Do About This Impending Mass Displacement?

The introduction of AI presents both opportunities and challenges for society-the key is capitalizing on the former while minimizing the latter. We need clear policies that promote technological progress without leaving vulnerable members of society behind.

Governments must take decisive action to minimize potential social unrest due to anticipated job losses from automation. They can provide retraining programs for those affected by the shift. Moreover, they can help create new employment opportunities based on technology skill sets such as coding or machine learning algorithms development.

Further, they could offer income support for those who lose their jobs due to automation. Another is incentivizing companies that retain human workers instead of replacing them with machines. Governments could give them tax credits and other targeted waivers for maintaining human labor.

The Role of Employees and Their Employers

There are also steps that workers can also take to mitigate the potential negative impact of AI adoption. These include upskilling themselves in areas where demand is expected to remain high such as data science. Alternatively, they can seek career advice to help them stay ahead of technological trends affecting their livelihood later.

On their part, companies should weigh the potential impacts of AI on their operations now more than ever before. They must assess the benefits of AI and Automation against potential risks related to job loss or reduced wages and benefits for workers left behind in the wake of digital transformation.

Artificial intelligence presents both promise and peril regarding its potential impact on the global workforce. But its trajectory can still be shaped through careful consideration today. This will allow everyone to reap its rewards going forward into a brighter tomorrow powered by smarter machines working alongside us collaboratively rather than against us competitively.

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AI Could Displace 800 Million Jobs by 2030 | Bankless Times

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Last updated 15th Dec 2022 Artificial intelligence (AI) could displace 800 million jobs (30% of the global workforce) by 2030. That's according to a new BanklessTimes.com report. The site holds that increased AI adoption will greatly impact people's lives and their ability to make a living in the years ahead.

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Negative

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Main idea:
AI is increasingly present in everyday life in 2022, and is being used to improve various aspects of daily life

Arguments/facts:
More than 72 percent of Americans are concerned about machines doing many human tasks in the future.

Dialogue:
The text mentions concerns about AI, such as its impact on human tasks and the dangers of not regulating it. It also mentions the benefits of AI, such as its ability to improve daily life.

Summary:
The text discusses the increasing presence of AI in daily life in 2022 and its applications in various aspects such as social media and transportation. It also mentions concerns and benefits of AI.

APA:Yousif, N. (2023, January 13). ChatGPT: Student builds app to Sniff Out Ai-Written essays. BBC News. Retrieved January 24, 2023, from https://www.bbc.com/news/world-us-canada-64252570

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There are so many great applications of Artificial Intelligence in daily life, by using machine learning and other techniques in the background. AI is everywhere in our lives, from reading our emails to receiving driving directions to obtaining music or movie suggestions. Don't be scared of AI jargon; we've created a detailed AI glossary for the most commonly used Artificial Intelligence terms and the basics of Artificial Intelligence. Now if you're ready, let's look at how we use AI in 2022.

Best usages of Artificial Intelligence in everyday life (2022)

Artificial intelligence (AI) appears in popular culture most often as a group of intelligent robots bent on destroying humanity, or at the very least a stunning theme park. We're safe for now because machines with general artificial intelligence don't yet exist, and they aren't expected to anytime soon. You can learn the risk and benefits of Artificial Intelligence with this article.

We're nowhere near living in Westworld, but that doesn't make AI's possible impact on our future trivial. According to a recent poll, more than 72 percent of Americans are concerned about machines doing many human tasks in the future. Additionally, tech entrepreneur Elon Musk, who has long pushed for government regulation of AI, recently compared AI to nuclear weapons in terms of its danger. It is reasonable when we think about the impact of Artificial Intelligence in industry 4.0, but we aren't there yet. If you want, you can check the Artificial Intelligence trends in 2022.

Artificial intelligence is now part of our everyday environment, whether we are aware of it or not. AI lurks in the background every time we open our Facebook newsfeed, conduct a Google search, purchase a suggestion from Amazon, or book a trip online. There are also popular AI apps that help us. So, what is the best Artificial Intelligence app? If you ask what is the point of all these apps and wonder is Artificial Intelligence better than human intelligence, go to this article.

AI usage types

We can distinguish two types of AI applications to enhance people's daily lives.

Software/Methodology:

Voice assistants, image recognition for face unlock in cellphones, and ML-based financial fraud detection are examples of AI software currently being used in everyday life. Typically, just downloading AI software from an online store and having no other devices is required.

Embodied

Drones, self-driven vehicles, assembly-line robots, and the Internet of Things (IoT) are examples of AI implementation in hardware. This includes the development of specific devices that incorporate AI capabilities.

We need AI in our lives such as Artificial Intelligence call centers or Artificial Intelligence games. So, let's take a deep dive into the usage of Artificial Intelligence in everyday life.

Examples of how AI improves our everyday life

AI and ML-driven software and devices emulate human thought processes to help society move forward with the digital revolution. AI systems recognize their surroundings, handle what they see, resolve issues, and take action to assist with chores to make daily life more comfortable.

How Artificial Intelligence improves Social Media?

People regularly check their social media accounts, including Facebook, Twitter, Instagram, and other platforms. AI is not only working behind the scenes to customize your feeds, but it's also detecting and eliminating false news.

Artificial Intelligence in everyday life: Social Media

Twitter

Twitter has started to rely on artificial intelligence behind the scenes to improve its product, from suggesting tweets to fighting offensive or racist material and improving the user experience. To learn in time what customers' preferences are, they use advanced neural networks that process a large amount of data.

Facebook

Deep learning assists Facebook in extracting value from a growing number of its unstructured data sets, acquired from almost 2 billion users updating their statuses 293,000 times a minute. Most of Facebook's deep learning technology is based on the Torch framework, which focuses on deep learning and neural networks.

Instagram

Instagram also makes use of big data and artificial intelligence to target advertising and combat cyberbullying, as well as remove abusive comments. As the number of posts on the platform increases, artificial intelligence is becoming increasingly important in showing people information they might be interested in, removing spam, and improving user experience.

Chatbots

Chatbots are AI programs that can answer questions and deliver relevant content to consumers with common queries. Sometimes, chatbots are so successful that it appears as if you're interacting with a real person.

Autonomous vehicles and aircraft

Drones, or unmanned aerial vehicles (UAVs), are already present in our skies, conducting surveillance and providing delivery services in various plans, among them the delivery of medicines and necessities to confined-to-home elderly persons COVID-19 restricts their mobility.

While the autonomous vehicle market is still in its early phases, there are already enough prototype and pilot projects to indicate that such vehicles will become more common as artificial intelligence and IoT (Internet of Things) technologies improve. Artificial Intelligence in everyday life is increasing day by day.

Digital assistants

Virtual assistants like Siri, Cortana, Google Assistant, and others have made our lives easier. They've acted as a fantastic friend, reminding us to pick up a package and telling us jokes. This software can recognize speech patterns and offers natural language processing functionality. It also has the potential to learn about the user via monitoring working hours, screentime, and other related data. Because of its use of artificial intelligence, it can practice learning and listen like a person.

Food ordering sites

Around mealtime, tempting notifications about breakfast, lunch, and dinner frequently arrive from online ordering applications and sites. This is made possible by artificial intelligence software that keeps track of the time when you're most likely to want food. Not only that but AI also keeps track of the types of foods you enjoy eating and offers comparable cuisines based on your preferences.

Music and media streaming services

Other excellent examples of AI are the music and video streaming services that we use daily. AI makes judgments for you when you use Spotify, Netflix, or YouTube. These platforms provide suggestions based on your preferences.

Remember that AI is involved the next time you play a recommended video on YouTube, watch a suggested show on Netflix, or any other media for that matter.

Plagiarism

The college student's (or, perhaps, the professor's) nightmare. Whether you're a content manager or a teacher grading essays, you've probably faced the same issue: the internet has made plagiarism more convenient.

There is an almost limitless amount of knowledge and data available, which unscrupulous students and employees will take advantage of it.

To be honest, no person on Earth can compare and contrast someone's essay with all the data accessible. AIs are a completely different species. AI can analyze an enormous amount of data, compare it to the relevant text, and see whether a match exists or not.

Furthermore, due to progress and development in this field, certain tools can now check sources in other languages and pictures and audio.

Banking

Today, many larger banks allow you to deposit checks using your smartphone. You may deposit cheques with just a few taps rather than going to the bank. Aside from the obvious precautions when utilizing your phone to access your bank account, a check also requires your signature.

Banks now employ artificial intelligence (AI) and machine learning software to read your handwritten signature, compare it with the one you provided the bank previously, and authorize a check without risk.

In general, machine learning and AI technology enable computers to complete many tasks much more quickly. As a result, operations are completed more efficiently, wait times are reduced, and costs are reduced. Artificial Intelligence in everyday life is increasing day by day.

Credit and fraud

While we're on the subject of banking, let's speak about fraud for a moment. Every day, a bank handles millions of transactions. It's tough for a typical person to keep track of all that and analyze it.

Furthermore, the appearance of fraudulent transactions varies on a daily basis. With AI and machine learning algorithms, you may process thousands of transactions in a second. Furthermore, they may be trained to recognize what problematic activities might appear like and prepare for future problems.

Finally, when you apply for a loan or obtain a credit card, a bank must verify your application. The software can now handle numerous elements, such as your credit score, financial history, etc. This means that approval waiting periods are reduced, and less room for error.

Online commerce (eCommerce) and shopping

Consumers' online shopping experience is becoming more personalized and streamlined, owing to artificial intelligence (AI) technologies such as machine learning.

AI-powered automated warehousing and supply chain management systems assist commercial enterprises in better managing their logistics. At the same time, sentiment analysis allows them to better understand and react to their consumers' needs and behavior.

Navigation and travel

The job of AI engineers behind navigation apps like Google Maps and Waze never ends. Only satellite images, which are updated every second, can effectively be cross-checked by ML algorithms unleashed on them.

Researchers at MIT recently developed a navigation method that tags road features in digital maps, all in real-time. These digital maps are also produced simultaneously based on satellite pictures with incorporated information about cycling lanes and parking places.

Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN) have assisted with the reduction of repetitive route changes. AI also aids in determining routes on satellite images covered by vegetation growth using predictive models.

Transportation

Vehicle rentals, such as Uber and Lyft, are extremely useful since they can provide you with a car nearly every time you need one. However, we underestimate the AI-based programs that operate on them. We frequently get a notification to hail a cab before leaving for home from the workplace. How do these applications know when we need a taxi? Because these apps use deep learning technology and have already identified our routine behavior, they can do so. Artificial Intelligence in everyday life is increasing day by day.

Video games

In video games, we often compete with a virtual competitor. This virtual player is an embodiment of a real gamer and is designed to duplicate the gaming behavior throughout time with the help of machine learning and programming, providing the user with a delightful gaming experience.

Email system

The email communication system is a great deal of fun. Unwanted emails are immediately filtered out and tagged as spam or non-urgent. When creating a new email, the software suggests possible replies. Some email systems also include functions that notify users when it's time to submit their messages. Artificial intelligence is required for all of these useful features to existing.

Video games

AI or similar technologies may be found in various video games, including racing games, shooting games, and strategy games. The main aim of incorporating AI into gaming is to provide players with a realistic gaming experience to compete on a digital platform. Some businesses are now creating computer games to analyze their habits and improve their algorithms.

Job seeking apps

Many job-search engines are employing deep learning to understand more and more about the user and his needs. These applications use software that allows consumers to discover the finest possibilities by suggesting jobs, roles, employees, and other pertinent information.

Audio to text converters

Have you ever wondered how a non-living device responds to your voice command and converts it into text? What happens to the sound of your voice when it's converted to text? Why don't we encounter any language difficulties while interacting with our digital assistants? The most popular response to all of these inquiries is that speech recognition and natural language processing are utilized to develop these programs. As we said before, Artificial Intelligence Technology is largely based on these two technologies. As a result, it gives us a variety of intelligent characteristics.

Security and surveillance

Every country has its own set of rules, but whether or not you think it's a good idea to have a massive surveillance system is up for debate. We may all disagree about the ethics of utilizing such a system, but there's no doubt that it's being utilized, and AI plays an important role in this.

Because humans cannot keep track of many monitors at once, AI makes perfect sense since it allows machines to do so. Thanks to technological advances such as object recognition and face recognition, it won't be long before all security camera feeds are watched by AI rather than people. While AI will take some time to become fully accepted, it will eventually become our future.

Smart Home

When it comes to utilizing AI in creating smart homes, Alexa and Bixby are at the top of our thoughts. However, these applications of AI aren't limited to Smart-voice assistants like Alexa and Bixby.

AI is also being used to make homes smarter in a variety of different ways, including smart thermostat devices that regulate temperature automatically using AI. These AI applications save energy by automatically switching on/off lights based on human presence, smart speakers, apps that adjust the light color based on time of day, and so forth. Artificial Intelligence in everyday life is increasing day by day.

Google predictive search algorithms

Whenever you try to look something up on Google, you've undoubtedly seen a few of the auto-populated search terms in the search bar. This is Google's Autocomplete feature, which displays prognostications as you type each character of your query.

RankBrain is the algorithm for Artificial Intelligence employed by Google's Predictive Search. Its job is to retrieve results based on the data supplied in the query. It improves search results with each search, bringing in more relevant results for the user.

Google uses various technologies in its search engine, including Neural Networks, Deep Learning, Machine Learning, and Artificial Intelligence. It would be hard to imagine life without Google's search algorithms because they are effective.

Internet of Things

The confluence of AI and the Internet of Things (IoT) provides a wealth of possibilities for creating smarter home technologies that need less human intervention to function. The AI component, on the other hand, aids these gadgets in learning from data.

Create, communicate, aggregate, analyze, and act are the five major phases in IoT-enabling. The effectiveness of the "act" step is dependent on how thorough the analysis was; AI adds a lot of value to this process.

The value of data gathered by IoT devices is unlocked via IoT's adaptability. Learnings from this information over time help IoT technology to respond to human signals and need more effectively.

Recipes and cooking

AI has the potential to improve various things, including home management. However, it may also assist with various activities outside the realm of typical AI research. For example, AI might be used to help chefs prepare meals more efficiently.

Rasa has created an AI system that analyzes food and then presents recipes based on what you have in your kitchen and pantry. This sort of AI is great for individuals who enjoy cooking but don't want to spend much time planning ahead of time.

Autocorrect

Autocorrect does more than just correct typos; it also suggests the user with the next word in a sentence, making it an essential aid for anyone who types quickly. Have you ever wondered how autocorrect understands what you want to write? It's simple: With the help of pattern recognition (AI), it can save your most frequently used letters and send you the most frequent texts. This establishes a pattern in the device's artificial memory, which is later used for suggestions and forecasts.

Medical applications

The medical application of AI is incredible. Devices are being utilized to identify and treat damaged tissues, including Google AI Eye Doctor, which works with an Indian eye health chain to develop a cure for diabetic retinopathy, a disease that causes blindness. IBM Watson Health also uses computer algorithms to monitor patients' health. With AI and cutting-edge cognitive capabilities like data processing, healthcare has been revolutionized extraordinarily.

Conclusion

Thanks to the continuous increase in data available to machine learning algorithms, Artificial Intelligence is evolving. This allows Artificial Intelligence to function more effectively because trained ML models may be molded to work better with users' wide array of real data. The following are a few of the most prominent examples of AI in everyday life that are widespread and easy to use. This proves that AI is already changing our lives by allowing us to be more productive while also putting our efforts toward actual problems.

The future is now. AI technology will only accelerate, expand, and become more important to all industries and virtually every aspect of our everyday lives in the future.

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Best Usages Of Artificial Intelligence In Everyday Life (2022) - Dataconomy

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There are so many great applications of Artificial Intelligence in daily life, by using machine learning and other techniques in the background. AI is everywhere in our lives, from reading our emails to receiving driving directions to obtaining music or movie suggestions.

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Main idea:
A system that recommends products.
Arguments/facts:
-  A recommender system can be used in different places, like online shops or streaming apps.
-A recommender system takes a look at what you are normally interested in, and recommends the best to fit you.
-This could be a very simple way to increase engagement.

Dialogue:
A recommender system can be very convenient and useful, but can they also be used to manipulate a person into buying more and using more of the company's app.

Summary:
A recommender system can be used in different apps such as online shops or websites you can watch movies and tv shows in. For example, when you watch a show about guns, the system will analyze the show and recommend more that are similar to it. It can be used to manipulate the user to use more of the app.

APA

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A recommendation engine is a powerful tool for increasing your customer loyalty and improving the shopping behavior on your e-commerce platform. According to McKinsey, 35% of what consumers purchase on Amazon and 75% of what they watch on Netflix come from product recommendations. AI recommendation engines are the future of personalization, so don't miss your chance to take your business to the next level.

What is a Recommendation Engine

Let's start with the recommendation engine definition. A recommender system with AI is a system that suggests products, services, information based on the user data. The recommendation algorithm retrieves such data as the user's history and the behavior of similar users, their preferences, interests, and buying experience. Based on this data, a recommendation engine provides an efficient way for e-commerce companies to provide consumers with personalized products and services and cloud recommendations ai examples.

Collaborative Filtering

So if you have enough data on your customers and the items they buy you can implement a recommendation system right away. The type of AI recommendations will depend on the following factors:

Type of data you have about your users/items

Your ability to scale

The transparency of your recommendations

What is a Recommendation Engine and How does it Work

So how does AI recommendation engine work?

Usually, there are two types of data recommendations AI system collects: implicit and explicit. Implicit data includes information collected from user activities such as web search history, clicks, cart events, search log, and order history. Explicit data is collected from customer behavior, such as reviews and ratings, likes and dislikes, and product comments.

Recommendation engines also use such customer data as demographics (age, gender) and psychographics (interests, values) to identify similar patterns between different customers, as well as feature data (genre, item type) to identify similarities between products.

" Polls have shown some really powerful numbers in regards to AR too: 35% of people say that they would be shopping online more if they could virtually try on a product before buying it, and 22% would be less likely to visit a brick-and-mortar store if AR was available via their favorite e-commerce store. AR grants a person the ability not just to see a 3D model of a product but lets a user see how it looks if they were actually wearing it. Some products and industries lend themself better to traditional shopping methods, but AR is going to shake things up sooner than later. "
- Michael Prusich, Director of Business Development at 1Digital Agency.

Then, this data is stored

You need scalable storage that will grow together with your data. Over time, the amount of data will only increase, so make sure you have different types of storage for each data set that is scalable and reliable.

After that, the data is processed and analyzed in different ways: real-time analysis (data is processed as it is created), batch analysis (data is processed periodically), and near-real-time analysis: Data is processed in minutes after it's gathered.

" Technology and data analysis helps your people focus time and effort on their areas of strength and adds more value. "
- Sayer Martin, Director of Product Management at Conga.

At the final stage, the data is filtered using different matrixes or mathematical rules and formulas depending on the model of recommendation filtering. The outcome of this filtering is the recommendations.

Recommendation Engine Algorithm Categories

The process of predicting the utility of items for a particular user depends on the recommendation algorithm selected. There are three main categories of recommendation engines: content-based, collaborative filtering (CF)-based, and knowledge-based approaches. Let's review each of them.

Recommendation Techniques

Content-Based

As you may have guessed from its name, content-based recommender systems use the content of an item's description based on a user's profile. Content-based recommender systems recommend items that are similar to items the user has been previously interested in.

First, the AI recommender systems extract data from documents/descriptions. For example, a movie consists of the attributes such as genre, the director, writer, etc. Content-based recommender systems mostly use a keyword-based model known as the vector space model with term frequency-inverse document frequency weighting.

For example, Pandora, the online streaming music company, has engineered features from all songs in their catalog as part of the Music Genome Project. They use traditional machine learning techniques to analyze a certain user's probability of liking a specific song based on a training set of their song listening history.

Demographic Based

If you have a lot of demographic information about your users, you may recommend products or services based on similar users and their past behavior. Like the content-based method, the suggestion engine derives data set for each of your users and generates models that predict probabilities of liking certain things. Keep in mind that this requires a lot of information about your users. If you don't have a large amount of such data, try collaborative filtering.

Collaborative Filtering

This method is based on the relationship between users and items, and the interesting thing here is that you don't need information about the users or the items. All you need is a rating of some kind for each user/item interaction: either explicit (rating or like) or implicit (click or view).

There are two types of collaborative filtering: user-user and item-item. The user vector includes all the items purchased by the user and the rating given for each particular product when the item is all about finding similar items to the ones user was already interested in.

Benefits of Recommender Systems

The product recommendation engine retrieves data to understand your website visitor's shopping behavior better and automates your business. The main benefits include:

Understand the behavioral patterns of your website visitors during their whole session on your platform and provide them with better content recommendation

Collect shoppers' behavior and compile the data for the analytics process to use it for the proper recommendations.

Understand and interpret your website's social interaction and value to your customers.

Better evaluate their preferences and the products purchased via a particular social network. You can use this for targeting a specific demographic to better understand the real needs of your users.

The product recommendation engine can also monitor the cart abandonment activities to help you interact with the users and don't let them go without a purchase.

Recommendation Systems with Machine Learning

Machine Learning (ML) is the AI field concerned with using computers to simulate human learning, allowing computers to identify and acquire knowledge from the real world and improve their performance on some tasks based on the new knowledge. Today, ML algorithms are used in different industries, such as business, advertisement, and medicine, divided into supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised learning happens when algorithms are provided with training data and correct answers.

Recommenders can be evaluated similarly as classical machine learning models on historical data (offline evaluation). In reality, using one algorithm for creating a deep learning recommendation system will not deliver the desired result, so it's better to mix them to solve different problems.

Any system based on Machine Learning principles requires initial data to learn. If there are no such data, the system will not be able to deliver results. In recommendation systems, this problem is known under the "cold start" name. Each algorithm requires a certain set of data, and errors may arise when launching the service in production with the minimal number of users and absence of algorithms. There are a few solutions to the problem. So, the items can be recommended by the date of adding to the cart or randomly choosing them from the whole item set, hoping the user will like some of them. Second, the system can be started after the service works for a while, and there will be enough data to analyze.

AI Recommendations Examples

If you are looking for the type of recommender system to implement, here are the most common use cases:

You have certainly seen it when purchasing one product and then seeing a "You may also like" section with similar recommended products. This type of AI-based recommender engine can analyze the individual purchase behavior and detect patterns that will help provide a certain user with suggestions of products that will most likely match his or her interests.

Alternative products recommendation

Online stores may suggest similar products when the product is out of stock. It's mostly combined with a "notify me" feature when a product appears in stock again, such an ML-based recommender engine brings great results!

Weekly products recommendations

Recommending the top items is a popular strategy used by music streaming services. It generates the most popular items/services/products to grow customer loyalty and improve conversion rates.

Not sure what type of recommender system to choose? We at Proxet have over a decade of experience in developing state-of-the-art software solutions for startups, SMBs, and enterprises. 60% of our projects have AI/ML components, including everything from chatbot implementation to image recognition and sentiment analysis. We expertly guide our clients through their digital journey to success, so don't hesitate to contact us for a free consultation!

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[2021] What As a Recommender System With AI And Why Do You Need It | Proxet

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A recommendation engine is a powerful tool for increasing your customer loyalty and improving the shopping behavior on your e-commerce platform. According to McKinsey, 35% of what consumers purchase on Amazon and 75% of what they watch on Netflix come from product recommendations.

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Main idea:Can we trust AI

Arguments/facts:
-Copy writing
-AI pictures
-worid about the future

Dialogue:If you take pictures in an Ai app they can youse you’re face as advertisement or as product.It is crazy how they can make a picture is 5 seconds and a painter can take up to a month.

Summary:AI can take over the world if it can take a description and turn it into a image in seconds and that is crazy.The fascination of ai just starts here the future relies on the robots and the persons programming them

APA

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Could the future of art lie in AI technology? - BBC News

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Main idea:AI can be helpful

Arguments/facts:
-Imprisonment in costumer service
-Research and data analysis
-Reduces Errors

Dialogue:
In ai there are not all all ways bad it can also be good like shown in this paper.The best things that revolve around AI is the small margin of error because the error the AI would make is actually from a human that programmed it.

Summary:AI is computer science that aims to create machines that can think and act like humans. It has many things, such as self-driving cars and virtual assistants, and can revolutionize various industries by increasing productivity, improving customer service, and solving complex problems. Businesses can benefit from AI in many ways, such as automation, smarter decision making, research and data analysis..

APA
Koch, R. (2022, December 12). Benefits of AI: How it contributes to our society and economy. clickworker.com. Retrieved January 25, 2023, from https://www.clickworker.com/customer-blog/benefits-of-ai/

APA

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Benefits of AI: How it Contributes to Our Society and Economy

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Benefits of AI: Examples for Industries, Businesses, Society and Economy. Innovations in the AI space have led to several benefits.

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Professor catches student cheating with ChatGPT

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Professor catches student cheating with ChatGPT

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Welcome to the new age of academic dishonesty.

A college professor in South Carolina is sounding the alarm after catching a student using ChatGPT - a new artificial intelligence chat bot that can quickly digest and spit out written information about a vast array of subjects - to write an essay for his philosophy class.

The weeks-old technology, released by OpenAI and readily available to the public, comes as yet another blow to higher learning, already plagued by rampant cheating.

"Academia did not see this coming. So we're sort of blindsided by it," Furman University assistant philosophy professor Darren Hick told The Post. "As soon as I reported this on Facebook, my [academic] friends said, 'Yeah, I caught one too.'"

Earlier this month, Hick had instructed his class to write a 500-word essay on the 18th-century philosopher David Hume and the paradox of horror, which examines how people can get enjoyment from something they fear, for a take-home test.

But one submission, he said, featured a few hallmarks that "flagged" AI usage in the student's "rudimentary" answer.

"It's a clean style. But it's recognizable. I would say it writes like a very smart 12th-grader," Hick said of ChatGPT's written responses to questions.

"There's particular odd wording used that was not wrong, just peculiar ... if you were teaching somebody how to write an essay, this is how you tell them to write it before they figure out their own style."

Despite having a background in the ethics of copyright law, Hick said proving that the paper was concocted by ChatGPT was nearly impossible.

First, the professor plugged the suspect text into software made by the producers of ChatGPT to determine if the written response was formulated by AI.

He was given a 99.9% likely match. But unlike in standard plagiarism detection software - or a well-crafted college paper - the software offered no citations.

see also

Hick then tried producing the same essay by asking ChatGPT a series of questions he imagined his student had asked. The move yielded similar answers, but no direct matches, since the tool formulates unique responses.

Ultimately, he confronted the student, who copped to using ChatGPT and failed the class as a result. The undergrad was also turned over to the school's academic dean.

But Hick fears that other cases will be almost impossible to prove, and that he and his colleagues will soon be inundated with fraudulent work, as universities like Furman struggle to establish formal academic protocols for the developing technology.

For now, Hick says that the best he can do is surprise suspected students with impromptu oral exams, hoping to catch them off-guard without their tech armor.

"What's going to be the difficulty is that, unlike convincing a friend to write your essay because they took the class before or paying somebody online to write the essay for you, this is free and instantaneous," he said.

Even more frightening, Hick fears that as ChatGPT keeps learning, irregularities in its work will become less and less obvious on a student's paper.

"This is learning software - in a month, it'll be smarter. In a year, it'll be smarter," he said. "I feel the mix myself between abject terror and what this is going to mean for my day-to-day job - but it's also fascinating, it's endlessly fascinating."

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Professor catches student cheating with ChatGPT: 'I feel abject terror'

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Darren Hick, an assistant professor of philosophy at Furman University in South Carolina, is sounding the alarm after a student attempted to pass off the relatively sophisticated work of new artificial intelligence chat bot ChatGPT as their own.

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Main idea:
An explanation of robotics and artificial intelligence.
Arguments/facts:
- An artificial intelligence is an intelligent machine that is able to perform like a human.
- A robot is a machine that looks like a human, and is able to perform human actions.
-Robotics can be applied in many different places that require labor

Dialogue:
Will artificial intelligence be so advanced that it may one day be self aware, and be free from our control? And if so, would it be dangerous or beneficial?

Summary:
This article explains the key points to differentiating robotics and artificial intelligence. Artificial intelligence is a sort of “mind”, whereas robots are able to carry out actual human movements, such as picking objects up.

APA

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Main idea:AI can be used more for bad then for good.

Arguments/facts:
-Unemployment
-Profiling
-Domination by Big Tech companies

Dialogue:estimating that between 75 and 375 million people, or 3 and 14% of the global workforce, will need to learn new skills and switch jobs.

Summary:
People can be profiled with frightening precision using artificial intelligence. Algorithms are made to find patterns, so when they were put to the test in a contest by collecting personal data, it became clear that they could predict a user's likely future location by looking at where they've been in the past. This drawback of artificial intelligence is frequently minimized. You might believe that because you have nothing to hide, you don't care who knows your movements. It's possible that you don't want your personal information to be made public, even if you didn't do anything wrong or illegal. In any case, you wouldn't move into a house with walls that were transparent.
People are like getting scanned and read from up to down they can predict what we do each day.From an estimated global total of $39 billion, Google, Apple, Facebook, Microsoft, Amazon, and the Chinese megaplayers spent up to $30 billion in 2016 on AI-related R&D and acquisitions.

APA
Platform, E. L. (n.d.). 7 disadvantages of Artificial Intelligence Everyone should know about. Liberties.eu. Retrieved January 31, 2023, from https://www.liberties.eu/en/stories/disadvantages-of-artificial-intelligence/44289

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What are the disadvantages of artificial intelligence?

Artificial intelligence is already having a profound effect on society, an impact that promises to become even greater as the technology becomes more sophisticated. But not all of it is guaranteed to be positive.

We've put together a list of our 7 disadvantages of artificial intelligence, which we all should be watching out for.

1. Unemployment

With growing fears that automation and AI will change the way we work and force people into unemployment, questions about which jobs will be replaced by machines in the future are being raised. Some experts point out that potential shifts in occupations are imminent by 2030, estimating that between 75 million to 375 million workers (3 to 14 percent of the global workforce) will need to switch jobs and learn new skills. This shows a large gap in predictions, ranging from optimistic to very pessimistic, and highlights that many experts from technology and business sectors do not share a common view on the future of our labor market. In short: it's really hard to say how many jobs will actually be lost.

The transition to a more automated world will be a major challenge for many countries as ensuring that workers have the skills and support needed to transition to new jobs is anything but easy. This is especially so because the impact of automation is more pronounced for low-skilled jobs, such as administrative tasks, construction or logistical services. Hence, the diffusion of robotics and AI contributes to the reduction in available jobs for the less-educated and has a negative effect on lower waged jobs. This disadvantage of artificial intelligence could lead to a growth in income polarization and mass unemployment. Economic insecurity - as we know from the past - can be a huge threat to our democracies, causing loss in trust in political institutions, but also discontent with the system at large. Consequently, the way AI changes the way we work could pave the way for voters to sympathize with populist parties, and create the conditions for them to develop a contemptuous stance towards representative liberal democracies.

2. Lack of transparency

Al can be faulty in many ways which is why transparency is extremely important. The input data can be riddled with errors or poorly cleansed. Or perhaps the data scientists and engineers that trained the model inadvertently selected biased data sets in the first place. But with so many things that could go wrong the real problem is the lack of visibility: not knowing why the AI is performing poorly. Or sometimes not even that it is performing poorly. In a typical application development, there is quality assurance as well as testing processes and tools that can quickly spot any bugs.

But AI is not just code, the underlying models can't just be examined to see where the bugs are - some machine learning algorithms are unexplainable, kept in secret (as this is in the business interests of their producers), or both. This leads us to limited understanding of the bias or faults AI can cause. In the United States, courts started implementing algorithms to determine a defendant's "risk" to commit another crime, and inform decisions about bail, sentencing and parole. The problem is such that there is little oversight and transparency regarding how these tools work.

Without proper safeguards and no federal laws that set standards or require inspection, these tools risk eroding the rule of law and diminishing individual rights. In the case of defendant Eric Loomis, for example, the trial judge gave Loomis a long sentence, because of the "high risk" score he received after answering a series of questions that were then entered into Compas, a risk-assessment tool. Compas is a black-box risk assessment tool - the judge, or anyone else for that matter, certainly did not know how Compas arrived at the decision that Loomis is 'high risk' to society. For all we know, Compas may base its decisions on factors we think it is unfair to consider - it may be racist, agist, or sexist without us knowing.

3. Biased and discriminatory algorithms

This leads us to our next topic. "Bias" is not just a social or cultural problem, it is equally found within the technical sphere. Design flaws or faulty and imbalanced data that is being fed into algorithms can lead to biased software and technical artifacts. So AI just reproduces race, gender and age bias that already exists in society and deepens social and economic inequalities. You have probably read about Amazon's experimental hiring a few years ago. That tool used artificial intelligence to find candidates by ranking them from one to five stars - much like shoppers rate products on Amazon. It was discriminatory against women, because Amazon's computer models were trained to vet applicants by observing patterns in resumes submitted to the company over a 10-year period, effectively preferring male candidates and penalizing resumes that included the word "women".

In addition to the biased data basis, homogeneous non-representative developer teams also pose an issue. With their low diversity, they weave their cultural blind spots and unconscious biases into the DNA of technology. Companies that lack diversity therefore risk developing products that exclude their customers. Four years ago, a study found that some facial recognition programs incorrectly classify less than 1 percent of light-skinned men but more than one-third of dark-skinned women. The producers claimed that the program is proficient, but the data set they used to assess performance was more than 77 percent male and more than 83 percent white.

4. Profiling

AI can be used to build frighteningly precise profiles of people. Algorithms are developed to find patterns, so when testing their abilities in gathering personal data in a contest, it became clear that they were able to predict a user's likely future location by observing past location history. The prediction was even more accurate when also using the location data of friends and social contacts. Sometimes this disadvantage of artificial intelligence is downplayed. You might think that you do not care who knows your movements, after all you have nothing to hide. First of all, chances are that this is not completely true. Even if you do not do anything wrong or illegal, you may not want your personal information available at large. After all, you would not move in a house with transparent walls. So is it really the case that you do not care about sharing your device's location history? How about your teenage daughter's location history? Would you really be comfortable if someone published her location data including predictions? Surely not. Information is power, and information we give up is power over us.

5. Disinformation

A rise in disinformation is a disadvantage of artificial intelligence that we are already witnessing. In 2020, the Activist Group Extinction Rebellion created a deepfake to produce a fictional speech by Belgian prime minister Sophie Wilmès. The group took an authentic video address made by Wilmès and used AI to manipulate her words. The result: disinformation. In the fake video Wilmès can be seen talking about Covid-19, claiming that the pandemic is directly linked to the "exploitation and destruction by humans of our natural environment". Unfortunately, this hasn't been the only case. Deepfakes will be incrementally used for targeted disinformation campaigns in the future, threatening our democratic processes and causing societal polarization. Adding to these issues of misinformation are online bots, who can generate fake texts including news articles altered to push scamming perspectives or tweets. The AI language tool, GPT-3 recently composed tweets saying "They can't talk about temperature increases because they're no longer happening", aiming to create skepticism about climate change. In recent years with Trump constantly calling out the media as fake, such technologies could mean, as the Atlantic put it, the "collapse of reality". With deepfakes and online bots spreading disinformation, society could face blurring lines between reality and fiction, destabilizing trust in our political institutions.

6. Environmental impact

Although AI can have a positive environmental impact, for example by enabling smart grids to match electrical demand or enabling smart and low-carbon cities. However, one of the disadvantages of artificial intelligence is that it can also cause significant environmental damage due to intensive energy use. A 2019 study found that a particular type of AI (deep learning in natural language processing) has a huge carbon footprint due to the fuel the hardware requires. Experts say that training a single AI model produces 300,000 kg of CO2 emissionsroughly equivalent to 125 round trip flights from NYC to Beijing or 5 times the lifetime emissions of an average (American) car. And the training of the models of course, is not the only source of emissions. The carbon impact of the infrastructure around big tech's deployment of AI is also significant: the data centers need to be built up, and materials used need to be mined and transported.

Keep an eye on big tech. Sign up!

7. Domination by Big Tech companies

AI is dominated by Big Tech companies. Since 2007, Google has bought at least 30 AI companies working on everything from image recognition to more human-sounding computer voices - building a huge monopoly of AI tech. But Google isn't the only gatekeeper. In 2016, Google, Apple, Facebook, Microsoft, and Amazon together with the Chinese megaplayers spent up to $30 billion out of an estimated global total of $39 billion on AI-related research, development, and acquisitions. Companies snatching up AI startups globally is dangerous, because consequently they will play an oversized role determining the direction AI technology takes. With dominance in search, social media, online retail, and app stores, these companies have near-monopolies on user data and are becoming the primary AI suppliers to everyone else in the industry. Such a concentration of power is dangerous, as it risks huge tech companies dictating to democratically elected governments.

Further reading:
10 Impacts of Artificial Intelligence on Our Everyday Life Are Decision-Making Algorithms Always Right, Fair and Reliable or NOT? Artificial Intelligence vs Human Rights?
Photo credits: Alena Darmel, Anastasia Shuraeva, Darina Belonogova, Keira Burton, Ron Lach, Tim Douglas, Vazhnik /Pexels.com

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7 Disadvantages of Artificial Intelligence Everyone Should Know About

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Artificial intelligence is already having a profound effect on society, an impact that promises to become even greater as the technology becomes more sophisticated. But not all of it is guaranteed to be positive. We've put together a list of our 7 disadvantages of artificial intelligence, which we all should be watching out for.

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ChatGPT: Student builds app to sniff out AI-written essays

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ChatGPT: Student builds app to sniff out AI-written essays

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Main idea:
To find out ho is cheating or using this robot
Arguments/facts:
-GBTZero
-Checks for Ai written things
-Help full for checking plagiarism

Dialogue:
Edward Tian, a Princeton student, created GPT-Zero to distinguish human-written text from AI-generated text, due to concerns about AI misuse as it becomes more accessible.

Summary:Edward Tian, a computer science student at Princeton University, has created an application called GPTZero that can accurately identify if a text was written by a human or a bot. The app uses all the chats to establish if it’s I a writen .

APA
Yousif, N. (2023, January 13). ChatGPT: Student builds app to Sniff Out Ai-Written essays. BBC News. Retrieved January 24, 2023, from https://www.bbc.com/news/world-us-canada-64252570

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ChatGPT: Student builds app to sniff out AI-written essays

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Edward Tian, 22, has built a new tool to help teachers detect plagiarism.

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Main idea:
Makes it possible to computers to understant human language.
Arguments/facts:
- NLP analyzes the grammatical structure of sentences and words’ individual meanings, tries to make sense of it and automatically performs tasks.
-Many examples of NLP already exist, such as Google Assist, Siri and Alexa.
-NLP can also be used on chatbots, which are AIs that chat with you depending on your message requests.

Dialogue:
This is very convenient and already very advanced, but some people think it could be a bad idea, should we allow NLP to offer help where we need it?

Summary:
NLP stands for Natural Language Processing, which is basically a computer’s ability to understand human language and carry out automated tasks. This technology can be found in chatbots and smartphone assistants, such as Siri or Alexa. (editado)

APA

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Companies are increasingly using NLP-equipped tools to gain insights from data and to automate routine tasks.

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables machines to understand the human language. Its goal is to build systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification.

Automate routine tasks with NLP

But what exactly is Natural Language Processing? How does it differ from other related terms, like AI and machine learning?

1. What is Natural Language Processing (NLP)?

2. NLP, AI, Machine Learning: What's the Difference?

3. NLP Techniques

4. 5 Use Cases of NLP in Business

What Is Natural Language Processing?

Natural Language Processing (NLP) makes it possible for computers to understand the human language. Behind the scenes, NLP analyzes the grammatical structure of sentences and the individual meaning of words, then uses algorithms to extract meaning and deliver outputs. In other words, it makes sense of human language so that it can automatically perform different tasks.

Probably, the most popular examples of NLP in action are virtual assistants, like Google Assist, Siri, and Alexa. NLP understands written and spoken text like "Hey Siri, where is the nearest gas station?" and transforms it into numbers, making it easy for machines to understand.

Another well-known application of NLP is chatbots. They help support teams solve issues by understanding common language requests and responding automatically.

There are many other everyday apps you use, where you've probably encountered NLP without even noticing. Text recommendations when writing an email, offering to translate a Facebook post written in a different language, or filtering unwanted promotional emails into your spam folder.

In a nutshell, the goal of Natural Language Processing is to make human language ‒ which is complex, ambiguous, and extremely diverse ‒ easy for machines to understand.

NLP, AI, Machine Learning: What's the Difference?

Natural Language Processing (NLP), Artificial Intelligence (AI), and machine learning (ML) are sometimes used interchangeably, so you may get your wires crossed when trying to differentiate between the three.

The first thing to know is that NLP and machine learning are both subsets of Artificial Intelligence.

AI is an umbrella term for machines that can simulate human intelligence. AI encompasses systems that mimic cognitive capabilities, like learning from examples and solving problems. This covers a wide range of applications, from self-driving cars to predictive systems.

Natural Language Processing (NLP) deals with how computers understand and translate human language. With NLP, machines can make sense of written or spoken text and perform tasks like translation, keyword extraction, topic classification, and more.

But to automate these processes and deliver accurate responses, you'll need machine learning. Machine learning is the process of applying algorithms that teach machines how to automatically learn and improve from experience without being explicitly programmed.

AI-powered chatbots, for example, use NLP to interpret what users say and what they intend to do, and machine learning to automatically deliver more accurate responses by learning from past interactions.

NLP Techniques

Natural Language Processing (NLP) applies two techniques to help computers understand text: syntactic analysis and semantic analysis.

Syntactic Analysis

Syntactic analysis ‒ or parsing ‒ analyzes text using basic grammar rules to identify sentence structure, how words are organized, and how words relate to each other.

Some of its main sub-tasks include:

Tokenization consists of breaking up a text into smaller parts called tokens (which can be sentences or words) to make text easier to handle.

Part of speech tagging (PoS tagging) labels tokens as verb, adverb, adjective, noun, etc. This helps infer the meaning of a word (for example, the word "book" means different things if used as a verb or a noun).

Lemmatization & stemming consist of reducing inflected words to their base form to make them easier to analyze.

Stop-word removal removes frequently occuring words that don't add any semantic value, such as I, they, have, like, yours, etc.

Semantic Analysis

Semantic analysis focuses on capturing the meaning of text. First, it studies the meaning of each individual word (lexical semantics). Then, it looks at the combination of words and what they mean in context. The main sub-tasks of semantic analysis are:

Word sense disambiguation tries to identify in which sense a word is being used in a given context.

Relationship extraction attempts to understand how entities (places, persons, organizations, etc) relate to each other in a text.

5 Use Cases of NLP in Business

NLP tools are helping companies understand how their customers perceive them across all channels of communication, whether emails, product reviews, social media posts, surveys, and more.

Not only can AI tools be used to understand online conversations and how customers are talking about businesses, they can also be used to automate repetitive and time-consuming tasks, increase efficiency, and enable workers to focus on more fulfilling tasks.

Here are some of the main applications of NLP in business:

Sentiment Analysis

Sentiment analysis identifies emotions in text and classifies opinions as positive, negative, or neutral. You can see how it works by pasting text into this free sentiment analysis tool.

By analyzing social media posts, product reviews, or online surveys, companies can gain insight into how customers feel about brands or products. For example, you could analyze tweets mentioning your brand in real-time and detect comments from angry customers right away.

Maybe you want to send out a survey to find out how customers feel about your level of customer service. By analyzing open-ended responses to NPS surveys, you can determine which aspects of your customer service receive positive or negative feedback.

Language Translation

Machine translation technology has seen great improvement over the past few years, with Facebook's translations achieving superhuman performance in 2019.

Translation tools enable businesses to communicate in different languages, helping them improve their global communication or break into new markets.

You can also train translation tools to understand specific terminology in any given industry, like finance or medicine. So you don't have to worry about inaccurate translations that are common with generic translation tools.

Text Extraction

Text extraction enables you to pull out pre-defined information from text. If you deal with large amounts of data, this tool helps you recognize and extract relevant keywords and features (like product codes, colors, and specs), and named entities (like names of people, locations, company names, emails, etc).

Companies can use text extraction to automatically find key terms in legal documents, identify the main words mentioned in customer support tickets, or pull out product specifications from a paragraph of text, among many other applications. Sounds interesting? Here's a keyword extraction tool you can try.

Chatbots

Chatbots are AI systems designed to interact with humans through text or speech.

The use of chatbots for customer care is on the rise, due to their ability to offer 24/7 assistance (speeding up response times), handle multiple queries simultaneously, and free up human agents from answering repetitive questions.

Chatbots actively learn from each interaction and get better at understanding user intent, so you can rely on them to perform repetitive and simple tasks. If they come across a customer query they're not able to respond to, they'll pass it onto a human agent.

Topic Classification

Topic classification helps you organize unstructured text into categories. For companies, it's a great way of gaining insights from customer feedback.

Imagine you'd like to analyze hundreds of open-ended responses to NPS surveys. How many responses mention your customer support? What percentage of customers talk about "Pricing"? With this topic classifier for NPS feedback, you'll have all your data tagged in seconds.

Also, you can use topic classification to automate the process of tagging incoming support tickets and automatically route them to the right person.

Closing

Natural Language Processing (NLP) is the part of AI that studies how machines interact with human language. NLP works behind the scenes to enhance tools we use every day, like chatbots, spell-checkers, or language translators.

Combined with machine learning algorithms, NLP creates systems that learn to perform tasks on their own and get better through experience. NLP-powered tools can help you classify social media posts by sentiment, or extract named entities from business emails, among many other things.

MonkeyLearn is a user-friendly AI platform that helps you get started with NLP in a very simple way, using pre-trained models or building customized solutions to fit your needs.

Sign-up to MonkeyLearn for free and explore all you can do with your data!

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Companies are increasingly using NLP-equipped tools to gain insights from data and to automate routine tasks. Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables machines to understand the human language. Its goal is to build systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification.

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How good is ChatGPT?

Millions have been using the new AI technology ChatGPT for everything from songwriting to this CNN commentary. "It's good for people trying to generate huge amounts of text that doesn't have to be especially convincing very quickly," says Bloomberg Businessweek Columnist Max Chafkin, but also "it makes stuff up."

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computer science and information technology

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Millions have been using the new AI technology ChatGPT for everything from songwriting to this CNN commentary. "It's good for people trying to generate huge amounts of text that doesn't have to be especially convincing very quickly," says Bloomberg Businessweek Columnist Max Chafkin, but also "it makes stuff up."

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I encourage my team to keep learning. Ideas in different topics or fields can often inspire new ideas and broaden the potential solution space.

Lilian Weng Applied AI at OpenAI

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Creating safe AGI that benefits all of humanity