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https://www.analyticsvidhya.com/blog/2023/04/machine-learning-for-social-media/

This Article highlights how AI is prevalent within the sphere of Social Media

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Introduction

We live in a world where social media platforms shape our interests, tailor our news feeds, and provide customized content, all thanks to machine learning! With machine learning (ML), a branch of artificial intelligence (AI), software programs can predict outcomes more accurately without being explicitly instructed. Machine learning algorithms use previous data as input to forecast new output values.

It has become significant in developing new ideas and provides businesses with a picture of trends in consumer behavior and operational business patterns. A significant portion of operations in leading organizations like Facebook, Google, and Uber rely on machine learning.

Another area where this technology has become highly relevant is social media. Have you ever wondered how? The posts, advertisements, and products on your social media feed are studied and calculated by machine learning algorithms. These algorithms analyze your search data and cluster the type of content you want to watch. This analysis is not limited to your search history; even your messages, likes, and posting patterns on social media help ML algorithms study and identify patterns for the future.

Get ready to explore how this technology revolutionizes how you consume, share, and interact with content online.! We will dive deep into the role of machine learning in social media platforms and its transformative impact.

Source: Pianalytix

Table of Contents

Overview of Machine Learning Techniques

Source: Spiceworks

Machine learning allows computer systems to improve their performance through repeated learning experiences. The learning processes are categorized into three major types: supervised learning, unsupervised learning, and reinforcement learning.
Supervised Learning
This technique involves training a model with labeled data to make predictions on new, unseen data. Supervised learning algorithms include regression, classification, and support vector machines.
Unsupervised Learning
In this technique, the model works with unlabeled data and tries to identify patterns, clusters, or relationships within the data. Unsupervised learning algorithms include clustering, dimensionality reduction, and anomaly detection.
Reinforcement Learning
This technique involves training a model through trial and error by providing it with rewards or penalties based on its actions. The model learns to make the best decisions in a given environment to maximize its cumulative reward.

There is another relatively nascent subset of machine learning that is widely used for more complex models called deep learning.

It utilizes artificial neural networks with multiple hidden layers, allowing the model to learn complex patterns from vast amounts of data. Deep learning incorporates convolutional neural networks (CNNs) for image recognition and recurrent neural networks (RNNs) for sequence analysis.

Source: Iterators

It's no secret that social media has emerged as a brand-new area for commercial operations. Take a moment to look at these astounding numbers, suggesting the vitality of social media platforms in shaping people's lives. There are 1.65 billion social media users, over 96% of firms use social media, and 71% of consumers with positive social media experiences are inclined to recommend the same to others. It may not be extensively apparent; however, machine learning has a significant role here.

With the help of machine learning, social media platforms can provide a better user experience, use data to forecast future states, and predict more accurate results. Below are a few ways machine learning can improve social media platforms.

Source: Mentionlytics

Improves Potential Customers' Visual Experience

Customer experience is the holy grail for a product's performance. Social media platforms deploy machine learning models to identify and predict the most relevant content for each user, provide better recommendations, reduce junk emails, and add very little to their inbox spam. Moreover, they use AI and ML to determine the most captivating visual effects for viewers.

According to data, 82% of Twitter users view videos, and 90% do it on a handheld device. As a result, Twitter bought Magic Pony Technology, a technology company with offices in London that has created machine learning methods for visual augmentation to enhance the visual experience further.

Source: freepik

Enables Social Networking Giants to Promote their Goods to the Target Audience

Machine Learning enables social networking giants to promote their goods to niche audiences by analyzing user data such as demographics, interests, and preferences. This data is then used to create targeted ads that are shown to specific groups of users, increasing the chances that they will be interested in the promoted goods.ML algorithms can also analyze user behavior and predict which products or services they are most likely to be interested in, allowing for even more precise targeting.

For instance, Facebook uses deep neural networks to determine which advertisements should be shown to which people. This frees skilled professionals from labor-intensive tasks by letting the machines do them.

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Protects the Data and the Reputation

Machine learning algorithms can help protect social media platforms by detecting and flagging potentially harmful or inappropriate content before it spreads. This not only helps prevent the spread of harmful content but also helps maintain the platform's reputation by promoting a safe and welcoming online community.

Additionally, machine learning can be used to identify and prevent unethical activities on social media by analyzing historical hacking malicious attacks, further protecting user data and the platform's integrity.

Pinterest uses machine learning to ensure data security. With ML, the business can identify spam users and content, promote the content, and gauge the possibility that a user will pin it.

Source: primex manufacturing

Facilitates Data Automation

Machine Learning facilitates data automation for social media platforms by using algorithms to analyze a large amount of data and identify patterns and insights. This can help social media platforms automate content recommendation, moderation, and ad targeting tasks. By automating these tasks, the platforms can improve the user experience and increase engagement while also maximizing their revenue potential.

Google has adopted a suite of machine-learning techniques for automatic tagging without the need for further data input. It is to distinguish abstract ideas (like sunsets and beaches) and automatically attach metadata to them.

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Machine learning algorithms have become an integral part of social media platforms, enabling them to analyze vast amounts of user-generated data and make informed decisions. Some of the commonly used ML algorithms are:

Natural Language Processing (NLP)

NLP aids in understanding and interpreting human language patterns. Social media companies utilize NLP to analyze text data, including tweets, comments, and posts, to extract sentiment, categorize content, or identify trends.

Platforms like Twitter and Facebook analyze the content of posts, identify trends, and offer personalized advertisements based on user preferences. For instance, Twitter employs NLP to filter out spam content and prioritize relevant tweets for users.

Source: Nexocode

Linear Regression

Linear regression uses statistical models to establish relationships between variables. In social media, it can be applied in scenarios like predicting user engagement based on post features or optimizing advertising strategies by analyzing click-through rates or cost per click.

This algorithm helps companies like LinkedIn predict user engagement probability with various content offerings. By analyzing factors such as user activity, historical engagement patterns, and connections within a network, linear regression allows LinkedIn to present highly personalized content feeds for its users.

Source: LinkedIn

Support Vector Machines (SVM)

SVM is a robust machine learning algorithm for classification tasks. These algorithms are beneficial for distinguishing between categories or sorting content into groups. In social media applications, SVMs can be utilized to filter spam messages or analyze user behavior patterns to detect fraudulent activities. With SVM algorithms, social media platforms can also sort content into categories or clusters based on visual aesthetics or similarity to other images.

Instagram employs SVM algorithms in their Explore tab to recommend pictures that users may find attractive or visually appealing based on their browsing history and preferences.

Source: Hecettepe Edu

Overall, machine learning algorithms like NLP, Linear Regression, and SVM continue transforming how social media platforms analyze and process their vast quantities of data, ultimately leading to more tailored and efficient user experiences.

Now that you know how widespread is the use of machine learning in social media platforms, let's move on to see how the technology actually benefits them.

Improved Search Algorithms

Machine learning algorithms enable social media platforms to provide more relevant search results to users. They can identify and prompt a word in the search keyword that does not look right and suggest corrections. With each search, the algorithm learns, implying that it gets better every time. This helps users find the content they are looking for quickly and efficiently, resulting in a better experience and higher satisfaction rates.

Efficient Spam Filtering and Moderation

Machine learning algorithms can help social media platforms detect and filter spam more efficiently. Algorithms like Decision Trees cluster content that is often flagged as spam and prevent it from surfacing on the users' social media profiles. This helps the platforms moderate content based on the user's preferences, resulting in a cleaner, safer, and more trustworthy environment for users to engage with each other.

Enhanced User Experience and Engagement

Machine learning algorithms can analyze user behavior to provide personalized notifications, recommendations, and content. They can streamline the type of posts that most users engage with. For instance, they can shortlist shopping experiences based on user data and accordingly allow advertisements to appear on their feed.

Real-time Data Analysis for Better Decision-making

Machine learning algorithms can analyze vast amounts of data in real-time to provide valuable insights and help social media platforms to strategize better. As these platforms also need to work on their algorithms to rank content based on user engagement, the insights provided by machine learning help them tailor their platform algorithms. For example, Instagram, Snapchat, Facebook, etc., have platform algorithms devised based on user data and insights.

Source: SlideTeam

Image and Speech Recognition for Better Content Moderation

Machine learning helps social media platforms analyze images and speech patterns to automatically detect and flag inappropriate content, thereby ensuring a safe online environment for users. Convolutional Neural Networks (CNN), Viterbi search, and Deep Neural Networks (DNNs) are examples of machine learning algorithms that can be used to analyze images and speech patterns.

Yelp, an online reviews website, heavily relies on machine learning to sort through millions of social media data points and categorizes them as food, menus, or outside photos.

Sentiment Analysis for Understanding User Emotions and Feedback

Machine learning algorithms can analyze text data from social media posts to determine user sentiments. This allows companies to gauge public opinion and better understand their customers' needs. To test the precision of predicting sentiment scores, researchers use several machine learning algorithms, including Support Vector Machines (SVM), Multilayer Perceptron Neural Networks (MLP Neural Nets), Naive Bayes (NB), and Decision Tree (DT) algorithms.

Source: Data camp

Predictive Analytics for Targeted Advertising

Machine learning can predict consumer preferences and offer personalized advertisements by analyzing user behavior patterns, leading to higher click-through rates and increased sales. K-means clustering is a popular machine-learning technique that can be used to analyze user behavior for targeted advertising.

Natural Language Processing for Personalized Customer Support

Through natural language processing, machine learning can help social media platforms understand user inquiries and provide tailored support, creating a more efficient customer service experience. Support Vector Machines, Maximum Entropy, and Bayesian Networks are the most popular supervised NLP machine learning algorithms.

Chatbots for Automated Customer Service

Machine learning-powered chatbots can handle common customer queries on social media, reducing response times and freeing up human support staff to focus on more complex issues. Support vector Machine, Natural language processing (NLP), and Recurrent neural networks (RNN) are some of the most popular machine learning algorithms for chatbots.

Source: Nimble Works

Biases and Ethical Concerns

Ethics is one of the main concerns with machine learning. Trusting and depending on machine learning algorithms for automation, analysis, and decision-making is beneficial. Yet, there are certain difficulties because these algorithms may be biased in their development, especially given that they are trained on input data. If the input data is corrupted or unethical, the machine learning model can give terrible results.

Data Privacy and Security Issues

With the development of social media and machine learning, data breaches and security issues have also increased. Machine learning in social media platforms is based on large amounts of data. This data is typically private and user confidential. This data, if leaked, can compromise user information to a large extent.

Need for Skilled Professionals in Machine Learning

Applying machine learning in social media analysis requires highly skilled professionals and data scientists who can create, develop, and maintain machine learning models. This is because developing successful models involves an advanced understanding of algorithms, data processing techniques, and coding languages. The need for such professionals may pose a challenge to utilizing machine learning in social media analysis fully.

High Cost of Implementing Machine Learning Technologies

Implementing machine learning solutions in social media platforms can involve significant financial investment. These costs generally cover data storage services, infrastructure development, hiring a skilled workforce, regular system updates, and maintenance expenses. For some businesses, these high costs deter effectively adopting and utilizing the potential benefits of machine learning technologies in social media analysis.

As it has in the last ten years, social media's future is filled with pivotal moments, like the onset of machine learning in improving these platforms. In the coming years, the future of machine learning in social media is poised for significant growth and advancement. You will witness an increased integration of AI-powered chatbots and personalized content feeds to cater to individual preferences, enhancing user engagement.

Moreover, ML has enabled geolocation targeting and collecting accurate data on population density. This data is then utilized for safety and health measures in emergent situations. In 2016, the practice covered only 22 regions; however, it now provides worldwide coverage! Facebook is one such social media platform that actively contributes to this cause.

Last, ML and AI have offered numerous tools that take over redundant human tasks and provide insights that none of us could have imagined. These tools assist in everything, from content creation (for example, ChatGPT, Flick, Contentbot.ai, etc.), to content analysis (for example, Lately, Socialbakers, etc.). They have revolutionized the way content works and is consumed by the people.

Further, down the line, social media will also be integrated with other technologies like augmented reality, virtual reality, and blockchain to enhance social media experiences. Just like Snapchat was the first social media company to incorporate AR-based filters, effects, and creator profiles, many other platforms are to follow these trends. This integration will enable more competent virtual assistants, immersive content, and even new mechanisms for tracking engagement and verifying user identities.

Conclusion

In essence, social media operates on websites and applications encouraging users to produce and distribute content to participate in the social system. Today, machine learning plays a significant role in social media platforms, as it helps in content personalization, user experience improvement, targeted advertising, and moderation of online communities. The continued research and development in this field are crucial to drive the evolution of social media and enhancing its capabilities.

As machine learning advances, the understanding of user behavior and preferences will become more refined, resulting in more engaging and relevant content for users. In the future, machine learning has the potential to revolutionize social media and many other industries by enabling advanced forms of communication, interaction, and content discovery that can foster a more connected and informed society.

To learn more about machine learning and other related technology like artificial intelligence and data science, head over to Analytics Vidhya (AV). AV is an excellent educational platform and a one-stop destination for all ML, AI, and Data Science related learning resources and career guidance. They offer engaging blogs, courses, and a community of experts to train you for a tech-focused future.

Frequently Asked Questions

A. Social media platforms use ML to analyze likes, comments, messages, images, videos, and audio. The algorithms then identify certain features and classify them into "clusters." Using this information, the platforms decide the content to be shown to a particular user.

A. ML and AI help marketers create more personalized social media campaigns in a fraction of the time taken by a human. For example, Heinz, a global producer of ketchup, pickles, and vinegar, created an ad campaign with DALL-E 2, showcasing "ketchup in outer space!"

Additionally, these technologies allow companies to extract deeper insights from user data and activity helping them to achieve better marketing ROI.

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How Machine Learning is Used on Social Media Platforms in 2024?

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Alongside finding more accurate ways to anticipate sporting event outcomes, machine learning in sports betting also covers ways to improve many operational processes for operators. Let's investigate each of the cases.

Forecasting Matches

From an operator's perspective, machine learning offers the tantalizing possibility of automating the generation of accurate predictions - more accurate than human analysts (oddsmakers) could reliably generate. Hence, sports betting operators can create automated odds that maximize their average returns from events.

Such machine learning models are multivariate, meaning they factor in many variables when making predictions. In some models, this can be a quantitative analysis of historical performance by an athlete or a team, such as average possession and pass rates in soccer or the history two players have in matchups in games like tennis. In other models, machine learning prices in factors such as the likelihood of player injury or how probable weather conditions can affect outcomes.

The results of these models can be stark. Since the 1990s, computer scientists and machine learning researchers have developed models across a range of sports that have provided above average accuracy for match outcomes. The accuracy of these models range from 50 up to 90 percent depending on the sport. In the case of tennis, the prediction accuracy of models has averaged 70-75 percent. This can mean reliable returns for bettors and sports betting operators (if odds are adjusted properly), with the effectiveness of models only increasing as they are exposed to more data.

The Use of Machine Learning in Predicting Outcomes of Sports Events

Machine learning, however, is still a nascent technology, and it is essential to mitigate the risks of using models. One challenge is that of "explainability." There is a substantial risk of models becoming "black boxes" with predictive reasoning that is incomprehensible to outside observers. Tech teams need to offset this and ensure that their models and infrastructure include "explainability" functionality so that non-technical stakeholders can make sense of the rationale for a model's decision making. Basically, converting 1's and 0's to layman terms.

Another challenge is the risk of concept drift. Concept drift weighs new data heavily which could prompt a model to redefine the concepts it uses in such a way to reassign the classification of existing data points incorrectly and systematically misallocate new data points to the wrong categories. The risk posed by concept drift in models for sports betting operators is that they can generate inexplainable and systematic errors in their predictions. To put it simply, as new data points are added to the dataset, there is a risk of misclassification which has to be mitigated.

It is also worth mentioning that most of the underlying technology and code for machine learning in sports betting is open source, meaning that everyone has access to the code and can also employ similar models. Therefore, operators also have to consider bettors who may pick up a systematic advantage from machine learning models.

Offsetting this risk requires both human and automated monitoring of bettors' performance, identifying those whose pattern of betting outcomes is significantly beyond the norm, and looking into the types of bets they place. Betting operators can then limit the amount of money a bettor can place on a particular outcome, limit the number of bets they place for a particular game or sport, or impose deposit or withdrawal limits to discourage high volume automated bets.

Fan Segmentation

Beyond the direct use of machine learning for sports betting, operators can also benefit from machine learning to overhaul their marketing, sales, and content functions. Machine learning models can analyze customer journeys on operator websites, a customer's betting patterns with particular teams or sports, and the customer's appetite for risk and return based on historical bets.

Using this data, it is possible to leverage machine learning to personalize a bettor's experience to reflect their preferences. This can include advertisements and banner content that is better suited to a bettor's interests and values or automated and personalized incentives to encourage them to place more bets. It can also include targeting a particular club, fanbase, or community for platform onboarding based on their advertisement clickthrough rates or analyzing historical betting volumes.

Automated Content

Rapid engagement with audiences is an excellent way to drum up real-time interest in a sports team or a betting operator. Leading up to and during match time, the creation of live content to mirror the ebbs and flows of a game can generate massive fan engagement and direct traffic towards a brand.

Of course, the challenge here is being able to rapidly create content on-the-fly, given how short the window can be to generate content that quickly resonates with the viewing public. But alas, machine learning can play a significant role with companies like WSC Sports pioneering machine learning to generate clippings of high intensity moments for near-instant distribution on social platforms.

Automated content can help sports betting operators ride the crest of high momentum sporting moments, generating traffic and building up a reputation as an authority on a given sport with fans and the general public.

Simulations and Fantasy Sports

The rise of fantasy sports and simulated sporting events has also proved an enormous opportunity when to use machine learning models. Machine learning can be used to improve the selection of team formations in fantasy sports

There are some fantastic opportunities here as such platforms can provide simulations in real-time. This means bettors can enjoy a more realistic and exciting simulated experience, and betting operators can have a product that will encourage trust from the public while optimizing long-term revenue.

One example of such a platform is Betradar's simulated reality soccer product, which plays out simulated fixtures in real-time to maximize engagement. It also provides pre-match and live betting markets on simulated fixtures, encouraging bettor participation in fantasy sports.

Sports Data Integrity

Another crucial way that machine learning can support sports betting operators is by minimizing fraud and reducing the risk of match fixing. Models can detect and flag particularly anomalous match outcomes and any peculiar trends in athlete performance over time.

While operators have been increasing the number and types of wager offerings over the years, machine learning solutions have become increasingly important in this regard. One example of machine learning being used to flag anomalous match outcomes to sporting authorities is Sportsradar. In 2020, the Sportsradar team flagged over 521 suspicious matches to relevant sporting authorities, providing crucial third-party oversight for sports events.

Operators face some notable challenges when deploying machine learning for fraud detection; these include problems such as regulatory inconsistencies and lack of data mandates. You can listen to our recent webinar on sports data integrity to find out how they are responding to these problems.

The Future of Machine Learning in Sports Betting

Machine learning can help sportsbooks improve crucial business functions like fraud detection, audience personalization, and marketing while unlocking new business functions, such as real-time simulated and fantasy sports games.

The challenge facing operators is to ensure they move to deploy machine learning in their operations swiftly and effectively while also ensuring machine learning is deployed in a robust, responsible manner to mitigate risks for customers and themselves.

Are you looking to elevate your sports betting operations to the next level? DataArt is here to empower you with cutting-edge solutions that can boost efficiency, expand your market reach, and provide a technological advantage over your competitors. Our team covers every aspect of sports betting software development, from scalable and secure platforms to advanced analytics. Explore DataArt's strategic sports betting solutions.

If you have any questions or if you need a professional consultation on implementing machine learning capabilities into your sportsbook, drop us a line.

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Machine Learning in Sports Betting: 5 Winning Cases

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What is artificial intelligence (AI)?

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind

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While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in this 2004 paper (link resides outside ibm.com), " It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."

However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing's seminal work, " Computing Machinery and Intelligence"(link resides outside ibm.com), which was published in 1950. In this paper, Turing, often referred to as the "father of computer science", asks the following question, "Can machines think?" From there, he offers a test, now famously known as the "Turing Test", where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since its publish, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.

Stuart Russell and Peter Norvig then proceeded to publish, Artificial Intelligence: A Modern Approach (link resides outside ibm.com), becoming one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting:

Human approach:

Systems that think like humans

Systems that act like humans

Ideal approach:

Systems that think rationally

Systems that act rationally

Alan Turing's definition would have fallen under the category of "systems that act like humans."

At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

Over the years, artificial intelligence has gone through many cycles of hype, but even to
skeptics, the release of OpenAI's ChatGPT seems to mark a turning point. The last time generative AI loomed this large, the breakthroughs were in computer vision, but now the leap forward is in natural language processing. And it's not just language: Generative models can also learn the grammar of software code, molecules, natural images, and a variety of other data types.

The applications for this technology are growing every day, and we're just starting to
explore the possibilities. But as the hype around the use of AI in business takes off,
conversations around ethics become critically important. To read more on where IBM stands within the conversation around AI ethics, read more here.

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Weak AI-also called Narrow AI or Artificial Narrow Intelligence (ANI)-is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. 'Narrow' might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM watson, and autonomous vehicles.

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equaled to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)-also known as superintelligence-would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman, rogue computer assistant in 2001: A Space Odyssey.

Since deep learning and machine learning tend to be used interchangeably, it's worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is actually a sub-field of machine learning.

Deep learning is actually comprised of neural networks. "Deep" in deep learning refers to a neural network comprised of more than three layers-which would be inclusive of the inputs and the output-can be considered a deep learning algorithm. This is generally represented using the diagram below.

The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman noted in same MIT lecture from above. Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn.

"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn't necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the hierarchy of features which distinguish different categories of data from one another. Unlike machine learning, it doesn't require human intervention to process data, allowing us to scale machine learning in more interesting ways.

Generative AI refers to deep-learning models that can take raw data - say, all of Wikipedia or the collected works of Rembrandt - and "learn" to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified
representation of their training data and draw from it to create a new work that's similar,
but not identical, to the original data.

Generative models have been used for years in statistics to analyze numerical data. The rise of deep learning, however, made it possible to extend them to images, speech, and other complex data types. Among the first class of models to achieve this cross-over feat were variational autoencoders, or VAEs, introduced in 2013. VAEs were the first deep-learning models to be widely used for generating realistic images and speech.

"VAEs opened the floodgates to deep generative modeling by making models easier to
scale," said Akash Srivastava, an expert on generative AI at the MIT-IBM Watson AI Lab.
"Much of what we think of today as generative AI started here."

Early examples of models, like GPT-3, BERT, or DALL-E 2, have shown what's possible. The future is models that are trained on a broad set of unlabeled data that can be used for different tasks, with minimal fine-tuning. Systems that execute specific tasks in a single domain are giving way to broad AI that learns more generally and works across domains and problems. Foundation models, trained on large, unlabeled datasets and fine-tuned for an array of applications, are driving this shift.

When it comes to generative AI, it is predicted that foundation models will dramatically
accelerate AI adoption in enterprise. Reducing labeling requirements will make it much
easier for businesses to dive in, and the highly accurate, efficient AI-driven automation they enable will mean that far more companies will be able to deploy AI in a wider range of mission-critical situations. For IBM, the hope is that the power of foundation models can eventually be brought to every enterprise in a frictionless hybrid-cloud environment.

There are numerous, real-world applications of AI systems today. Below are some of the most common use cases:

Speech recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search-e.g. Siri-or provide more accessibility around texting.

Customer service: Online virtual agents are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.

Computer vision: This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry.

Recommendation engines: Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.

Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.

The idea of 'a machine that thinks' dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

1950: Alan Turing publishes Computing Machinery and Intelligence. In the paper, Turing-famous for breaking the Nazi's ENIGMA code during WWII-proposes to answer the question 'can machines think?' and introduces the Turing Test to determine if a computer can demonstrate the same intelligence (or the results of the same intelligence) as a human. The value of the Turing test has been debated ever since.

1956: John McCarthy coins the term 'artificial intelligence' at the first-ever AI conference at Dartmouth College. (McCarthy would go on to invent the Lisp language.) Later that year, Allen Newell, J.C. Shaw, and Herbert Simon create the Logic Theorist, the first-ever running AI software program.

1967: Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that 'learned' though trial and error. Just a year later, Marvin Minsky and Seymour Papert publish a book titled Perceptrons, which becomes both the landmark work on neural networks and, at least for a while, an argument against future neural network research projects.

1980s: Neural networks which use a backpropagation algorithm to train itself become widely used in AI applications.

1997: IBM's Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch).

2011: IBM watson beats champions Ken Jennings and Brad Rutter at Jeopardy!

2015: Baidu's Minwa supercomputer uses a special kind of deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human.

2016: DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves!). Later, Google purchased DeepMind for a reported USD 400 million.

2023: A rise in large language models, or LLMs, such as ChatGPT, create an
enormous change in performance of AI and its potential to drive enterprise value.
With these new generative AI practices, deep-learning models can be pre-trained on
vast amounts of raw, unlabeled data.

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