Bench TalkBench Talk for Design Engineers | The Official Blog of Mouser Electronics for People with Physical Disabilities,Computing,EIT 2020: The Intelligent RevolutionWed, 17 Mar 2021 16:05:14 GMT<p><img alt="" src="/blog/Portals/11/Matuschek_AI%20for%20Physical%20Disabilities_Theme%20Image_min.png" style="width: 600px;" title="" /></p> <p style="font-size:12px;"><em><small>(Source: Andrey_Popov/</small></em></p> <p>Artificial intelligence (AI) has influenced or even revolutionized many areas of our society, and with recent developments such as deep learning or reinforcement learning, this trend will continue to accelerate. These developments are particularly interesting for people with physical disabilities, such as impaired vision, speech disorders or missing or damaged limbs. They open up opportunities for inclusion and participation in society that until recently seemed unattainable for disabled people. Technology can do so much more than just making life easier or more convenient. Instead, the aim is to enable them to do activities that were previously difficult or impossible.</p> <div> <h2>AI Solutions for People with Impaired Vision</h2> </div> <p>Although assistive technologies for the blind, such as screen readers or braille displays, have been around for some time, AI has recently led to significant improvements. With more powerful speech synthesis algorithms and smartphones and dedicated assistive devices, the use of text-to-speech (TTS) technology has become much more flexible, enabling mobile ad-hoc use, such as listening to an e-book hands-free. This is also closely related to advances in image processing, especially in optical character recognition (OCR), to make text accessible for speech synthesis. A good example of this is Google Lens, which can, for example, recognize and process text in a camera image of a traffic sign. The same logic applies to speech-to-text (STT) apps, which enable mobile and hassle-free text communication without the need for a keyboard or special software, as was the case in the past.</p> <p>However, a crucial aspect that requires even more attention is the user interface design and, in particular, error tolerance. For ordinary users, the wrong recognition of a command or a failure to load are simple annoyances, but this can be a serious obstacle or even life-threatening for a disabled person if they cannot make an emergency call. An example of this gap in interface design is when a voice interface requires a touch input, which a blind person might not be able to perform at all, in the event of an error.</p> <div> <h2>AI Solutions for Speech and Hearing Impairments</h2> </div> <p>TTS and STT can also be used to support people with speech and hearing impairments. For people with the motor skills for typing, TTS can give them a voice. One of the best-known examples is the late Stephen Hawking, who used specially designed speech synthesis software to make himself heard. Similar software is now widely used.</p> <p>Live transcription is an example of STT. This is a popular field of research primarily for commercial reasons, but reliable and flexible transcription also allows deaf people to consume previously inaccessible media. For example, YouTube now enables live subtitles for many languages, making many entertainment and educational resources available to a wider audience.</p> <div> <h2>AI Solutions for Prosthetics</h2> </div> <p>Although many advances have been made that indirectly help people with impairments, areas of AI research are directly aimed at alleviating the impairment in close cooperation with medical research. One of the most interesting fields of research in this regard is the development of prostheses. Unlike early prostheses, which were completely immobile, mechanically flexible limbs have become commonplace, and recent advances in algorithm design combined with more powerful and compact chips lead to significant improvements in this area. A modern prosthesis reacts much faster to input from the nervous system because of improved signal processing algorithms and adapts appropriately to the environment, for example, to different floor conditions. This allows for much more natural, intuitive movement and reduces the disparity between artificial and natural limbs.</p> <p>Another traditional approach to improving the lives of disabled people is, of course, physical therapy. In the past, assessing a person&rsquo;s physical condition was the responsibility of the doctor or therapist. However, with time constraints and incomplete information, it is often difficult to create a tailored, optimized treatment plan. This often results in the use of textbook approaches that do not do justice to the specific situation.</p> <p>This can be better managed with AI. For example, image recognition software can analyze minor inconsistencies in gait and posture to suggest custom exercises, and progress can be tracked much more closely, allowing for faster improvements. This also leads to greater motivation, as the person being treated achieves success faster and plays a more active role in setting goals and providing feedback on what is working and what is not.</p> <div> <h2>Conclusion</h2> </div> <p>Many AI technologies already exist that improve the lives of disabled people, either as by-products of regular technologies or that are designed specifically for them. However, many technological challenges still prevent AI from having a greater impact on this large and particularly vulnerable segment of the population. To make significant progress toward inclusion, policymakers and companies must focus more on taking disabilities into account during product development and, ideally, also include disabled people in the development process.</p> 1741Reinforcement Learning Is Advancing AI Applications,EIT 2020: The Intelligent RevolutionThu, 25 Feb 2021 08:49:41 GMT<p><img alt="" src="/blog/Portals/11/shutterstock_1155296422_022521.jpg" style="width: 600px; height: 424px;" title="" /></p> <p style="font-size:12px;"><em><small>(Source: a-image/</small></em></p> <p>Just a few years ago, technological applications that can perceive the surroundings, recognize important details&mdash;and ignore the rest&mdash;and then use those details to accomplish a task seemed like the stuff of science fiction.</p> <p>However, several technologies have now become an integral part of our daily lives: intelligent voice assistants that understand and respond to the many nuances of human language, medical applications that use imaging to predict cancer more accurately than human doctors, and self-driving cars that navigate dynamic environments. They are just some of the technologies making headlines.</p> <p>Reinforcement learning, one of the three branches of machine learning, is advancing many of these innovations. It enables computers to recognize important features of their environment to make optimal decisions&mdash;a skill that did not exist until recently. A more detailed look at reinforcement learning (RL), artificial neural networks (ANNs), and deep learning (DL) reveals new potentials&mdash;as well as remaining challenges&mdash;for artificial intelligence applications aiming to achieve AI on a human level.</p> <h2>Approaches to Machine Learning</h2> <p>Machine learning (ML) is a subset of AI that enables computers to learn from examples and experiences. Of the three branches of ML, supervised and unsupervised learning are perhaps the best known and are used for well-defined problems and relatively predictable.</p> <h3>Supervised Learning</h3> <p>Supervised learning (SL) approaches are used for solving problems for which annotated input data are available. The algorithms try to learn patterns and associations from these known examples to, in turn, process unknown examples. A classic example of this is image recognition, in which manually annotated images are used to train models to classify freshly captured images correctly.</p> <h3>Unsupervised Learning</h3> <p>Unsupervised learning (UL) approaches are used to infer hidden structures or relationships in non-annotated data records. These approaches can be applied without much preparation but are generally more descriptive and exploratory. They are typically used to prepare for the use of supervised approaches. A common example is identifying different customer groups in transactional data, which can later facilitate various targeted marketing campaigns.</p> <h3>Reinforcement Learning</h3> <p>Reinforcement learning (RL), ML&rsquo;s third branch, enables some of the most complex and human applications making headlines today. RL is a type of machine learning in which rewards and penalties evaluate individual actions and input variables on which future actions can be planned. Rather than explicitly telling how to solve a problem, RL is based on maximizing rewards and minimizing penalties. Not limited to specific problems or environments, RL focuses on machines that make optimal decisions based on complex inputs from dynamic environments.</p> <p>The basic idea of RL is to model learning in a similar way to how a human&mdash;or any sufficiently intelligent being&mdash;learns: by attempting to achieve a specific goal&mdash;connected to a reward&mdash;with the skills and tools provided, but with no clear instructions on exactly how to solve the problem. A simple example is a robot that can open and close a hand to place a ball in a box. The robot has to learn that it can grab the ball, move its arm into the correct position, and then allow it to drop. This usually involves many iterations and re-starts of the experiments. The robot only receives feedback on whether its behavior was successful or not and tries to adjust its movements until the goal is achieved.</p> <p>This is in marked contrast to SL, where a good result requires many examples&mdash;such as a large, diverse collection of annotated cat images&mdash;to describe the problem in all its dimensions. This is the only way for algorithms to learn exactly which features&mdash;such as shapes or colors&mdash;are relevant to the correct decision. For the robot example, the equivalent would be to accurately and carefully describe each step of the process&mdash;such as where to move the hand, how much pressure to apply, etc. For this example with few variables, it might be possible to achieve this level of detail, but relearning would be required if the variables changed. Given a larger ball, the robot would be at a loss.</p> <p>In real-world applications, the balancing of inputs, outputs, and training data becomes surprisingly complex. For example, autonomous vehicles process a large amount of sensor data almost in real-time. Overlooking nuances in the environment can have significant consequences, and a great deal is at stake. That is why reinforcement learning is the tool of choice in an environment where creating training examples or instructions is prohibitive or impossible.</p> <h2>Sub-genres of Reinforcement Learning</h2> <p>Like other branches of ML, RL has sub-genres that work together to drive innovation. In particular, feature learning (FL) enables systems to recognize differentiated details of input data. Artificial neural networks (ANNs) and deep learning (DL) provide the required framework for advanced parsing, processing, and learning, and enable the subfield of deep reinforcement learning (DRL).</p> <h3>Feature Learning</h3> <p>Feature learning&mdash;also known as representation learning&mdash;is an ML technique that enables machines to recognize characteristic and independent components of input data that often cannot be represented in algorithms. For example, in a self-driving car, surroundings are perceived by several cameras, radar, and other sensors. This means that a lot of information is available to decide on the next action, but only a fraction of it is relevant. For example, the sky&rsquo;s color is usually irrelevant, while the color of a traffic light is highly relevant. The speed of a bird flying past is much less important than the speed of a pedestrian approaching the curb.</p> <p>Why is the ability to represent this level of input functions so important? Data sets used for training play a key role in the accuracy of the models. The more training data, the better. In particular, the more diverse examples with clear and identifying features included in the data set, the better. In other words, the distinctive and independent features of the input data help computers bridge the gap between what they have already learned and what needs to be learned to ensure 100 percent accuracy and consistency regardless of the context. The recognition of distinctive features also helps identify any characteristics and outliers that can be ignored, which can in turn help to reduce the data volume over time significantly.</p> <h3>Artificial Neural Networks and Deep Learning</h3> <p>These highly variable applications require a robust and scalable framework. One approach that has received considerable attention, particularly in supervised learning, is deep learning. When combined with the principle of reinforcement learning, we refer to it as deep reinforcement learning.</p> <p>The basic idea of artificial neural networks (ANNs) dates back to the 1960s and is loosely based on the human brain&rsquo;s network-like neural structure. ANNs comprise a huge network of artificial neurons called perceptrons that receive input signals, evaluate various input features, and then relay the signal through the network until an output signal is reached.</p> <p>The network is defined by the number of neurons, the strength and number of their connections, and the neurons&rsquo; activation threshold. This is the strength that the input signal must have to be passed on. ANNs have a scalable structure containing multiple input and output levels, using hidden levels in between that translate the input into something that the output level can use. The specialized term deep learning is derived from networks with many successive layers of neurons and are therefore deep.</p> <p>ANNs are particularly suitable for generating optimal answers from complex input data and dynamic environments because of how they learn&mdash;through backpropagation. For any given training signal&mdash;for example, a vector describing coordinates and color values of an image&mdash;the network checks that the generated output is correct and then adjusts the weights in the network slightly to achieve the desired result. After enough training iterations, the network becomes stable and can now recognize previously unknown situations.</p> <h3>Limitations of ANNs, DL, and RL</h3> <p>ANNs and DL offer enormous potential because of their ability to represent characteristics and arrive at optimal responses in dynamic environments. However, their abilities point to more challenges and reveal some remaining gaps in mimicking certain aspects of human intelligence.</p> <h4>Millions of Nodes, Connections, and Training Iterations Are Required</h4> <p>Modeling relevant problems requires ANNs to have significant numbers of nodes and connections to handle the millions of different variables that need to be analyzed and stored. Modern computers have only recently made this possible. Similarly, the number of training loops required can reach billions and grow exponentially with the number of environment variables. It is no coincidence that the first major breakthroughs in reinforcement learning were made in games such as Go, where the AI called AlphaGo now manages to beat the best human players: The rules of the game&mdash;such as the possible actions and outcomes&mdash;as well as the objectives are clearly defined, and it is easy to quickly execute many simulated games by letting the AI play against itself. The next evolutionary step was in playing video games such as Super Mario&trade; or StarCraft, in which the relationship between actions and outcomes is more complex. Still, the environment remains limited, and the rapid simulation of many iterations is also possible.</p> <p>However, with a real-world problem such as autonomous driving, the situation is different. The primary task of safely reaching the destination is still relatively easy to formulate. However, the environment is significantly more diverse, and simulations need to be much more sophisticated to make them useful for learning about the actual problem. Ultimately, the simulations still have to be replaced with actual driving to take into account other factors that cannot be modeled, and close monitoring will continue to be required until human performance is achieved. For example, autonomous vehicle manufacturer Waymo stated in a 2020 press release that its cars need 1,400 years of driving experience to compete with human drivers. This is surprising because a person can drive a car safely after just a few weeks of practice. Why isn&#39;t this possible for R&mdash;or is it?</p> <h4>Abilities Related to Abstraction and Inference</h4> <p>People can learn to play a game or drive a car quickly because the human brain can learn through abstraction and inference. Through this type of learning, a driver can, for example, imagine what a traffic light would look like from another point of view or in another context because of the innate spatial awareness of humans. A human can also spot cars on the road that are different in color from those previously seen and draw conclusions from observations and experience.</p> <p>Such functions have only been recently explored in ANNs. Although different levels of the network can capture different aspects of the input, such as shapes and colors, the network can only process characteristics explicitly contained in the training data. If the AI is trained during the daytime, the model is unlikely to handle the other conditions at night. Even with DL, such differences must be taken into account in the training data, and the degree of acceptable deviation from the training data is very small.</p> <p>Various techniques for learning by abstraction and inference are currently being explored, but they reveal even more challenges and limitations. A popular example of ANN failure was a computer vision system that detected Siberian Husky dogs with extremely high reliability&mdash;much more reliably than other dog breeds. Closer inspection revealed that the network had focused on the snow present in almost all of the Husky images and was ignoring the dog itself. In other words, the model failed to see that the color of the ground&mdash;a trivial detail to humans&mdash;is not an intrinsic property of the dog.</p> <p>This example seems banal and artificial, but the real-world consequences can be dire. Let&#39;s look again at the example of self-driving cars, where accidents are rare but can be traced back to ambiguous situations. The 2018 accidental death of a pedestrian pushing a bicycle down a four-lane highway was an example of a situation that would have been easy for a human driver to handle but resulted in collision and death because an ANN incorrectly processed it. The situation was not observed during the many hours of training and, no adequate failover&mdash;&ldquo;<em>If you don&#39;t know what to do, stop!</em>&rdquo;&mdash;was implemented. As a result, the system appeared to react irrationally because it lacked that fundamental cornerstone of human intelligence.</p> <p>Worse still, such blind spots in AI can be exploited by those seeking to harm. For example, the image classification can be completely misdirected if manipulated images are inserted during training. Although minor changes in images are imperceptible to humans, the same changes could be perceived and interpreted differently in ANNs. In one example, stop signs with nondescript stickers were incorrectly recognized as other signs. This could have led to accidents if this trained model had been used in an actual car. A human driver, on the other hand, would, of course, still recognize the stop sign without any problems.</p> <div> <h2>Overcoming Obstacles and Limitations</h2> </div> <p>These and other obstacles and limitations give rise to the question of how to move forward and enable ANNs to further fill the gap in making optimal decisions. The simple answer is more training. If the variability and quality of the training data are good enough, it can reduce the error rate to such an extent that the model&rsquo;s accuracy is acceptable. It has been shown that autonomous cars are already less frequently involved in accidents than human drivers, but the potential for &ldquo;freak accidents&rdquo; prevents wider acceptance.</p> <p>Another systematic approach would be to explicitly encode the required background knowledge and make it available in the ML process. For example, a knowledge base created by Cycorp has been around for many years and contains millions of concepts and relationships, including the meaning of the stop sign mentioned previously. The aim is to manually encode human knowledge in a machine-readable form so that AI can fall back on training data and draw conclusions and assess unknown situations, at least in part, in a way that is similar to human intuition.</p> <div> <h2>Conclusion</h2> </div> <p>Technologies that can perceive the surroundings recognize important details, and make optimal decisions are no longer science fiction. Reinforcement learning, one of machine learning&rsquo;s three branches, provides tools and frameworks that can handle high-dimensional variables and dynamic environments. However, these solutions also lead to new challenges, particularly the need for extensive neural networks, comprehensive training, and the imitation of human learning abilities through abstraction and inference to adapt to new situations. Although AI is capable of remarkable achievements and is becoming increasingly indispensable in many real-world applications, it is still a long way from achieving human-level learning abilities. Experiencing the intermediate steps is perhaps even more interesting than science fiction itself.</p> 1699Transferring Human Knowledge to AI,EIT 2020: The Intelligent RevolutionFri, 29 Jan 2021 02:16:12 GMT<h2 style="color:#aaa; font-style:italic; font-size:16px;">Sentiment Analysis: The Case for Context- and Culture-Sensitive AI</h2> <h2>&nbsp;</h2> <p><img alt="" src="/blog/Portals/11/Transferring%20Human%20AI%20_Theme%20Image.png" style="width: 600px; height: 400px;" title="" /></p> <p style="font-size:12px;"><em>(Source: hoelixDE/</em></p> <p>In 2020, more than 4.4 billion internet users were producing a staggering amount of data through social-media posts, reviews, recommendations, and similar interactions. The insight gathered from this data is invaluable in guiding businesses and innovators through product development, marketing, and customer support. However, extracting that insight is challenging as opinion-oriented, customer-provided data is difficult for machines to understand and interpret because of complexities in human language and cultural context. Tools such as natural language processing (NLP) and machine learning (ML) enable computers to understand and derive meaning from human language. Furthermore, an advancing research area in artificial intelligence (AI) called sentiment analysis helps machines understand unstructured, customer-provided data and interpret opinions as positive, negative, or neutral.</p> <div> <h2>Language Complexities in Semantic Analysis</h2> </div> <p>To understand sentiment analysis in NLP, let&rsquo;s look at this simple statement from a restaurant review: &ldquo;The soup was good.&rdquo; An analysis of the sentiment requires three actions:</p> <ul> <li>Identify whether a statement, sentence or the whole text contains an opinion.</li> <li>Understand whether the opinion is positive, negative, or neutral (called polarity).</li> <li>Identify the target of the opinion.</li> </ul> <p>In this instance, the sentiment analysis is unambiguously positive concerning a particular food served at the restaurant. However, other examples are less straightforward, as in a seemingly similar clause, &ldquo;The beer is cold.&rdquo; Many would consider this opinion positive because they like beer this way, but cold can have a negative polarity in other contexts. For example, &ldquo;The coffee is cold&rdquo; uses an identical sentence structure and adjective, but many people would consider cold coffee to be negative.</p> <p>Other language complexities create additional challenges, such as sentences that contain multiple sentiments, for example: &ldquo;The food was good, but the soup was cold.&rdquo; Here, we have a positive, negative, and an ambiguous sentiment, depending on the customer&rsquo;s preference for soup temperature. Similarly, &ldquo;The soup was hot, but the beer was cold&rdquo; would be positive sentiments for most people but ambiguous given potential customer contexts.</p> <p>Modifiers further blur the line between polarities. For instance, consider the opinion: &ldquo;The staff were almost too friendly.&rdquo; Here, we must also think about irony, sarcasm, or figures of speech, making it challenging to identify sentiment correctly. Examples such as &ldquo;We waited for more than an hour, really great service!&rdquo; tend to be rare in training data and extremely difficult to encode manually in a systematic manner.</p> <div> <h2>Cultural Variables in Semantic Analysis</h2> </div> <p>Assigning polarity to opinions becomes even more challenging when considering personal, cultural, or circumstantial preferences. For example, consider analyzing customer reviews for a <em>ryokan,</em> a traditional Japanese guest house that is typically fancy and expensive but features a common bathing area rather than private bathrooms. Categorizing the absence or presence of something as positive or negative seems straightforward&mdash;for example, &ldquo;There was dirt in the shower&rdquo; or &ldquo;There was a pool for the kids.&rdquo; However, the <em>ryokan</em> example demonstrates how accounting for cultural variables and personal preferences is essential in attaining useable insights for data. In Japan, guests believe shared bathing areas to be a positive attribute. By contrast, most European travelers would view it negatively, particularly at an expensive hotel. This example highlights just one feature and two cultures.</p> <div> <h2>Addressing Language and Cultural Variables in NLP</h2> </div> <p>In NLP, sentiments can be analyzed at the whole-document level and at the paragraph and sentence levels, with results often then aggregated. Although whole-document analysis is useful, paragraph and sentence-level analysis can yield more granular and correspondingly accurate results (such as&nbsp;identifying sentiment about a particular product feature in addition to the complete product). The challenge comes in developing a lexicon&mdash;the set of rules that machines use to classify sentiments as positive, negative, or neutral. Many free tools and resources are trained on public data as a starting point. For instance, software libraries such as Natural Language Toolkit, <a href="" target="_blank">spaCy</a>, and <a href="" target="_blank">TextBlob</a> include sentiment models and retraining with user data. If you prefer not to code, cloud offerings such as <a href=";utm_medium=cpc&amp;utm_campaign=na-US-all-en-dr-bkws-all-all-trial-e-dr-1008076&amp;utm_content=text-ad-lpsitelinkCCexp2-any-DEV_c-CRE_113120492767-ADGP_Hybrid+%7C+AW+SEM+%7C+BKWS+%7C+US+%7C+en+%7C+EXA+~+Google+Cloud+Platform-KWID_43700009942847400-kwd-26415313501&amp;utm_term=KW_google%20cloud%20platform-ST_google+cloud+platform&amp;&amp;gclid=EAIaIQobChMIw9GxsbD96AIVCr7ACh0A2Q0mEAAYASAAEgK7-PD_BwE" target="_blank">Google Cloud Platform</a> or <a href=";ef_id=EAIaIQobChMIwc-9v7D96AIVQdbACh102An7EAAYASAAEgIolPD_BwE:G:s&amp;OCID=AID2000128_SEM_FtLZf4aO&amp;MarinID=FtLZf4aO_287508794052_ms%20azure_e_c__54853277188_kwd-298008906618&amp;lnkd=Google_Azure_Brand&amp;gclid=EAIaIQobChMIwc-9v7D96AIVQdbACh102An7EAAYASAAEgIolPD_BwE" target="_blank">Microsoft Azure</a> enable you to get started with sentiment analysis immediately: Simply paste the text to be analyzed into a browser and build your application from there.</p> <p>Beyond prototyping, data sets and ML models should address language and culture complexities. This means:</p> <ul> <li><strong>For planning: </strong>Find structured approaches to discovering variables and useful insights. For instance, analyze your data for underlying languages and cultures, tone, sources, author demographics, and then consult linguists to interpret those elements. Further improve your approach by interviewing people who belong to the author group to get a precise understanding of nuance and context.</li> <li><strong>For training data: </strong>Identify examples needed to address variables and include human-provided annotations. It might also mean revisiting knowledge bases such as dictionaries, adding more training data for the particular problem, or in some cases, removing problematic or misleading examples from your data if they do more harm than good.</li> <li><strong>For modeling: </strong>Find a method of representing sentences in a mathematically processable way. For example, word embeddings, which represent arbitrary text as numerical vectors, are useful for mapping words used in context to corresponding positive, negative, or neutral sentiments. Ideally, data analysis would be based explicitly or implicitly on individual customers&rsquo; preferences; however, this analysis is cumbersome and, in many cases, not possible if a user is not identifiable. A more accessible approach is to analyze data according to region and language. Then, model cultural differences with separate training examples.</li> </ul> <h2>Conclusion</h2> <p>Customer-provided data from media posts, reviews, recommendations, and the like provide invaluable insights for businesses and innovators. Complexities in natural language and cultures make it difficult for AI-driven machines to understand customer opinions. However, sentiment analysis can help ensure that these aspects are captured and reflected in insights. You can get started by using freely available tools and resources, but addressing complexities in language and culture is challenging, requiring significant planning, data prep, and modeling. Raising awareness about language and culture complexities is an excellent start in gaining useful insights and a highly valuable way to better understand your customers and their needs.</p> 1690Using Adaptive AI to Improve the Gaming Experience,Computing,EIT 2020: The Intelligent RevolutionMon, 21 Dec 2020 22:08:16 GMT<p><img alt="" src="/blog/Portals/11/EIT%202020%20EMEA%20Blog%205_Theme%20Image.jpg" style="width: 600px; height: 400px;" title="" /></p> <p>(Source: Andrey_Popov/</p> <div> <h2>Introduction</h2> </div> <p>Except for pure multiplayer games where human opponents largely determine the gaming experience, artificial intelligence is a fundamental cornerstone of game design. However, AI in commercial game design is fundamentally different from AI in general. While notable breakthroughs have been made in games such as chess, Go, and StarCraft&mdash;where AI effortlessly beats the best human players&mdash;this is not the intention with commercial games. On the contrary, a game AI that is unbeatable can quickly frustrate players and lead to poor sales&mdash;and the same applies to an AI that is too weak to be competitive, making the game boring.</p> <p>Therefore, the current focus is on adaptive AI. That is, AI that adapts to the style of play and skill of individual players to offer as many players as possible a challenging yet rewarding experience (the sweet spot). This is also known as hyper-personalization, a strategy designed to maximize potential audiences, and commercial success. We will discuss how adaptive AI hyper-personalization improves games, enhances user experience, meets the player&rsquo;s skill level, intensifies the level of immersion, and creates an overall customized gaming experience for most.</p> <div> <h2>AI in Games</h2> </div> <p>In games, AI is mainly used to control non-player characters, such as determining the attack patterns of enemies. In computer games&rsquo; early days, the different levels of difficulty mostly involved adding more enemies or strengthening them to withstand more damage, move faster, etc. Simple scripts and rules based on the game&rsquo;s current status rather than the behavior of the players mainly determined the behavior, meaning there was virtually no personalization. In more complex games&mdash;for example, strategy games&mdash;the cheating AI was also very common. This served to mask weaknesses in the AI by giving it more starting resources or allowing it to build units more quickly because, otherwise, it couldn&#39;t compete with good human players.</p> <h3>Adaptive Using AI to Improve the User Experience</h3> <p>Adaptive AI is first and foremost based on technological advances made in recent years&mdash;modern PCs and game consoles are now widely available to stream games directly to end devices, freeing them from the restrictions of running on local hardware. This allows games to take advantage of the cloud&rsquo;s computing power to enable more advanced AI behavior, especially when it comes to gaming graphics. More powerful processors and increased memory now enable much more sophisticated algorithms than a few years ago. Another major leap in gaming AI is likely because of cloud gaming trends from services such as Google Stadia and GeforceNow. &nbsp;</p> <div> <h2>Adaptation to the Player&#39;s Skills</h2> </div> <p>As a result of these developments, levels of ability and different gaming styles have rendered inflexible levels of difficulty a thing of the past. The current challenge is to design hyper-personalized games to players with different ability levels and different gaming styles.</p> <p>At either end of the spectrum of gamer types are, for example, wanderers, players who play mainly for relaxation and are content with absorbing the game&rsquo;s storyline and aesthetics. On the other hand, conquerors are players who seek the greatest possible challenge as long as it seems achievable.</p> <p>Catering to all types of gamers requires today&#39;s adaptive AI to measure how successful a player is (X represents percentage of the games were lost) and which techniques were used to be successful (&quot;Prefers weapon Y over the others&quot;). This can be used to adapt not only the abilities of AI opponents&mdash; including what make them miss the target more or less often&mdash;but also their strategy. For example, if a player prefers a long-range weapon, AI opponents can change their behavior to hand-to-hand combat to provide a new challenge.</p> <h3>Maintaining and Advancing Immersion</h3> <p>However, the most important aspect about game design is always to stay within the boundaries of the game world so as not to endanger the player&#39;s immersion in that world. For example, if a racing game is too easy, it is helpful for opponents to make fewer mistakes or overtake more aggressively to make the races more exciting. However, if an opponent&#39;s car is suddenly able to drive faster than the player was previously aware of, this is perceived as unfair, and interest in the game quickly wanes.&nbsp;</p> <div> <h2>Creating Unique Experiences</h2> </div> <p>Adaptive AI that learns from player behavior has also opened up another way to make games more interesting: mirroring player behavior. Mirroring player behavior is exemplified in racing games that learn the maneuvers of real race car drivers. This allows regular players to get as close as possible to a real race&rsquo;s hypothetical scenario with famous opponents.</p> <p>Another example is professional e-athletes who create AI fingerprints of their individual gaming styles to make them commercially available. This enables several players to test their skills against notable e-athletes without actually participating in a multiplayer match against them. Releasing these new AI patterns to add new challenges to an existing game is clearly another way to improve a game&#39;s commercial longevity. It prevents players from losing interest once they have played through the game or investigated all the possible strategies.</p> <div> <h2>Conclusion</h2> </div> <p>The use of AI in gaming has come a long way, from inflexible levels of difficulty to highly adaptive AI algorithms. These days, the aim is to provide as many players as possible with a rewarding experience to maximize a game&#39;s lifespan and commercial return on investment. Advances in hardware, especially in cloud gaming services, provide increasing opportunities for this so-called hyper-personalization, culminating in the fingerprinting of individual gaming styles to create unique gaming experiences.</p> 1631