Welcome to the Artificial Intelligence and Games book. This book aims to be the first comprehensive textbook on the application and use of artificial intelligence (AI) in, and for, games. Our hope is that the book will be used by educators and students of graduate or advanced undergraduate courses on game AI as well as game AI practitioners at large.

The book is now available from Springer in digital and printed versions. Click here to access the SpringerLink edition or to buy the hardcopy.

If your institutiondoes not have access to SpringerLink, a pdf version of the book is available here (but please try the link above first).

You can also buy the book from Amazon, though buying directly from Springer may be cheaper.

We are running a summer school on AI and Games, based on the content of the book, in May 2018.

To cite this book you may usethe following bibtex entry:

which yields the following reference (e.g.inChicagostyle):

Yannakakis, Georgios N.,and Julian Togelius. Artificial Intelligence and Games. Springer, 2018.

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Artificial Intelligence and Games A Springer Textbook ...

The future is intelligent: By 2030, artificial intelligence (AI) will add $15.7 trillion to the global GDP, with $6.6 trillion projected to be from increased productivity and $9.1 trillion from consumption effects. Furthermore, augmentation, which allows people and AI to work together to enhance performance, will create $2.9 trillion of business value and 6.2 billion hours of worker productivity globally. In a world that is increasingly characterized by enhanced connectivity and where data is as pervasive as it is valuable, Africa has a unique opportunity to leverage new digital technologies to drive large-scale transformation and competitiveness. Africa cannot and should not be left behind.

There are 10 key enabling technologies that will drive Africas digital economy, including cybersecurity, cloud computing, big data analytics, blockchain, the Internet of Things, 3D printing, biotechnology, robotics, energy storage, and AI. AI in particular presents countless avenues for both the public and private sectors to optimize solutions to the most crucial problems facing the continent today, especially for struggling industries. For example, in health care, AI solutions can help scarce personnel and facilities do more with less by speeding initial processing, triage, diagnosis, and post-care follow up. Furthermore, AI-based pharmacogenomics applications, which focus on the likely response of an individual to therapeutic drugs based on certain genetic markers, can be used to tailor treatments. Considering the genetic diversity found on the African continent, it is highly likely that the application of these technologies in Africa will result in considerable advancement in medical treatment on a global level.

In agriculture, Abdoulaye Banir Diallo, co-founder and chief scientific officer of the AI startup My Intelligent Machines, is working with advanced algorithms and machine learning methods to leverage genomic precision in livestock production models. With genomic precision, it is possible to build intelligent breeding programs that minimize the ecological footprint, address changing consumer demands, and contribute to the well-being of people and animals alike through the selection of good genetic characteristics at an early stage of the livestock production process. These are just a few examples that illustrate the transformative potential of AI technology in Africa.

In a world that is increasingly characterized by enhanced connectivity and where data is as pervasive as it is valuable, Africa has a unique opportunity to leverage new digital technologies to drive large-scale transformation and competitiveness. Africa cannot and should not be left behind.

However, a number of structural challenges undermine rapid adoption and implementation of AI on the continent. Inadequate basic and digital infrastructure seriously erodes efforts to activate AI-powered solutions as it reduces crucial connectivity. (For more on strategies to improve Africas digital infrastructure, see the viewpoint on page 67 of the full report). A lack of flexible and dynamic regulatory systems also frustrates the growth of a digital ecosystem that favors AI technology, especially as tech leaders want to scale across borders. Furthermore, lack of relevant technical skills, particularly for young people, is a growing threat. This skills gap means that those who would have otherwise been at the forefront of building AI are left out, preventing the continent from harnessing the full potential of transformative technologies and industries.

Similarly, the lack of adequate investments in research and development is an important obstacle. Africa must develop innovative financial instruments and public-private partnerships to fund human capital development, including a focus on industrial research and innovation hubs that bridge the gap between higher education institutions and the private sector to ensure the transition of AI products from lab to market.

At the same time, we must be careful that priority sectors drive the AI strategy in Africa with accompanying productsnot the other way around. We believe the health care industry presents by far the most urgent need and promising market opportunity, and, as such, should be put at the top of the list for the continents decisionmakers. A large portion of the African population is still unable to access proper health care, with a low patient ratio of one physician per 5,000 patients, and there is almost no country with a fully integrated health management platform. AI could intervene directly to improve personalized health care and product development. Importantly, the health management platform precedes the leveraging of AI, so we must equally invest in cybersecurity, Big Data, cloud computing, and blockchain.

Artificial intelligence for Africa presents opportunities to put the continent at the forefront of the Fourth Industrial Revolution. Before Africa can lead this transformation, though, there are important steps that must be undertaken. First, the region needs to formulate a comprehensive continental blueprint to guide its AI strategy by involving key Pan-African institutions, academia, and the private and public sectors in its conception.

In addition, these stakeholders must also invest in creating a digital identity platform for all Africans with reliable data banks for AI to be a viable economic option. For this, it is imperative to leverage readily available local talent as a means to promote and democratize AI technology continent-wide. Finally, we must harmonize regulatory policies that encourage ethically built AI systems so as to guarantee a more inclusive economic development for Africa. With these important steps, the next decade for Africa will be intelligent.

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The future is intelligent: Harnessing the potential of artificial intelligence in Africa - Brookings Institution

With the introduction of new export controls on artificial intelligence software last week, the White House appealed to lawmakers, businesses, and European allies to avoid overregulation of artificial intelligence. It also maintained its refusal to participate in a project proposed by the Group of Seven leading economies, which seeks to establish shared principles and regulations on artificial intelligence, as the U.S. prepares to take over the presidency of the organization this year.

The U.S. has rejected working with other G-7 nations on the project, known as the Global Partnership on Artificial Intelligence, maintaining that the plan would be overly restrictive.

Kay Mathiesen is an associate professor of philosophy and religion in the College of Social Sciences and Humanities. Photo by Matthew Modoono/Northeastern University

Kay Mathiesen, an associate professor at Northeastern who focuses on information and computer ethics and justice, contends that the U.S.s refusal to cooperate with other nations on a united plan could come back to hurt its residents.

Advocates of the plan say it would help government leaders remain apprised of the development of the technology. The project, they say, could also help build consensus among the international community on limiting certain uses of artificial intelligence, especially in cases where its found to be controlling citizens or violating their privacy and autonomy.

U.S. leaders, including deputy chief technology officer Lynne Parker, counter that the proposal appears overly bureaucratic and could hinder the development of artificial intelligence at U.S. tech companies.

But Mathiesen says that many companies are already ahead of the curve in considering or implementing oversight mechanisms to guide the ethical development of their products. She says that its important to rein in the potentially harmful effects of artificial intelligence to ensure that the benefits of the technology are not overridden by the cost.

The idea that we should just not regulate at all or not even think about this, because maybe then we might limit ourselves, I think thats a pretty simplistic view, says Mathiesen, a professor of philosophy who studies political philosophy and ethics. Its not like the G-7 is going to have the power to all of a sudden impose regulations on U.S. industry. So that argument that merely by joining this [group] and beginning to think these things through, and do research on this, and develop [policy] recommendationsthat that by itself is going to put us behind on artificial intelligence doesnt hold a lot of water.

Mathiesen suggests that failing to work with other countries in addressing privacy issues stemming from the unchecked spread of artificial intelligence productssuch as facial recognitioncould result in consumer backlash, and thereby slow down the development of artificial intelligence in the U.S.

The technology is advancing incredibly rapidly and we want to make sure that were thinking ahead, and were building at the beginning protections for consumers before these things come out and its too late and we have to try to fix problems that we couldve prevented, she says.

The plan for the Global Partnership on Artificial Intelligence, which was introduced in December 2018, is to ensure that artificial intelligence projects are designed responsibly and transparently, in a way that prioritizes human values, such as privacy. The initiative received a major boost from Canada, which held the G-7s rotating presidency at the time, and was kept alive by France the following year. The U.S. will take over the presidency of the organization this year.

In addition to Canada and France, the other G-7 countries, including Germany, Italy, Japan, and the U.K., are on board with the project. The European Union, India, and New Zealand have also expressed interest. Mathiesen says that while she understands the concerns of some U.S. government officials about being out-competed, its important for the U.S. to be a participating member in this effort, especially while the technology is still in its nascent stages.

In a way, its better that the U.S. has buy-in at the beginning and is at the table to make these arguments about how do we balance concerns about things like privacy, security, and possible harm that could be produced by artificial intelligence? How do we balance that with also wanting to enable companies and inventors to create new things with artificial intelligence that can be economically and socially beneficial? she says.

Mathiesen suggested that failing to engage in these conversations with the wider international community could leave the U.S. trailing behind.

I think that the American citizens are going to suffer for that, just like they do now with the lack of data privacy, she says.

In conjunction with global professional services company Accenture, researchers at Northeasterns Ethics Institute last year produced a report that provided organizations a framework for creating ethics committees to help guide the development of smart machines.

For media inquiries, please contact Marirose Sartoretto at m.sartoretto@northeastern.edu or 617-373-5718.

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The G7 wants to regulate artificial intelligence. Should the US get on board? - News@Northeastern

live from last vegas: during CES 2020, LG unveiled their framework for the future of artificial intelligence (AI) development. throughout its both massive and impressive booth, the south korean multinational electronics company showcased the LG ThinQ technology one that goes a step further by using deep learning to predict and play a proactive role in the users life. from a fridge that can tell you when to buy milk, to a dressing room that picks the best outfits for your body type, LG is giving us a glimpse into the future.

live from las vegas:designboom is covering all the latest launches ofCES 2020 the worlds largest consumer technology fair from the trade show grounds. stay connected for the future of technology as it debuts.

images courtesy of LG unless otherwise stated

at CES 2020, the LG ThinQ features were on show, displaying everyday products for attendants to try out for themselves. each one of them followed the four levels of AI experience (AIX) stated by the company efficiency, personalization, reasoning and exploration. efficiency is where specific device and system functions can be automated through single commands, like voice recognition. personalization focuses on pattern learning to optimize and personalize device functions. reasoning envisions an AI that can perceive the cause of certain patterns and behaviors to predict and promote positive outcomes for users. and although still far in the future, level four, exploration, is the ultimate destination for LGs AI. by using a concept called experimental learning based on the scientific method, AI-enabled systems will be able to develop new capabilities through forming and testing hypotheses to uncover new inferences, enabling them to learn and improve, adding more value to users lives.

image designboom

LGs massive exhibition space at CES 2020 embodied the companys anywhere is home concept with the LG ThinQ zone, showcasing a truly connected lifestyle that extends beyond the front door. the experience began with the smart door, which verifies visitors with both facial recognition and vein authentication before unlocking. when exiting, users can see a screen on the inside of the door that displays useful information such as weather and traffic conditions. when set to depart mode, the smart door instructs LG ThinQ appliances to go into low power when all residents have left the house.

image designboom

when it comes to fashion, the LG ThinQ fit collection zone allowed visitors to experience virtual fashion without having to step into a fitting room. an evolution of LGs original smart mirror concept, the LG ThinQ fit system uses cameras to accurately measure the users body to generate a realistic avatar for virtual fittings.

LGs robotic solutions impressed attendees with their culinary skills, efficiency and first-class hospitality at CLOis table zone. the futuristic restaurant featured the LG CLOi robots managing the entire operation from taking orders, cooking, serving and cleaning. potential diners would make reservations remotely via the ThinQ app and browse the menu via a smart speaker, smart TV or smartphone.

last but not least, the connected car zone, LG demonstrated a personalized in-car experience that allows users to take a piece of home on the road. for example, the vehicle features OLED displays inside it where users can continue enjoying the TV programs and movies they started watching at home. moreover, the personal sound zone offers a unique multimedia experience for the rider with voice-activated virtual personal assistant.

as pioneers in the field of AI it is our responsibility to consider the importance of the human experience whilst pushing the boundaries of AI research and development, commented jean-franois gagn, co-founder and CEO of element AI. together with LG electronics we hope that this work helps to set forth standards and principles that guide AI practitioners to consider a human centric approach when building the future.

image designboom

project info:

name: LG ThinkQ

company: LG

presented at: CES 2020

juliana neira I designboom

jan 13, 2020

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LG's ThinQ technology showcases the virtue of artificial intelligence in fashion and food at CES 2020 - Designboom

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Welcome to TechTalksAI book reviews, a series of posts that explore the latest literature on AI.

It wouldnt be an overstatement to say that artificial intelligence is one of the most confusing and least understood fields of science. On the one hand, we have headlines that warn of deep learning outperforming medical experts, creating their own language and spinning fake news stories. On the other hand, AI experts point out that artificial neural networks, the key innovation of current AI techniques, fail at some of the most basic tasks that any human child can perform.

Artificial intelligence is also marked with some of the most divisive disputes and rivalries. Scientists and researchers are constantly quarreling over the virtues and shortcomings of different AI techniques, further adding to the confusion and chaos.

Between tales of killer drones and robots that cant brew a cup of coffee, a book that sheds light on the real capabilities and limits of artificial intelligence is an invaluable and necessary read. And this is exactly how I would describe Artificial Intelligence: A Guide for Thinking Humans, a book by computer science professor Melanie Mitchell.

With a rich background in artificial intelligence and computer science, Mitchell sorts out, in her own words, how far artificial intelligence has come, and elucidates AIs disparateand sometimes conflictinggoals. As AI is drawing growing attention from investors, governments and the media, Mitchells Guide for Thinking Humans lays out the good, bad and ugly of artificial intelligence.

Most of us reading news headlines view artificial intelligence in the context sensational articles that have started to appear in mainstream media in the past few years. But theres much more to AI, which has a history that dates back to the early days of computing.

A Guide for Thinking Humans demystifies some of the least understood facts about artificial intelligence. As you read through the chapters, Mitchell eloquently takes you through the six-decade history of AI. You become acquainted with the original vision of AI, the early efforts at creating symbolic AI and expert systems, and parallel efforts to develop artificial neural networks.

You go through the AI winters, where overpromising and underdelivering dampened interest and funding in artificial intelligence. One of the most important parts of the book is the chapter convolutional neural networks (CNN), the AI technique that triggered the deep learning revolution in the early 2010s. While digging into the inner workings of CNNs, Mitchell also explains how other scientific fields such as neuroscience and cognitive science have played a crucial role in advancing AI.

Today convolutional neural networks and deep learning, in general, are a pertinent component of many applications we use every day.

It turns out that the recent success of deep learning is due less to new breakthroughs in AI than to the availability of huge amounts of data (thank you, internet!) and very fast parallel computer hardware, Mitchell notes in A Guide for Thinking Humans. These factors, along with improvements in training methods, allow hundred-plus-layer networks to be trained on millions of images in just a few days.

The trend, sparked by the ImageNet competition, has gradually morphed the field from an academic contest to a high-profile sparring match for tech companies commercializing computer vision, Mitchell explains.

The commercialization of AI has had bad effects on the field (as Ive also argued in these pages). Mitchell points to some of the other negative effects of the race to beat tests and benchmarks. Precision at ImageNet has become a de facto ticket to getting funding and improving stock prices and product sales. It has also led some companies and organizations to cheat their way to better test results without proving robustness in real-world situations.

These systems can make unpredictable errors that are not easy for humans to understand, Mitchell said in written comments to TechTalks. The machines often are not able to deal with input that is different from the kind of input they have been trained on. A Guide for Thinking Humans provides several examples of AIs failures.

There are several studies that show deep learning models optimized for ImageNet do not necessarily perform well when faced with objects in real life. There are also numerous papers that show how neural networks can make dangerous mistakes.

Mitchell also points out that, while very efficient at processing vast amounts of data, current AI models lack the generalization abilities of human intelligence, which makes them vulnerable to the long-tail problem: the vast range of possible unexpected situations an AI system could be faced with. Unfortunately, current approaches to AI only try to solve these problems by throwing more data and compute at the problem.

Often these benchmark datasets dont force the learning systems to solve the actual full problem that humans want them to solve (e.g., object recognition) but allow the learning systems to use shortcuts (e.g., distinguishing textures) that work well on the benchmark dataset, but dont generalize as well as they should, Mitchell says.

The obsession with creating bigger datasets and bigger neural networks has sidelined some of the important questions and areas of research regarding AI. Some of these topics include causality, reasoning, commonsense, learning from few examples and other fundamental elements that todays AI technology lacks.

But at least, the ImageNet race has taught us one thing. Says Mitchell: It seems that visual intelligence isnt easily separable from the rest of intelligence, especially general knowledge, abstraction, and language Additionally, it could be that the knowledge needed for humanlike visual intelligence cant be learned from millions of pictures downloaded from the web, but has to be experienced in some way in the real world.

Fortunately, these are topics that have been gaining increasing attention in the past year. In his 2019 NeurIPS keynote speech, deep learning pioneer Yoshua Bengio discussed system 2 deep learning, which aims to solve some of these fundamental problems. While not everyone agrees with Bengios approach (and its not clear which approach will work), the fact that these things are being discussed is a positive development.

One of the least-understood aspects of artificial intelligence is its handling of human language. The advances in the field have been tremendous. Machine translation has taken leaps and bound thanks to deep learning. Search engines are producing much more meaningful results. There are AI algorithms that can pass science tests. And of course, theres that OpenAI text generation algorithm that threatens to create a massive fake new crisis.

There has also been remarkable progress in speech recognition, an area where neural networks perform especially well (Mitchell calls it AIs most significant success to date in any domain). It is thanks to deep learning that you can utter commands to Alexa, Siri, and Cortana. Your Gmails Smart Compose and sentence completion features are powered by AI. And the numerous chatbot applications that have found a stable user base all leverage advances in natural language processing (NLP).

As Mitchell told TechTalks, I think some of these advances are very positive developments; applications such as automated translation, speech recognition, etc. certainly make life better. Indeed, human-machine combination is much better today than at any time in the past.

But whats less understood is how much todays artificial intelligence systems understand the meaning of language.

Understanding languageincluding the parts that are left unsaidis a fundamental part of human intelligence, Mitchell explains in A Guide for Thinking Humans. Language relies on commonsense knowledge and understanding of the world, two areas where todays AI lacks sorely.

Todays machines lack the detailed, interrelated concepts and commonsense knowledge that even a four-year-old child brings to understanding language, Mitchell writes.

And its true. Even the most sophisticated language models start to break as soon as you test their limits. For the moment, AI is limited to handling small amounts of text. Alexa can perform thousands of tasks, but it cant hold a meaningful conversation. Smart Compose provides interesting reply suggestions, but theyre only short answers to basic queries. Google Translate produces decent results when you want to translate simple sentences. But it cant translate an article that contains the rich and complicated nuances of language and culture. And the text generated by OpenAIs famous GPT-2 language model loses coherence as it becomes longer.

This is because todays AI still lacks the understanding of language. So how is AI performing such feats? It is basically the same pattern-matching that neural networks are performing on images (though in a different manner and with some added tricks). Again, recent years have proven that bigger data sets and larger neural networks will help push the limits of NLP applications. But they wont result in breakthroughs.

Whats stunning to me is that speech-recognition systems are accomplishing all this without any understanding of the meaning of the speech they are transcribing Many people in AI, myself included, had previously believed that AI speech recognition would never reach such a high level of performance without actually understanding language. But weve been proven wrong, Mitchell writes in A Guide for Thinking Humans.

But as she later explains in the book (and the failures of AI show), theres only so much you can achieve with statistics and pattern matching. For the moment, AI systems might have solved 90 percent of the problem of solving language problems. But that last 10 percent, dealing with the implicit subtleties and hidden meanings of language, remain unsolved.

Whats needed to power through that last stubborn 10 percent? More data? More network layers? Or, dare I ask, will that last 10 percent require an actual understanding of what the speaker is saying? Mitchell reflects. Im leaning toward this last one, but Ive been wrong before.

So while we enjoy the applications of artificial intelligence in natural language processing, theres no reason to worry that robots will soon replace human writers or interpreters. While neural machine translation can be impressively effective and useful in many applications, the translations, without post-editing by knowledgeable humans, are still fundamentally unreliable, Mitchell observes.

A Guide for Thinking Humans delves into many more topics, including the ethics of AI, the intricacies of the human mind, and the meaning of intelligence. Like several other scientists, Mitchell notes in her book that in intelligence, the central notion of AI, remains ill-defined, with various meanings, an over-packed suitcase, zipper on the verge of breaking.

The attempt to create artificial intelligence has, at the very least, helped elucidate how complex and subtle are our own minds, Mitchell writes.

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Artificial intelligence: The good, the bad and the ugly - TechTalks

This is the second part in aseries looking at the investments for the future sectors that will grow to become major industries and provide returns along the way. Part onelooks at clean energy. We will also focus on, water security, ageing populations and nutrition

"Alexa, can I make money investing in companiesthat buildartificial intelligence (AI) programmes?"

There is a lot of hype around the technology andit has thepotential to transform our lives. This naturally has led to investors approaching the sector with interest, looking to see whether they can invest in the next big technological change.

Investing in something as specific as AI is known as thematic investing or trend investing. Thisis a way of getting exposure to one niche area that is expected to expand significantly over time and therefore grow an investment.

Investing in AI is the second part in aseries looking at trend investing. Telegraph Money studies the outlook AI companies, how they would withstand a recession and what is the best way to invest for those enamoured with the sector.

AI is beginning to touch all areas of our lives, from suggesting films on Netflix to helping doctors diagnose diseases. It even interacts with us in our homes via smart speakers, which can play music and answer questionsamong an increasingly sophisticated array of "skills".

While AI dates back to the work of computer pioneer Alan Turing in the 1940s, its usefulness for the masses could be at a turning point. Research from Microsoft showed that around half of British businesses are now using AI in some form.

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Artificial intelligence is changing the world here's how to invest - Telegraph.co.uk