Top Artificial Intelligence books you must read
Artificial Intelligence (AI) has taken the world by storm. Almost every industry across the globe is incorporating AI for a variety of applications and use cases. Some of its wide range of applications includes process automation, predictive analysis, fraud detection, improving customer experience, etc.
AI is being foreseen as the future of technological and economic development. As a result, the career opportunities for AI engineers and programmers are bound to drastically increase in the next few years. If you are a person who has no prior knowledge about AI but is very much interested to learn and start a career in this field, the following ten Books on Artificial Intelligence will be quite helpful:
1. Life 3.0: Being Human In The Age of Artificial Intelligence by Max Tegmark
The book begins by positing a scenario in which AI has exceeded human intelligence and become pervasive in society. Tegmark refers to different stages of human life since its inception: Life 1.0 referring to biological origins, Life 2.0 referring to cultural developments in humanity, and Life 3.0 referring to the technological age of humans. The book focuses on “Life 3.0”, and on emerging technology such as Artificial general intelligence that may someday, in addition to being able to learn, be able to also redesign its own hardware and internal structure.
The first part of the book looks at the origin of intelligence billions of years ago and goes on to project the future development of intelligence. Tegmark considers short-term effects of the development of advanced technology, such as technological unemployment, AI weapons, and the quest for human-level AGI (Artificial General Intelligence). The book cites examples like Deepmind and OpenAI, self-driving cars, and AI players that can defeat humans in Chess, Jeopardy, and Go.
After reviewing current issues in AI, Tegmark then considers a range of possible futures that feature intelligent machines and/or humans. The fifth chapter describes a number of potential outcomes that could occur, such altered social structures, integration of humans and machines, and both positive and negative scenarios like Friendly AI or an AI apocalypse. Tegmark argues that the risks of AI come not from malevolence or conscious behavior per se, but rather from the misalignment of the goals of AI with those of humans. Many of the goals of the book align with those of the Future of Life Institute.
The remaining chapters explore concepts in physics, goals, consciousness and meaning, and investigate what society can do to help create a desirable future for humanity.
2:The Master Algorithm by Pedro Domingos
The book outlines five tribes of machine learning: inductive reasoning, connectionism, evolutionary computation, Bayes’ theorem and analogical modelling. The author explains these tribes to the reader by referring to more understandable processes of logic, connections made in the brain, natural selection, probability and similarity judgements. Throughout the book, it is suggested that each different tribe has the potential to contribute to a unifying “master algorithm”.
Towards the end of the book the author pictures a “master algorithm” in the near future, where machine learning algorithms asymptotically grow to a perfect understanding of how the world and people in it work. Although the algorithm doesn’t yet exist, he briefly reviews his own invention of the Markov logic network.
3 Deep Medicine by Eric Topol
One of America’s top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship–the heart of medicine–is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard.Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved.
4. Prediction Machines by Ajay Agrawal, Avi Goldfarb and Joshua Gans
“What does AI mean for your business? Read this book to find out.” — Hal Varian, Chief Economist, Google. Artificial intelligence does the seemingly impossible, magically bringing machines to life-driving cars, trading stocks, and teaching children. But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future. But in “Prediction Machines,” three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs. When AI is framed as cheap prediction, its extraordinary potential becomes clear: Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions.; Prediction tools increase productivity–operating machines, handling documents, communicating with customers.; Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete. Penetrating, fun, and always insightful and practical, “Prediction Machines” follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.
5. AI Superpowers by Kai-Fu Lee
With driverless cars, firefighting drones and email programs that can finish your sentences, there’s no escaping that artificial intelligence (AI) is going to continue being applicable in our everyday lives. There’s also little debate about the US and China currently possessing the largest concentration of brilliant minds working to develop these applications.China is so determined to become the world’s superpower in AI that they’re doing whatever is necessary to pave the way for a booming AI industry. This involves heavily subsidizing rent for AI-tech start-ups and installing one-stop-shops to make it easy to launch new start-ups. The Chinese government is even ensuring placements at competitive schools for start-up executives’ children.Is that all enough to topple the Silicon Valley giants? Author Kai-Fu Lee spent years in both Silicon Valley and the Chinese equivalent, Zhongguancun. He believes China is in an excellent position to surpass Silicon Valley, reign supreme in the current AI-based economy and overall change the world order. In this book summary, you’ll find out:
- how the Chinese version of Groupon grew to be one of the world’s largest start-ups;
- how WeChat became the biggest super-app in the world; and
- how a cancer scare caused the author to reconsider our relationship to AI.
A Breakthrough in Deep Learning
Until recently, when people talked about artificial intelligence (AI), it was often in the context of science-fiction. But lately, everyone from schoolkids to CEOs is curious about what type of changes that AI has in store for us in the next several years. When the author gives lectures at schools and executive conferences, he’s noticed that Chinese kindergarten students ask him similar questions as the CEOs do, like, “Are we going to have AI teachers?” and “What type of jobs are we going to have in the future?” While the development of real-world AI may seem like a relatively new concept, it’s actually been brewing for decades and has only now started to become a major business tool thanks to a breakthrough indeep learning.The story of how we arrived at deep learning goes back to the 1950s when researchers like Marvin Minsky and John McCarthy had the goal of instilling human intelligence into computers. And when the author began getting involved in the field during the early eighties, there were two camps working toward that purpose: therule-basedand theneural network. Rule-based AI thought the best results would come from programming machines with one rule at a time, like “cats have triangular-shaped ears.” The neural network camp, however, favored letting the machine learn on its own, similar to humans, through experience. This way, a machine is capable of analyzing a picture of a cat and responding incorrectly, but the error will become data it learns from.What neural network-based AI truly needed was a large amount of data to interpret and faster computing power, which ultimately arrived in the mid-2000s. With these improved circumstances, researcher Geoffrey Hinton was able to finally add the correct amount of layers to the “neurons” and basically multiply AI processing power to an entirely new level. When this occurred, theneural networkrebranded todeep learning. This major development went public at a 2012 contest when Hinton’s new AI algorithm blew the competition away at visual recognition. Suddenly, AI was able to process complex problems, recognize patterns and come up with amazing results. It was apparent that this technology was now applicable to an array of everyday functions, including visual and audio recognition, executing complex financial decisions and even driving a car. Thanks to deep learning, an AI economy was on the way.
How China Went From Copycat to Top Contender
In China, artificial intelligence had what the author describes as a “Sputnik moment” in 2016. This occurred when the AI program, AlphaGo beat the champion Go, player, Lee Sedol over the span of a three-game tournament. These games had 280 million Chinese spectators glued to their televisions, and many were heartbroken when the visibly emotional Lee conceded his defeat. But instead of breaking their spirit, the people of China became motivated to harness the power of AI to their advantage – similar to the launch of the Russian satellite, Sputnik, that encouraged Americans to be the first ones on the moon. Just as John F. Kennedy declared US intentions to land on the moon, after the Go tournament, the Chinese government issued a rallying cry announcing their goal to become the global leader of AI innovation in the next ten years. This is especially remarkable because only a few years prior, China was more well known for being a hub of copycat technology than for innovation. In the early 2000s, China was mimicking all of the successful Silicon Valley products. It caused many people in the West to write off China’s capabilities as a competitor. What the skeptics failed to recognize, however, is that by being copycats, Chinese entrepreneurs were actually learning how to make their own unique world-class goods. The best example might be Wang Xing, who created the copycats of Friendster, Facebook, Twitter, and Groupon sites. In doing so, Wang not only figured out how to design seamless products, but he also grew into a battle-hardened competitor that knew how to thrive in the cutthroat Chinese market. When he directed his focus on his group discount service Meituan, he was ready to outperform Groupon. This time around, Wang didn’t imitate the interface; instead choosing to make it Chinese-friendly with densely-packed page layouts. He also held back from early overspending to woo customers and chose to spend money for the long-term win by signing exclusive deals with vendors and creating a quick, secure payment system. Unlike Groupon, Wang didn’t attempt to coast on one idea. He expanded and proposed new products based on what was popular at the moment, including movies, food delivery, and local tourism. By 2014, Groupon was in a slump, selling for less than half its IPO, while Meituan was on its way to becoming the fourth most valuable start-up in the world.
Why China’s Unique Online World Makes it a Goldmine
There are some significant variations between Silicon Valley’s and China’s start-ups, and a major one is possessing what’s called a ‘light’ or ‘heavy’ touch. When a business has a light touch, it takes care of one aspect and leaves a lot of the particulars surrounding the service for other people to handle. This is the method of Silicon Valley companies such as Uber, which connects people with a ride, but doesn’t administer gas and car maintenance. Didi, the Chinese equivalent of Uber, also owns the gas stations and repair shops that keep their rides in service. This heavy touch approach is favored in China because it generally makes it harder for a copycat start-up to completely duplicate a service. Having a heavy touch and controlling each feature to a service can also lead to more data, which is necessary for a good AI product. China is already sitting on the world’s largest data goldmine. This is particularly true when it comes to Tencent, the company behind WeChat, a certifiable super-app that people utilize for almost everything. To understand the phenomenon WeChat, it’s essential to know that most Chinese people are mobile-first internet users, meaning that their primary internet experience was through a cheap smartphone, rather than a computer. With that in mind, WeChat has grown into the mobile app that allows you to do everything you’d want to do on a computer. Thanks to mini-apps within WeChat, you don’t have to only chat with friends, but you can also order food delivery, unlock a shared bike, buy groceries, movie tickets, or plane tickets, book a doctor’s appointment, order a prescription, and secure stocks – all without leaving the app. Many of those functions are possible through another mini app: the WeChat Wallet, which was launched on Chinese New Year 2014. Every New Year’s Day, the Chinese have the tradition of sending loved ones a red envelope with money. WeChat enabled users to do this electronically, with no transaction fees, and it was so successful that on the launch day, five million people linked their bank accounts to WeChat, sending 16 million electronic red envelopes. Since the debut of the WeChat Wallet, China has increasingly become a cash-free society. It’s a lot of data under one roof, making it very clear what people like to purchase, where they’re traveling and a lot more.
Internet AI and Business AI
The arrival of AI in our daily lives is approaching in four waves. The first wave isinternet AI, and it’s here already. YouTube can recommend the next video for you to watch based off of an AI algorithm, and services like the app, Toutiao doesn’t just recommend articles, but automatically generates them too. As for whothe leader is in internet AI, the author perceives the US and China to be neck and neck at this point. However, in five years, he predicts that China will have a 60-40 advantage in terms of ability to dominate the market. This is thanks to China possessing more internet users than both the US and Europe combined, as well as a population prepared to make mobile payments to content creators. Apps such as WeChat Wallet already allow people to transfer micropayments of a few cents to online content creators they like. That type of environment is going to drive innovative content from empowered creators, thus giving China a slight edge. The second wave isbusiness AI, and this category is where the US truly has the advantage. Business AI is currently emerging, with algorithms making the decisions on financial portfolios and bank loans. China does own some impressive mobile services right now, like Smart Finance, which offers loans without taking financial history or zip code into account. Instead, it employs unique metrics like how long it takes you to answer specific questions and how much battery power your device has. In doing so, it’s demonstrated itself to be a reliable loan service for migrant workers and other populations underserved by traditional banks. Not to mention the percentage of defaults is only in the single digits. Nevertheless, one section of data China is lacking in is business records. Comparatively, the US has had an excellent history of record keeping, with databases full of banking, hospital and other business transactions. For that reason, the US is in a great position for business AI and it’s why the author gives America the 90-10 advantage. The five-year prediction is somewhat better for China, with the US advantage decreased to 70-30.
Perception AI and Autonomous AI
The third wave of AI isperception AI, which incorporates voice and facial recognition programs. China has the edge here, which is in part due to cultural differences. Americans have numerous “Big Brother” concerns regarding their image and voice being captured, while the Chinese are more amenable to the idea of relinquishing some privacy in return for convenience. Perception AI has the potential to be an impressive field because it blurs the boundaries between online and offline. That’s why this technology frequently falls under the classification ofonline-merge-offline(OMO). An OMO application we’ll start noticing more often is the smart grocery store. Imagine getting a grocery cart that can scan your face, recognize you and bring up your shopping list. It also will greet you in your favorite actor’s voice. And since it scans everything you place in the cart, it can stop you before you get to the checkout counter if you forget something. It could even remind you what your partner’s favorite brand of wine is as you approach that section of the store.China is currently building the Xiaomi line of products, which convert your house into a voice-activated, AI-enhanced environment. Due to a local manufacturing hub in Shenzhen, those products, including speakers, refrigerators, rice cookers, and vacuum cleaners, are quite affordable. China’s manufacturing advantage and the US’s privacy fears, give China the 60-40 lead now, and the author presumes it will increase to 80-20 in five years’ time. The fourth and last wave isautonomous AI. So far, we haven’t even gotten close to the type of technology that supplies robots with human-like intelligence, and it’s possible we never will. But we do have drones, which are growing more advanced and machines that are able to identify the color of ripe strawberries and gently pick them. Google and Tesla are additionally transforming our motorways with driverless cars, which will be released to the public in the years to come. The US currently has a significant lead in autonomous AI, which the author puts at about 90-10, but China is ardent in catching up. The Chinese government is especially proactive in declaring AI-friendly policies and regulations, so it will be simpler to implement the technology on a broad scale. Already, China is developing a highway and a city the size of Chicago primarily designed for AI vehicles. In five years’ time, it’ll be more like a 50-50 split.
Will AI Lead to a Utopia or Dystopia?
Lately, when economists and researchers discuss what a world with an AI economy will look like, they generally fall into two camps. Famed geneticist and researcher, Ray Kurzweil, is on the side of utopia. He views machines as being the best tool for us to enhance our bodies and minds, empowering us to become smarter and live longer. Similarly, AI researcher Demis Hassabis regards AI as a tool that will allow us to finally cure disease and solve issues like global warming. From the dystopia viewpoint, entrepreneur Elon Musk and physicist Stephen Hawking believe AI’s potential signifies a very serious threat to humanity. For instance, an AI program could be asked to resolve global warming and determine that wiping out the human race is the best option.Opinions vary amongst economists too, and a lot of the debate stems from a 2013 study from Oxford University that found 47 percent of US jobs to be at risk over the next 20 years due to rising automation. Of course, most companies would be eager to lower costs and boost profits if they could automate some tasks. And this brings us to an essential distinction in the reports that followed the 2013 Oxford study: most of the automation that AI is currently capable of allots for specifictasksto be automated, but not entire jobs. For example, an automated tax advisor could do some things, like calculate tax returns and check for inconsistencies, but it couldn’t hold nuanced conversations with clients. Keeping in mind the difference between tasks and entire jobs, more reports emerged. According to the Organization for Economic Cooperation and Development (OECD), only 9 percent of US jobs were at risk because of automation. In a 2017 report by PriceWaterhouseCoopers (PWC), 38 percent of US jobs were in danger, while McKinsey Global said that about 50 percent of tasks globally are “already automatable.” That’s quite a range, and it’s a prominent reason why economists remain divided on the subject. The author tends to agree with the PWC report and thinks the actual number of displaced workers might even be higher. That’s because the reports didn’t takeground-up displacementinto consideration which will come from businesses such as Smart Finance and Toutiao that don’t employ any loan officers or editors. These businesses won’t be adding automation and letting their employees go; instead, they’ll displace loan officers and editors from the ground-up by not offering them a position at all.
Working in Harmony With AI in the Future
HiIn 2013, the author was diagnosed with cancer: stage IV lymphoma and up to that point, he was practically a workaholic, but it all changed. He realized so much of the effort he placed towards his career was meaningless now. Facing his own mortality, he understood now that being productive wasn’t what made him human and that was actually was what made him more like a machine. What made him human was his relationships with family, friends and the people close to him.Thanks to a course of chemotherapy, he’s in remission now, but the experience transformed how he envisioned AI and humans operating in harmony. The emergence of AI gives us an amazing opportunity to relinquish so many of our unpleasant mechanical tasks to algorithms so we can focus on the more human aspects of our lives: interacting with each other, being apart of a community and making the world a better place. However, this would entail a fundamental change in the value that we put on particular jobs. Currently, highly-paid jobs are ordinarily the ones that generate profit, and they’re also the jobs that can often be completed by AI. Meanwhile, jobs that can’t be automated as easily, like caregivers and personal aides are undervalued and underpaid. It’s a booming field in the US with 1.2 million home health aide and personal care jobs to be added over the next ten years. Yet, these positions come with an average salary of approximately $20,000. If we can increase that salary while permitting AI to generate profit in the corporate sector, we could concurrently ease the job displacement problems and care for our communities better. There are many ideas of how to cope with dismissed workers, like taxing the wealthy in order to issue a universal basic income, which would see that everyone gets enough money to get by. And while some sort of a basic income might be necessary, solely relying on that as a solution would be a shame. Doing so would be bypassing the chance to enact some true social change that could help the whole world by building human-centered labor markets not as driven by profit. Instead of concentrating solely on money, maybe we should be more like Bhutan, which looks at “Gross National Happiness” as the true mark of progress.
6. The Book Of Why by Dana Mackenzie and Judea Pearl
Everyone knows that the cock’s crowing at dawn does not “cause” the sun to rise. Conversely, we have equal confidence “that flipping a switch will cause a light to turn on or off and that a sultry summer afternoon will cause sales to go up at the local ice cream parlor.” Such intuitions are integral to countless practical and moral judgments that fill our daily lives. And yet, as Prof. Judea Pearl and the science writer Dana Mackenzie note in their illuminating new work, “The Book of Why: The New Science of Cause and Effect,” scientists and statisticians lacked a common language until recently to distinguish between these very different kinds of observation. Indeed, within academia, “causal vocabulary was virtually prohibited for more than half a century.”
The absence of an accepted scientific approach to analyzing cause and effect is not merely of historical or theoretical interest. It explains the delay in the surgeon general’s reports on smoking, for example, and likely led to untold avoidable early deaths. New modeling tools have vastly expanded what can be learned from the proliferation of “big data” and will define the potential reach of artificial intelligence more broadly.
The subject of causation has preoccupied philosophers at least since Aristotle. Professor Pearl has deftly used the arc of his own career — first at RCA Laboratories and for the last 50 years at the University of California, Los Angeles (initially in the engineering department and since 1970 in computer science) — to chart the recent history of the subject.
This period broadly coincides with what Professor Pearl terms “the causal revolution.” Three ascending rungs of what he calls the “ladder of causation” serve as the central metaphor driving the narrative of “The Book of Why.” The “revolution” charted in the book, and in which Professor Pearl and his disciples played a crucial role, is what has allowed researchers across a vast range of disciplines to move beyond the first rung of the causal ladder, where they had been perennially stuck.ADVERTISEMENT
This lowest rung deals simply with observation — basically looking for regularities in past behavior. Professor Pearl places “present-day learning machines squarely on rung one.” While it is true that the explosion of computing power and accessible deep data sets have yielded many surprising and important results, the mechanics still operate “in much the same way that a statistician tries to fit a line to a collection of points.”
“Deep neural networks have added many layers of complexity of the fitted function, but raw data still drives the fitting process,” according to Professor Pearl. The causal revolution is what has enabled researchers to explore the higher rungs of the ladder.
The second rung of the ladder of causation moves from seeing to doing. That is, it goes from asking what happened to asking what would happen based on possible interventions. Professor Pearl notes that “many scientists have been traumatized to learn that none of the methods they learned in statistics is sufficient to articulate, let alone answer, a simple question like ‘What happens if we double the price?’” “The Book of Why” provides a detailed explanation and history of how and when a model alone can answer such questions in the absence of live experiments.
The top rung of the ladder involves something called “counterfactual” questions: What would the world be like if a different path had been taken? These are “the building blocks of moral behavior as well as scientific thought.” The ability to look backward and imagine what could have been governs our judgments on success and failure, right and wrong.
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