Anyone who watched the last week unfold will realize that the new Bing has (or had1) a tendency to get really wild, from declaring a love that it didn't really have to encouraging people to get divorced to blackmailing them to teaching people how to commit crimes, and so on. A lot of us were left scratching our heads. ChatGPT tended not to do this kind of stuff (unless you used "jailbreaking" techniques to try to trick it), whereas from what I can tell, Bing went off the rails really fast. And the thing is, the two systems are basically close siblings; OpenAI built ChatGPT, and is now presumed to be working very closely with Microsoft, using the same technology. ChatGPT was, I believe, mainly powered by GPT 3.5 plus a module known as RLHF (which combines Reinforcement learning with human feedback, to put some guardrails in place).

Long before the technology even existed in the real world, the concept of artificial intelligence has long been a topic of fixation for writers. From cautionary tales and science fiction epics to nonfictional explorations of the implications of AI in our modern world, artificial intelligence seems to be an endlessly fascinating subject of books both big and small. As such, there are all kinds of truly exceptional books about artificial intelligence out there for you to read, enjoy, and maybe even learn a thing or two from. As to be expected, these books about artificial intelligence truly run the gamut. Beyond simply falling under both fiction and nonfiction, artificial intelligence books cover topics ranging from the future to the past, from work to society, from computing to critiques… and all sorts of other topics along the way.

When Elon Musk and Jack Ma famously sat down for a chat about AI, their thoughts were inspiring for some and excruciating for others. They discussed AI in an all-too-common display of fantastical forecasts and philosophical musings. The status quo of conversations about AI shoots us into the future, where we're allowed to get far ahead of ourselves simply because such discussions are admittedly more fun. We need to get back to the present, so that we may actually solve the problems standing in the way of our projected future. For that, I recommend a new book, Rebooting AI: Building Artificial Intelligence We Can Trust, which has set off a buzz in the land of AI enthusiasts and skeptics alike.

To judge from the hype, artificial intelligence is inches away from ripping through the economy and destroying everyone's jobs--save for the AI scientists who build the technology and the baristas and yoga instructors who minister to them. But one critic of that view comes from within the tent of AI itself: Gary Marcus. From an academic background in psychology and neuroscience--rather than computer science--Mr Marcus has long been an AI gadfly. He relishes poking holes in the popular AI technique of deep-learning because of its inability to perform abstractions even as it does an impressive job at pattern-matching. Yet his unease with the state of the art didn't prevent him from advancing the art with his own AI startup, Geometric Intelligence, which he sold to Uber in 2016.

To judge from the hype, artificial intelligence is inches away from ripping through the economy and destroying everyone's jobs--save for the AI scientists who build the technology and the baristas and yoga instructors who minister to them. But one critic of that view comes from within the tent of AI itself: Gary Marcus. From an academic background in psychology and neuroscience--rather than computer science--Mr Marcus has long been an AI gadfly. He relishes poking holes in the popular AI technique of deep-learning because of its inability to perform abstractions even as it does an impressive job at pattern-matching. Yet his unease with the state of the art didn't prevent him from advancing the art with his own AI startup, Geometric Intelligence, which he sold to Uber in 2016.

The book Rebooting AI explains why a different approach other than deep learning is needed to unlock the potential of AI. Authors Gary Marcus and Ernest Davis propose that AI programs will have to have a large body of knowledge about the world in general, represented symbolically. Some of the basic elements of that knowledge should be built in. InfoQ readers can read excerpts of Rebooting AI to get an impression of the book. InfoQ interviewed Marcus and Davis about the state of the practice of AI and main concerns, the limitations of deep learning and their suggestion for bringing "common sense" to machine learning, what's needed to make AI safe and trustworthy, and what they expect AI can bring us in the near future and what will take a longer time.

First of all, apologies for not posting as frequently as I used to. As you might imagine, blogging is not my full time job and I'm currently extremely involved in a very exciting startup (something I'm going to write about soon). On weekends and evening I'm busy with 7mo infant to help care for and altogether that leaves me with very little time. But I'll try to make it better soon, since a lot is going on in the AI space and signs of cooling are visible now all over the place. In this post I'd like to focus on the recent book by Gary Marcus and Ernest Davis, Rebooting AI.

Welcome to TechTalks' AI book reviews, a series of posts that explore the latest literature on AI. The media is rife with stories that warn of AI algorithms bringing people back from the dead, AI algorithms developing secret languages, mass technological unemployment, and a looming robot apocalypse. Movies and TV series like Her, The Circleand Westworld,which present a mystic portrayal of conscious machines and human-level AI being just around the corner. Rebooting AI is a refreshing read and a much-needed reality check on the current confusing state of artificial intelligence. Consider the following text, mentioned in Rebooting AI: "Elsie tried to reach her aunt on the phone, but she didn't answer." You don't need to be a genius to quickly make the following assumptions after reading this sentence: But even the most sophisticated AI algorithm would struggle to draw the same conclusions.

It's been 7 years since my first interview with Gary Marcus and I felt it's time to catch up. Gary is the youngest Professor Emeritus at NYU and I wanted to get his contrarian views on the major things that have happened in AI as well as those that haven't happened. Prof. Marcus is an interesting interviewee not only because he is an expert in the field but also because he is a skeptic on the current approaches and progress towards Artificial General Intelligence but an optimist that we will eventually figure it all out. I can honestly say that I have learned a lot from Gary and hope that you will too. During this 90 min interview with Gary Marcus we cover a variety of interesting topics such as: Gary's interest in the human mind, natural and artificial intelligence; Deep Mind's victory in Go and what it does and doesn't mean for AGI; the need for Rebooting AI; trusting AI and the AI chasms; Asimov's Laws and Bostrom's paper-clip-maximizing AI; the Turing Test and Ray Kurzweil's singularity timeline; Mastering Go Without Human Knowledge; closed vs open systems; Chomsky, Minsky and Ferrucci on AGI; the limits of deep learning and the myth of the master algorithm; the problem of defining (artificial) intelligence; human and machine consciousness; the team behind and the mission of Robust AI.

Welcome to TechTalks' AI book reviews, a series of posts that explore the latest literature on AI. The media is rife with stories that warn of AI algorithms bringing people back from the dead, AI algorithms developing secret languages, mass technological unemployment, and a looming robot apocalypse. Movies and TV series like Her, The Circle and Westworld, which present a mystic portrayal of conscious machines and human-level AI being just around the corner. Rebooting AI is a refreshing read and a much-needed reality check on the current confusing state of artificial intelligence. Consider the following text, mentioned in Rebooting AI: "Elsie tried to reach her aunt on the phone, but she didn't answer." You don't need to be a genius to quickly make the following assumptions after reading this sentence: But even the most sophisticated AI algorithm would struggle to draw the same conclusions.