An artificial intelligence can beat the best human players at the racing video game Gran Turismo 7 using only the images and information that players can see. In 2022, researchers at Sony created GT Sophy, a driving AI that could beat the best human players at Gran Turismo Sport, a previous version of the game. However, the AI had access to information that human players didn't, such as real-time information of other cars and the layout of the racetrack beyond the driver's view.

Shall we play a game? A single artificial intelligence can beat human players in chess, Go, poker and other games that require a variety of strategies to win. The AI, called Student of Games, was created by Google DeepMind, which says it is a step towards an artificial general intelligence capable of carrying out any task with superhuman performance. Martin Schmid, who worked at DeepMind on the AI but who is now at a start-up called EquiLibre Technologies, says that the Student of Games (SoG) model can trace its lineage back to two projects. One was DeepStack, the AI created by a team including Schmid at the University of Alberta in Canada and which was the first to beat human professional players at poker.

I am writing this post to organize and share my thoughts about the extraordinary progress in artificial intelligence over the last years and especially the last few months (link to a lot of my prior writing). First, I want to come right out and say that anyone still dismissing what we are now seeing as a "parlor trick" or a "statistical parrot" is engaging in the most epic goal post moving ever. We are not talking a few extra yards here, the goal posts are not in the stadium anymore, they are in a far away city. Growing up I was extremely fortunate that my parents supported my interest in computers by buying an Apple II for me and that a local computer science student took me under his wing. Through him I found two early AI books: one in German by Stoyan and Goerz (I don't recall the title) and Winston and Horn's "Artifical Intelligence." I still have both of these although locating them among the thousand or more books in our home will require a lot of time or hopefully soon a highly intelligent robot (ideally running the VIAM operating system – shameless plug for a USV portfolio company).

Development of artificial intelligence agents tends to frequently be measured by their performance in games, but there's a good reason for that: Games tend to offer a wide proficiency curve, in terms of being relatively simple to grasp the basics, but difficult to master, and they almost always have a built-in scoring system to evaluate performance. DeepMind's agents have tackled board game Go, as well as real-time strategy video game StarCraft. But the Alphabet company's most recent feat is Agent57, a learning agent that can beat the average human on each of 57 Atari games with a wide range of difficulty, characteristics and gameplay styles. Being better than humans at 57 Atari games may seem like an odd benchmark against which to measure the performance of a deep learning agent, but it's actually a standard that goes all the way back to 2012, with a selection of Atari classics including Pitfall, Solaris, Montezuma's Revenge and many others. Taken together, these games represent a broad range of difficulty levels, as well as requiring a range of different strategies in order to achieve success.

Today, it is difficult to imagine a technology that is as enthralling and terrifying as machine learning. While media coverage and research papers consistently tout the potential of machine learning to become the biggest driver of positive change in business and society, the lingering question on everyone's mind is: "Well, what if it all goes terribly wrong?" For years, experts have warned against the unanticipated effects of general artificial intelligence (AI) on society. Ray Kurzweil predicts that by 2029 intelligent machines will be able to outsmart human beings. Stephen Hawking argues that "once humans develop full AI, it will take off on its own and redesign itself at an ever-increasing rate".

Jane Zavalishina is the CEO of Yandex Data Factory. Vyacheslav Polonski is a PhD student at the University of Oxford and the CEO of Avantgarde Analytics. For years, experts have warned against the unanticipated effects of general artificial intelligence (AI) on society. Ray Kurzweil predicts that by 2029 intelligent machines will be able to outsmart human beings. Stephen Hawking argues that "once humans develop full AI, it will take off on its own and redesign itself at an ever-increasing rate."

Google's DeepMind Artificial Intelligence AlphaGo Zero recently attained an important milestone--the Artificial Intelligence (AI) taught itself how to play the strategy game Go without any human interaction and was able to beat the world's best Go players. The ability to reach this level of performance with human input is a significant step forward in the maturation of AI. Over the past several years, AI has made significant progress in a wide variety of areas such as image and speech recognition, drug discovery, and algorithmic trading. In most of these cases, the AI relies on vast existing data sets and some degree of human engagement. A long-standing ambition of AI researchers has been to create algorithms that do not rely on already existing data sets nor the need for human input.

Google's DeepMind team that specializes in machine learning and artificial intelligence has created an AI called AlphaGo Zero that is able to teach itself the Chinese strategy game Go. Not only that, it can teach itself so effectively that it is able to beat the previous iteration of AlphaGo that successfully beat the world's best human players. The previous AlphaGo was taught to play by inputting the data of how the best human players in the world played certain moves, effectively creating a compendium of the best players in the world. AlphaGo Zero however, according to the Guardian, learnt completely differently, by being given the rules to Go, and being left to its own devices. Obviously, it started by making some pretty foolish and ill-advised moves, but quickly learnt which moves were more likely to lead to victory, and which to failure.

I meant to write this blog several months ago. However as a compulsive procrastinator I kept putting it to another day, until today. On January 27, an article in Nature reported on a computer that had beat a human player at Go. It is an ancient board game that has long been viewed as a hard nut to crack for Artificial Intelligence (AI). Till then, computers had already beaten the best human players of backgammon, draughts, and chess.

Inside a simple computer simulation, a group of self-driving cars are performing a crazy-looking maneuver on a four-lane virtual highway. Half are trying to move from the right-hand lanes just as the other half try to merge from the left. It seems like just the sort of tricky thing that might flummox a robot vehicle, but they manage it with precision. I'm watching the driving simulation at the biggest artificial-intelligence conference of the year, held in Barcelona this past December. What's most amazing is that the software governing the cars' behavior wasn't programmed in the conventional sense at all.