As you have doubtless noticed, we are in the middle of a feeding frenzy about something called generative AI. Legions of hitherto normal people – and economists – are surfing a wave of irrational exuberance about its transformative potential. For anyone suffering from the fever, two antidotes are recommended. The first is the hype cycle monitor produced by consultants Gartner, which shows the technology currently perched on the "peak of inflated expectations", before a steep decline into the "trough of disillusionment". The other is Hofstadter's law, about the difficulty of estimating how long difficult tasks will take, which says that "It always takes longer than you expect, even when you take into account Hofstadter's law".

I have always taken it for granted that, just as my parents made sure that I could read and write, I would make sure that my kids could program computers. It is among the newer arts but also among the most essential, and ever more so by the day, encompassing everything from filmmaking to physics. Fluency with code would round out my children's literacy--and keep them employable. But as I write this my wife is pregnant with our first child, due in about three weeks. I code professionally, but, by the time that child can type, coding as a valuable skill might have faded from the world.

In 2016 a computer named AlphaGo made headlines for defeating then world champion Lee Sedol at the ancient, popular strategy game Go. The "superhuman" artificial intelligence, developed by Google DeepMind, lost only one of the five rounds to Sedol, generating comparisons to Garry Kasparov's 1997 chess loss to IBM's Deep Blue. Go, which involves players facing off by moving black and white pieces called stones with the goal of occupying territory on the game board, had been viewed as a more intractable challenge to a machine opponent than chess. Much agonizing about the threat of AI to human ingenuity and livelihood followed AlphaGo's victory, not unlike what's happening right now with ChatGPT and its kin. In a 2016 news conference after the loss, though, a subdued Sedol offered a comment with a kernel of positivity.

A human player has comprehensively defeated a top-ranked AI system at the board game Go, in a surprise reversal of the 2016 computer victory that was seen as a milestone in the rise of artificial intelligence. Kellin Pelrine, an American player who is one level below the top amateur ranking, beat the machine by taking advantage of a previously unknown flaw that had been identified by another computer. But the head-to-head confrontation in which he won 14 of 15 games was undertaken without direct computer support. The triumph, which has not previously been reported, highlighted a weakness in the best Go computer programs that is shared by most of today's widely used AI systems, including the ChatGPT chatbot created by San Francisco-based OpenAI. The tactics that put a human back on top on the Go board were suggested by a computer program that had probed the AI systems looking for weaknesses.

In March 2016, to much fanfare, AlphaGo beat Go world champion Lee Sedol, convincingly, in a 5 game match, 4 to 1. Computers have only gotten faster since then; one might have thought that the matter was settled. And of course computers have only gotten faster ever since. And the human that won wasn't even a professional Go player, let along a World Champion, just a strong amateur named Kellin Pelrine. And the match wasn't even close; Pelrine beat a top AI system 14 games to 1, in a 15 match series. Almost seven years to the day after AlphaGo beat Sedol.

There's no doubt that artificial intelligence is one of the most fascinating and rapidly-growing fields in technology today. But there are still many things about AI that remain unknown to the average person. In this blog post, we will explore 10 interesting facts about artificial intelligence that you may not know. Keep reading to learn more! The most powerful artificial intelligence-based text generator available today, OpenAI's GPT-2, can write entire paragraphs and is error-free, but it has difficulty establishing causal relationships.

DeepMind, a Google-owned British company, might be on the verge of creating human-level artificial intelligence. The revelation was made by the company's lead researcher Dr. Nando de Freitas in response to The Next Web columnist Tristan Greene who claimed humans will never achieve AGI. For anyone who doesn't know, AGI refers to a machine or program that can understand or learn any intellectual task that humans can. It can also do so without training. Addressing the somewhat pessimistic op-ed, and the decades-long quest to develop artificial general intelligence, Dr de Freitas said the game is over.

We already expect that humans to exhibit flashes of brilliance. It might not happen all the time, but the act itself is welcomed and not altogether disturbing when it occurs. What about when Artificial Intelligence (AI) seems to display an act of novelty? Any such instance is bound to get our attention; questions arise right away. How did the AI come up with the apparent out-of-the-blue insight or novel indication? Was it a mistake, or did it fit within the parameters of what the AI was expected to produce?

The recent advancements in Artificial Intelligence (AI) have been a boon for various applied fields. Artificially intelligent systems today are present everywhere around us, chatbots, for instance, replying back to queries in accordance with the interaction made, processing a result in return. Recently, one of the most popular areas of research in artificial intelligence has been in the field of video games. Challenging yet easy to formalize, this platform can be very well used to develop new AI methods and measure how well they work. Video games can also help demonstrate that machines today are capable of behaviour that is thought to require intelligence without putting human lives or property at risk.

Well, you clicked this, so obviously you're interested in some of the finer nuances of artificial intelligence. Little wonder; it's popping up everywhere, taking on applications as far ranging as trying to catch asymptomatic COVID infections via cough, creating maps of wildfires faster, and beating up on esports pros. It also listens when you ask Alexa or summon Siri, and unlocks your phone with a glance. But artificial intelligence is an umbrella term, and when we start moving down the specificity chain, things can get confusing -- especially when the names are so similar, e.g. Let's make that distinction between deep learning vs machine learning; they're pretty closely related.