In July 2015, two founders of DeepMind, a division of Alphabet with a reputation for pushing the boundaries of artificial intelligence, were among the first to sign an open letter urging the world's governments to ban work on lethal AI weapons. Notable signatories included Stephen Hawking, Elon Musk, and Jack Dorsey. Last week, a technique popularized by DeepMind was adapted to control an autonomous F-16 fighter plane in a Pentagon-funded contest to show off the capabilities of AI systems. In the final stage of the event, a similar algorithm went head-to-head with a real F-16 pilot using a VR headset and simulator controls. The AI pilot won, 5-0.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Building machine learning systems differs from traditional software development in many aspects of its lifecycle. Established software methodologies for testing, debugging and troubleshooting result simply impractical when applied to machine learning models.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Last week, many tech publications broke news about a blog generated by artificial intelligence that fooled thousands of users and landed on top of the Hacker News forum. GPT-3, the massive language model developed by AI research lab OpenAI, had written the articles. Since its release in July, GPT-3 has caused a lot of excitement in the AI community. Developers who have received early access to the language model have used to do many interesting things, showing just how far AI research has come. But like many other developments in AI, there's also a lot of hype and misunderstanding surrounding GPT-3, and many of the stories published about it misrepresent its capabilities. The blog written by GPT-3 resurfaced worries about fake news onslaughts, robots deceiving humans, and technological unemployment, which have become the hallmark of AI reporting.

Despite the buzz around GPT-3, it is, in and of itself, not AGI. In many ways, this makes it similar to AlphaGo or Deep Blue; while approaching human ability in one domain (playing Chess/Go, or writing really impressively), it doesn't really seem like it will do Scary AGI Things any more than AlphaGo is going to be turning the Earth into paperclips anytime soon. While its writings are impressive at emulating humans, GPT-3 (or any potential future GPT-x) has no memory of past interactions, nor is it able to follow goals or maximize utility. However, language modelling has one crucial difference from Chess or Go or image classification. By harnessing the world model embedded in the language model, it may be possible to build a proto-AGI.

OpenAI doesn't stop to amaze me. Last year they made headlines providing GPT-2, an NLP framework with powerful writing skills. The one is GPT-3, a big-scaled language models with serious creative potential. Another one made some buzz but got lost in the background quickly -- pretty unfairly, in my opinion: JukeBox, a Generative Model for Audio. There were -- and are -- various approaches to generate music using algorithms and Artificial Intelligence.

A new Philosopher AI could help you find meaning in a meaningless world -- as long as you don't ask it any controversial questions. The system provides musings on subjects that have plagued humanity since its inception. You can ask it about a topic that's filling you with existential angst. The system is the brainchild of a Vancouver-based programmer called Murat Ayfer, who describes it as an experiment in "prompt engineering." Ayfer admits the AI doesn't have any specific opinions or knowledge of its own.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Building machine learning systems differs from traditional software development in many aspects of its lifecycle. Established software methodologies for testing, debugging and troubleshooting result simply impractical when applied to machine learning models.

Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information about human belief in a consistently interrogatable form. If these models could be shown to accurately reflect the underlying beliefs of the human beings that produced the data used to train these models, then such models become a powerful sociological tool in ways that are distinct from traditional methods, such as interviews and surveys. In this study, We train a version of the GPT-2 on a corpora of historical chess games, and then compare the learned relationships of words in the model to the known ground truth of the chess board, move legality, and historical patterns of play. We find that the percentages of moves by piece using the model are substantially similar from human patterns. We further find that the model creates an accurate latent representation of the chessboard, and that it is possible to plot trajectories of legal moves across the board using this knowledge.

"GPT-3 is not a mind, but it is also not entirely a machine. It's something else: a statistically abstracted representation of the contents of millions of minds, as expressed in their writing." In recent years, the AI circus really has come to town and we've been treated to a veritable parade of technical aberrations seeking to dazzle us with their human-like intelligence. Many of these sideshows have been "embodied" AI, where the physical form usually functions as a cunning disguise for a clunky, pre-programmed bot. Like the world's first "AI anchor," launched by a Chinese TV network and -- how could we ever forget -- Sophia, Saudi Arabia's first robotic citizen.

The only way you can really "debug" humans is conversationally though? If some employee screwed up the assembly line and messed up production, you might call him into the office and ask him why he made that mistake, you could get the same "oh, I'm sorry I got distracted and wasn't paying attention.", There could be some bad synaptic weights that caused some of his neurons to misfire and cause him to make the mistake. This is just explained by "getting distracted". I don't know if you've played around with GPT-3, but if you push it on something it's gotten wrong, it usually gets very defensive and will bullshit it's way out of it, just as well as a human would.