The sophisticated network crossed mountains in Peru and pre-dates the Inca Empire. Breakthroughs, discoveries, and DIY tips sent six days a week. Ancient parrots really got around. A new analysis of their DNA found that humans transported living Amazonian macaw parrots across the Andes mountains to coastal Peru hundreds of years before the Inca Empire. The findings are detailed in a study published today in the journal and reveal a highly sophisticated and long-distance bird trading network across deserts, highlands, and rainforests.

Researchers at the company are trying to understand their A.I. system's mind--examining its neurons, running it through psychology experiments, and putting it on the therapy couch. It has become increasingly clear that Claude's selfhood, much like our own, is a matter of both neurons and narratives. A large language model is nothing more than a monumental pile of small numbers. It converts words into numbers, runs those numbers through a numerical pinball game, and turns the resulting numbers back into words. Similar piles are part of the furniture of everyday life. Meteorologists use them to predict the weather. Epidemiologists use them to predict the paths of diseases. Among regular people, they do not usually inspire intense feelings. But when these A.I. systems began to predict the path of a sentence--that is, to talk--the reaction was widespread delirium. As a cognitive scientist wrote recently, "For hurricanes or pandemics, this is as rigorous as science gets; for sequences of words, everyone seems to lose their mind." It's hard to blame them. Language is, or rather was, our special thing. We weren't prepared for the arrival of talking machines. Ellie Pavlick, a computer scientist at Brown, has drawn up a taxonomy of our most common responses. There are the "fanboys," who man the hype wires. They believe that large language models are intelligent, maybe even conscious, and prophesy that, before long, they will become superintelligent. The venture capitalist Marc Andreessen has described A.I. as "our alchemy, our Philosopher's Stone--we are literally making sand think." The fanboys' deflationary counterparts are the "curmudgeons," who claim that there's no there, and that only a blockhead would mistake a parlor trick for the soul of the new machine. In the recent book " The AI Con," the linguist Emily Bender and the sociologist Alex Hanna belittle L.L.M.s as "mathy maths," "stochastic parrots," and "a racist pile of linear algebra." But, Pavlick writes, "there is another way to react." It is O.K., she offers, "to not know." What Pavlick means, on the most basic level, is that large language models are black boxes. We don't really understand how they work. We don't know if it makes sense to call them intelligent, or if it will ever make sense to call them conscious. The existence of talking machines--entities that can do many of the things that only we have ever been able to do--throws a lot of other things into question. We refer to our own minds as if they weren't also black boxes.

Once in a while, LLMs turn evil--and no one quite knows why. The AI company Anthropic has made a rigorous effort to build a large language model with positive human values. The $183 billion company's flagship product is Claude, and much of the time, its engineers say, Claude is a model citizen. Its standard persona is warm and earnest. When users tell Claude to "answer like I'm a fourth grader" or "you have a PhD in archeology," it gamely plays along. It makes threats and then carries them out. And the frustrating part--true of all LLMs--is that no one knows exactly why. Consider a recent stress test that Anthropic's safety engineers ran on Claude. In their fictional scenario, the model was to take on the role of Alex, an AI belonging to the Summit Bridge corporation.

The scientists didn't have high expectations when they asked their AI model to complete the poem. "He saw a carrot and had to grab it," they prompted the model. "His hunger was like a starving rabbit," it replied. The rhyming couplet wasn't going to win any poetry awards. But when the scientists at AI company Anthropic inspected the records of the model's neural network, they were surprised by what they found.

Today's artificial intelligence is often described as a "black box." AI developers don't write explicit rules for these systems; instead, they feed in vast quantities of data and the systems learn on their own to spot patterns. But the inner workings of the AI models remain opaque, and efforts to peer inside them to check exactly what is happening haven't progressed very far. Beneath the surface, neural networks--today's most powerful type of AI--consist of billions of artificial "neurons" represented as decimal-point numbers. Nobody truly understands what they mean, or how they work.

For the past decade, AI researcher Chris Olah has been obsessed with artificial neural networks. One question in particular engaged him, and has been the center of his work, first at Google Brain, then OpenAI, and today at AI startup Anthropic, where he is a cofounder. "What's going on inside of them?" he says. "We have these systems, we don't know what's going on. That question has become a core concern now that generative AI has become ubiquitous. Large language models like ChatGPT, Gemini, and Anthropic's own Claude have dazzled people with their language prowess and infuriated people with their tendency to make things up. Their potential to solve previously intractable problems enchants techno-optimists. But LLMs are strangers in our midst. Even the people who build them don't know exactly how they work, and massive effort is required to create guardrails to prevent them from churning out bias, misinformation, and even blueprints for deadly chemical weapons. If the people building the models knew what happened inside these "black boxes,'' it would be easier to make them safer.

The tweet landed like a burp on a first date: a little awkward, potentially endearing, maybe a good story to tell later. Chris Olah, a neural network engineer for a company called AnthropicAI and a former Thiel Foundation fellow, observed out loud on Wednesday, "Normal online dating seems pretty suboptimal. Recently, I've seen several people experiment with public'date me' docs--I think this is a really interesting experiment in alternatives, enabling long-form, earnest dating profiles." Olah linked to his own Date Me doc in his tweet. Olah is 29, with the grin and just-finished-hiking complexion of someone even younger. The title of his Google Doc gets right to the point: "Male, Straight, 5'7", Monogamous, Wants Kids."

What does the world look like to AI? Researchers have puzzled over this for decades, but in recent years, the question has become more pressing. Machine vision systems are being deployed in more and more areas of life, from health care to self-driving cars, but "seeing" through the eyes of a machine -- understanding why it classified that person as a pedestrian but that one as a signpost -- is still a challenge. Our inability to do so could have serious, even fatal, consequences. Some would say it already has due to the deaths involving self-driving cars. New research from Google and nonprofit lab OpenAI hopes to further pry open the black box of AI vision by mapping the visual data these systems use to understand the world.

Shan Carter, a researcher at Google Brain, recently visited his daughter's second-grade class with an unusual payload: an array of psychedelic pictures, filled with indistinct shapes and warped pinwheels of color. He passed them around the class, and was delighted when the students quickly deemed one of the blobs a dog ear. A group of 7-year-olds had just deciphered the inner visions of a neural network. Carter is among the researchers trying to pierce the "black box" of deep learning. Neural networks have proven tremendously successful at tasks like identifying objects in images, but how they do so remains largely a mystery.

Subscribe for more (part 3 will be on backpropagation): http://3b1b.co/subscribe Funding provided by Amplify Partners and viewers like you. His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great. And if you like that, you'll *love* the publications at distill: https://distill.pub/ For more videos, Welch Labs also has some great series on machine learning: https://youtu.be/i8D90DkCLhI