OpenAI has released a powerful new image-and text-understanding AI model, GPT-4, that the company calls “the latest milestone in its effort in scaling up deep learning.”

GPT-4 is available today to OpenAI’s paying users via ChatGPT Plus (with a usage cap), and developers can sign up on a waitlist to access the API.

Pricing is $0.03 per 1,000 “prompt” tokens (about 750 words) and $0.06 per 1,000 “completion” tokens (again, about 750 words). Tokens represent raw text; for example, the word “fantastic” would be split into the tokens “fan,” “tas” and “tic.” Prompt tokens are the parts of words fed into GPT-4 while completion tokens are the content generated by GPT-4.

Anthropic, a startup co-founded by ex-OpenAI employees, today launched something of a rival to the viral sensation ChatGPT.

Called Claude, Anthropic’s AI — a chatbot — can be instructed to perform a range of tasks, including searching across documents, summarizing, writing and coding, and answering questions about particular topics. In these ways, it’s similar to OpenAI’s ChatGPT. But Anthropic makes the case that Claude is “much less likely to produce harmful outputs,” “easier to converse with” and “more steerable.”

Organizations can request access. Pricing has yet to be detailed.

https://www.tweaktown.com/news/90714/ai-apologizes-for-killi…index.html.

A robot that dissolves into a puddle after exposing itself to heat and UV light could one day be used to securely gather intelligence and then destroy itself before it falls into the wrong hands.

By Lauren Barack

How will superhuman artificial intelligence (AI) affect human decision-making? And what will be the mechanisms behind this effect? We address these questions in a domain where AI already exceeds human performance, analyzing more than 5.8 million move decisions made by professional Go players over the past 71 y (1950 to 2021). To address the first question, we use a superhuman AI program to estimate the quality of human decisions across time, generating 58 billion counterfactual game patterns and comparing the win rates of actual human decisions with those of counterfactual AI decisions. We find that humans began to make significantly better decisions following the advent of superhuman AI. We then examine human players’ strategies across time and find that novel decisions (i.e., previously unobserved moves) occurred more frequently and became associated with higher decision quality after the advent of superhuman AI. Our findings suggest that the development of superhuman AI programs may have prompted human players to break away from traditional strategies and induced them to explore novel moves, which in turn may have improved their decision-making.

Join Greg Brockman, President and Co-Founder of OpenAI, at 1 pm PT for a developer demo showcasing GPT-4 and some of its capabilities/limitations. Join the co…

The fruit fly larva connectome showed circuit features that were strikingly reminiscent of prominent and powerful machine learning architectures. “Some of the architectural features observed in the Drosophila larval brain, including multilayer shortcuts and prominent nested recurrent loops, are found in state-of-the-art artificial neural networks, where they can compensate for a lack of network depth and support arbitrary, task-dependent computations,” they wrote. The team expects continued study will reveal even more computational principles and potentially inspire new artificial intelligence systems. “What we learned about code for fruit flies will have implications for the code for humans,” Vogelstein said. “That’s what we want to understand—how to write a program that leads to a human brain network.”