You got to hand it to DeepMind. They’re the Apple of laboratories. While everyone else is clamoring to occupy space in the public head, it’s toiling mysteriously in the background. 

I only have one problem with the Big Bang theory: nobody knows what “big” means. Is Earth big? What about the Sun? Is the Hercules-Corona Borealis Great Wall big?

Hugging Face recently bought Pollen Robotics, a startup developing “open source” humanoid robots.

Users were able to navigate 25% faster with this system than using a traditional cane.

In other words: You’re the middle segment in a human-AI centipede.

If the basic idea is to get the most utility out of chatbots, it seems counterintuitive to train them to waste tokens and time outputting polite language. 

Chatbots don’t actually exist. No, we’re not trying to create a conspiracy theory. What we mean is this: large language models (LLMs) aren’t “entities” or “beings.”

A team of researchers at RespAI Lab, KIIT Bhubaneswar, KIMS Bhubaneswar, and Monash University, Australia today published a fascinating preprint research paper that takes a mighty bludgeon to the notion that AI can predict medical diagnosis. 

According to a fresh preprint from Xinghong Fu, Ziming Liu, and Max Tegmark over at MIT’s Institute of Artificial Intelligence and Fundamental Interactions, when formulating initial theories, AI agents trained to conduct research tend to gravitate toward Hamiltonian mechanics. 

OpenAI says 500 million people use ChatGPT. And, even though it’s exactly as smart as a toaster, everyone seems to think it’s the cat’s meow.

The average American doesn’t believe that AI will make their lives better any time soon, according to a recently published survey from Pew Research.

And more money and more money and more money…

A team of researchers from Gaoling School of Artificial Intelligence at the Renmin University of China, with support from the Alibaba Group, published a preprint on Monday, March 31, that seems to indicate the impossible: algorithms can think.

SoftBank bets $1T on US robotics infrastructure, China pours $888B into its own, and Tesla calls for a national robotics strategy. 

In two new papers, Anthropic describes a new methodology by which developers are attempting to peek under the models’ hoods to discern exactly what’s occurring inside.

Recent comments from world-renowned British computer scientist Stuart Russell indicates that the sector of AI developers and organizations dedicated to scaling large language models to “human-level” may have reached that point, financially speaking. 

There’s nothing you can think that can’t be thunk, scale is all you need.

AI agents? Check. Action, combat, and treasure? Check, check, and check. Unleashing hidden flocks of angels on unwitting hordes of zombies to prove the existence of artificial “thought?” 

Check?

Joe Tsai, the chairman of Alibaba, a big tech company based in China, recently took the stage at a speaking gig aimed at young entrepreneurs in Hong Kong. In a surprise twist, he spent a significant portion of his time criticizing the AGI and robotics industries. This, despite the fact that Alibaba is a global leader in both.

Google launched Gemini Pro Experimental 2.5, a “thinking model,” none of today’s generative AI models have scored higher than 4% against a human average of 60% on the new ARC-AGI-2 test. And Jack Clark is becoming increasingly convinced that AGI is right around the corner.