But does the lack of eyes mean that language models can’t ever “understand” that a lion is “larger” than a house cat? Philosophers and scientists alike have long considered the ability to assign meaning to language a hallmark of human intelligence — and pondered what essential ingredients enable us to do so.

Peering into this enigma, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have uncovered intriguing results suggesting that language models may develop their own understanding of reality as a way to improve their generative abilities. The team first developed a set of small Karel puzzles, which consisted of coming up with instructions to control a robot in a simulated environment. They then trained an LLM on the solutions, but without demonstrating how the solutions actually worked. Finally, using a machine learning technique called “probing,” they looked inside the model’s “thought process” as it generates new solutions.

After training on over 1 million random puzzles, they found that the model spontaneously developed its own conception of the underlying simulation, despite never being exposed to this reality during training. Such findings call into question our intuitions about what types of information are necessary for learning linguistic meaning — and whether LLMs may someday understand language at a deeper level than they do today.

A team of AI researchers at Sakana AI, in Japan, working with colleagues from the University of Oxford and the University of British Columbia, has developed an AI system that can conduct scientific research autonomously.

The group has posted a paper to the arXiv preprint server describing their system, which they call “The AI Scientist”. They have also posted an overview of their system on Sakana’s corporate website.

Scientific research is generally a long and involved process. It tends to start with a simple idea, such as, “Is there a way to stop the buildup of plaque on human teeth?” Scientists then research other studies to determine what research has been done on the topic.

U-net is a convolutional neural network (CNN) model and not transformer model however it have encoder decoder structure that that make it confusing and correlate it with transformers, it is specifically designed for image segmentation. Structure Encoder (Contracting Path) Function: The encoder part.

Anduril Industries is planning to construct a new factory to scale weapons production for the United States defense base. Anduril Industries Co-Founder and Executive Chairman Trae Stephens joins Market Domination Overtime to discuss this initiative.

Stephens explains that the legacy defense industry has traditionally focused on \.

Visualizing Global Attitudes Towards AI 🤖

From the archive:

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We visualize survey results from over 19,000 adults across 28 countries to see how attitudes towards AI differ around the world.