GPT and Dall-e are just the first baby steps in the world of human-like AI.

Tesla’s Optimus is taking baby steps while OpenAI’s Figure 1 is doing burnouts on the track.
All non-Google chat GPTs affected by side channel that leaks responses sent to users.
Amazon.com’s self-driving car unit, Zoox, is seeking to stay abreast of rival Waymo by expanding its vehicles’ testing in California and Nevada to include a wider area, higher speeds and nighttime driving.
ANYmal is a truly remarkable robot, capable of standing and lifting things like a humanoid, or slinking around on all fours like a quadruped, with or without wheels. But what’s really surprised us now is the eerie grace it’s starting to move with.
Their AI is able to recognize patterns in complex data sets and to formulate them in a physical theory. The development of a new theory is typically associated with the greats of physics. You might think of Isaac Newton or Albert Einstein, for example. Many Nobel Prizes have already been awarded for new theories. Researchers at Forschungszentrum Jülich have now programmed an artificial intelligence that has also mastered this feat. Their AI is able to recognize patterns in complex data sets and to formulate them in a physical theory.
In the following interview, Prof. Moritz Helias from Forschungszentrum Jülich’s Institute for Advanced Simulation (IAS-6) explains what the “Physics of AI” is all about and to what extent it differs from conventional approaches.