AI capabilities made progress in 2024 but there was a temporary slowdown in massive compute scaling for AI training or test time compute. 2025 will see massive AI training scaling.
Dr. Alan Thompson is a trusted thought leader in AI, frequently cited by institutions like Microsoft, RAND Corporation, and the University of Oxford. He publishes the Memo newsletter to update Fortune 500 companies and business and government leaders about AI. He tracks AI progress toward AGI and beyond.
Alan determines that we are 84% of the way to AGI after the release of Google Genie 2 with world models and other releases that advance agent capabilities.
World models have largely been confined to modeling narrow domains. In Genie 1, Google introduced an approach for generating a diverse array of 2D worlds. Google Deep mind introduced Genie 2, which represents a significant leap forward in generality. Genie 2 can generate a vast diversity of rich 3D worlds.
Genie 2 is a world model, meaning it can simulate virtual worlds, including the consequences of taking any action (e.g. jump, swim, etc.). It was trained on a large-scale video dataset and, like other generative models, demonstrates various emergent capabilities at scale, such as object interactions, complex character animation, physics, and the ability to model and thus predict the behavior of other agents.
Below are example videos of people interacting with Genie 2. For every example, the model is prompted with a single image generated by Imagen 3, GDM’s state-of-the-art text-to-image model. This means anyone can describe a world they want in text, select their favorite rendering of that idea, and then step into and interact with that newly created world (or have an AI agent be trained or evaluated in it). At each step, a person or agent provides a keyboard and mouse action, and Genie 2 simulates the next observation. Genie 2 can generate consistent worlds for up to a minute, with the majority of examples shown lasting 10-20s.
Action controls the World Model
Genie 2 responds intelligently to actions taken by pressing keys on a keyboard, identifying the character and moving it correctly. For example, our model has to figure out that arrow keys should move the robot and not the trees or clouds.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.