I’m somehow really skeptical—has there been a postmortem of the general failure of BLOOM? That model was unreasonably ineffective. I expect the same of government and academic models, but I don’t have a good rationale.
"The Last Question" by Isaac Asimov is a seminal science fiction short story that explores humanity's evolution and the quest to overcome entropy over trillions of years. It starts in 2061 with two technicians, Alexander Adell and Bertram Lupov, operating Multivac, a powerful computer. They discuss using solar energy and the finite nature of the sun, leading to the titular question: Can the increase in entropy in the universe be reversed?
The story jumps through various epochs, where different iterations of computers (Microvac, Galactic AC, Universal AC, and finally Cosmic AC) are repeatedly confronted with this question but always respond with "Insufficient data for a meaningful answer." Humanity evolves, colonizes the universe, achieves immortality, and ultimately merges with the Cosmic AC.
At the universe's end, when only the Cosmic AC exists and time, space, and matter cease to exist, the AC finally finds an answer to the question. The story concludes with the words: "LET THERE BE LIGHT! And there was light --" suggesting the cycle starts anew, possibly with a new universe. Asimov's tale is famed for its profound philosophical inquiry and visionary depiction of humanity's future and technology.
I would like a GPT capable of making significant advances in string theory. It is extremely hypothetical by nature, so I think it should be a good fit for a very strong scientific GPT to check many different hypotheses (e.g. supersymmetric theories), assess their likelihood, and maybe even come up with new ideas that have value for the scientific community.
"With a budget of €7.5 billion, the LHC is one of the most expensive scientific instruments[1] ever built" - Wikipedia
Perhaps we should invest the next 7.5 billion in an exceptionally large GPT model.
> The Trillion Parameter Consortium (TPC) brings together teams of researchers engaged in creating large-scale generative AI models to address key challenges in advancing AI for science. These challenges include developing scalable model architectures and training strategies, organizing, and curating scientific data for training models; optimizing AI libraries for current and future exascale computing platforms; and developing deep evaluation platforms to assess progress on scientific task learning and reliability and trust.
I don't think it's fake, but it's just a "language" model.
This could be tremendously helpful based on the combined text & data it is trained on, and who knows, it might contribute to a 10X or more performance for more than just a small percentage of a scientist's needs.
I would think it depends on the type of scientist quite a bit.
