| We already have breakthroughs. Benchmark results which have been unheard of before ML. Alone language translation got so much better, voice syntesis, voice transcription. All my meetings now are searchable and i can ask 'ai' to summarize my meetings in a relative accurate way impossible before that. Alphafold made a breakthrough in protein folding. Image and Video generation can now do unbelievable things. Realtime voice communication with computer. Our internal company search suddenly became usefull. I have 0 use case for NFT and Crypto. I have tons of use case for ML. |
Sort of. Alphafold is a prediction tool, or, alternatively framed, a hypothesis generation tool. Then you run an experiment to compare.
It doesn't represent a scientific theory, not in the sense that humans use them. It does not have anywhere near something like the accuracy rate for hypotheses to qualify as akin to the typical scientific testing paradigm. It's an incredibly powerful and efficient tool in certain contexts and used correctly in the discovery phase, but not the understanding or confirmation phase.
It's also got the usual pitfalls with differentiable neural nets. E.g. you flip one amino acid and it doesn't really provide a proper measure of impact.
Ultimately, one major prediction breakthrough is not that crazy. If we compare that to e.g. Random Forest and similar models, the impact in science is infinitely more with them.