Some people just believe there is no innate knowledge or we dont need it if we just scale/learn better (in the direction of Bitter Lesson)
(ML) Academia is also heavily biased against it due to mainly two reasons:
- Its harder to publish, since if you learn Task X with innate Knowledge, its not as general, so reviewer can claims its just (feature) engineering - Which hurts acceptance, so people always try to frame their work as general as possible
- Historical reasons due to the conflict the symbolic community (which rely heavily on innate knowledge)
Seems like a band-aid solution for a broken system.
But in general science will have to deal with that problem. Written text used to "proof" that the author spend some level of thought into the topic. With AI that promise is broken.
Its quite funny to see that LLMs reviewed interest in KnowledgeGraphs/Reasoning/Triple Stores etc... since (on a high level) they both are often pitched to solve the same goal. (E.g. Ask an AI about a topic...)
And sampling from a (now fixed) distribution can be made deterministic...
So the total generation of text from an LLM can be made fully deterministic. The problem for scientists is that we cant do that in the deployed systems...
"The goal of Automated driving is not to drive automatically but to understand how anyone can drive well"...
The goal of DeepBlue was to beat the human with a machine, nothing more.
While the conquest of deeper understanding is used for a lot of research, most AI (read modern DL) research is not about understanding human intelligence, but automatic things we could not do before. (Understanding human intelligence is nowadays a different field)
Start-ups deliver something much more complicated/different than these research projects.
If the whole research project at the end actually delivers a somewhat coherent prototype, it's seen as a huge success.
Most start-ups start with a proof-of-concept prototype to transform it into an economically viable product.
So, comparing these success rates does not make sense. Multiple research groups can deliver rough prototypes at the end and celebrate their "huge success". In most fields, there can be only a few economically surviving startups...
Teaching is not really relevant in the hiring process of professors.
I saw several committees for prof position and teaching is treated like a checkmark. You should done it and provide a small sample lecture (which you prepare much more than your average lecture) and don't have to suck at it. After this checkbox, the differentiating factors are about citations and how much grant money you can/could/do have... (Western Europe, maybe somewhere else it's different).
Some people just believe there is no innate knowledge or we dont need it if we just scale/learn better (in the direction of Bitter Lesson)
(ML) Academia is also heavily biased against it due to mainly two reasons: - Its harder to publish, since if you learn Task X with innate Knowledge, its not as general, so reviewer can claims its just (feature) engineering - Which hurts acceptance, so people always try to frame their work as general as possible - Historical reasons due to the conflict the symbolic community (which rely heavily on innate knowledge)