I believe it’s mostly due to closeness to the work and actually turning the problem over in my mind. When I use an LLM to code much of the context is lost and my understanding of the solution is diminished. The longer I use it in a code base the less and less aware of the code base I become.
This is also born out in the research which has demonstrated reduced retention of content when authoring was facilitated by an LLM. Over the long term, I believe this is widen a chasm between experienced and inexperienced engineers.
While not all CEOs will feel this way, it will not surprise me that knowledge workers will be treated as disposable, even if their work is invaluable. If they can hire a junior escort for the AI they will. That junior will also be scale goated when things go wrong.
Big caveat to the productivity claim is that it’s concentrated in lesser experienced engineers and vanishes or goes negative with highly experienced engineers.
This intuitively makes sense and generally agrees with my experience. LLMs move your baseline towards the mean. If you are below average it improves and if you are above it hinders.
But also in my experience, my memory retention of the work done with an LLM is worse than doing it myself self. This leads me to believe that the lesser experienced engineers are not gaining experience!
My tinfoil hat says that this is what tech CEOs want. They want workers that are low skilled and can be paid less.
Sodium ion batteries are less energy dense than lithium ion and are not prone to dendrite formation. They are also more thermally stable and less likely to do thermal run away reactions.
Also, since they are less energy dense they don’t store the same potential energy.
You are probably thinking of metallic sodium batteries which are completely different.
Crypto investors are migrated to the newest unregulated speculative asset: the stock market.
I don’t know for certain but the amount of retail investing on vibes around AI companies and metals feels just like the crypto peaks. Just the other day Marvell jumped on the words of Jensen Huang and metal ETFs randomly lost tons of investment earlier this year.
Without a lot of the guardrails of the US regulatory apparatus that has been gutted by the current administration, the market is riff with manipulation. Truly another gilded age.
My best guess is that Gemini was trained on the textbooks that the questions are meant to test against, thus they are probably better at explicit recall of those questions or related questions.
This is a pretty limited introductory course based on what it says in the methods of the paper itself.
Just a few more GPUs ought to fix it.