The gradient of current moment is that whatever approach is optimized to use more data and more compute is much easier to invest in than something which can do more with less, but with a significant number of possible dead-ends.
At some point, this will have diminishing returns, but until that is hit, this makes sense as a purely return-on-investment for both research progress and business returns.
The second is our Lord and Savior.
Turns out you sometimes you need a top down, centralised vision to execute on projects. When the goal is undefined, you can allow researchers to run free and explore, now its full on wartime, with clear goals (make GPT-5,6,7....).
Last time Google got spooked by a competitor was Facebook, and they built Google Plus in response. We all know that was an utter failure. Googlers could escape that one with their egos in tact because winning in "social" is just some UX junk, not hard-core engineering like ML.
It's gonna be super hard for them to come to grips with the fact that they are way behind on something that they should be good at. Plan for lots of cognitive dissonance ahead.
If you ask a googler about this, they typically assume GPT is just as stupid as bard. Or say something like "so GPT is just trained on more data - we can do that." As if nothing's wrong.
> We’re releasing it initially with our lightweight model version of LaMDA. This much smaller model requires significantly less computing power, enabling us to scale to more users, allowing for more feedback. [1]
> Bard is powered by a research large language model (LLM), specifically a lightweight and optimized version of LaMDA, and will be updated with newer, more capable models over time. [2]
[1] https://blog.google/technology/ai/bard-google-ai-search-upda...
Is it only me that sees a problem here?
OpenAI just focused on making it a great product.
(In case it's not clear, I think you might be underestimating the size of the subsequent contributions)