Well, for one thing, I’d point to all the other anecdotes of developers being made 10x more efficient due to LLM use.
But really I’m just comparing against my personal experience. Once I learned how to use the tools well, and what to expect (and what not to), I’ve found LLMs to be very powerful.
> Don't use it to write code. It's a terrible programmer.
I see this sentiment often, and I’m honestly not sure where this comes from, as it’s really not been my experience. I feel like this must come from people feeling threatened and just moving the goals posts, or not knowing how to use the tools effectively.
Yeah, historically it’s been software that’s limited AMD here. Not surprised to hear that may still be the issue. NVidia’s biggest edge was really CUDA.
Software aside, does AMD have a potential strategic advantage in the long term since they are also producing top tier CPUs? Is there some future benefit to tightly integrating their products? IIRC NVidia partnered with intel for this purpose, right?
I’ve been wondering about this. It used to be that different chip makers had different efficiencies, and so measuring two different companies chips in gigawatts wouldn’t be a good apples to apples comparison.
Is that still true or has the gap narrowed? Or have GPUs always been similar across the board and it’s CPUs that have more disparity?
Franklin Templeton | Quant Implementation Lead | Boston, MA | In-Person | $145k - $190k + Bonus
We're building an end-to-end portfolio construction system to support FTIS, a $100bn multi-asset investment manager within FT. This is a front office role that will help guide our quantitative staff as we build out this new system. This project is a strategic objective for FTIS, highly visible, and frankly one of the most fun jobs in the industry.
We're looking for mid to senior level researchers or developers with strong buy-side backgrounds who can help translate our investment process into a robust, scalable, and approachable design.
Reach out with any questions - dan.nadler (at) franklintempleton (dot) com
I agree with you that AGI is not around the corner. I think the people who do believe that are generally falling for a behavioral bias. They see the advances in previously difficult problems, and extrapolate that progress forward, when in reality we are likely to come against significant hurdles before we get to AGI.
Also, seeing computers perform tasks they haven't done before can convince people that the model behind the scenes is closer to AGI than it really is. The fact that deep neural networks are very hard to decifer only furthers the mystical nature of the "intelligence" of the model.
Also, tasks like playing starcraft are very impressive, but are not very close to true AGI in my opinion. Perhaps theres a more formal definition that I'm not aware of, but in my mind, AGI is not being good at playing starcraft, AGI is deciding to learn to play starcraft in the first place.
But really I’m just comparing against my personal experience. Once I learned how to use the tools well, and what to expect (and what not to), I’ve found LLMs to be very powerful.