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Two questions that need answering: 1. Are chatbots going to get much more effective than they already are? It seems like all the major players are plateauing a

Two questions that need answering: 1. Are chatbots going to get much more effective than they already are? It seems like all the major players are plateauing and the different models are becoming commoditized. That doesn't bode well for sustainable GPU sales. Also if the hallucination problem can't be solved, it's not clear that this generation of AI will ever be deployable at scale. 2. Are there genuine at scale use cases for AI outside of LLM's? Autonomous navigation seems like a major one, but I'm not sure how close that is to production ready. I know drug discovery and other applications are talked about, but not sure how much GPU consumption they can realistically generate. As we leave the novelty phase of the adoption curve, it's clear that a lot of the use of the image generators was unsustainable experimentation. My personal experience has been, a year ago my friends were creating tons of images but now we hardly do at all.

deleted 2y ago
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krasin 2y ago
> Are there genuine at scale use cases for AI outside of LLM's Assuming that "outside of LLMs" means "outside of text processing". Yes. Robotics. Imitation learning with LLMs is working surprisingly well. It will require a lot of investments in data and training to get to a practical state, but all the early signs point that new revenue streams will be unlocked in Robotics. One limitation that still stands on the way is the inference speed. My estimate is that we need ~10k tokens/sec prompt processing speed to get these smart robots working reasonably fast. We're getting there for 8B models (Groq & Cerebras silicon), but these 8B models are too dumb (especially, after being finetuned on robotics data), and 70B models are still 20x slower than practical.