How will this possible government action affect American AI development if a case is brought against Nvidia? Wouldn't a case kill AI development by years, if not decades?
Isn't this timing weird in that they just got charged by the DOJ with monopoly? "Oh we had a hack, that's why we cannot find the documents, your honor."
Doesn't it seem familiar, like something Google would do? They should have someone like Larry Page, similar to how Mark Zuckerberg or Elon Musk handle things. A decision is made and you go forward. Google seems incapable of taking action without the approval of a committee and middle managers...reminds me of IBM back in the '90s.
I'm not impressed. When comparing Gemini/Bard to ChatGPT + GPTs, Bard/Gemini feel more like a search engine. I asked Gemini for help in planning a date with my date, who is famous enough that GPT4 knows her and her art. However, Gemini immediately started giving me step-by-step instructions to plan the date. I had to tell it to slow down and ask me questions first before giving an answer. It complied this time, but after the Q&A, it provided nearly the same response as before, without any personalization. Next, I asked about my artist friend, but Gemini had no clue. I even said, "come on, you have to know her," but it simply repeated that it didn't know her. Another issue I encountered was with images. I tried sending a few, but Gemini couldn't describe them. I spend around 8-10 hours a day playing with LLMs, but so far, I'm not impressed.
Has anyone considered the potential of integrating the recent ILP breakthrough into transformer models? Given ILP's prowess in optimization, I'm curious about its application in enhancing transformer efficiency, especially in inference speed. Could this ILP method streamline computational resource allocation in transformers, leading to a significant leap in AI model optimization? Keen to hear thoughts on practical challenges and theoretical implications of merging these two fields.