21–30 of 336 posts
I find Gemini 3 to be really good. I'm impressed. However, the responses still seem to be bounded by the existing literature and data. If asked to come up with new ideas to improve on existing results for some math problems, it tends to recite known results only. Maybe I didn't challenge it enough or present problems that have scope for new ideas?
It is interesting that most of our modes of interaction with AI is still just textboxes. The only big UX change in that the last three years has been the introduction of the Claude Code / OpenAI Codex tools. They feel amazing to use, like you're working with another independent mind. I am curious what the user interfaces of AI in the future will be, I think whoever can crack that will create immense value.
There's a reason keyboards haven't changed much since the 1860s when typewriters were invented. We keep coming up with other fun UI like touchscreens and VR, but pretty much all real work happens on boring old keyboards.
I find Gemini 3 to be really good. I'm impressed. However, the responses still seem to be bounded by the existing literature and data. If asked to come up with new ideas to improve on existing results for some math problems, it tends to recite known results only. Maybe I didn't challenge it enough or present problems that have scope for new ideas?
https://mathstodon.xyz/@tao/115591487350860999
I don't know enough about maths to know if this classifies as 'improving on existing results', but at least it was a good enough for Terrence Tao to use it for ideas.
It is interesting that most of our modes of interaction with AI is still just textboxes. The only big UX change in that the last three years has been the introduction of the Claude Code / OpenAI Codex tools. They feel amazing to use, like you're working with another independent mind. I am curious what the user interfaces of AI in the future will be, I think whoever can crack that will create immense value.
We see similar tendency toward the most general interfaces in "operator mode" and similar the-AI-uses-the-mouse-and-keyboard schemes. It's entirely possible for every application to provide a dedicated interface for AI use, but it turns out to be more powerful to teach the AI to understand the interfaces humans already use.
> So is this a PhD-level intelligence? In some ways, yes, if you define a PhD level intelligence as doing the work of a competent grad student at a research university. But it also had some of the weaknesses of a grad student. As a current graduate student, I have seen similar comments in academia. My colleagues agree that a conversation with these recent models feels like chatting with an expert in their subfields.…
When I did it last week with Gemini-3 and chatGPT-5.1, they got on the topic of what they are going to do in the future with humans who don't want to do any cognitive task. That beyond just AI safety, there is also a concern of "neural atrophy", where humans just rely on AI to answer every question that comes to them.
The models then went on discussing if they should just artificially string the humans along, so that they have to use their mind somewhat to get an answer. But of course, humans being humans, are just going to demand the answer with minimal work. It presents a pretty intractable problem.
I find Gemini 3 to be really good. I'm impressed. However, the responses still seem to be bounded by the existing literature and data. If asked to come up with new ideas to improve on existing results for some math problems, it tends to recite known results only. Maybe I didn't challenge it enough or present problems that have scope for new ideas?