Love it. I was really surprised to see the traction typesafe got in the first place. I had built something similar a year ago for a client and thought it was nothing groundbreaking. The client bought it, still uses it and that was it. I had also spent considerable time training and fine tuning zero shot NLI classifiers. Anyway, after typesafe was launched I decided to start building this open source library - https://github.com/deepanwadhwa/OpenDecision . The context length for the underlying model is 8k.
>>can't believe there is a class of software engineers who wake up everyday and tell themselves, "today is the day I am going to automate the rest of my job"
>>The whole point of software engineering is automating processes
There is a difference between 'automating a process' and 'automating oneself' and making one obsolete a benchmark. I can sympathize if it's confusing for you to understand that.
While I agree with this and appreciate Tao and other mathematicians to take the time to do this. There are similar concerns for many many other fields aka there is a general misalignment of technology. Take Software engineering for example, I can't believe there is a class of software engineers who wake up everyday and tell themselves, "today is the day I am going to automate the rest of my job".
Lets forget the hyper intellectual fields like maths and software engineering for a moment. What about taxi drivers? The best minds in silicon valley wake up everyday to automate the jobs of taxi drivers - TFA can be reworded as - 'The misalignment of AI/Tech in Transportation'. Remember the Nepal disaster that happened a couple weeks ago - the largest cranes that they had were stuck in the mud and couldn't move. There were no tools which could help the rescue teams at that time. Its weird that billions have been spent on making a ride automated to make a taxi driver redundant but no improvement in tech for rescue teams.
there are a few assumptions in this future- the biggest one is - we will have to attain a baseline intelligence which is useful for everyone which is not true today. even fable fumbles hard!!
second is that everyone will have to own a medium to use this intelligence (aka robots). like a currency is useless if you can't use it.
to me this is the least daunting scenario. all other scenarios lead to mass slavery.
I guess this makes sense for the scenario when we will use machine intelligence as a currency, maybe 15-20 years from now? - like I can pay 15 minutes of inference for a dozen bananas? and the banana seller uses those 15 minutes to do banana shelling or removing weeds from their farms or whatever i guess when robots are doing everything. right??
This is awesome. I worked on something similar a few months ago. It is a general purpose navigation system based on TERCOM and dead reckoning - https://github.com/deepanwadhwa/anumaan
I was going to cancel my gemini membership today ..... still going ahead. In my experience, gemini 3.1 pro, 3.5, 3.6 flash constantly lie too much about completing their tasks whereas sol (even though equally dumb) never claims something has been done when it hasn't been.
i looked at the animations, they look cool, and i don't think i will enjoy learning things that way. as someone else said, there's a lot of content already produced on these topics. i also think the level at which these animations are playing, they are actually hiding the 'complexity' of these topics.
Dario, as your unpaid therapist I would tell you that models are a commodity and you are having a hard time coming to terms with it. You are doing everything except accepting it. It's a common defense mechanism, but as your unpaid therapist, i will tell you that it's not going to work. Your company will cease to exist or exist like how ferrari or buggati exist.
In absolute terms, you are absolutely right but relatively i don't think so. if we were to compute private money divided by state resources for AI, i think china might have more share than the US. also, even if government spend in the US on AI is so high, shouldn't we get then some models for free? maybe thinking machines is doing that, but its funded privately by a16z.
In the end its really VC money (US) versus State resources (China). In my personal opinion, building reliable LLMs is kind of a fundamental science problem which if done right has the potential to help everyone regardless of the background, so it should definitely be funded by states resources (taxes etc), which is what China is doing. In them doing so, the rest of the world also benefits, I think its a net win.
this is good to see. i also trained a stt under 500kb for sub dollar chips. it had about 20 words that it could understand(like start, stop, left, right, go, up etc) and then the spell mode where you could say the word spell and then say the individual english alphabets and close with spell. it was super fun to work on. these tend to be extremely unstable though, like confusion between p and t (at least for my accent). will have to try this one now.
Antigravity sucks so bad that I have started to feel that google really doesn't wanna compete, they just wanna hang in there at number 2 or 3, to just annoy the number 1 and 2.
Its really easy to argue against local models because when it comes to quality, you can argue using the tokens/sec. and when it comes to speed, you can argue using the parameter count. This is not compared to the frontier stuff but it is the frontier of last year that now runs on a local machine. It was impossible to do this last year.
It will, but the process at this point is SSD bound rather than compute bound. On a bigger machine, Apple silicon must help but I don't have a bigger machine. I can think about this more and will make changes if that helps.
>>Yes, it’s technically running, but not in a way that would be useful by normal LLM standards.
What are the LLM standards?
Do you know how many people use perplexity? I know many people who are not software engineers or tech workers and have a LLM subscription for rewriting their stuff (non-native english speakers) in english. There are many use cases for running good models locally. Maybe not for you, but someone might find this beneficial.