411–420 of 472 posts
Earlier quoted context omitted.
Hamas isn't the only path to freedom for Palestinians. In fact, they seem to be the major impediment to it.
If we're going to be reductive, at least include the other main roadblock to a solution which is the current government of Israel.
Earlier quoted context omitted.
Who cares about speed if you’re wrong? This isn’t a race to write the most lines of code or the most lines of text. It’s a race to write the most correct lines of code. I’ll wait half an hour for a response if I know I’m getting at least staff engineer level tier of code for every question
That’s the correct answer. Years ago I worked on inference efficiency on edge hardware at a startup. Time after time I saw that users vastly prefer slower, but more accurate and robust systems. Put succinctly: nobody cares how quick a model is if it doesn’t do a good job. Another thing I discovered is it can be very difficult to convince software engineers of this obvious fact.
So the assertion that small models aren’t as good just isn’t correct. They are amazing at certain things, and are incredibly faster and cheaper than larger models.
Earlier quoted context omitted.
Less compute also means lower cost, though. I see how most people would prefer a better but slower model when price is equal, but I'm sure many prefer a worse $2/mo model over a better $20/mo model.
That’s the thing I’m finding so hard to explain. Nobody would ever pay even $2 for a system that is worse at solving the problem. There is some baseline compute you need to deliver certain types of models. Going below that level for lower cost at the expense of accuracy and robustness is a fool’s errand. In LLMs it’s even worse. To make it concrete, for how I use LLMs I will not only not pay for anything with less ca…
Earlier quoted context omitted.
What Mistral has though is speed, and with speed comes scale.
I’ll wait 5 seconds for the right code over 1 sec for bad code.
Earlier quoted context omitted.
Neural networks were already big 10 years ago, you have to go back 15 years to see before they started being popular. From wikipedia: > Between 2009 and 2012, ANNs began winning prizes in image recognition contests, approaching human level performance on various tasks, initially in pattern recognition and handwriting recognition. That was when Neural networks became a big thing every tech person knew about, 2014 it w…
NN were already a casual topic in my high school computer science class more than 20 years ago. I've always assumed they were already fairly common by that point. (~2000)
Earlier quoted context omitted.
That’s the thing I’m finding so hard to explain. Nobody would ever pay even $2 for a system that is worse at solving the problem. There is some baseline compute you need to deliver certain types of models. Going below that level for lower cost at the expense of accuracy and robustness is a fool’s errand. In LLMs it’s even worse. To make it concrete, for how I use LLMs I will not only not pay for anything with less ca…
So I think that’s a “your problem isn’t right for the tool” issue, not a “Mistral isn’t capable” issue.
Andrej inspires millions. He’s the Taylor Swift of AI/NNs. I really hope he’s next gig is at an actually Open AI company.
Earlier quoted context omitted.
[flagged]
Would you cringe if he’d said “the Michael Jordan of AI”? Perhaps you cringing says more about you?
Let me say, he's a great teacher! I took a CV class with him. He should teach more, and take it seriously. Being a popular AI influencer is not necessarily correlated with being a good researcher though. And I would argue there is a strong indication that it is negatively correlated with being a good business leader / founder. Here's to hoping he chills out and goes back to the sorely needed lost art of explaining co…
>He should teach more, and take it seriously. if only we compensated that knowledge properly. Youtube seems to come the closest, but Youtube educators also show how much time you have to spend attracting views instead of teaching expertise. > It makes you a target for offers and opportunities because of your name/influence, but not necessarily because of your underlying "best fit" That's unfortunately life in a nutsh…
This can be self-fulfilling.
In an organization beyond a certain size, there will be more almost-adequate-fits than there are leadership positions. This could be about like a recognized baseline which seems like it really needs to be scrutinized closely to see exactly who might be slightly above or below the line.
Or in a small company where there is not any almost-fit whatsoever, imagination can result in an ideal that is equally recognizable, but also might not be fully attainable.
Either way it could be OK but not exactly the best-fit.
If good fortune smiles and the rare more-than-adequate-fit appears anywhere on the horizon though, it's so unfamiliar they fly right over the radar.