🚨🚨 Lets Talk o3 🚨🚨

AI beating programmers on leet code is like that guy who won the French and Spanish scrabble tournaments despite speaking neither French nor Spanish.

Apparently AGI has been acheived and all jobs are going to be automated by next year... but Devin still can't push to master

i hope o3 will help make svg in webpack easier

Prime I really appreciate your calm and reasoned review of the wild AI ride we are subject to these days.

That graph shows a similar limitation curve to what we already know to be the limit of AI. I wonder what it looks like in comparison to the rest of our results. A wild guess is it falls right in line.

these AGI headlines made me go blind after rolling my eyes so hard they fell off.

Man that single piece empty puzzle gave me flashbacks to windows vista.

On the "laser" task issue; there are other tasks that do line extensions like that, and I don't think any of them complete along the side as suggested. But on that note, each test is allowed 2 answers, so when in doubt, provide both answers.

AI has all the same issues as parallel scientific computing. It took 15 years to get from petascale to exascale computing. Yes, that's 1000 times more raw operations. That doesn't mean you can do matrix multiplication 1000x faster, it mainly means you can do matrix multiplications for bigger matrices. So, let's say you make the parallel parts of using a LLM model 1000x faster (that's is a massive ask), and ninety nine percent (that's generous by the way) of the model computation is parallel. You get 90 times total speedup. You are not getting massive speedup in *both* model size and execution time. Amdahl's Law is just that. This ignores the issue of the power it takes to run these systems. It's just a given you will be using more power. None of the current work even addresses another critical step: Showing your work. Why do you think this tissue slide is 96% likely to have this pathology? You need to know to rule out tissue processing error, containments and so on. This was considered for quite sometime to be a key part of general intelligence, not sure why it is getting pushed to the wayside. Of course, it's not an issue in machine learning, because it is a given that you are creating a predictive model, not a model of the process of predicting a thing itself. At some point, you have to ask what doing all the linear algebra is best used for. Is it for modeling climate, protein folding, drug development doing the things the humans have no chance at, or is it trying to do what humans do pretty well already. For me, don't need the chatbots and generated art, music and crap articles. I really don't. The research will move forward, but not all ideas are worth really bring out of the lab really. There's always value to making people afraid about their jobs and livelihoods. "Oh, we will replace your pesky labor force" is a pretty good selling point. But it's all a race to the bottom. Have a bot for sales pitches. Have a bot for support. Have a bot for HR. Have a bot for coding. The only thing that nobody seems to replace with a bot is the people that have to money to buy all the bots in the first place.

So in my spare time I occasionally spend some time getting paid to do AI training. While solving the tasks themselves often take only a few seconds where it takes longer is that you have to write up the solution while also assessing how well the AI is doing. Further you are paid by the hour and not by the task so you might have a task that pays $40/hour and you submit 5-10 tasks per hour (after you account for the additional task work besides just solving the problem yourself) thus a $5/task would be for $40/hr if on average the annotators solve 8 tasks/hr. So I don't think they are necessary exaggerating when they say they are paying $5/task on average