Fun story, but I really wish OpenAI got its act together and started making actual AI breakthroughs instead of funneling compute into LLMs. I'd really like some new algorithms to get me excited about the field again. Kuddos to them for making LLMs really useful, but this is not the ride I wanted to get on.
This is the internet, so I cannot tell at all whether you're being sarcastic or not. In my view, what LLMs should get us to reconsider isn't whether there is more to intelligence, but whether there is more to language. It's the latter which I underestimated.
Dumb people aren't going to be doing the work of the smart people ever, no matter how tech evolves. The actual implication of AI being on the level of sentient beings instead of hyped-up token predictors is that the era of humanity is over. If AIs were literally capable of doing what CEOs want them to, we'd have far bigger problems to deal with than employment.
People often want to share their seeds so that players can play the same game they did. If there was an interesting series of results for example, which gave you a good set of cards.
Minecraft does this too with world generation for example.
> Probably one of the most ego-crushing realizations (if you're a nerd) is to discover that there are people out there MUCH more talented and higher performing than what you'll ever be, but with none of the obsession or pride. In other profession that's not really a topic. You can be a top performer in other professions, without a deep interest, clock out 4 daily, and never think about work outside work.
You could clock out, but I don't think the top performers ever stop thinking about work. Everything you've written here has to be wrong.
Depends on the work. I've worked places (military intel) where you leave work at work, simply because it is impossible to take work with you home. Some of the people I worked with said that was exactly why they chose that line of work - so that they never had to think about work when they came home. Some of those were also top performers.
But I also knew other top performers that basically had geopolitics as their hobby, and would study OSINT (open-source intelligence) when they came home.
And obviously there are many other professions where you can do really well, and don't think a second about work when your day is over. Really depends on how your work is structured!
Some people are naturally talented at things. It’s no different than an average athlete who works extremely hard and an elite athlete who puts in half the work but still outperforms the average.
I think what he meant was that you can take a group of people and train them for a sport. Some of those people (genetically elite athletes) will improve very quickly with minimal training, others can do massive amounts of training and never reach beyond a certain level.
Yes, this is what I mean. It's not that talent doesn't exist, but it's never the case that people on the top of their domain work less hard than others. In fact, it's the opposite. People don't have talent, the talent has them.
I promise you top performers aren't always thinking about work. There is proof in going detached from work problems and doing other things can help produce novel solutions. Same principle as getting a good night's sleep vs cramming the night before a test. Your subconscious does a lot of lifting. Never being able to put down work is just anxiety masked as dedication, in my book.
The job market is somewhat of a crapshot. A dice roll if you will. I got rejected from all kinds of place before I got a highly paid one from a Reddit post of all places. I got rejected from all kinds of places that only paid like 1/2 or 1/3 of what that job was paying, and I wasn't even looking for it at that time. It's been over for over half a year, and I wouldn't be able to replicate that sort of performance at will. Though at this point, I am not even looking for one and just working on my own projects since I have a lot saved up and barely spend anything.
One thing that I understand at my age of nearly 39 now is that success comes pretty hard, and luck plays a large role in it. As programmers all we can do is build and develop our skills, even if the world doesn't validate our efforts.
You're asking yourself what you are doing wrong, but you should be asking yourself what your goal is and focus on that. Is it really to just work for other people?
Opus has been amazingly useful at answering various statistics question that I had for it, and my current idea is a nested auction market theory inspired model. My biggest discovery is that replacing time with volume on the x axis (on a chart) and putting the bar duration on the bottom panel instead of volume normalizes the price movements and makes some of the profitable setups I've seen described in tape reading/price ladder trading courses actually visible on naked charts. A great insight I've gleamed is that variance should be proportional to volume instead of time or trade count. When plotted, it has the effect of expanding high volume areas, and compressing low volatility ones, which exposes trending price action much more readily. It honestly amazing, it's making me think that I could actually win at the trading game.
One takeaway from the book is that trend following strategies are really difficult to follow. Jesse Livermore had a 3 yearlong losing streak from 2011 - 2014 despite him following his rules. After the events of the book, he went short in 1929 and was reportedly worth over 100m in that time, a huge amount. Then he lost it all in the strongly mean reverting markets of the 1930s where his trend following strategy didn't work.
He was a problem gambler, but I think if we looked at top poker players of today, they'd all have some love the gamble in them. Jesse had godly tape reading skills that allowed him to beat the bucket shops at the start of his career.
After being kicked out of the bucket shops, he should have just become a floor trader and in all likelihood, he'd have had lower highs but would have fared a lot better overall. A lot of the trading cliches like cutting trading losses quickly, letting profits run, averaging up rather than down originate from this book. There is a reason people still talk about it 100 years after its publication. It's a good contender for the best trading book of all time.
All it takes to make and lose a huge fortune is capital and a high variance strategy. Whether that strategy is informed by legitimate genius is immaterial to the final outcome - you bust badly, exhaust your credit, and are unable to stay in the market and score next big the windfall which you were counting on.
> Investment Strategy: Organizations should invest more in computing infrastructure than in complex algorithmic development.
> Competitive Advantage: The winners in AI won’t be those with the cleverest algorithms, but those who can effectively harness the most compute power.
> Career Focus: As AI engineers, our value lies not in crafting perfect algorithms but in building systems that can effectively leverage massive computational resources. That is a fundamental shift in mental models of how to build software.
I think the author has a fundamental misconception what making best use of computational resources requires. It's algorithms. His recommendation boils down to not do the one thing that would allow us to make the best use of computational resources.
His assumptions would only be correct if all the best algorithms were already known, which is clearly not the case at present.
Rich Sutton said something similar, but when he said it, he was thinking of old engineering intensive approaches, so it made sense in the context in which he said it and for the audience he directed it at. It was hardly groundbreaking either, the people whom he wrote the article for all thought the same thing already.
People like the author of this article don't understand the context and are taking his words as gospel. There is no reason not to think that there won't be different machine learning methods to supplant the current ones, and it's certain they won't be found by people who are convinced that algorithmic development is useless.
I dare say ChatGPT 3.0 and 4.0 are the only recent examples where pure computing produced a significant edge compared to algorithmic improvements. And that edge lasted a solid year before others caught up. Even among the recent improvements;
1. Gaussian splashing, a hand-crafted method threw the entire field of Nerf models out the water.
2. Deepseek o1 is used for training reasoning without a reasoning dataset.
3. Inception-labs 16x speedup is done using a diffusion model instead of the next token prediction.
4. Deepseek distillation, compressing a larger model into a smaller model.
That sets aside the introduction of the Transformer and diffusion model themselves, which triggered the current wave in the first place.
AI is still a vastly immature field. We have not formally explored it carefully but rather randomly tested things. Good ideas are being dismissed for whatever randomly worked elsewhere. I suspect we are still missing a lot of fundamental understanding, even at the activation function level.
We need clever ideas more than compute. But the stock market seems to have mixed them up.
>There is no reason not to think that there won't be different machine learning methods to supplant the current ones,
Sorry, is that a triple negative? I'm confused, but I think you're saying there WILL be improved algorithms in the future? That seems to jive better with the rest of your comment, but I just wanted to make sure I understood you correctly!
reply