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!
To me, the current LLMs aren't qualitatively different from the char RNNs that Karpathy showcased all the way back in 2015. They've gotten a lot more useful, but that is about it. Current LLMs will have as much to do with GAI as computer games have to do with NNs. Which is to say, games were necessary to develop GPUs which were then used to train NNs, and current LLMs are necessary to incentivize even more powerful hardware to come into existence, but there isn't much gratitude involved in that process.
The strengths and weaknesses of the algorithmic niche that artificial NNs are in hasn't changed a bit since a decade ago. They are still bad at anything I'd want to actually use them for that you'd imagine actual AI would be good at. The only thing that has changed is people's perception. LLMs found a market fit, but if you notice, compared to last decade where we had Deepmind and OpenAI competing at actual AI in games like Go and Starcraft, they've pretty much given up on that in favor on hyping text predictors. For anybody in the field, it should be an obvious bubble.
Underneath it all, there is some hope that an innovation might come about to keep the wave going, and indeed, a new branch of ML being discovered could revolutionize AI and actually be worthy of the hype that LLMs have now, but that has nothing to do with the LLM craze.
It's cool that we have them, and I also appreciate what Stable Diffusion has brought to the world, but in terms of how much LLMs influenced me, they only shorted the time it takes for me to read the documentation.
I don't think that machines cannot be more intelligent than humans. I don't think that the fact that they use linear algebra and mathematical functions makes the computers inferior to humans. I just think that the current algorithms suck. I want better algos so we can have actual AI instead of this trash.
I've thought about adding record row polymorphism to Spiral, but I am not familiar with it and couldn't figure out how to make it work well in the presence of generics.
Why is generics the tricky bit? Isn't that the bread-and-butter of this type system? You should just be able to substitute the term 'type variable' in the article for 'generics'.
Some of the stuff in this playlist might be relevant to you, though it is mostly about programming GPUs in a functional language that compiles to Cuda. The author (me) sometimes works on the language during the video, either fixing bugs or adding new features.
Net/30 is 30 days after the contract is signed in this case, Net/60 is 60 days. Sometimes instead of "after the contract is signed" it is "after work is delivered" but my case was the former.
I am doing a fully fused ML GPU library along with a poker game to run it on in my own programming language that I've worked on for many years. Currently, right at this very moment, I am trying to optimize compilation times along with register usage by doing more on the heap, so I am creating a reference counting Cuda backend for Spiral.
Both the ML library and the poker game are designed to run completely on GPU for the sake of getting large speedups.
Once I am done with this and have trained the agent, I'll test it out on play money sites, and if that doesn't get it eaten by the rake, with real money.
I am doing fairly sophisticated functional programming in the videos, the kind you could only do in the Spiral language. Many parts of the series involve me working and improving the language itself in F#.
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.
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