Why Strawberry is not interesting to me - Community - OpenAI
Imho, if it’s for real, it’s the singularity. If it’s not, then it’s just a marginal agent framework among zillions of marginal agent frameworks. I mean… for real. If it works, DO something. Solve a critical and novel problem. Make zillions in the stock market. Don’t just publish YAFAF.
A Need for Proof
Where’s the paper from OpenAI where they’ve proven some challenging math problem? I mean, come on already. Get real. For those saying leave it to experts, well then it’s the experts solving the problems. We have 8th graders who are doing amazing things with GPT4 and Claude, mind-boggling things. If 8th graders can do it, then OpenAI PhDs should be able to use the vaunted Strawberry to make SOTA advancements in some field they are vaguely familiar with.
Progress and Expectations
I think we’re now at a point where the BS walks and SOTA talks. Yes, better models and cheaper models are appreciated. But if the folks at OpenAI can’t truly achieve something novel with it, it’s probably marginal and asymptotic. I say this because I know - we all know - what is capable with the current models. I’m not saying Strawberry can’t do it, I’m just saying proof is in the pudding at this point.
Technological Advancements and Real-world Impact
Early computers amazed people by performing precise calculations at speeds far beyond those of humans. For a long time, calculation was thought to be uniquely human, so computers capable of such intellectual tasks were believed to possess human-like consciousness. Later, when a computer defeated the human chess champion, it initially caused a stir as it seemed humans had lost in an intellectual arena. However, people soon realized that this achievement was the result of an accumulation of processes, rather than true intelligence.
That technological progress does not create more problems than it solves is my fervent hope. This is because we are facing an overwhelming number of urgent issues, and time - as precious as life itself - is rapidly slipping away. The market will adapt quickly once people start using advanced agents. While there may be arbitrage opportunities for early adopters, it’s important to recognize that trying to consistently outsmart the market, especially when everyone has access to the same tools, is unlikely to succeed. Dollar-cost averaging and diversification are strategies that will always keep pace with the market’s developments. It’s best to approach the market with a long-term perspective and find enjoyment in other aspects of life. If you’re using the stock market primarily for entertainment, it might be worth reconsidering your approach.
Post-training and Model Improvement
If you follow the LLM bubble, we have GPT or Generative Pre-trained Transformers. The problem here is the weights are frozen, and the model can only regurgitate the information contained in those frozen weights. But if you could also Post-train the model, say by unfreezing some of the layers in the network and let them be dynamic, you can unlock some rapid improvement pathways.
Revolutionary Thinking
What I do think is revolutionary is actually studying how our minds work and try to mimic this (hint: I’m a big fan of neuromorphic computing). One recent blurb that came out is how they think quantum entanglement arises in our brains and gives rise to consciousness. This controversial idea could completely change how we understand the mind.
Powered by Discourse, best viewed with JavaScript enabled
