Moore’s law is the observation that the number of transistors in an integrated circuit (IC) doubles about every two years.
Is there anything similar for the sophistication of AI, or AGI in particular?
Some in the AI industry have proposed concepts similar to Moore’s Law to describe the rapid growth of AI capabilities.
Although there is no universally accepted law or principle akin to Moore’s Law for AI, people often refer to trends that describe the doubling of model sizes or capabilities over a specific time frame.
For instance, OpenAI has previously described a trend where the amount of computing power used to train the largest AI models has been doubling roughly every 3.5 months since 2012.
Thank you!
But does that equate to the power of AI doubling every 3.5 months?
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which completely killed the field of AI for decades
uh, maybe if you forgot about natural language processing
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I guess it’s hard to measure the power of AI anyway but I would say a strong no: it doesn’t equate to the power of AI doubling every 3.5 months 😅
Personally I don’t believe we’ve had the technology long enough to make such a prediction.
There isn’t really a way of quantifying the “sophistication” of AI
The advancements in this space have moved so fast, it’s hard to extract a predictive model on where we’ll end up and how fast it’ll get there.
Meta releasing LLaMA produced a ton of innovation from open source that showed you could run models that were nearly the same level as ChatGPT with less parameters, on smaller and smaller hardware. At the same time, almost every large company you can think of has prioritized integrating generative AI as a high strategic priority with blank cheque budgets. Whole industries (also deeply funded) are popping up around solving the context window memory deficiencies, prompt stuffing for better steerability, better summarization and embedding of your personal or corporate data.
We’re going to see LLM tech everywhere in everything, even if it makes no sense and becomes annoying. After a few years, maybe it’ll seem normal to have a conversation with your shoes?
We’ve reached far beyond practical necessity in model sizes for Moore’s Law to apply there. That is, model sizes have become so huge that they are performing at 99% of the capability they ever will be able to.
Context size however, has a lot farther to go. You can think of context size as “working memory” where model sizes are more akin to “long term memory”. The larger the context size, the more a model is able to understand beyond the scope of it’s original model training in one go.
That is a pretty wild assumption. There’s absolutely no reason, why a larger model wouldn’t produce drastically better results. Maybe not next month, maybe not with this architecture, but it’s almost certain that they will grow.
This has hard “256kb is enough” vibes.
What drastically better results are you thinking of?
Actual understanding of the prompts, for example? LLMs are just text generators, they have no concepts of what’s being the words.
Thing is, you seem to be completely uncreative or rather deny the designers and developers any creativity if you just assume “now we’re done”. Would you have thought the same about Siri ten years ago? “Well, it understands that I’m planning a meeting, AI is done.”
I see your point. Rereading the OP, it looks like I jumped to a conclusion about LLMs and not AI in general.
My takeaway still stands for LLMs. These models have gotten huge with little net gain on each increase. But a Moore’s Law equivalent should apply to context sizes. That has a long way to go.
