this post was submitted on 11 Oct 2023
505 points (92.6% liked)
Technology
59587 readers
2553 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related content.
- Be excellent to each another!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, to ask if your bot can be added please contact us.
- Check for duplicates before posting, duplicates may be removed
Approved Bots
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
Hm. I think you should zoom out a bit and try to recognize that AI isn't stagnant.
Voice recognition and translation programs to years before they were appropriate for real-world applications. AI is also going to require years before it's ready. But that time is coming. We haven't reached a 'ceiling' for AI's capabilities.
Breakthrough technological development usually can be described as a sigmoid function (s-shaped curve), while there is an exponential progress in the beginning, it usually hit a climax then slow down and plateau until the next breakthrough.
There are certain problem that are not possible to resolve with the current level of technology for which development progress has slowed to a crawl, such as level 5 autonomous driving (by the way, better public transport is a way less complex solution.), and I think we are hitting the limit of what far transformer based generative AI can do since training has become more and more expensive for smaller and smaller gains, whereas hallucination seems to be an inherent problem that is ultimately unfixable with the current level of technology.
One thing that I think makes AI a possibility to deviate from that S model is that it can be honed against itself to magnify improvements. The better it gets the better the next gen can get.
that is a studied, documented, surefire way to very quickly destroy your model. It just does not work that way. If you train an llm on the output of another llm (or itself) it will implode.
Also at best it's an refinement, not a new sigmoid. So are new hardware/software designs for even faster dot products or advancements in network topology within the current framework. T3 networks would be a new sigmoid but so far all we know is why our stuff fundamentally doesn't scale to the realm of AGI, and the wider industry (and even much of AI research going on in practice) absolutely doesn't care as there's still refinements to be had on the current sigmoid.