That’s a bold prediction.
Comment on Has Generative AI Already Peaked? - Computerphile
dick_stitches@lemm.ee 4 months ago
In 30 years, we’re going to look back at this headline like we look back at articles about the internet or smart phones being fads.
sexy_peach@beehaw.org 4 months ago
dick_stitches@lemm.ee 4 months ago
I think most people underestimate how big of a deal it’s going to be when this tech is pervasive in things like search engines or digital assistants. There are many times when I can’t figure out the right combination of words to put into a search engine to find the results. ChatGPT is already my go to when I want to figure out a movie or song from some random combination of foggy memories. Imagine after 10 more years of cpu/gpu innovations, and chat applications that have actually been designed for information retrieval, how much that is going to transform how we interact with data and information.
Full disclosure, I didn’t watch the video. I just can’t imagine that that headline isn’t going to look silly in 30 years.
FreeFacts@sopuli.xyz 4 months ago
Imagine after 10 more years of cpu/gpu innovations, and chat applications that have actually been designed for information retrieval, how much that is going to transform how we interact with data and information.
LLMs are going to change how we interact with data and information, but not the way you think. The AI-generated spam will ruin the whole concept of internet search completely. Only information that we can trust is going to be human-curated.
jarfil@beehaw.org 4 months ago
You will need an LLM to tell that apart, so… 🤷
eleitl@lemmy.ml 4 months ago
There are diminishing returns in semiconductor photolitho. Moore scaling is long over, absolute real estate see WSI with Cerebras, DC costs and power envelope are all sending a clear message. Quantization is there, so you can go from digital multipliers to analog and go spiking networks, but transformers and Co have little power there.
Also, the kind of economy that can carry Gen AI as business model is not a given, long term.
jarfil@beehaw.org 4 months ago
Neuromorphic hardware is going to jump many orders of magnitude over classic hardware. When we get a RAM that can execute multiple layers in parallel at once, per clock tick, we’ll see whole AI ecosystems cooperating to get a solution in a fraction of the time a single modern NN would take.
anachronist@midwest.social 4 months ago
Alternate theory we’ll look back the same way we looked back on the claims that IBM watson was intelligent, or the claims in the 60s, 70s, 80s, 90s, 2000s, 2010s, that <insert technology x> was going to make computers truly intelligent.
darkphotonstudio@beehaw.org 4 months ago
They are discussing a very specific approach and a paper that lays out the issues with pursuing this one specific type of generative AI. It’s not about AI in general. The headline is a bit click-baity.
sonori@beehaw.org 4 months ago
While the paper demonstrated strong diminishing returns in adding more data to modern neural networks in terms of image classifers, the video host is explaining how the same may effect apply to any nureal network based system with modern transformers.
While there are technically methods of generative AI that don’t use a neural network, they haven’t made much progress in recent decades and arn’t what most people mean when they hear or say generative AI, and as such I would say the title is accurate enough for a video meant for a general audience, though “Is there a fundamental limit to modern neural networks” might be more technically correct.