One thing I have found myself wondering is why this didn't play out similarly with Google in the early days. I guess their secret sauce was just so good and so secret that neither established players nor copycat startups were able to replicate it, the way it happened with ChatGPT? Why is the same not the case here, is it just because the whole LLM thing grew out of a relatively open research culture where the fundame…
There were no cloud services to buy and no free open source software infrastructure, so the CAPEX for running PageRank on the whole Internet was huge.
I lead an applied AI research team where I work - which is a mid-sized public enterprise products company. I've been saying this in my professional circles quite often. We talk about scaling laws, superintelligence, AGI etc. But there is another threshold - the ability for humans to leverage super-intelligence. It's just incredibly hard to innovate on products that fully leverage superintelligence. At some point, AI…
> In my mind, already with GPT-4, we're not generating ideas fast enough on how best to leverage it. This is just another way of saying this technology, like Blockchain before it, is a solution in search of a problem.
Or, more drastically, another way of saying it’s useless.
Personally, I think the more interesting question is: if not AI then what? I doubt we will go back to Metaverse (lol) or Crypto, B2B SaaS seems safe but stagnant (and competing over a largely fixed pie of IT spend). It feels like it doesn't matter if the companies work-- until there is something shinier to invest in it will be the defacto choice for LPs chasing returns (and VCs chasing carry).
How about improving the software, both the development process and the final product? We hear claims of upcoming AGI, but most everyday software still feels embarrassingly broken.
I am wondering where exactly are the AI products. I am not talking about boring, nerd stuff but rather about products that are well-known (to the point where non-technical users have heard of them) and are used on a daily basis by a significant amount of people. Currently only ChatGPT fits this bill despite the fact that the "AI revolution" is nearly two years old at this point.
I lead an applied AI research team where I work - which is a mid-sized public enterprise products company. I've been saying this in my professional circles quite often. We talk about scaling laws, superintelligence, AGI etc. But there is another threshold - the ability for humans to leverage super-intelligence. It's just incredibly hard to innovate on products that fully leverage superintelligence. At some point, AI…
> Anyone using a chatbot already has felt the frustration of "it doesn't get what I want". And also "I have to explain so much that I might as well just do it myself
Ehm yes. That's because it actually doesn't work as well as the hype suggests, not because it's too "high bandwidth".
I've said it before, AI is the default way to hype technology. A hundred years ago computers were 'electronic brains'. It goes dormant after loosing its edge and then re-emerges when some research project gains traction again.
"Computer" in (simplified) Chinese is still "电脑" -- literally "electric/electronic brain".
In Icelandic, computer is "tölva" which is a combination of "tala" meaning "number" and "völva" meaning "oracle".
I lead an applied AI research team where I work - which is a mid-sized public enterprise products company. I've been saying this in my professional circles quite often. We talk about scaling laws, superintelligence, AGI etc. But there is another threshold - the ability for humans to leverage super-intelligence. It's just incredibly hard to innovate on products that fully leverage superintelligence. At some point, AI…
> Anyone using a chatbot already has felt the frustration of "it doesn't get what I want". And also "I have to explain so much that I might as well just do it myself Ehm yes. That's because it actually doesn't work as well as the hype suggests, not because it's too "high bandwidth".
I'd contribute that we, the engineering class, are as a whole terrible communicators that confuse and cannot explain their own work. LLMs require clear communications, while the majority of LLM users attempt to use them with a large host of implied context that anyone would have a hard time following, not to mention a non-human software construct. The key is clear communications, which is a phrase that many in STEM don't have the education to know what that phrase really means, technically, realistically.
I lead an applied AI research team where I work - which is a mid-sized public enterprise products company. I've been saying this in my professional circles quite often. We talk about scaling laws, superintelligence, AGI etc. But there is another threshold - the ability for humans to leverage super-intelligence. It's just incredibly hard to innovate on products that fully leverage superintelligence. At some point, AI…
The "it's making mistakes" phase might be based on the testing strategy. Remember the old bit about the media-- the stories are always 100% infalliable except strangely in YOUR personal field of expertise. I suspect it's something similar with AI products. People test them with toy problems -- "Hey ChatGPT, what's the square root of 36", and then with something close to their core knowledge. It might learn to solve a…
> Remember the old bit about the media-- the stories are always 100% infalliable except strangely in YOUR personal field of expertise.
I lead an applied AI research team where I work - which is a mid-sized public enterprise products company. I've been saying this in my professional circles quite often. We talk about scaling laws, superintelligence, AGI etc. But there is another threshold - the ability for humans to leverage super-intelligence. It's just incredibly hard to innovate on products that fully leverage superintelligence. At some point, AI…
Have we surpassed Clippy "It looks like you're trying to write a letter" yet?
I lead an applied AI research team where I work - which is a mid-sized public enterprise products company. I've been saying this in my professional circles quite often. We talk about scaling laws, superintelligence, AGI etc. But there is another threshold - the ability for humans to leverage super-intelligence. It's just incredibly hard to innovate on products that fully leverage superintelligence. At some point, AI…
>Anyone using a chatbot already has felt the frustration of "it doesn't get what I want". And also "I have to explain so much that I might as well just do it myself"
the problem is really, can it learn "I need to turn this over to a human because it is such an edge case that there will not be an automated solution."