1. We know they have more big breakthroughs already that have not been released.
2. We know the current tech can keep scaling. They have not hit a limit with the current approach yet.
Given gpt-4 is already ridiculously useful and we’ve barely scratched the surface, it makes complete sense to me. More capacity + faster gpt responses unlocks massive amounts of more potential/use cases.
> Climate change disaster timelines are longer than AI timelines.
Sure ... and we've already let Koch and Co. piss 50 years of lead time up against the wall since the first global recognition of the problem in the 1970s.
Now that it's starting to bite and properly ramp up there's far too many that are stretched out lizard like on Titanic deckchairs asking AI Jeeves for another drink.
The biggest contributor to climate change was widespread protests against nuclear power, which arrested what had been rapid growth in a safe non-CO2-emitting base load power source that could have replaced hydrocarbon generators within two generations.
The Green parties in Europe successfully stopped the expansion of every nuclear energy program in the EU. Greenpeace engaged in a number of terrorist attacks to sabotage nuclear energy.
Seems unlikely given the expansion in standard of living in developing countries, the use of fossil fuels in ICE's .. and elsewhere outside of power generation in the EU.
It's a factor, sure, but "biggest" .. not so much.
The rate of growth in nuclear power was putting on a trajectory to replace all or nearly all hydrocarbon based base load power sources. And it was political opposition to nuclear power that put a stop to that. No other factor comes close as a contributor to climate change in my estimation.
The transition from ICEs to electric battery cars is largely orthogonal to base load power, but even electric battery cars depend on a base load source, so the extent that CO2 emitting energy sources have been replaced by non-emitting ones is highly dependent on the base load sources.
Climate change will became very bad possibly as soon as 5-10 years. Widespread starvation, resource war, bad.
At the rate AI is accelerating we may not get even 3. It's the final force multiplier. If we end create runaway automation feedback loops, there may not be much of a recognizable planet left to have a climate in ten years. A spot of hull rust quickly consumes the entire ship once it takes root.
I grasp the looming disaster of climate-driven global collapse. We simply found a way to speedrun disaster even more efficiently.
I think you are overestimating its usefulness and underestimating how much the surface has been metaphorically breached.
Where's the killer app? The only one I can think of off hand is co-pilot and the reception I've seen is that it's pretty mid. Most of the proposed applications require human checking to get right which is a huge limitation to the adoption of these systems unless you accept a 3-5% error rate which is terrible. I've not met anyone who is interested in something like a book written using this thing and the main use case I've seen basically amounts to denial of service attacks with believable bullshit.
Frankly the only people I've seen who are super excited about this stuff are people in the field or the uninformed.
Not sure if you’re technical but the only thing I have to say to this is: Tinker with it yourself. Try different experiments. I’ve built a ton of tools at this point with AI, some have not been very useful in the end and others have made me significantly more productive and effective.
In terms of error rate: gpt 3.5 had a high hallucination rate that made use cases fairly narrow. It then got faster which opened up some more use cases. Then gpt 4 came out that had a significantly smaller hallucination rate which opened up a gigantic number of additional possibilities. And had a larger context window and output size that made it significantly more useful. Then it got faster with an even larger context size… each of these iterative improvements just continue to add more and more possibility in a gigantic range of cases that have literally never existed before.
I am guessing an incredibly talented team that is incredibly networked and incredibly well funded and proven agile in the tech hub of the world can find hardware experts. Don’t know why anyone would bet against that.
We would have heard if they had hired/bought the size of team necessary to design a system large enough to be a significant impact. Modern (eve sub 28nm much less 2nm) design is hugely complex and the range of things that an AI compute engine needs to do are very broad.
Perhaps they could design a core and license it out? I'm trying to come up with a way they can do something significant without 100 people. Just the memory and serial connections are complex enough ignoring the GPU or heat/power issues.
It took apple like 10 years to go from their first chips to actually using them in laptops, and they are literally the most well capitalized company on the planet. Sorry if I'm skeptical that some relative up starts with a billion in compute from Microsoft can compete with trillion dollar companies that have been around for decades.
Nobody can even define what AI is, why we need it, or how to achieve it. Usually it makes sense to seek funding to execute on a plan. Making a fancy chat bot that scrapes the web to synthesize sometimes accurate and sometimes useful information is not worth trillions of dollars.
What is essentially happening in my opinion is technical innovation has slowed so silicon valley is seeking money to prop up a house of cards that doesn't make much new that is useful or needed.
Can anyone specifically say what trillions of dollars invested in "AI" would buy for society?
It seems to me there are so many higher priorities.
I don't remember LLM's claiming to replace GPU's. This is more like arguing with a landowner why your assembly line is so innovative and needs to be built on their land for free. They need the land, the land doesn't necessarily need them yet.
I agree some of these are fluff. However many here are great to have basic knowledge of for specific scenarios one may run into and then know where to dig deeper when needed.
Agree though to marry being a ferocious reader / learner with being a do’er is the way to go. Personally I did not grow up with an opportunity to develop an intuition about business and self-motivated action. Has taken a lot of work to move that direction over the years.
It’s very likely this person doesn’t care about the credentials but rather just wants to gain the knowledge to do something themselves.
I more or less did this for years by reading a ton of books + many years of HN / articles / interviews / videos + startup weekend events + tried starting things + coding hobby for years + enterprise consulting work, and now I’m fairly well equipped to co-lead the company that I do.
The first 5+ years of doing this I debated attempting to go to a prestigious school for an MBA, primary reason (95%) was for the networking opportunity (meeting great likeminded or driven people), (0% because I would’ve been able to tell people I had an MBA from somewhere for job purposes, the snobbery from that would have been a detriment often instead I believe), and 5% for the education which I think has value but when it’s free and I had the motivation there was no reason to pay $100ks for that. At this point this company is doing fairly well so although I would love to meet more driven and likeminded people, I’m not sure it the best path to do so anymore for me.
Most initiatives are not purely good without any downsides. A principle to consider is: Just because there are some negative aspects sometimes, do those outweigh the good? In this case, how many negatives actually come from patent law? (Btw, a monopoly most often is not the result - there are often many solutions to a similar problem/need.). And consider the side of the creator (which I’ve been finding is surprisingly rare in hn) - creating a company and product is already incredibly hard, how much harder do we want to make that? What and who are we sacrificing?
Maybe we just adjust the patent law some. Decrease the number of years? I’m sure it could be better.
Not public but internally I wrote a tool to help us respond to RFPs. You pass in a question from a new RFP and it outputs surprisingly great answers most of the time. Is writing 75%+ of our RFP responses now (naturally we review and adjust sometimes and as needed). And best of all it was very quickly hacked together and it’s actually useful. Copied questions/answers from all previous ones into a doc, and am using OpenAI embeddings api + FAISS vector db + GPT-4 to load the chunks + store the embeddings + process the resulting chunks.
He praised it many times, but just a few weeks ago was complaining when he posted BS and community notes essentially called him out. In any case, I think it's a good feature and hope it doesn't go away.
Given gpt-4 is already ridiculously useful and we’ve barely scratched the surface, it makes complete sense to me. More capacity + faster gpt responses unlocks massive amounts of more potential/use cases.