351–360 of 581 posts
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There’s a lot of money to be made if you can reliable distinguish the two in advance.
My grandma loved to remind my dad about how he didn't think CDs would become a thing, and to not invest in Philips (as i recall) And he was tech savy, building circuits, computers etc, he just didn't think it would be reliable or better than what existed... definitely a vinyl guy.
The current AI hype wave has really hit a nerd soft spot - that we're steps away from AGI. Surely if a computer can make plausible-looking but incorrect sentences we're days away from those sentences being factually accurate! The winter after this is gonna be harsh.
> The winter after this is gonna be harsh. The winter is going to be warm because of all the heat generated by GPUs ;)
global warming is killing us all
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Current LLMs fail if what you're coding is not the most common of tasks. And a simple web app is about as basic as it gets. I've tried using LLMs for some libraries I'm working on, and they failed miserably. Trying to make an LLM implement a trait with a generic type in Rust is a game of luck with very poor chances. I'm sure LLMs can massively speed up tasks like front-end JavaScript development, simple Python script…
I had similar experiences: 1. Aasked ChatGPT to write a simple echo server in C but with this twist: use io_uring rather than the classic sendmsg/recvmsg. The code it spat out wouldn't compile, let alone work. It was wrong on many points. It was clearly pieces of who-knows-what cut and pasted together. However after having banged my head on the docs for a while I could clearly determine from which sources the code io…
This is probably why there’s such a divide when you try to talk about software dev online. One camp believes that it boils down to duct taping as many ready made components together all in pursuit of impact and business value. Another wants to really understand all the moving parts to ensure it doesn’t fall apart.
7 trillion is absurd. The recent advancements in AI are real, but not on the order of 7T.
I think the 7T number is his concept of what he can probably get a bunch of oil sheiks to give him.
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> With current tech there is, there can not be anything novel. I wasn't talking about current tech, which is obviously not at human levels of intelligence yet. I would still say that our progress in the last 100 years, and the last 50 in particular, has been astonishing. What's preposterous is expecting that we can crack a problem we've been thinking about for millennia in just 100 years. Do you honestly think that o…
> Do you honestly think that once we're able to build AI that _fully_ mimics humans by every measurement we have, What made you think that is measurable and if it is then we can build something like that ever ? I already linked https://garymarcus.substack.com/p/this-one-important-fact-ab... did you read it?
What makes you think it isn't, and that we can't? The Turing test was proposed 75 years ago, and we have many cognitive tests today which current gen AI also passes. So we clearly have ways of measuring intelligence by whatever criteria we deem important. Even if those measurements are flawed, and we can agree that current AI systems don't truly understand anything but are just regurgitation machines, this doesn't matter for practical purposes. The appearance of intelligence can be as useful as actual intelligence in many situations. Humans know this well.
Yes, I read the article. There's nothing novel about saying that current ML tech is bad at outliers, and showcasing hallucinations. We can argue about whether the current approaches will lead to AGI or not, but that is beside the point I was making originally, which you keep ignoring.
Again, the point is: if we can build AI that mimics biological intelligence it won't matter that it's not biological. And a sidenote of: even if we're not 100% there, it can still be very useful.
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IBM Watson was more than a couple years back. The Jeopardy event was in 2011. It's currently 2024. As for cars, I don't know what you're referring to specifically, and the hype is still ongoing as far as I can tell. It has taken 10+ years to get to present day, from the start of the "deep learning revolution" around 2010. I vaguely recall Uber promising self-driving pickups somewhere around 8-10 years ago. A main dif…
> Waymo alone services tens of thousands of autonomous rides per month (edit: see sibling comment, I was out of date, it's currently hundreds of thousands of rides per month -- but see, increasingly) But they aren't particularly autonomous, there's a fleet of humans watching the Waymos carefully and frequently intervening for the case where every 10-20 miles or so the system makes a stupid decision that needs human i…
What "critical" intervention rate are you talking about? What network magically supports the required low latencies to remotely respond to an imminent accident?
How does your theory square with events like https://www.sfchronicle.com/sf/article/s-f-waymo-robotaxis-f... that required a service team to physically go and deal with the stuck cars, rather than just dealing with them via some giant remotely intervening team that's managed to scale to 10x rides in a year? (Hundreds of thousands per month absolutely.)
Sure, there's no doubt a lot of human oversight going on still, probably "remote interventions" of all sorts (but not tele-operating) that include things like humans marking off areas of a map to avoid and pushing out the update for the fleet, the company is run by humans... But to say they aren't particularly autonomous is deeply wrong.
I would be interested if you can dig up some old skeptics, plural, saying probably centuries. May take centuries, sure, I've seen such takes, they were usually backed by an assumption that getting all the way there requires full AGI and that'll take who knows how long. It's worth noticing that a lot of such tasks assumed to be "AGI-complete" have been falling lately. It's helpful to be focused on capabilities, not vague "what even is intelligence" philosophizing.
Your parenthetical seems pretty irrelevant. First, models work outside their training sets. Second, these companies test such scenarios all the time. You'll even note in the link I shared that Waymo cars were at the time programmed to not enter the freeway without a human behind the wheel, because they were still doing testing. And it's not like "live test on the freeway with a human backup" is the first step in testing strategy, either.
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Also causing lots of harm, too, e.g. police departments using AI systems that get the wrong suspect.
Ideally your court system does not permit AI testimony.
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Imagine how much food you get for every person in U.S. with 7 trillion. You probably get some roofs too.
The US has -- if anything -- too much food. If someone is starving in America, it's 100% due to them having no interest in acquiring the free food that is widely available almost everywhere.
So you end up with a whole bunch of poor overweight people who need 4500 cals a day to sustain their mass, reporting that they have a hard time sustaining their diet. Obesity is a huge problem in lower income demographics, the same demographics that report high food insecurity.
Like, bro, all the sovereign wealth funds of the Arab Gulf hold less than $3 trillion, mostly in illiquid assets that you could take a 50% haircut if you need to liquidate. And after the $100b Vision Fund fiasco where Saudi Arabia was subsidizing "Uber for Dog-walking" startups for years (no thanks to Masayoshi Son), I'm not sure they're going to be keen subsidizing yet another bubble.
But, isn't that the default in VC? Aggressive, toxic optimism because it's not your money and you need to promise otherworldly yield to get capital.
The current AI hype wave has really hit a nerd soft spot - that we're steps away from AGI. Surely if a computer can make plausible-looking but incorrect sentences we're days away from those sentences being factually accurate! The winter after this is gonna be harsh.
Like, are we using entirely different products? How are we getting such different results?