badgermurphy

That seems to be the preferred road to riches today. Make an illegal business, make money as fast as you can while you trip up the legal system as best you can to maximize the time until you have to pay the piper, and if you can make enough before the hammer drops, you pay off the hammer and keep doing it. If not, you pay the hammer its cut (the slap on the wrist fine), then make off with the rest.

That depends on how carefully youre checking and what youre checking for. I’ve seen debugging a single problem with a project take nearly as long as the whole rest of the project, so I don’t find that claim broadly true. Again, reality supports my observation because there are no companies turbocharging their productivity this way, or the people selling the AIs would make absolutely sure everyone sees that company doing it and its rising revenue and value to match.

Because the LLMs are error-prone, and the the types of errors they may make are not limited to the types a human is likely to make, you can’t do the standard supervisor “glance, nod, say it looks good” routine you might do with an apprentice’s work on a basic task, but LLMs can make any kind of mistake anywhere, so I have to use a fine-toothed comb or risk rookie mistakes on even rookie work submitted with my name on them.

Like I said before and still contend, AIs today are great for tasks where mistakes are not events and high accuracy is not critical to success. I think for anything not like that, they’re bad and are constrained by their very design to stay that way. They are no different than any other tool: good at a specific type of task and bad at everything else. _Un_like any other tool, though, widespread attempts are being made to use it for a broad variety of things that it is bad for in one or more ways.

ETA: In this post, “bad” can also include inefficient, overkill, or wasteful, like killing a bug with a bomb.

I do see what you mean. You’re saying that AI allows you to do something much faster or perform more work in the same time by essentially doing parts of it for you. While that is true, it is only so for certain values of “more”. For example, I could get an AI to write most or all of a configuration for a server deployment for me in about 2 minutes, instead of maybe an hour. However, if I do not spend about an hour reviewing its work, I will get a failure rate dramatically higher than if I had just done it myself. Similarly, if I decide to start pencil-whipping my work to do many times more work in the same time, I could do that and get much more deliverable work out the door. Whether or not you would call that more productive depends on what factors you’re accounting for. So, for applications I am familiar with, the primary value proposition of LLMs is that I can maximize quantity over quality even more than would be possible without it, by allowing me to not even do much of it. The overall quality of the output will likely improve incrementally over time as it has so far, but given its current design, its propensity for errors and hallucinations can only be mitigated, never eliminated or made trivial.

Because of this limitation inherent in its design, I don’t believe it can ever have real applications for tasks where quality is important. That said, there are many tasks where it is not. For example, fuzzy correlations run over vast data sets can be riddled with false correlations and still be useful, like Flock does. It is also effective at filling “gaps” in human knowledge, such as a mathematical truth that we have yet to devise a mathematical proof for, or structural designs that a human could come up with, but never did because of how unintuitive the solution is to humans. Basically, any problem that can be solved by slinging tons of shit at the wall to see what sticks is fertile ground for AIs to help us.

I, personally, enjoy creating something much more than I enjoy proofreading someone else’s creation for the same amount of time. I, and I’m sure many, many others, don’t want their 40 hour work week of designing things and solving problems to be replaced with 40 hours of proofreading and debugging to achieve a comparable amount of productivity. Once again, the proof is in the pudding. Tech workers (and likely all workers) like tools that make their lives easier. If these tools were doing that, our bosses would not need to force us to use them; we would want to use them so we can go home early. I just simply don’t agree that they let most workers get more done in less time, and I cite the fact that nobody is, when you actually measure it rather than going on feeling, getting more done in less time. If this were happening anywhere in any numbers, these AI marketers would be shouting it from the rooftops. As mentioned above, there are exceptions to that, but they are just that, exceptions.

It makes sense that they did away with that button, since they long ago did away with the thing that button actually does.

At least in the USA, livery workers were required to have a “medallion”, which is essentially an onerously expensive license with the stated purpose of restricting competition to the point of profitability for those workers. There was no loophole, and it was ridgidly enforced.

Then, Uber came in and started violating that law so flagrantly that governments seemed stunned into inaction. The government corruption comes in where they then decided not to enforce those rules on Uber for some reason around the same time Uber aggressively contributed to political campaigns around the world.

I went down this line of discussion to point out that Uber is not a good example of organic business success that started out financially underwater. Their ethics or lack of them is an aside to the discussion, but agreed, they are unrepentant scumbags.

Everyone’s claiming that! Every invention allows someone to do something that was previously impossible. Humans couldn’t travel faster than a horse can run until the car was invented. Humans couldn’t see microbes before the microscope was invented. People couldn’t drag more than 10x their body weight until the wheel was invented. People couldn’t catch a marlin until the net was invented.

I am trying to resist the notion that youre being willfully obtuse if you aren’t equating invention with increasing what is possible.

In my country, we could do so by enforcing laws that are already on the books that exist for this exact purpose. You start with the Sherman Antitrust act and break up any company with more than X% market share in any market they operate in. You enforce the Glass-Steagall and Frank-Dodd acts forbidding conventional banks from investing in speculative markets, and then sit back and watch the economy work like it does in the textbooks.

Of course, all this is predicated on the government functioning. If it does not, the laws are irrelevant and the markets are just lawless warzones with non-corporate entities (like individual humans) acting as the blades of grass underfoot. So, step 1 is to restore the government to the point where the will of the governed is acted upon. Step 2 is the stuff I said above.

I’m not even drawing a conclusion anymore. I’m asking for anyone anywhere to point to the previously impossible thing that AI lets me do. Even if that thing is of questionable utility, what is it? I just want an answer to that question.

Then, we can start talking about if that’s worth the cost. Most previous inventions cost someone’s life savings, or corporate focus on technology. This one’s costing us the GDP of South Korea, so it better be good. Like “the microscope” or “the wheel” good.

But some could, and they could articulate their reasoning. “Machine learning” is not even necessarily a path to anything. Its hard to argue that against your example, which is literally unlocking secrets of the universe.

That’s a false dichotomy. I would rather have my subsistence at the whims of the entirety of the country’s economic activity than any government, which is one election away from changing its mind about how much to pay me. Markets are constrained by their environment. If my employer cuts my pay, I can leave and go elsewhere.

The billionaires are responsible for the fact we’re even talking about needing alternative economic systems in the first place. They and the governments are the same people, or at least their representatives.

Your example is another one that illustrates my point. When they invented the microscope, they could instantly demonstrate its utility in it’s ability to observe previously invisibly tiny things. The following inventions that enabled are great, but the microscope itself was great, too, and all could see why.

AI is almost all hype. When anyone asks what it can do that was impossible or too difficult before, people point to what they think it will do, yourself included.

So, again, what previously intractable problem does AI solve? Please do not include answers of problems you think it might one day solve. Like the microscope can immediately let you see tiny things, what does the AI let you do that was impossible before? Bonus points if what it does is worth spending a planet’s worth of resources on.

Uber is an example of government corruption leveraged to overthrow a market. The livery industry required special licensing, so only licensees were legally allowed to do it. Uber then started doing it without the $1 million per license overhead, bribed the government to ignore that, and naturally undercut their competitors that had huge government-imposed overheads that Uber didn’t. They were initially unprofitable because they had to compete for business, then became profitable when the government helped then destroy their competitors and structure their pay so as to have no minimum wage.

If you only look at modern examples, youre looking at a funhouse mirror reflection of real business operations. Running at a loss for a decade while you create a monopoly is the path to business success today, but this is not a functioning market. Real business success involves making money right out of the gate or at least right after. If your business only works if it is a near-monopoly, it is not a sound one.

Elon Musk, the apparent thought leader of the tech sector and mover of markets, claims we will be in a post-scarcity AI-fuelled utopia by 2036. Since his words move the market, clearly the market heeds his words.

In that context, it seems quite modest to expect obvious material gains from using AI by now. When the car was invented, you could prove its utility immediately by getting between to places faster than ever before. When space flight was invented, you could prove its utility by relaying messages around the planet. When insulin were invented, you could prove its utility by curing people bed-ridden awaiting death.

My point is, real paradigm-shifting inventions demonstrate how they’ll do that right away, in an undeniable way. So, AI is either not a paradigm shifting technology, or its not done yet and people are buying a half-baked product.

That is one thing it could look like which is captured in my examples. We dont see any company like that. Remember that stock valuation is not, in the present day, correlated to any business metric. There are companies with huge valuations that have never netted $1 (like these AI companies, for example). So, even in your narrower example, there are no companies I have heard of that meet its criteria.

I’m saying that if the amount of money spent on AI were spent on any other global societal problem, it would have been over-solved 100x by now.

The marketers are not using the word “sapience”, as that word would require defining for the layman. They are claiming AI will usher in an era of human prosperity by taking over our work and big decisions to do them more efficiently–tasks that sound to almost everyone to require advanced decision-making and thought, the hallmarks of sapience and cognition. This is largely besides the point, though.

I think that if this were successful, you would see at least some early adopting companies coming up aces. Some company would have double the productivity with half the workforce, and use that to absolutely devour their market segment by undercutting everyone else on price because their operating costs are so much lower than the competition. I’d see a software company adopt breakneck software release cycles with substantial, material stability and performance improvements with each. I’d see some company anywhere suddenly begin to outperform their former selves and be able to convincingly point to AI as where their improvements came from. The only one like that I can think of is NVidia, who are rich strictly because they’re selling shovels in the gold rush.

I dont believe it requires sapience; that is what the marketers are saying. The AI boom (and many historic boom cycles) is predicated on marketing and sentiment, not facts and data. That is why they always pop; the facts dont back up the hype.

The fact that your search results turn up results that align with the marketing is just the marketing working.

Look–neither of us are data scientists, but we do have eyes. If this is working, where’s the company with runaway success creating unimagined leaps in productivity and technology? We’re pouring a whole planet’s with of resources in and nothing much is coming out. If we spent this much on world hunger, everyone would be obese by now.

I think that is where the hopes and hype point. The evidence, that which is gathered through controlled studies, points to an upper limit to this technology that does have emergent properties, but not ones that amount to cognition. That evidence also points to other hurdles, such as cognitive damage to the user and context windows nowhere near that of even a simpleminded creature, let alone a sapient one like a human.

The core problem is that these models are fixed; they are the same on day 1000 as they were on day 1. All their “learning”, as it were, happens in training before it is released. Everything it appears to learn after that date is contained in the rolling context window. Since they already have literally all the RAM they can get their hands on and are still at least an order of magnitude away from where they need to be on that, this technology either can’t do it or, at best, is so inefficient an approach that it can’t be done with all the planet’s resources.

Sometimes, especially in abstract constructions like software, you can start down the wrong path early and have to start over, because there is no path from where you are to where you need to get. In this case, they may have done that to the extreme, blinded by the lucrative prospects.

I think there may be some confusion about scale here. His bubble encompasses the globe and is heavily leveraged globally. This time, I dont think there are any “other people” literally anywhere with reserves of money that could be conned into paying for this.

That analogy makes the assumption that LLMs are dormant baby AGIs. There is no evidence that AGI is just a bigger and better curated LLM.