I worked for a hardware startup ten years ago now, and a big problem that was rampant at the time (and seemingly has only gotten worse) is that basically the Contract Manufacturers (CMs) in China take the BOMs and plans they’re given, and since they already have the molds, the same product will mysteriously be produced with a knock-off name, within weeks of your product being produced in china. At the time (and still) I didn’t know enough about whether the CMs are doing it themselves or they’re selling the information to a company to produce, or what, but if you want to manufacture something in China, you’re begging for it to be copied immediately.
While I have my own disdain for the current length of copyright law, it’d be great if China at least had some variety of it. This sort of crap may be an eyesore for the big companies, but its a death-knell for small startups, and Amazon is enabling it.
I’m using the term “stochastic parrot” exactly as the author of the paper did, and incidentally an interview with her was on HN yesterday. https://news.ycombinator.com/item?id=48805401
To wit:
> Another one is that “stochastic parrot” got picked up and interpreted by other people as a minimization or an insult. It was not meant that way. Other people might be using it that way, but that’s not how I intended it, because it’s just a description of what these systems actually are. To see it as an insult requires either the belief that the large language model is the kind of thing that can take offense, which it isn’t, or that these large language models should be understood as steps toward this grand ideal that I don’t hold of artificial intelligence.
> What I have been doing in many places—the octopus thought experiment, stochastic parrots, the phrase “synthetic text-extruding machines”—it’s all about trying to make vivid to people who aren’t in the business of building language technology what these systems actually do, which is not the same thing as insulting the systems or insulting the people who like the systems.
Upvoted you, of course; but it’s worse than that. It’s vibes being marketed as correctness. To the lay person (and unfortunately, to more than a few folks who should know better), computers don’t “make up” information. Maybe some good (in some weird way) that comes from all of this is that we stop using LLMs for recitation of facts.
> nowadays AI chatbots and coding agents routinely assume they need to get up-to-date information in other ways, via web searches and other tool calling.
So I don’t see accuracy declining at least for programming.
How do those chat bots discern that the ‘web searches’ they’re using are returning human generated information only that’s been vetted instead of LLM output?
> The only thing that matters is if LLMs with sufficient scaling can become frontier AI researchers kicking off the exponential. Everything else is transient
As long as the term “AI” means by-and-large LLMs with additional features sprinkled on top, the answer is no. More likely (without careful vetting by the folks aggregating these models) is that the quality will go down as more and more AI-generated output gets subsumed into these models.
Even without that particular problem, LLMs-as-AI can only give us probabilistic outputs based on inputs; and by definition they’re reliant on humans to provide the training data for their model. Without specialized knowledge or training on that knowledge (And even with it, viz. Meta’s engineering), we don’t have to worry about AI itself. We do have to worry what investors who are looking for outsized returns will do to get those returns, job market be damned.
The problem for us isn’t that AI will take our jobs; it’s that snake-oil salesmen can sell the idea that AI will take our jobs, investors buy into it, companies try it, fire their folks, the snake-oil salesmen IPOs, the companies that bought into this idea implode in some form or fashion, and the salesmen have already taken the money and ran. Of course, we still lose our jobs, but maybe (!) we get them back when this all fails?
Having gone through the court system for a civil matter, I can tell you that “get a judgment” is a lot of time and money in even the easiest of cases, and let’s go at it from the person’s end who has to fight this, and let’s just focus on cases it’s a big tech company vs. a mom-and-pop or just a random person — who hasn’t actually done anything wrong. The big company has the money to sue, and now all of a sudden if you don’t want a default judgment, you have to spend money on a lawyer to fight the lawsuit, and guess what? It’s not as if it’s free to fight a lawsuit. It’s expensive to fight it.
The merits win in a lawsuit only if you don’t run out of money first.
I recommended an elastic demo for a client that would be well served by Elasticsearch. The Elastic sales folks completely torpedoed the presentation by trying to focus on their AI “capabilities” and not on the recommended talking points. This was 2 years ago.
They got duped into it partly because of the enthusiasm and demoware shown by folks like the ones that topped HN with their exhortations about the wonders of AI for coding.
We’re not innocent bystanders here, and it’s important to recognize that. Our hype added to the hype. Our optimism added to the optimism. After layoffs due to Section 172 and interest rates going up, technologists were looking for a reason to be in-demand again, and generative AI as a platform specialization provided that.
We can’t now criticize CEOs for being taken in by the same enthusiasm we pushed for our own purposes.
> Like businesses don't care about the tool/tech itself, how do I find and approach them, and for which niche.
You probably don't realize this, but you're asking one of the hardest questions when starting a business, and one of the questions others are least likely to be able to answer for you.
"finding" a niche, and connecting to the business folks inside that 'niche' is hard, and is inherently a personal journey.
There's an old writing adage, "Write about what you know", and the same adage works in business: Do business with what you know.
Your question goes into another issue that you have to resolve when building a business: going into a platform specialization necessarily means folks know about that platform or they know they need you to solve a problem they have with that platform.
In general, there are two ways out of each problem:
1. Build an ecosystem with DuckDB at its center that solves a business problem that a particular niche cares about.
2. Build a reputation solving problems with DuckDB that would attract those that know they have a problem with DuckDB.
Honestly, best of luck here, becoming successful at business is hard if you're not already in tune with why folks buy and ensuring you're selling something they want to buy from you.
> You're optimising for quality, where as companies optimise for some balance of quality and cost.
To be clear; in our current stage of capitalism, companies are largely optimizing for how much quality they can sacrifice before they lose too many customers to justify the sacrifice in quality.
Companies are optimizing for cost, and part of the backlash against AI is the backlash against the overt quality sacrifices made by companies.
When I use a computer to do work I want the computer to be right. I want to be able to trust the computer. With the inherit non-determinism and probabilistic nature of generative-AI, that fundamental reason why I engage a computer is lost.
If the spreadsheet is wrong, it’s because the math is wrong, it’s because I made a mistake. It’s not because all of a sudden the computer decided the nature of algebra should be different than it is.
Part of the reason why humans are rejecting AI is that we are putting it in places where it makes no sense, or places where humans prefer a human in the loop, there are plenty of places where machine learning algorithms make sense, but customer service is not one of them.
We are not at a point as a country to have a serious discussion about governance.
The administration in charge (as recently as yesterday) still blames Biden for issues happening on their watch, even though he hasn’t been in officer for 16 months now.
This is not an administration serious about governing, and until we have an administration serious about governing and taking responsibility for their actions, we will continue to have this situation where half the country blames the half not in power for decisions it is making.
Congress of course is somehow worse, as instead of treating the executive like a branch of government they are meant to have oversight of, they abdicate their oversight role and roll over to the wishes of the present administration.
The net effect is those of us that live paycheck to paycheck (which is 2/3rds of Americans) are caught in the middle of a situation that would be deemed fantastical and not realistic to write about if it was described in a dystopian novel.
The Iran war continues with no oversight from Congress, and no authorized war while we pay the price. Vote them all out.
The issue is that folks are substituting judgment and critical thinking for “vibe coding”, and having it spit out 10x more code than you could in the same amount of time is addictive and feels easy. The long term impacts and the issues of trusting the non-deterministic algorithms seem to be ignored by the folks addicted to the easy production of code. That is problematic and over time will come back to bite all of us.
In this case, not only is it a theory that fits the facts, it’s likely.
A content farm having a disparate range of websites, for the sole purpose of SEO needs to be able to create engaging content quickly. AI generation allows for that; and by purposefully keeping a name that we can try to trace back to a real human as the author, the post itself lends credence to the theory that it’s AI generated.
The comments on this HN post nicely color the problem Tim points out, from the comments that assume the exceptionalism of the USA, to comments that say “stay in Canada”, to comments that call the post “moral preening”.
I grew up in a very conservative household, and until the tea party/Trumpian alliance would have called myself a small-l libertarian.
Now? I won’t vote republican for a whole host of reasons, not the least of which is that it rhymes with the worst parts of the political parties we destroyed in world wars.
There’s something new almost every day that should, in a sane culture, cause folks to abandon the Republican Party en masse. Today’s example? The 1.776 Billion “anti-weaponization” fund that is a slush fund for Trump and his allies, including folks that stormed the Capitol on January 6, 2021. The grift of this administration is shocking, but the fact that rank-and-file conservatives aren’t abandoning it by the millions gives away the game. It isn’t about principles, it’s about one party winning, no matter what.
We used to fight for what’s right, but we have become the villain. Tim is right about the declination of America (realizing his title is a double-entendre), and I can’t help but wonder if there is even a line that Trump could cross to the modern “Republican” party.
If this wasn’t so last-stage capitalist dystopian, it would be funny.
“Let’s slap AI on it and see if we can make money” is…. Depressing as a world view. Besides the sheer amount of computational power it uses to produce a worse result than dedicated humans, the fact that if this wins out our future is promise to be replete with humans farming out any part of humanity they can to a dataset that promises to deliver a median outcome at the price the market is willing to bear to those that don’t care, from those that don’t care.
我不得不弄清楚是有人写了一些东西还是让人工智能为他们生成了一些东西，这让我筋疲力尽。
自从生成式人工智能出现以来，阅读这样的文章变得不再那么令人愉快。文章中没有感情或内心，这是我可以阅读标题、阅读文章并想知道为什么我花时间阅读它的情况之一。