The only reason any of us defend it is that's how we make money. Honestly a lot of people in tech should be a little ashamed.
I've worked in tech in banking, gambling, software development, internet service provision, and even the postal service. They were all extremely scummy compared to the sector I work in now.
"What did I do today? Helped a bunch of doctors get the information they needed to diagnose someone's obscure disease. What did you do?"
"Oh, I worked on ways to make my company's app more addictive to 12-year-olds, and look less like gambling."
I'm happy to be in the first group.
I think it's true that a lot of people are a little ashamed already, but that a lot of people in tech should be more actively opposed to what's happening. As a small example, part of the paycheck I get from placing hidden F_cebook tracking on my corporate website to analyze the behaviors of people who could not possibly consent, I donate to the EFF. Actually it's quite difficult to be politically engaged against abusive tech in the same way I may or may not be engaged in local politics, but money is one thing we all have.
Aren't we though? In my experience people that are super proud to be working close to things like adtech or social media are generally early-career, and haven't worked out for themselves yet how these things are a huge net-negative. At least you could make the argument that AI is more ambiguous.
And whether you're a data-scientist, application developer, or infrastructure engineer, the situation is the same. I think we'd all rather be curing cancer, working on climate change, maybe even doing meaningful work in the government sector. Those jobs mostly don't exist, and where they do: they don't pay market rates, and they aren't like other tech jobs where meaningful experience is much more important than things like academic credentials.
Regarding market rates, one could argue that tech workers are in a privileged position and should have the luxury of concluding that money isn't everything. But even without dependents, even with a tech salary, for lots of people housing is still pretty unaffordable, healthcare is nuts, retirement pushed back or seeming impossible. This kind of insecurity understandably leads to scarcity mentality where honesty/integrity and other ideals of how to be a morally upright citizen are on hold, and slimy behaviour or at least associating with slime is just normal.
Most money of any kind isn't clean if you really look into it, and it seems like there's fewer ways to earn an honest living in tech compared to say blue-collar work. Coding was still mostly fun and games maybe as recently as 10 years ago, but weaponized disinformation is bringing it closer to something like chemistry, where even if you're not working on nerve-gas you're probably still trafficking with adjacent big-pharma, oil companies, etc. But there's a lot more tech-workers than chem PhDs.
Shame seems like it's still required, but actually feeling guilty requires responsibility, and responsibility requires viable alternative options.
> The tech industry has long, long, long been a rapacious pump and dump scheme where the tech is barely relevant and most of the industry has been harvesting violations of social norms and regulations (tracking and ad tech, Uber, etc.).
The tech industry != the tech community. OpenAI is part of the tech industry, and I think you are accurately describing their behavior.
But the question was about the failures of the tech community, and I think those are different and exploited by the tech industry.
Basically I think a large fraction of the tech community is composed of gullible and easily propagandized software engineers who believe in sci-fi fantasy and consistently overestimate their own abilities and understanding. That kind of person is incapable of "clarify[ing] what [OpenAI's] products really are," because they're some of the people with the most personal (ideological?) investment in the hype.
Honestly, when was the last time that we got a new technology that was as user friendly as talking to an LLM for the average user?
For most users, its magical. It doesn't take marketing for them to start thinking this thing is "reasoning" or "thinking" after just spending a few minutes talking to it.
So personally I think the problem is less the marketing (not to downplay that), and how the industry has gravited towards it. And the lack of proper education on what is not.
We have to properly educate on what it is not actually doing while at the same time it sure appears to be doing exactly that since it is so good at faking it.
This even exists within tech circles, we see some of it here.
Even many smart technical people here on HN fell for that.
It's constant struggle to remain aware of what it really is under the covers because we're just so used to engaging with language creation entities as if they had minds
It's only when you need to iterate on quality and go from "amazing" to "correct" that you start seeing how limited they are.
I'm not sure, but the first time may have been around 1967.
> For most users, its magical. It doesn't take marketing for them to start thinking this thing is "reasoning" or "thinking" after just spending a few minutes talking to it.
People had the same reaction interacting with ELIZA[1].
Compare with an ordinary UI where it's clear that it only does one kind of thing. It doesn't have to refuse to do things when it's obvious that it doesn't do what you want.
Something like Copilot or Cody is more clearly a programmer's tool. That's still an extremely broad scope (any programming language? Any domain?) but it's at least less prone to imaginative flights of fancy.
Replying with "Sir, this is a Wendy's" is more reasonable when it's clearer up front what you do.
This is mostly an article about how unreleased features in software that are in development still don’t yet work in production and proving that the unreleased features are still unreleased, then dressing it up as a scandal.
acting like LLMs are responsible for the reliability of identifying the source document is disingenuous when a large part of the value of an LLM as an asset is its role laundering other peoples' intellectual property without attribution.
They accomplish this by training/fine-tuning the LLM to output special tokens, or special syntax, which is interpreted in code to perform some action, such as calling out to an API. This is how chatgpt and Gemini are able to automatically search the web and generate images and such. Yes, LLMs just output tokens, but those tokens can be interpreted to perform actions in code.
Don't modern LLMs like GPT4 have access to load web pages?
The downside is it will slow down the output I guess.
Most of the tech community is as clueless about ML as the general public.
One would imagine that the FTC could require warning labels of "AI" products: "This product will confidently lie and cannot be trusted for factual information," etc.
This warning would be especially useful for decision makers in commercial and government domains.