Yet the author thinks ‘ChatGPT, LLMs and all that crap’. Unbelievable.
I use LLMs everyday. It has not only boosted productivity, but most of all, it made workmore fun.
why is it incredible? we followed a bunch of lists of laws until we got ELIZA on virtual steroids ... after we cracked some milestones in hardware development.
people work towards that. money is put into that. we'd have cancer solved if the same amount of people and money flowed into the entangled fields.
next step is digital steroids (signal processing) and the physical level once again.
software-wise, the current and next generations of coders won't do anything "incredible" anymore. too few approaches on the playing field, no indie thinkers, and the capital is literally dumb.
(enforced) conformity has too many downsides that can only be balanced via variety in the wide wild; but that means cumulative competition, which was handled via sabotage ( a good analogy is doping in sports, US influence and the actually incredible cognitive & logical weakness as well as the cowardice of almost the entire rest of the world )
so I cannot agree, people give too much credit indeed.
I see these things and think, this is incredible, machines seem to be approximating or emulating conscious thought. There must be so much we can learn about ourselves, and so much they can do.
You see the same thing and say meh, useless pattern matching, what’s the point, spend the money elsewhere.
I wonder why we have this different perspective? I’ve seen these two reactions again and again—I suspect they evince two different worldviews, but I don’t know what the correlates are. I don’t think it’s techno optimism/pessimism, because I’m profoundly worried about what happens to us. It’s not purely an age thing—I’m not young. I see it on here all the time so I don’t think it’s field of work. So what is it, I wonder?
Potential litmus test; They like videoclips from Coldplay where every frame is drawn with real crayons.
Then it's hard to value a machine that can not feel pain or effort and just generates. It's not fair it didn't have to suffer, and then flip the arrow to say, therefore it's not valuable.
Happy to becorrected. I'm also very curious what mental models are behind such big differences in perspectives.
At the end of the day, we build (or should build) technology for the betterment of society. If at the end of the day, large swathes of the population are unhappy or upset at a given technology, that in and of itself should be a sign that something is wrong.
Obviously LLMs aren't going away so I think we should listen to the detractors and try to better understand how we can steer this technology for the better.
Before Google switched to Gemini AI it didn't offer making mustard gas recipe when prompted for cleaning advice.
LLM are tools, and highly unreliable one at that. I don't fear it. I fear the idiots that think it is a viable replacement.
> if LLMs were really that miraculous, we wouldn't have such detractors
I don't think you thought this through.
I also agree completely with your point on the socioeconomic context of LLMs causing the most contention and that a deployment giving more autonomy and control to its users would have a much more toned down reaction. Like, I don't really notice a strong reaction against locally deployed LLMs.
Productivity and efficiency gains at the short term expense of inefficient jobs have always made people upset, so taking only that into consideration is wrong.
> The Swing Riots were a widespread uprising in 1830 by agricultural workers in England against the mechanization of agriculture. The riots were a protest against the introduction of threshing machines, which had been increasing since the end of the 18th century. The threshing process was labor-intensive, and before the machines were introduced, it employed about 25% of all agricultural workers. The introduction of the machines gradually unemployed a large number of agricultural workers
> Jethro Tull invented the seed drill in 1701, but it was not immediately popular in England. Tull's ideas were controversial, and his theories fell into disrepute. Tull's servants resisted his new methods because they threatened their position as laborers and their skill with the plow
Personally (as someone who's worked in a ML-adjacent field for a number of years, and so seen the various ML waves), LLMs have so far been a resounding disappointment in comparison to other ML tools (the only cool thing I've seen come out of it was Google using LLMs to generate code to try to futz code even better), and have shown that while chasing the last 1% accuracy is interesting from both an engineering and mathematical perspective (which are entirely legitimate things to be interested in, though sadly they seem to have been lost to hype), simple-to-explain-and-understand less accurate methods (like a decision tree) are both cheaper to run and more predictable (which is what you usually want).
LLMs enable me to write more code than I ever have and in languages I don’t know. It’s a wonder.
That said I grew up with an 8-bit machine buying games on cassette. There were never updates and rarely ever a serious bug.
Yes programs were tiny, but they were also handwritten in assembler. Quality has gone down in software with the advent of modern tools as surely as it has in furniture with the introduction of the table saw.
Who geniunely thought that? Eliza could almost fool a human and that was in the 1960...
LLMs are a neat thing for doing some tedious task but I saw them making plain stupid mistakes that makes my skin crawl.
That aside, nothing you said about LLMs doesn't apply to humans as well.
Humans make mistakes in a diverse way, that makes groups of them much better than an LLM that will amplify its own mistakes.
Computer programs has to be extremely better than a single human since they lack the group dynamics of humans, so as long as you compare single humans to AI people will rightfully demand more from the AI.
If all humans ran the same program and gave the same answers then humans would be way less useful, that is where LLM are today.
IMHO, LLMs will grow immensely in power once someone comes up with the right way to wrap them in a negative feedback loop to keep the hallucination "gain" under control. The result may no longer be recognizable as an LLM, but I'm pretty sure that the GPT concept will be recognized historically as the foundation for what follows.
That's a matter of character. Other people team up and up your LLM-augmented productivity tenfold without LLMs.
I'm mostly solo myself and did get a bunch of good shit out of LLMs but that has nothing to do with them being good. Search Engines got me the same results after only little more time.
The one "good" thing about LLMs is that you can get algorithmic, logically straight, safe & sound FACTS about topics like how the insurance mafia monetizes on & cooperates with various other entities to create crisis, or how the smuggling of weapons is done and used to increase premiums on & thus prices in trade via tankers & shipping . Or why labile prices on gas are complete fucking bullshit.
And the list goes on and on and on. But who in the respective, theoretically respectable positions cares?
Hallucinations have been build into LLMs on purpose. Above is just one of them. And it's just cooperation of magic money & enforced conformity of engineers.
Can you share some of your prompts for the topics you listed? Asking about it straightforwardly doesn't get very far.
So we can stop making it bigger/smarter and instead focus on making it more acessible.
Our understanding of consciousness is about to undergo a revolutionary transformation, and a lot of people ... aren't going to like that, either.
You can make them deterministic by fixing the temperature. But even still, there is some intrinsic error rate: it's like an oracle machine where some subset of queries will simply give you the wrong answer... which is just like humans (how many times have answers on StackOverflow been wrong), but it's definitely not like "conventional" programs.
I guess just as people had to get used to "trusting" computers, I think there's probably going to be another adjustment period of "untrusting" them and finding the right balance of "trust but verify." I think this is also mirrored in adoption patterns: mathematicians seem to be more open to using LLMs because they're used to not blindly trusting supposed proofs. On the other hand, it's well known that writing code is easier than reading it, so developers are a bit more wary.