remember, when you interview for a job and they ask you, "do you have any questions?", you ask;
Sure, and at that level of accuracy it's also a description of how humans work. I didn't invent these words myself, I'm just stringing them together based on a stochastic process my brain was trained into.
Like LLMs, some of my speech is semi-random initialization (dada wawa googoo), some of that is mimicry (some of that is mimicry), some of that is reinforcement learning (downvotes incoming), and some of that is the output of a subprocess that uses the same systems prompted at the meta-level and without verbalization (maybe they won't get the analogy between thinking and LLM scratchpads... how about I use this space to clarify).
Calling an LLM a stochastic parrot has the same social-emotional role as calling a human an animal. Yes, it is correct. But people can infer the connotation.
I am a very lazy man, micromanagement takes so much fucking energy. Hire talented people, give clear and unambiguous guidance and trust them to do the work. It is amazing how easy management can be when you don't get in the way.
Being in leadership is way easier than being an engineer and the pay is better too. Some people really overcomplicate shit.
It's going to be a wonderful time to be a Freelance Senior Developer and above in a few years.
Aah, thanks. I really like the idea of this QUERY method.
Tbf, LLMs may be comparable in complexity to their specific brain.
The mistake has been thinking this implies LLMs can never do X task
As this article points out, an LLM can spit out chunks of regurgitated code that it scraped from the internet, but that does not make the LLM a programmer. The resulting output is an attempt to find an existing pattern in the database which fits with what the user has asked for, but it is not a product of actually understanding the use case for the code. It is just statistical correlation.
So, sure, an LLM can be set up to generate output related to X task. If you can collect and clean data that can be used to train the kind of output you want, it should be able to produce an approximate facsimile of the results you want. Is that valuable for your use case? Maybe.
We're still just talking about what is essentially a complex search function. The statistical model returns results from its database that correlate most closely to your input. That does not mean it returns the right answer. If there is no good correlation, it will still return a result.
As long as you understand that the result you get is just a correlation based on your input and may or may not be relevant to your specific problem, and you are not fooled into believing that the LLM actually understands what you're asking and produced a result by "thinking" about it, then you might be able to use an LLM as an effective tool - to search a large collection of information for something that is relevant(ish) to what you're asking for.
The real mistake has been broad misunderstanding of what LLMs actually do, and trying to use them as general-purpose problem solving tools (or worse, as accurate and reliable sources of information).
Not really.
A machine learning model is a computer program. It is fundamentally a math equation, which we understand completely.
A living brain is not fundamentally a math equation, and is not purely a statistical model, at least not in any empirically demonstrable way. We don't understand completely how it works, but we do know that it's more complex than what you're trying to imply.
The comparison is not valid. Machine learning models are not an equivalent to a biological brain.
Not in my experience.
They have failure modes that are easier for humans to spot.
invalid criticisms like “they are just stochastic parrots”
That's not a criticism per se, it's a description of how they work.
Probably fine if you review the code carefully. And if you're working in a domain that AI is decent at (e.g. web stuff). But even if it wasn't it doesn't mean AI cannot program.
If you’re letting your engineers find uses for it instead of constantly demanding that they generate lengthy “user stories” and decision documents and deferring thinking to agents instead of quickly planning stuff out using their experience then you’re probably quite an outlier by now.
This is true. But it doesn't invalidate any of my points. Humans have unique failure modes too.
This is like saying "but it is just a sophisticated network of neurons. All it can do is transform input signals into output signals. That's what it does."
Let them fail and then scramble to rehire.
They are not the automated from 0 to 100 coders that some people claim them to be. But they are quite capable, definitely much more capable than what anyone could have imagined ten years ago. Given well defined problems they can excel at even relatively complex tasks. I pointed Claude at a latex file of a somewhat complicated nonparametric statistical estimate calculation to look for any mistakes and it was actually able to find some. I then pointed it at a code that replicates the calculations and it was also able to correctly identify some issues with the code. I think this is the way one should use LLMs, not let it loose on coding tasks. In the former way you won't even be able to burn through your first tier account quota where as in the latter the LLM will likely end up getting in weird loops burning tokens like there is no tomorrow. Also this method of sane usage of LLMs is much more suitable for open local LLMs. I don't think there is any doubt anymore that LLMs can be very useful tools, not just for doing stuff but learning it too. People should move past the stage of invalid criticisms like "they are just stochastic parrots" and move to more serious matters like environmental impact, greedy fucking CEOs pretending LLMs are replacements for humans, degredation of skills, getting lazy at checking AI code, ethics of capitalizing on collective human knowledge and the unsustainable AI bubble that tech companies are pushing for.
part of the issue as well is that when they get something completely broken, people just re roll the output until they get something that’s broken in ways they don’t notice. Or re roll parts of it, or tell the system to judge if the output is broken and re roll the parts that it judges are broken automatically. Or increase the size of the context window to get it closer to that upper limit of accuracy.
All this together can get a more functional output with less effort, and as people find these tricks it gives them the illusion of an upward trend in capability, like this is all solvable issues that will improve as time goes on. Big problem with that though, theses tricks and methods explode the compute cost rapidly. That’s all fine and dandy when everyone is getting their compute costs for these tools subsidized by these model providers, but eventually they will need to charge the real cost of running this. The compute providers that host the model providers are also running at a loss, trying to help grow the market segment and maximize their market share. And then places that have the datacenters in them are giving tax breaks and discount utilities to attract new construction.
Everyone except the people making the chips is selling at a loss, and as people pile on usage to make up for the fundamental limitations of these systems, the demand balloons, validating to the providers at all levels that this is a growing market they should invest more in to.
But eventually… they need to make money. The bill comes due on all the debt and investment. What happens to the people who have fully embraced these to run their businesses? Or to all the people who have built their skill set around using these systems? It’s a crisis, a series of crisis, each time a debt wall gets hit by someone in the supply chain. A half decade of technical debt that just got really expensive to deal with, and not enough experienced people to handle it, since all the grey beared retired and not enough new people got brought in to replace them because the entry level work was automated.
Glad to see nore progress on this, but damn, it sure is slow goings. Not to mention that there's been an obvious need for this for at least a decade.
In tech leadership. I don't have to push it. My talented engineers took to it immediately.
They learned quickly that it is a tool. Instead of using a shovel and a wheelbarrow, they have a backhoe now. If you don't know how to dig a hole, the backhoe is just a way to make a mess faster. It doesn't replace intelligence.
They can use it to do the scutwork while they focus on the important stuff.
The duds are still typing shit into spreadsheets and emailing them as attachments while their coworkers are getting stuff done.
It is a tool. You can learn to use it or you can just be mad that it exists. In either case it isn't going away. Like the telephone, the car, the computer, the internet, it is here to stay.
I would love this. Had an excellent search system that required no server state... But i could only use POST for large requests. Which was a pain.
Now keep doing this for months, with non-trivial software that other people use.
Um… but it is just a sophisticated statistical model…
The mistake has been thinking this implies LLMs can never do X task, and using it as a catch-all argument for any value of X, but it isn't a good argument because it has been wrong for most of those.
This is very obvious unless you are in tech leadership, in which case your job is now to push this at all costs and suppress dissenting voices.
I missed it at first myself, but it’s right near the top under the title and author list: proposed standard.
