Comment by frakt0x90
4 days ago
To me, using AI to convert an idea or paper into working code is outsourcing the only enjoyable part of programming to a machine. Do we not appreciate problem solving anymore? Wild times.
4 days ago
To me, using AI to convert an idea or paper into working code is outsourcing the only enjoyable part of programming to a machine. Do we not appreciate problem solving anymore? Wild times.
i'm an undergrad, so when i need to implement a paper, the idea is that i'm supposed to learn something from implementing it. i feel fortunate in that ai is not yet effective enough to let me be lazy and skip that process, lol
When I was younger, we all had to memorize phone numbers. I still remember those numbers (even the defunct ones) but I haven't learned a single new number since getting a cellphone.
When I was younger, I had to memorize how to drive to work/the grocery store/new jersey. I still remember those routes but I haven't learned a single new route since getting a smartphone.
Are we ready to stop learning as programmers? I certainly am not and it sounds like you aren't either. I'll let myself plateau when I retire or move into management. Until then, every night debugging and experimenting has been building upon every previous night debugging and experimenting, ceaselessly progressing towards mastery.
I can largely relate... that said, I rarely rely on my phone for remembering routes to places I've been before. It does help that I've lived in different areas of my city and suburbs (Phoenix) so I'm generally familiar with most of the main streets, even if I haven't lived on a given side of town in decades.
The worst is when I get inclined to go to a specific restaurant I haven't been to in years and it's completely gone. I've started to look online to confirm before driving half an hour or more.
I noticed this also, and ever since, I've made it a point to always have memorized my SO's number and my best friend's number.
*Outsourcing to a parrot on steroids which will make mistakes, produce stale ugly ui with 100px border radius, 50px padding and rainbow hipster shadows, write code biased towards low quality training data and so on. It's the perfect recipe for disaster
Over the top humor duly acknowledged.
Disastrous? Quite possibly, but my concerns are based on different concerns.
Almost everything changes, so isn’t it better to rephrase these statements as metrics to avoid fixating on one snapshot in an evolving world?
As the metrics get better, what happens? Do you still have objections? What objections remain as AI capabilities get better and better without limit? The growth might be slow or irregular, but there are many scenarios where AIs reach the bar where they are better at almost all knowledge work.
Stepping back, do you really think of AI systems as stochastic parrots? What does this metaphor buy you? Is it mostly a card you automatically deal out when you pattern match on something? Or does serve as a reusable engine for better understanding the world?
We’ve been down this road; there is already much HN commentary on the SP metaphor. (Not that I recommend HN for this kind of thing. This is where I come to see how a subset of tech people are making sense of it, often imperfectly with correspondingly inappropriate overconfidence.)
TLDR: smart AI folks don’t anchor on the stochastic parrots metaphor. It is a catchy phrase and helped people’s papers get some attention, but it doesn’t mean what a lot of people think it means. Easily misunderstood, it serves as a convenient semantic stop sign so people don’t have to dig in to the more interesting aspects of modern AI systems. For example: (1) transformers build conceptual models of language that transcend any particular language. (2) They also build world models with spatial reasoning. (3) Many models are quite resilient to low quality training data. And more.
To make this very concrete: under the assumption of universal laws of physics, people are just following the laws of physics, and to a first approximation, our brains are just statistical pattern matchers. By this definition, humans would also be “stochastic parrots”. I go all this trouble to show that this metaphor doesn’t cut to the heart of the matter. There are clearer questions to ask: they require getting a lot more specific about various forms and applications of intelligent behavior. For example
- under what circumstances does self play lead to superhuman capability in a particular domain?
- what limits exist (if any) in the self supervised training paradigm used for sequential data? If the transformer trained in this way can write valid programs then it can create almost any Turing machine; limited only by time and space and energy. What more could you want? (Lots, but I’m genuinely curious as to people’s responses after reflecting on these.)
Until the thing can learn on its own and advance its capabilities to the same degree that a junior developer can, it is not intelligent enough to do that work. It doesn't learn our APIs, it doesn't learn our business domain, it doesn't learn from the countless mistakes I correct it on. What we have now is interesting, it is helping sometimes and wasteful others. It is not intelligent.
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Regarding the phrase statistical parrot, I would claim that statistical parrotism is an ideology. As with any ideology, what we see is a speciation event. The overpopulation of SEO parrots has driven out a minority of parrots who now respecialize in information dissemination rather than information pollution, leaving their former search-engine ecological niche and settling in a new one that allows them to operate at a higher level of density, compression and complexity. Thus it's a major step in evolution, but it would be a misunderstanding to claim that evolution is the emergence of intelligence.
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LLMs ARE stochastic parrots, throw whatever chatgpt slop answer but facts are facts
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Drawing blueprints is more enjoyable than putting up drywall.
The code is the blueprint.
“The final goal of any engineering activity is some type of documentation. When a design effort is complete, the design documentation is turned over to the manufacturing team. This is a completely different group with completely different skills from the design team. If the design documents truly represent a complete design, the manufacturing team can proceed to build the product. In fact, they can proceed to build lots of the product, all without any further intervention of the designers. After reviewing the software development life cycle as I understood it, I concluded that the only software documentation that actually seems to satisfy the criteria of an engineering design is the source code listings.” - Jack Reeves
depends. if i am converting it to then use it in my project, i don't care who writes it, as long as it works.