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Time will tell. As a GitHub Copilot user, I still review the code. SpaceX's advancements are impressive, from rocket blow up to successfully catching the Stars

Time will tell. As a GitHub Copilot user, I still review the code. SpaceX's advancements are impressive, from rocket blow up to successfully catching the Starship booster. Who knows what AI will be capable of in 5-10 years? Perhaps it will revolutionize code assistance or even replace developers

outworlder 2y ago
> SpaceX's advancements are impressive, from rocket blow up to successfully catching the Starship booster. That felt like it was LLM generated since that doesn't have anything to do with the subject being discussed. Not only it's on a different industry but it's a completely different set of problems. We know what's involved in catching a rocket. It's a massive engineering challenge yes, but we all know it can be done(whether or not it makes sense or is economically viable are different issues). Even going to the Moon – which was a massive project and took massive focus from an entire country to do – was a matter of developing the equipment, procedures, calculations (and yes, some software). We knew back then it could be done, and roughly how. Artificial intelligence? We don't know enough about "intelligence". There isn't even a target to reach right now. If we said "resources aren't a problem, let's build AI", there isn't a single person on this planet that can tell you how to build such an AI or even which technologies need to be developed. More to the point, current LLMs are able to probabilistically generate data based on prompts. That's pretty much it. They don't "know" anything about what they are generating, they can't reason about it. In order for "AI" to replace developers entirely, we need other big advancements in the field, which may or may not come.
- dspillett 2y ago
  > Artificial intelligence? We don't know enough about "intelligence". The problem I have with this objection is that it, like many discussions, conflates LLMs (glorified predictive text) and other technologies currently being referred to as AI, with AGI. Most of these technologies should still be called machine learning as they aren't really doing anything intelligent in the sense of general intelligence. As you say yourself: they don't know anything. And by inference, they aren't reasoning about anything. Boilerplate code for common problems, and some not so common ones, which is what LLMs are getting pretty OK at and might in the coming years be very good at, is a definable problem that we understand quite well. And much as we like to think of ourselves as "computer scientists", the vast majority of what we do boils down to boilerplate code using common primitives, that are remarkably similar across many problem domains that might on first look appear to be quite different, because many of the same primitives and compound structures are used. The bits that require actual intelligence are often quite small (this is how I survive as a dev!), or are away from the development coalface (for instance: discovering and defining the problems before we can solve them, or describing the problem & solution such that someone or an "AI" can do the legwork). > we need other big advancements in the field, which may or may not come. I'm waiting for an LLM being guided to create a better LLM, and eventually down that chain a real AGI popping into existence, much like the infinite improbability drive being created by clever use of a late version finite improbability generator. This is (hopefully) many years (in fact I'm hoping for at least a couple of decades so I can be safely retired or nearly there!) from happening, but it feels like such things are just over the next deep valley of disillusionment.
olivermuty 2y ago
Except cursor is the fireworks based on black powder here. It will look good, but as a technology to get you to the moon it seems to look like a dead end. NOTHING (of serious science) seems to indicate LLMs being anything but a dead end with the current hardware capabilites. So then I ask: What, in qualitative terms, makes you think AI in the current form will be capable of this in 5 or 10 years? Other than seeing the middle of what seems to be an S-curve and going «ooooh shiny exponential!»
- TeMPOraL 2y ago
  > NOTHING (of serious science) seems to indicate LLMs being anything but a dead end with the current hardware capabilites. In the same sense that black powder sucks as a rocket propellant - but it's enough to demonstrate that iterating on the same architecture and using better fuels will get you to the Moon eventually. LLMs of today are starting points, and many ideas for architectural improvements are being explored, and nothing in serious science suggests that will be a dead end any time soon.
  - zeroonetwothree 2y ago
    It’s easy to say with hindsight but if all you have is black powder I don’t think it’s obvious those better fuels even exist.
- dbmikus 2y ago
  If you look at LLM performance on benchmarks, they keep getting better at a fast rate.[1] We also now have models of various sizes trained in general matters, and those can now be tuned or fine-tuned to specific domains. The advances in multi-modal AI are also happening very quickly as well. Model specialization, model reflection (chain of thought, OpenAI's new O1 model, etc.) are also undergoing rapid experimentation. Two demonstrable things that LLMs don't do well currently, are (1) generalize quickly to out-of-distribution examples, (2) catch logic mistakes in questions that look very similar to training data, but are modified. This video talks about both of these things.[2] I think I-JEPA is a pretty interesting line of work towards solving these problems. I also think that multi-modal AI pushes in a similar direction. We need AI to learn abstractions that are more decoupled from the source format, and we need AI that can reflect and modify its plans and update itself in real time. All these lines of research and development are more-or-less underway. I think 5-10 years is reasonable for another big advancement in AI capability. We've shown that applying data at scale to simple models works, and now we can experiment with other representations of that data (ie other models or ways to combine LLM inferences). [1]: https://www.anthropic.com/news/3-5-models-and-computer-use https://www.anthropic.com/news/3-5-models-and-computer-use [2]: https://www.youtube.com/watch?v=s7_NlkBwdj8 https://www.youtube.com/watch?v=s7_NlkBwdj8
whimsicalism 2y ago
> or even replace developers I don't think there will be a 'replace developers, but other work remains extant' moment - not at least for very long at all.

outworlder 2y ago

> SpaceX's advancements are impressive, from rocket blow up to successfully catching the Starship booster. That felt like it was LLM generated since that doesn't have anything to do with the subject being discussed. Not only it's on a different industry but it's a completely different set of problems. We know what's involved in catching a rocket. It's a massive engineering challenge yes, but we all know it can be done(whether or not it makes sense or is economically viable are different issues). Even going to the Moon – which was a massive project and took massive focus from an entire country to do – was a matter of developing the equipment, procedures, calculations (and yes, some software). We knew back then it could be done, and roughly how. Artificial intelligence? We don't know enough about "intelligence". There isn't even a target to reach right now. If we said "resources aren't a problem, let's build AI", there isn't a single person on this planet that can tell you how to build such an AI or even which technologies need to be developed. More to the point, current LLMs are able to probabilistically generate data based on prompts. That's pretty much it. They don't "know" anything about what they are generating, they can't reason about it. In order for "AI" to replace developers entirely, we need other big advancements in the field, which may or may not come.

dspillett 2y ago
> Artificial intelligence? We don't know enough about "intelligence". The problem I have with this objection is that it, like many discussions, conflates LLMs (glorified predictive text) and other technologies currently being referred to as AI, with AGI. Most of these technologies should still be called machine learning as they aren't really doing anything intelligent in the sense of general intelligence. As you say yourself: they don't know anything. And by inference, they aren't reasoning about anything. Boilerplate code for common problems, and some not so common ones, which is what LLMs are getting pretty OK at and might in the coming years be very good at, is a definable problem that we understand quite well. And much as we like to think of ourselves as "computer scientists", the vast majority of what we do boils down to boilerplate code using common primitives, that are remarkably similar across many problem domains that might on first look appear to be quite different, because many of the same primitives and compound structures are used. The bits that require actual intelligence are often quite small (this is how I survive as a dev!), or are away from the development coalface (for instance: discovering and defining the problems before we can solve them, or describing the problem & solution such that someone or an "AI" can do the legwork). > we need other big advancements in the field, which may or may not come. I'm waiting for an LLM being guided to create a better LLM, and eventually down that chain a real AGI popping into existence, much like the infinite improbability drive being created by clever use of a late version finite improbability generator. This is (hopefully) many years (in fact I'm hoping for at least a couple of decades so I can be safely retired or nearly there!) from happening, but it feels like such things are just over the next deep valley of disillusionment.

olivermuty 2y ago

Except cursor is the fireworks based on black powder here. It will look good, but as a technology to get you to the moon it seems to look like a dead end. NOTHING (of serious science) seems to indicate LLMs being anything but a dead end with the current hardware capabilites. So then I ask: What, in qualitative terms, makes you think AI in the current form will be capable of this in 5 or 10 years? Other than seeing the middle of what seems to be an S-curve and going «ooooh shiny exponential!»

TeMPOraL 2y ago
> NOTHING (of serious science) seems to indicate LLMs being anything but a dead end with the current hardware capabilites. In the same sense that black powder sucks as a rocket propellant - but it's enough to demonstrate that iterating on the same architecture and using better fuels will get you to the Moon eventually. LLMs of today are starting points, and many ideas for architectural improvements are being explored, and nothing in serious science suggests that will be a dead end any time soon.
- zeroonetwothree 2y ago
  It’s easy to say with hindsight but if all you have is black powder I don’t think it’s obvious those better fuels even exist.
dbmikus 2y ago
If you look at LLM performance on benchmarks, they keep getting better at a fast rate.[1] We also now have models of various sizes trained in general matters, and those can now be tuned or fine-tuned to specific domains. The advances in multi-modal AI are also happening very quickly as well. Model specialization, model reflection (chain of thought, OpenAI's new O1 model, etc.) are also undergoing rapid experimentation. Two demonstrable things that LLMs don't do well currently, are (1) generalize quickly to out-of-distribution examples, (2) catch logic mistakes in questions that look very similar to training data, but are modified. This video talks about both of these things.[2] I think I-JEPA is a pretty interesting line of work towards solving these problems. I also think that multi-modal AI pushes in a similar direction. We need AI to learn abstractions that are more decoupled from the source format, and we need AI that can reflect and modify its plans and update itself in real time. All these lines of research and development are more-or-less underway. I think 5-10 years is reasonable for another big advancement in AI capability. We've shown that applying data at scale to simple models works, and now we can experiment with other representations of that data (ie other models or ways to combine LLM inferences). [1]: https://www.anthropic.com/news/3-5-models-and-computer-use https://www.anthropic.com/news/3-5-models-and-computer-use [2]: https://www.youtube.com/watch?v=s7_NlkBwdj8 https://www.youtube.com/watch?v=s7_NlkBwdj8

whimsicalism 2y ago

> or even replace developers I don't think there will be a 'replace developers, but other work remains extant' moment - not at least for very long at all.