> The Russia/Ukraine war has a goal, to make Ukraine either part of Russia or a client state.
No, it has a goal to keep Putin in power.
The Iran war happened to move people's interest from a certain set of files about a certain group people onto something else.
It succeeded by that standard, but now has created the mess that you can't just start a war with a country to distract your voters and not suffer any consequences from it.
Iran was not thrilled to be bombed to play a part in this distraction.
That does not at all look cherry picked or taken out of context...
LeCun's ideas cannot be reduced to a 6 second clip...
You're missing the forrest for the trees, taking a singular example of a problem and thinking that if an LLM can solve the singular example it completely disproves LeCun is comical...
I assumed LLMs should be able to rewrite a small amount of code ~5k dense LoC in Ruby to Rust.
It could not.
I suspect you'll see a wave of transpilers developed to mostly transpile code from one language to another.
You can have an LLM generate a 1-2k or so LoC transpiler that can translate 50%+ of code in place from most languages to another.
After doing that, it was able to actually get the job done relatively quickly.
I'm working on self-hosting a programming language I've been developing. The transpiler from the original language to the host language is ~12k LoC and translates ~99% the original compiler's ~80k LoC cleanly.
The total self-host looks like it might only take a couple of weeks and <$100... TBD.
His main LLM predictions have almost nothing to do with Arc AGI...
What exactly was he dead wrong about that is proven by any of this?
GPT getting better has absolutely nothing to do with completely disproving anything LeCun has been saying.
He never said LLMs couldn't get better. He never said they couldn't score 7.6% on Arc AGI 3.
He's merely said they don't think, and you probably want something that actually thinks if you want a model that can be trained cheaply on a small amount of data and provide a ton of value.
Spending $5B to train a model that scores better than an older model does not disprove any of that in any way.
> It's insane to me that Andrew thinks this post will somehow exonerate Zig when it really just makes them look childish.
Antirez made a post equivalent to: you'd be a fool not to use AI to increase test coverage.
Zig on the other hand has embarrassingly low test coverage given its adoption and time in development.
Their stance on AI is completely childish. They could benefit massively from it, yet refuse to even consider any potential usage.
It's one thing to try to stop PR spam. It's another thing to tie your hands behind your back and not even use it internally for the lowest hanging fruit where it could have major benefits.
They could use AI to triage potential real bugs from PR spam... but instead they just let real bugs go unnoticed for longer than need be because they won't even use AI to help triage...
I liked the article. It was a long (and entertaining) build up to the conclusion, but I'm scratching my head how the author got there.
AI needs more discipline, yes. But theoretically that discipline can be learned much easier than becoming a good engineer.
Think of it this way... 20 years ago, to write good, scalable C code - you needed to 1) either be a genius, or 2) dedicated to the craft.
You need to learn dozens of tools like the back of your hand.
* ASan
* LSan
* UBSan
* TSan
* GDB
etc... God forbid if you needed to manually read DWARF files. Unless you're a pure genius, this is not feasible to master in a short amount of time. And in parallel, you need to learn how to design systems, too, otherwise, you're still not very good, and that's an almost completely orthogonal skillset.
Now, you simply need to be aware of the hazards in your language/framework, tell your LLM to test for them, have the infrastructure set up to see if they've adequately tested for those hazards, and maybe read the actual tests and implementation.
It is pretty easy to be able to read and understand Rust compared to debugging all the sorcery-like errors that come during Rust development... It is easy to see that you need a Loom test for certain scenarios, and to write a tool to detect if you did it.
Even if you're still working in C or Zig, it far easier to know and detect when you need to use those tools then to learn to use them all individually.
It is not hard to learn to read SQL. Almost ~50% of business professionals can. Python is barely harder. Rust can look like sorcery if you don't read a 50 page guide to understand to read it, but that's a VERY small price to pay compared to spending ~10 years learning the craft painfully by trial and error.
I'm not sure how you get from "LLMs work in mysterious ways" to "So we need more discipline" to "everything is fine."
I agree that everything is fine. I just don't think this is the clear path and thought process.
Anyone who has the determination to get things to actually work, and takes a little bit of time to understand what makes them not, should be able to leverage LLMs to work wonders.
In my opinion, LLMs are going to make things far more complicated, because the cost of building something complicated is becoming almost free.
Engineering was always about discipline and getting things to work. But you needed a set of prerequisite skills to have much value. Most of those are gone now.
It is simply far easier than before. It does require discipline, yes. But discipline is cheap compared to ~10 years of trial by fire.
> At one end of the spectrum you have very talented, smart engineers who could easily get a job anywhere
Not exactly...
> devoting their lives to targeting ads, surveillance, brain-hacking the masses with the algorithm in order to sell more ads, etc.
Nice try, but most of engineering at Meta has almost as much to do with this as the food staff...
So the question remains - if you're an engineer working on nothing related to any of that - most of Meta - why is your work reduced to "destroying girls lives" but the TVC's working in the kitchen are not?
Why are people working at GM, who have a large ad spend on Meta, not destroying girls lives? But the people working on storage compression algorithms to save on hardware costs are??
Why is the TVC not bad, but the person working on decorating the offices is?
You're a TVC in the kitchen at Meta? All you do is give girls depression?
You work at a business that buys ads on Meta? Is all you do is give girls depression? Even if you work in a non-profit branch specifically to do out-reach for kids or something??
How far separated from Meta do you have to be to not be reduced to doing nothing but giving girls depression?
But you probably need >$10M to not HAVE to work and live a low-risk comfortable life in even modestly expensive parts of the US.
The funny thing about money is, it's really hard to save $1M and $10M, but once you get there, it's pretty easy to grow that substantially.
The fundamental problem in the West, IMO, is that we make it so hard to save even small amounts of money, and so easy to compound huge amounts of money (and no the EU is not much better on this front than the US).