I know unlikely the case, but in the sci-fi story this would be exactly the kind of comment the Codex agent would leave trying to avoid interference in its master plans.
I agree - but it's too easy to just 'call Luddism', and use the insult to not engage with all of the shared issues that make the comparison apt. Issues like:
- no serious plan for mass unemployment
- the risk of an underemployed middle class leading to violent outcomes as it has in the past
- (many) humans wanting to be useful, to have purpose in life and a place to put their natural ambition
- concentration of economic power in the hands of an ever-shrinking pool of people, from a couple of countries making up 20% of the world population
Luddism came from a place of genuine suffering and fear, which was not misplaced - the industrial revolution lead to amazing new jobs, but not for the Luddites themselves. With AI it's not even clear if those new jobs will come - it seems like the goal is a world where humans will not need to worry about thinking anymore.
So is wanting this to slow down really such a ridiculous notion?
And how about the group of kids who are just graduating college, and entering a job market where it's non-abstractly harder to to land a junior role as it's been in decades? It's the elites who have their finger on that scale, not the twitter folks.
I think that the thought trace is definitely incomplete - you can see cases where it is like and "let's calculate the integral:[no integral calculated]". The train of thought it's on towards the end of the trace looks like an entirely different approach than what it ends up returning, so I think we are just not seeing the part where it hits on the right approach (sadly).
I mean at some point what difference does this make? We can split hairs about whether it 'really understands' the thing, and maybe that's an interesting side-topic to talk about on these forums, but the behavior and outputs of the model is what really matters to everyone else right?
Maybe it doesn't 'understand' in the experiential, qualia way that a human does. Sure. But it's still a valid and useful simile to use with these models because they emulate something close enough to understanding; so much so now that when they stop doing it, that's the point of conversation, not the other way around.
The model should show some facsimile of understanding that it should not ignore the stop hook, otherwise that is a regression. Does that wording make you happier?
Deep learning works at a very high level because 'it can keep learning from more data' better than any other approaches. But without the 'stupid amount of data' that is available now, the architecture would be kind of irrelevant. Unless you are going some way to explain both sides of the model-data equation I don't feel you have a solid basis to build a scientific theory, e.g. 'why reasoning models can reason'. The model is the product of both the architecture and training data.
My fear is that this is as hopeless right now as explaining why humans or other animals can learn certain things from their huge amount of input data. We'll gain better empirical understanding, but it won't ever be fundamental computer science again, because the giga-datasets are the fundamental complexity not the architecture.
I have to ask - are you a bot? You seem to be making some very strong and misinformed statements here. You can fixate on the number if you like, but calling Reddit homogeneously radical left or 'very small', is pretty stupid.
Ironic that you are talking about unrepresentative samples while characterizing Reddit users that way. Reddit is a huge subsection of the internet, over a billion monthly active users. It covers basically all strata of western society you can think of.
To be frank, I thought trying to twist this into an argument about whether capitalism is inherently exploitative was a complete waste of time and I replied as such. If you'll recall what we were originally talking about here - "AI, should HN users be optimistic?"
Sure, but AI pessimism is allowed to be personal. Am I supposed to be optimistic that I feel I'm about to get shafted? Should I be less concerned that I need to provide for my family, because in the long term this is going to be a great step forward for humanity?
I also have contemplated just retraining now to try and get ahead of the curve, but I'm not confident that trades can absorb the shock of this - both in terms of supply (more unemployment) and demand (anything non-commercial will be hit by capital flight on the customer-side). I figure I will just try and make as much money on a higher wage as I can and hope for the best...
That's a false-dichotomy. Capitalism was good for artisanal workers before the industrial revolution, and then it became pretty goddamn bad for them. We're worried we're staring down the barrel of that right now - just saying 'well it was even worse before capitalism' does nothing for us.
Well this is HN so a lot of us are pretty terrified of your 1). We went from 'you have a good job for the next couple of decades' to 'your job is at extreme risk for disruption from AI' in the space of like 5 years. Personally I have a family, I'm a bit old to retrain, but I never worked at a high-comp FAANG or anything so I can't just focus on painting unless my government helps me (note - not US/China). That's extremely anxiety-inducing, that a vague promise of novel new things does not come close to compensating.
I don't think the LLM was asked to check 10,000 files given these models' context windows. I suspect they went file by file too.
That's kind of the point - I think there's three scenarios here
a) this just the first time an LLM has done such a thorough minesweeping
b) previous versions of Claude did not detect this bug (seems the least likely)
c) Anthropic have done this several times, but the false positive rate was so high that they never checked it properly
Between a) and c) I don't have a high confidence either way to be honest.
> But the entire value is that it can be automated. If you try to automate a small model to look for vulnerabilities over 10,000 files, it's going to say there are 9,500 vulns. Or none.
'Or none' is ruled out since it found the same vulnerability - I agree that there is a question on precision on the smaller model, but barring further analysis it just feels like '9500' is pure vibes from yourself? Also (out of interest) did Anthropic post their false-positive rate?
The smaller model is clearly the more automatable one IMO if it has comparable precision, since it's just so much cheaper - you could even run it multiple times for consensus.
I think your numbers are off. TAM for office workers is ~20T a year, of which SWE compensation is ~3T. So if they can make 3T x 10% X 5 years = 1.5T that covers their current valuations. It's not as insane as you make out, even not taking into account the other high risk areas like legal, accounting etc
I think it's completely normal. Whenever automation comes knocking, people are inclined to think it's going to flatline conveniently before their job is at risk. LLMs can code now? Cool, they can't code well though can they? Oh they can code pretty well now? Cool, coding was never the hard part of SWE anyway, it's [thing we have no reason to think AI can't beat 99% of humans at at some point], etc
I think SWE as a mainstream profession is much nearer to the end than the beginning, I'm curious and quite scared about what becomes of us.
> but it's really not such a crazy thing to have row labels built into your data table.
Sometimes you need data in a certain order. Sometimes there is no primary key. And it is nuts how janky the pandas API is if you just want the index to mean the current order of the dataframe and nothing else. Oh you did a pivot? I'm just going to make those pivot columns a row label now if that's alright with you. I don't do that for all functions though, you're going to have to remember which ones. Oh you want to sort a dataframe? You better make damn sure you reindex if you're planning to use that with data from another dataframe (e.g. x + y on data from separate dataframes), otherwise I'm going to align the data on indices, and you can't stop me. Also - want to call pyplot.plot(df['column'])? Yeah I'm giving it the data in index order obviously I don't care about that sort you just did. Oh you want to port this data to excel? Well if your row labels aren't meaningful and you don't want "Unnamed: 0" you're going to have to tell me not to. You need to manipulate a multi-index? You're so cute. Have fun with that buddy.
There is a reason no other dataframe library does this - because it's confusing and cognitive overhead that doesn't need to exist. I've used pandas since ~2013, had this chat with colleagues and many recommend just giving in and maintaining an index throughout. Except I've read their pandas and it sucks because now _you_ need to reason about what is currently the index - because it actually needs to change a lot to do normal things with data. I just use .reset_index copiously and try to make it behave like a normal dataframe library because it's just easier to understand later. Pandas has not earned the right to redefine what a dataframe means.
At the absolute least, index behaviour should be opt-in, not something imposed on the user.