They are the Tesla of the IT world, overpromise and under deliver.
"Capitalization is [...] indistinguishable from a commercialization of potentials, through which modern history is slanted (teleoplexically) in the direction of ever greater virtualization, operationalizing science fiction scenarios as integral components of production systems." [0]
"Within capitalist futures markets, the non-actual has effective currency. It is not an "imaginary" but an integral part of the virtual body of capital, an operationalized realization of the future." [1]
This corresponds to the idea that virtual is opposed to actual, not real.
What is the jump to O1 like, compared to GPT4/Claude 3.5? I distinctly remember the same (if not even greater) buzz around the announcement of O1, but I don't hear people singing its praises in practice these days.
Have an o3-mini model available to me on the other hand I'm very impressed with its fast, succinct, correct answers while tooling around in zsh on my mac. what things are called, why they exist. why is macports installing db48 etc. It still fails to write simple bash one liners. (I wanted to pipe the output of ffmpeg to a column of --enabled-features and it just couldn't do it)
It's a very helpful rubber duck but still not going to suffice as an agent, but I think its worth a subscription. I wanted to do everything local and self hosted and briefly owned a $3000 mac studio to run llama3.3-70B but it was only as good as GPT4 and too slow to be useful so returned it. In that context even $200/m is relatively cheap.
Not sure how accurate these numbers are but on https://openrouter.ai/ highest used "apps" basically can auto-accept generated code and apply it to the project. I was recently looking at top performers on https://www.swebench.com/ and noticed OpenHands basically does the same thing or similar. I think the trend is going to get much worse, and I don't think Moore's Law is going to save us from the resulting chaos.
So many people won’t be able to get paid for the quality of their work, but the people who buy the cheaper product will get what they pay for.
For example, I have little to no expectation that it will handle software architecture well. Especially refactoring legacy code, where two enormous contexts need to be held in mind at once.
when i was a team/project leader the largest part of my work was talking to the reports on what needs to be implemented and how they are going to implement it and the current progress of the implementation, how to interface the stuff, what are the issues and how to approach the troubleshooting, what are the next steps, etc. with occasional looking into/reviewing the code - it looks to me what working with coding LLM would soon be quite similar to that.
The persistence of indistinct ghost jobs that are generated by computer for pennies to flood and bind with prospective job seekers (similar to RNA interference), has resulted in severe brain drain in many fields. Worse, the fact these people have often been forced into poverty as a result will have a lasting impact. You might have planned for up to a year out of work pre-AI and had the financial resources, but now how long does it take? Conversion ratios for the first step have changed by two magnitudes or order (from x100 to x10,000). What are the odds of these people finding a job given their finite time, and requirements that are un-automatable for submission (nil). The media keeps claiming that everything is getting better, the stats say so (while neglecting the fact that the stats are being manipulated to the point of uselessness/fabricated), but you have 1/3 of welfare payouts now going to these people in California (in the US), just for basic food.
When you can't find work, you go where the work is abandoning the bad economic investment and choice you made regardless of how competent you were. It is a psychologically sticky decision. When there is no chance at finding work, you get desperate, and many desperate people turn to crime and unrest. This was foreseen by a number of very intelligent people many decades ago, and ignored following business as usual.
The mere demonstration that we are unable to react in time is what gave engineers such great pause to write about these things, as far back as in the 70s. Hysteresis is a lagging time problem where you can't react fast enough to avert catastrophic failure given chaotic conditions, leaving survival up to chance. Its the worst type of engineering problem with real consequences.
Given how western society is structured dependently on labor exchange, its a perfect weapon of chaos and debasement in the value of labor, that effectively destroys half of its underlying economic structure (factor markets). This forces sieving conditions of wealth that become spinodal, and eventually falter under their constraints and spiral into deflationary trends over time.
Business wins so much that they lose everything. Its quite a disadvantaged environment and the general trend is that everyone is ignoring the pink elephant. Actions (and inaction) have consequences. When people don't listen and take appropriate action, consequences get dire, it hits the fan.
People can choose to not place a bet by not participating in the economy, or tying physical assets to it. In other words, de-banking, off-grid farming, unemployment on welfare (not their money, printed guaranteed loss absorbed by the baseline).
The assumption that the market can always allocate the losses to the baseline has already been shown to be foundationally flawed. It depends upon whether the baseline can absorb the losses to keep the market going, not the other way around. Those who believe in MMT don't pay head to the fact that money printing has caused societies to fail many times in the distant past, in the ever quoted phrase, but it will be different this time.
When the economic engine stalls, so too does order and money-printing/debt issuance (without fractional reserve) drives this as a sieve (which we've seen over the past several decades in the form of bailout, marketshare concentration, and consolidation).
Central banks set reserve allocations to 0% in 2020, adopting a capital reserve, risk-weighted system based in fiat that is opaque and stock-market tied (Basel III modified). Value is subjective, and fiat may have store of value, right up until it doesn't.
Of particular note, societal order is required to produce enough food for 8bn globally, without order and its now brittle dependencies, we can only feed 4bn globally. Malthus has a lot to say about population dynamics in ecological overshoot.
TL;DR Half of all people die when modern chemical production (Haber-Bosch fertilizer) and other food dependencies (climate) fail.
AI drives chaos and disruption. Its like throwing a wrench into a gear system, maybe it will stall, maybe it gets thrown out (still slowing it), maybe it runs rough wearing it faster further degrading the system towards failure.
When the baseline cannot absorb the cost in terms of purchasing power, it absorbs the cost from the resulting chaos in lives.
Intelligent people pay attention to history because the outcomes that repeat in history occur as a result of dynamics that repeat, and in matters where lives or survival are on the line risk management shifts from permissive to restrictive (where the requirements of proof are flipped).
As for who cares to rationally think and act when they can easily observe and react?
The problem with the latter is that its a false choice. The latter simply isn't possible in any effective sense. Certain systems and dynamics become a hysteresis problem, where the indicator is lagging ahead of when it actually happens; by the time you see the indicator it can be perceived, but ultimately impossible to react to in time.
There are also simultaneous issues with rational thought being deprived broadly through induction of psychological stress using sophisticated mental coercion and torture (which isn't physical). Rational thought is the first thing to vanish, and these methods act like HIV does in cellular systems (i.e. destroying the memory of the immune system making it unable to act, instead it destroys perception blinding people).
For some reason these things remind me of the Tower of Babel story in the book of genesis. It makes god out to be the bad guy, when it seems far more likely that the dynamics became destructive. All of Humanity has psychological blindspots that can be used to manipulate them in collective and through unity. Pride often lends itself to delusion, and blindness. Destruction usually follows, and confusion occurs naturally when delusion breaks towards a witnessed reality (as survivors, where others kept dying).
It seems like the translation is off, where instead of god, they meant the inescapable forces of reality. Albeit this is getting a bit into the weeds, its an interesting perspective.
Getting back to things, the major difference today when comparing to history with regards to revolution, we are in extreme ecological overshoot (globally).
Breakdown of order translates to famine so severe that half globally die from starvation. To make matters worse, nearly every economic system on the planet is controlled indirectly by one nation through money printing, and those distortions created are chaotic (fundamentally it shares many characteristics as that of a n-body immeasurable astrophysics system that has limited visibility).
When these things happened in the past, they were largely in isolation, and outside the geographical affected areas, assistance could be leveraged for survival. This is no longer the case now. If these things collapses, it all happens to everyone at the same time. Not enough resources exist to resolve the failure, and there are no tools that would allow correcting the situation after the dynamics have passed a point of no return.
Thinking about these things rationally, preparing while we can (before it happens), is the only tool that might allow survival long term for a few. Its important that survivors know what happened and how it happened, or it will happen again given sufficient time, and that requires a foundation. Needless to say, we have many dark times ahead.
A line appears, the order wanes, the empire falls, and chaos reigns.
I do not envy those who would have to somehow live through chaos, where nuclear weapons might be used by the delusional or insane.
AI might be the excuse but the reason is the end of zero interest rates and blitzscaling along with resentment among business leadership that some members of the labor force were actually getting a good deal for once.
You can't claim wage earners have received a good deal when they are unable to support themselves with basic necessities, let alone a wife and three children (required for risk managing 1 surviving to have children themselves). This is largely why we have a problem with birth rate today with the old crowding out all opportunities for the young.
The problem you mention doesn't really have to do with AI. It comes down to purchasing power in the economy, not wages, and business has shown over decades they will not or cannot be flexible when it comes to profit.
Additionally, money printing puts both parties at each others necks through debasement of the currency. When the currency debasement (inflation) exceeds profit legitimate business not tied to a money printer leaves the market (no competition is possible).
When the only entities left in some proposed market cooperate, the market isn't a market, its non-market socialism without the requirements for economic calculation. This fails.
Neither parties in my opinion are getting a reasonable deal. Whose to blame? The cohorts of people printing money from nothing that call themselves central bankers.
This time it's different. The naysayers are wrong.
LLMs today can already automate many desk jobs. They already massively boost productivity for people like us on HN. LLMs will certainly get better, faster and cheaper in the coming years. It will take time for society to adapt and for people to realize how to take advantage of AI, but this will happen. It doesn't matter whether you can "test AI in part" or whether you can do "exhaustive whole system testing". It doesn't matter whether AIs are capable of real reasoning or are just good enough at faking it. AI is already incredibly powerful and with improved tooling the limitations will matter much less.
We still use calculators even though the profession we used to call "computer" was replaced by them.
LLMs do not guarantee any quality in the output even when processing text, and should in my opinion be verified before used in any serious applications.
That isn't really true.[0] The application of calculators to a subject matter is something that does need to be considered in some use cases.
LLMs also have accuracy considerations, and although it may be to a different degree, the subject matter to which they're applicable has a broad range of acceptable accuracies. While some textual subject matter demands a very specific answer, some doesn't: For example, there may be hundreds or thousands of various ways to summarize a text that could be accurate for a particular application.
0: example: https://www.reddit.com/r/calculus/comments/upjdn4/why_do_all...
The issue with LLMs is that they can be so unpredictable in their behaviour. Take the following prompt that asks GPT-4 to validate the response to "calculate 2+3+5 and only display the result":
https://beta.gitsense.com/?chat=6d8af370-1ae6-4a36-961d-2902...
GPT-4o mini contradicts itself, which is not something one would expect for something we believe to be extremely simple. However, if you ask it to validate the response to "calculate 2+3+5," it will get it right.
https://beta.gitsense.com/?chat=43221de5-bff6-487a-8c0f-48ca...
By adding "and only display the result," GPT-4o mini was thrown for a loop; examples like this should give us pause.
If I ask my TI-89 to "Summarize the plot in Harry Potter and the Chamber of Secrets" it responds "ERR"! :D
LLMs are good text processors, pocket calculators are good number processors. Both have limitations, and neither are good at problem sets that are outside of their design strengths. The biggest problem with LLMs aren't that they are bad at a lot of things, it's that they look like they are good at things they aren't good at.
I've personally found it is extremely unlikely for multiple good LLMs to fail at the same time, so if you want to process text and be confident in the results, I would just run the same task across 5 good models and if you have a super majority, you can be confident that it was done right.
“Previous generations of cars were useless because one guy rode a bike to work.” Pre-transformer neural nets were obviously useful. CNNs and RNNs were SOTA in most vision and audio processing tasks.
I was involved in ANN and equivalent based face recognition (not on the computational side, on the psychophysics side) briefly. Face recognition is one of these bigger more difficult jobs, but still more constrained than the things ANNs are useful for.
As far as I understand none of the face recognition algorithms in use these days are ANN based, but are instead computationally efficient versions of the brute force the maths implementations instead.
No they can't because they make stuff up, fail to follow directions, need to be minutely supervised, all output checked and workflow integrated with your companies shitty over complicated procedures and systems.
This makes them suitable at best as an assistant to your current worker or more likely an input for your foo as a service which will be consumed by your current worker. In the ideal case this helps increase the output of your worker and means you will need less of them.
An even greater likelihood is someone dishonest at some company will convince someone stupid at your company that it will be more efficacious and less expensive than it will ultimately be leading your company to spend a mint trying to save money. They will spend more than they save with the expectation of being able to lay off some of their workers with the net result of increasing workload on workers and shifting money upward to the firms exploiting executives too stupid to recognize snake oil.
See outsourcing to underperforming overseas workers because the desirable workers who could have ably done the work are A) in management because it pays more B) in country or working remotely for real money or C) cost almost as much as locals once the increased costs of doing it externally are factored in.
What’s the difference between what you describe and what’s needed for a fresh hire off the street, especially one just starting their career?
The fresh hire has the potential that after training and working for a while to become a much more valuable and reliable senior.
Good choice of wording! Definitely not a given though.
We don't mention this in techie circles, probably because it is gauche. However you can hold a person responsible, and there is a chance you can figure out what they got wrong and ensure they are trained.
I can’t do squat to OpenAI if a bot gets something wrong, nor could I figure out why it got it wrong in the first place.
Do you have a source on this? Spotify also seems to employ a few different recomendation algorithms, for example Discover Weekly vs. continuing to play after a playlist ends. I'd be surprised if Discover Weekly didn't employ some sort of ML as it does recommend songs I have never heard before many times.
I call: show me five actual "desk jobs" that LLMs have "already automated". Not merely tasks, but desk jobs - jobs with titles, pay scales, retirement plans, etc. in real companies.
I guess we will see the actual difference in 5-10 years in the stats. Big companies are mostly still evaluating and waiting. Maybe it will remain just a few blibs and it'll fizzle out, or maybe, and this is what I expect, the effect will be a lot larger, moving many to other roles and many completely out of work.
On a small (we see many companies inside, but many is relative, of course), but real life examples I see are translators, programmers, seo/marketing writers, data entry (copying content from pdf to excel, human webscraping etc) being replaced now.
Hopefully that'll finish off big companies
If you think about it, human calculators (the job title!) were entirely replaced by digital electronic calculators. Translators are simply "language calculators" that perform mechanical transformations, the ideal scenario for something like an LLM to replace.
> This time it's different. The naysayers are wrong.
This has been said every time. What is fundamentally different this time?
> LLMs today can already automate many desk jobs.
Which desk jobs existed 2 years ago that don’t exist today because LLMs have automated them away?
If there's a job that can be entirely replaced by AI, it was already outsourced to an emerging market with meager labor costs (which at this point, is likely still cheaper than a fully automated AI).
In what sense is the relationship between neurons and human function more “meaningful” than the relationship between matrices and LLM function?
You’re correct that LLMs are probably a dead end with respect to AGI, but this is completely the wrong reason.
Organic neural networks are pretty energy efficient in comparison- although still decently inefficient compared to other body systems - so there is the capacity to build things out to the scale required, assuming my read on what's going on there is correct, that is. So it's not clear to me that the energy inefficiency of ANNs can be sufficiently resolved to enable these multiple quasi-independent subsystems to be built at the scale required. Not even if these interesting looking trinomial neural nets which are matrix addition based rather than multiplication come to dominate the ANN scene.
While I was thinking this comment through I realised there's a possible interpretation wherin human activity induced climate change is an emergent property of the relative energy inefficiency of neural architecture.
I'm curious as to why you feel this needs to be true?
Or to put it another way, what would an AI structure look like to be more meaningfully connected to its function?
Not trying to flame. I always feel that I can't think quite deeply enough about these issues, so I'm worried that I'm missing something 'obvious'.
This isn't addressing what the original commenter was referring to.
I found this episode of the nature podcast - "How AI works is often a mystery — that's a problem": https://www.nature.com/articles/d41586-023-04154-4 - very useful in a 'thank goodness someone else has done the work of being coherent so I don't have to' way.
That's a really interesting (and understandable) explanation.
Heck we have everything from fields of study, to professions that cover this. Neurology, psychology, counseling, teaching, amongst a few.
All things being equal, If a kid didn’t pick up a concept, I can sit with them and figure out what happened, and we can both work towards making sure its cleared up.
“ the fact that this can not map to any kind of mechanistic explanation of what the ANN is doing in a way that can facilitate intuition.”
Will remain true imho. We will never fully intuit AI or understand it outside of some brute force abstraction like a token predictor or best fit curve.
My observation is that every wave of neural networks has resulted in a dead end. In my view, this is in large part caused by the (inevitable) brute force mathematical approach used and the fact that this can not map to any kind of mechanistic explanation of what the ANN is doing in a way that can facilitate intuition. Or as put in the article "Current AI systems have no internal structure that relates meaningfully to their functionality". This is the most important thing. Maybe layers of indirection can fix that, but I kind of doubt it.
I am however quite excited about what LLMs can do to make semantic search much easier, and impressed at how much better they've made the tooling around natural language processing. Nonetheless, I feel I can already see the dead end pretty close ahead.