Every few years a new technology goes mainstream and quickly changes the world around us rather fundamentally.
I’ve lived through the web in 1995. Google in 2000. Web 2.0 in 2005. Smartphones in 2009. Now it’s generative AI in 2023.
Every time, there’s an argument about whether it’s the end of the world or a total nothingburger. Every time, industry leaders are knocked off their perch. Every time, there are surprise developments and innovations, both positive and negative. Every time, within a few years, the new technology goes from seeming magical even in the wealthiest and most advanced locations to commonplace in a rural farm in the developing world.
It’s an incredible, terrifying and fascinating time to be alive.
This is how you know it is different. Because the people that were not treating mongo, graphql, microservices, the block chain, and web 3 as miracles are impressed by llm. Just like people that are not tech saavy.
There's hype and then there's overhype. We are clearly in the overhype category for LLMs, but there's absolutely no doubt they are going to change things.
In the 90s, the web was overhyped in the short run and under-hyped in the long run. We wrongly predicted 100% of people would do their shopping on the internet by 2005, but never dreamed Somali market traders would use it to discuss the price of fish, or that foreign hackers would use it to influence US elections.
It's funny you mention that because one of many concerns I can see with OpenAI might be, "... but is it web scale?" in that I could envision tight rate limits being imposed and then businesses having to pay exponentially more to get higher limits and passing those costs on to someone and these costs climb over time on some scale. New tech often operate at a loss until they get businesses dependent on their service.
I mean, it's true that we still don't know whether self-driving cars are a few years away, but the analogy with "LLMs are the beginning of AGI" is apt.
Yahoo, Compuserve, AOL, Gateway Computers, Nokia, SEGA, Blockbuster, KMart, and countless more medium-large business that didn't have a strategy for the technology shift.
That's not the question was asked though. Sure, they were an industry leader in the 70s/80s but in the period 1990-2005? It's hard to claim they really were. It's one of the rare examples of a fallen giant getting back on their feet, most just slowly dissolve over a few decades. AT&T is the only other such example that comes to mind.
> AT&T is the only other such example that comes to mind.
If you're talking about the current AT&T, then I'd argue it's not a great example. The relationship between old AT&T and the company currently calling itself AT&T is tenuous. It would be more accurate to say the brand name survived, because it has good name recognition value.
Yahoo was more of a slow decline, and missed opportunities. They could have actually acquired Google and/or Facebook at one point, and could have been acquired by Microsoft at a later point.
The comment talks about "quickly changes the world around us rather fundamentally", "knocked off their perch", and "within a few years", which doesn't seem like an accurate description of what happened with Yahoo.
Both Apple and Microsoft have had ups and downs since then. Even IBM, Intel, have been through ups and downs even if they are probably down forever now.
Apple had the obvious comeback, but even Microsoft had to reinvent itself away from windows.
Also the wealth of the average worker has been stagnant since the 70's - computers, the internet, etc came and brought tons of wealth, and didn't raise the standard of living.
I've never thought about computers that way - they got here, and didn't make everyone's lives better - they just accelerated wealth concentration in the top 1%.
I'd say that would depend on one's point of view in a couple of key respects:
- Are we just discussing the first-world middle class? Obviously if we extend our scope to all of humanity there have been remarkable jumps in standards of living.
- Does the idea of 'standard of living' include only access to goods, services, and information or also intangibles like access to safety and (more pertinently to this particular discussion) security? As a 34 year old American, I feel that my material standard of living is much better than it was in 1992, but my sense of security in said standard of living has taken a nosedive.
I agree with you, but a reasonable case could be made for viewing standard of living as material access so I think that's where a lot of us talk past one another.
Also American society is pretty disdainful of security, historically.
Isn’t housing part of that material access which has become harder to maintain and access by more and more? People living paycheck to paycheck to pay huge rents, people living in tents and so on seems to indicate some decrease in the standard of living.
The argument against this would be things like homes being larger now and housing standards having risen (even slum units have things like fridges and electricity). I don't think those are compelling arguments because often going without those things is not allowed/not an option, but that's what the other side's argument would be.
The people that live in tents generally used to live in state mandated mental institutions. Their standard of living decreased (well depending on how much you weigh autonomy), but that’s not really relevant to the standard of living for the middle class.
Eh. Depends on which part. I definitely have fallen into doomscrolling on occasion, but I have reasons to believe our society is less stable than it was. This has good and bad aspects - the 'stability' offered was in many ways the stability of a less rich way of living (think people without plumbing or with dirt floors), but at the same time the lack of security is a major stressor and that's not good for your mental health any more than material deprivation is.
There's also that I am a disabled American and we're generally not in great spots. As an American, your ability to have things like housing is directly tied to your ability to be economically productive, and as someone with MS, I have to roll the dice each morning on whether or not I will be able to work. I don't know how long my career can last which creates a lot of stress. But then that's also true for all Americans: How many of us could survive our main/only earner getting cancer? That's the sort of stability I mean: The ability to be wiped out by things outside of your control is higher (which is partially down to society deciding everything is an individual problem and if a tornado flattens your house that sounds like a you problem) and we're moving towards a lower-trust society and the community bonds that were existent but frayed in the 90s (see Bowling Alone) are pretty torched now. Who and what can you rely on?
Which is more relevant if you consider that a lot of Americans have medical conditions and that things like the move from a single-income to dual-income household as the standard means that losing one adult can cripple a household financially. I'm also basing this on things like my peers' ability to do things like pay for child care since mid-30s are prime 'give all your money to daycare' years.
I also was in school during 2008, so I entered college under the 'get a degree and you'll be fine' years and exited it into the bloodbath of the Great Recession. There are many realities and my personal one is less secure than it was and this is true for many Americans.
What's your opinion on the following assertion: countries (such as mine) with socialised medicine make sure everyone has a certain standard of care, but that standard only goes so far. The US lets people pay for any possible treatment, and so people who would be put on palliative care in, say, the UK, would be bankrupted in the US by trying to beat cancer with expensive drugs.
It's phrased as an assertion, but it's really just something I've been thinking about. What do you think?
It's probably accurate, and of course there are issues with socialized medicine as well. I have an interesting POV because I actually had my first relapse while I was living in Canada - I wasn't allowed to stay due to my disability but I have some experience with both systems. The waiting is definitely a problem - I didn't get into a neuro before I had to leave the country 9 months later, but at the same time I never worried about paying for treatment and I went to the ER when I couldn't feel half my body/couldn't walk without being concerned.
It isn't just socialized healthcare, though. It's also things like the US not mandating time off - for instance, I haven't had my Ocreveus infusion because I literally can't afford to take the time off to go to the infusion center. And the US is also very binary: Either you work full time or you get nothing benefits wise. For those of us who could comfortably work ~25-30 hours a week this results in either us overworking ourselves to the detriment of our health or not working at all. I'd love to work and just have a little bit of help for when I have a bad week or month, but that doesn't happen here.
The other problem with the American healthcare system is that it siphons off wealth from those in their end of life. Good luck planning a good life for your children: The government will take everything you own for your nursing home. That also has major impacts in term of stability.
But of course in socialized healthcare you do need to decide what is and isn't worth treating in some way and when cuts are needed you end up in dark places like Canada is with MAiD.
It's hard to say. I'm inclined to believe there are benefits and drawbacks to each approach.
Yeah, totally agree. The American system seems to have, despite its amazing innovations and advances, that we all benefit from sooner or later, the worst of both worlds: neither an efficient market-driven approach not a particularly socialised one either. And that's despite the US government spending more per head than I think any other country on healthcare.
The ties between government, big pharma, insurance companies and hospital systems must be so entrenched that it's very difficult to see a way out.
Add that to America disintegrating into a low-trust society where one almost has to assume that any stranger one meets is out to scam you/get your money somehow and it results in a country where it's terrible to be vulnerable. Which is one of the ways we grow as people, thus leading to emotional stunting amongst the populace, which in turn makes us less resilient and willing to act (particularly since we can't cooperate anymore) and therefore in my opinion sow the seeds of our own destruction.
> And that's despite the US government spending more per head than I think any other country on healthcare.
Far more per head — part of which is higher cost of labor and that healthcare is extraordinarily labor intensive. But, its also fairly high as a GDP share, for which excuses like that don’t work. The US just genuinely has a healthcare system that is ludicrously inefficient unless your goal is maximizing the wealth-based quality differential, in which case Mission Accomplished.
I really think this cynical take on wealth needs to stop. This is far more likely explained by politics.
Or more precisely: efficiency is distorted by compassion. Which is then recovered by efficiency. Which is again modified by compassion. If your system is a bewildering mish-mash of cost increasing political interventions and cost reducing market innovations then it's never going to work. But lawmakers need something to do to stand out, and so do business people.
> I really think this cynical take on wealth needs to stop. This is far more likely explained by politics.
The only mention of wealth was describing the effect. Yes, politics is a key part of how that effect is acheived. (Of course, advocacy by those with wealth is a key part of how the politics happens, and... well, the politics/wealth interactions here are basically a near-infinite chain. They aren’t competing explanations.)
It depends on what you're look at. This [1] is a graph of real median wages, for full time workers, since 1979 (date the Fed data begins). They've only increased by about 10%, and that's misleadingly high since 1979 was a local low of the era. The figures they give are in ~1983 constant dollars, so it works out to about $54k real median earnings in 1979 and $58.5k real median earnings today.
And since 1979 people have far more basic 'necessities' like internet and electronic devices. And other necessities like housing, education, and healthcare have all increased in cost far beyond nominal inflation figures. So a person in this situation is most certainly going to have a substantially lower quality of life in modern times. You have to keep in mind that in the past most families were single bread-winner, so wages and life were organized around the vision of one working individual being able to support at least 3 others, in a nuclear household. It was a very different economic vision than what has emerged in more modern times.
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The one thing I'd say these numbers miss out on is opportunity. I think in modern times there is dramatically more opportunity, but if you're just going to live a normal, as expected, life without really aggressively going after the opportunities we all have available - then you'd almost certainly be better off living on the median in the 70s than today. And since most people should be expected to live these sort of perfectly average lives, as that's precisely what the average is, that's a real problem.
If you buy the bullshit about being able to afford a faster computer as increased standard of living, sure.
If you consider housing, food, medical insurance, car prices, education - all of these things are ridiculously expensive compared to what they used to be.
"But we have the Nintendo Switch!" does not alleviate what we're going through right now.
Depends on whether you mean access to giant TVs or stuff like education and healthcare and housing. It’s hard to usefully turn it into a single metric.
Wages have remained flat, cost of consumer electronics have gone down, cost of education, healthcare, and housing has gone way up.
This is true only if you look narrowly at developed countries. If you look more broadly, a tremendous number of people have had huge increases in income in places like China.
That might even be related (i.e., wages for workers are being depressed in developing world as workers see competition from other workers in developing nations). And capital owners benefiting from increased availability of labor.
TBC, agree that gini coefficient re: wealth in developing world is increasing.
In the 70s the top 1% couldn’t even conceive of the power they’d have in their pockets one day. Today both billionaires and the rest of the world can have access to that same power. We’re all carrying the same phones in our pockets after all.
You missed the point. In the 70s you didn’t have the ability to give someone the power of your phone. If you did, the ultra wealthy would have paid a billion for it (inflation adjusted).
The technology you take for granted is huge. Warren Buffet said he would rather give up flying on a private jet, than give up his smartphone.
No, you missed the point. Something that is a baseline today is being used to justify absolutely colossal disparity in how people live on the unimaginative base of "gee, you are not a caveman".
Your phone does not mean that you don't have to work three jobs to have a roof over your head and can't have a family. Having a phone does not make you rich.
You need to reread the thread. There are some things that are significantly better for common people today. So much better that the richest people would have given up significant chunks of their fortunes for them in the 70s.
This does not mean it’s still difficult to get housing. It’s just a point that one aspect of our lives is unfathomably better than before and now we take it for granted.
Haha, poor billionaires. Just can't improve your iPhone no matter how many $$$$ you throw at it. The most expensive smartphone to date is an iPhone6 with a huge diamond protruding from it.
I’m not a fan of wealth concentration but a lot of the wealth increase at the bottom was by increasing the technological power of their possessions. While this isn’t the same as political or financial power, it’s something.
Careful. That claim about half of AI researchers was probably overestimating:
To summarize: The claim—that half of AI researchers believe that there is at least a 10% chance that rogue AI will kill us all—is based on a survey question that included 162 respondents. All we know about these respondents is that they were authors on papers published at 2021 machine learning conferences. Possible confounding issues are vagueness of the question, small sample size, response bias, confidence of responses, and level of expertise.
Anybody else feel like smartphones were overall net negative? I like cell phones they fit a particular need that I think most people would agree is a net good. But smart phones? I don't fucking want to see any of this stuff on my phone its just like cigarettes, feels nice but in the long run just makes you feel sicker. And people would say ok just don't get one but I sort of feel like its not just a me thing, most people would be better off if most people didn't have a phone. Facebook would not be a problem without smartphones IMO.
Yes I agree, most of what I do on smartphone I'm better off doing on computer, and everything I do exclusively on smartphone is toxic for my mental health and for my time spending habits.
My internet-connected smart phone allows me to pay securely for things, unlock the public city bikes for local transit, find quickly directions for a place, provide me answers to questions, read books at the cafe, etc...
All this without a single wallet or bag of "stuff" dangling over my shoulders.
Now... I don't have a facebook account ... YMMV ... :)
A co-worker had to skip lunch last month because he relied on his phone for payments and the app wouldn't let him pay - no explanation given. I keep critical stuff like payment methods separate and have no plans to stop carrying a wallet because of stuff like this.
It's amazing to me that people who work closely with software are happy to introduce a software powered single point of failure in their ability to function.
But how is it terrifying, if you've seen this four times and we're all still here? I'm inclined to think this AI hype way more fear mongering and over extrapolation than anything else.
There are other things that are terrifying, besides annihilation. Like the idea that you really cannot predict the future to any strong extent, because society is becoming extremely fluid.
Your timeline is interesting. I've heard people complain about nothing new happening. So I like seeing it laid out like that, so regularly every five years. Then not for fourteen.
I still think it's rubbish for people to demand two revolutions a decade or they get bored but I understand the scope a bit better.
People have been flailing around for the "next thing" for a while. First there was crypto, then the metaverse, but now it seems like it will be generative AI. The real world uses I've seen people using it for already outstrip crypto or the metaverse.
It is amazing to think that people have already forgotten about things like the introduction of smartphones and the internet. The military applications of AI could be civilisation shattering (once someone wins a war with a fully mechanised army all bets are off as to what happens to democracy as a political system and the human race in general). Apart from that risk, there is no reason yet to think that this will be more transformative than what we've seen in the past.
We forget about the "invention" of open source, Linux et al (e.g. GCC, Python, Curl, etc) that makes it possible to run those Androids, macOS, and infrastructure and zillions of devices around the world. Don't forget ;-)
Geez people. Please learn to identify hypes, but most importantly the causes of hypes.
LLM is fundamentally information processing technology. Not the path to AGI, not an emerging sentience.
The reason "this time" feels so amazing is that the unwashed masses suddenly got access to new information processing technology in a context where their tools been stagnant for decades.
Not because it was not possible, but because there was no money in it.
To understand my argument and its implications, humor me for a moment and imagine a universe in which everyone was already using linux computers and for a decade now published papers of ML/DL were available for people to use. So there were various crowdsourced indices and models of all sorts, which people incrementally embedded in their information processing workflows.
In that universe there would be no room for the delirious reaction we have here. It would be incremental evolution of search, knowledge bases, algorithmic content generation etc.
What we have experienced instead in these past decades is information tool starvation. These incrementally improving tools, while nothing but known and ultimately mundane algorithms were not available except within a tiny elite.
In fact, people's information processing capability arguably declined as the desktop platforms got downgraded, adtech toxic waste covered the information landscape etc.
What is happening now is that a socioeconomic and artificially induced scarcity is now being broken (for reasons that require serious piecing together of events).
So while on the surface this hype is as distastful as any illustration of
human lemming behaviour, there is enormous silver lining if we succeed to read its causes.
These tools are here, have been here for a while and they can be inserted into our ever growing information processing toolkit. The risks are there to match the opportunities.
The biggest risk of them all is precisely what has led to the current situation. Technology not diffusing normally, but controlled by gatekeepers.
We have spent the last seventy or more years working on machine translation.
All the way back in the 1950s, big money went into it. Decades of hard work, mathematical models of human language, all manner of study, enormous bilingual corpuses of text with phonetic annotation, programmed in general-knowledge databases, fuzzy reasoning algorithms. The amount of work put into it is quite staggering, in hindsight. I remember the cutting edge in the 1990s - SYSTRAN for example, could with some significant human guidance and a limited context domain, translate technical material sometimes usefully.
All of that work has been rendered moot by deep learning. All of it. A machine can, simply with the correct deep learning algorithm and mass exposure to language plus a few bilingual texts, learn an algorithm for translation. It does so automatically, no verb conjugation algorithms, no general knowledge databases, no expert systems with fuzzy reasoning, no parsers, not like a specifically-designed old-school translator had.
And yet these deep-learning systems are vastly superior to the old school architectures, completely supplanting them a couple years after their development.
It is the same story in many other areas. Chess, Go? They learn to play chess and go better than any AI designed specifically to do so. Image classification? Better than the previous 60 years of work on machine vision, and again, accidentally falls out of it. Speech recognition? An algorithm to write a bad poem? Well, we now have an algorithm to find an algorithm to write you that bad poem, if you want it.
That's the thing. These are algorithms to solve very tricky problems, and we didn't have to discover, find, or otherwise create the algorithm. The machine did it for us. I am not sure I'm communicating it well, but to me that's probably the most significant advance since the computer. It was understood - theoretically - that this was possible for a long time; but personally at least, I assumed it would forever require more data and compute than could be realized.
> It is the same story in many other areas. Chess, Go? They learn to play chess and go better than any AI designed specifically to do so.
Take GP's "stagnant for decades" with tech and turn it into "stagnant for centuries" with these. When it first started I remember professional Go players talking about just how big a shakeup it was.
Even if the core difference is accessibility, that is huge. I see AI in 2023 as a pivotal moment similar to 1977 with personal computers, 1994 for the internet and 2007 for mobile computing. All of these technologies had a history before their pivotal moments but when the time was right things changed fast.
Having easy access to the technology is starting to change the way I think about what is possible for computing. One of the skills that has helped me though my career is understanding the scope of what a computer can do or automate. Most of the time it isn't that the thing cannot be done but that the cost associated with it is too high. I recently started solving an issue that would have taken hundreds of hours of human intervention but now I can do it with ten bucks and two hundred lines of python.
2023 is the year where I personally benefit from LLMs, and might spend money on them as casually as others spend on video streaming, but the pivotal moment could still be a year or few away.
Your post boils down to “This is the next iPhone moment”.
The tech existed but was clunky and unavailable. Now it’s good and accessible enough to be used by most people. This is how technological revolutions happen!
Edison didn’t invent the lightbulb. But he made it useful and invented power distribution. That’s the important part. Consumers don’t care about technology that exists in a lab that they cannot even begin to fathom how to use.
Mind you last year even experts didn’t think LLMs would be able to do what they’re doing right now for at least another 5 to 6 years. This also is in part a side-effect of the hype and extra attention. All this attention causes an acceleration.
> LLM is fundamentally information processing technology. Not the path to AGI, not an emerging sentience.
This logic doesn't follow - AGI will be, fundamentally, information processing technology, just as the human brain is, fundamentally, information processing technology.
> "Not the path to AGI, not an emerging sentience".
These are HUGE claims. Define AGI. Define sentience. What is the path and why isn't LLM on it?
Right now those words are only dangerous misinformation - originating from a brain trying to protect its shaking world view. A reminder: you can talk with computers now. Think about that for a moment.
LLM are mathematical models that, given your question, return a sequence of words based on a very complex probability model. The many examples on the internet show that ChatGPT doesn't understand most concepts. For instance, it doesn't know what a book is; it just knows what humans say about books.
Finally, OpenAI has already stated there is a limit to how far the current AI models can progress. ChatGPT 5 will eventually be released but will be hitting the limits. A paradigm shift is needed to move to the next phase of AI.
There is no point in arguing whether LLMs have minds or are conscious, and neither is arguing whether it actually understands any concepts. Neither are LLMs are the first tech to be useful to humans - caculators are invented much earlier.
What is interesting is that it’s answering questions in a meaningful way that is competitive to humans. It replaces about 50% of jobs for each field where all work can be done on computers (like drawing and writing texts) and that number will only grow, until the point where only the most competent people with lots of training and experience can be trusted to do better.
And we don’t need GPT-5 to hit the likit - I think OpenAI said that they don’t have GPT-5 because GPT-4 already hit the limits. The next area for improvement would be multimodal, I think.
The type writer, or the PC didn't replace the need for secretaries. PAs just have more responsibilities beyond typing letters.
As translation is now cheaper, as it can be part automated, there is more demand for translation, whereas this was previously too expensive.
The problem with ChatGPT is that you need to specify exactly what you want. Writing text and drawing is often a creative process where you will only know at the end what you want.
We are true and well within the hype curve at the moment.
> LLM are mathematical models that, given your question, return a sequence of words based on a very complex probability model.
How is this different from how humans respond? Our model could be just some orders of magnitude more complex. Or do you think there some _fundamentally_ different things are going on in the human brain?
I think that's moot: based on the universal approximation theorem [1], a big enough model is indistinguishable from the human brain, regardless of whether the mechanism of action is fundamentally the same or not. I believe this applies to anything that can somehow be modeled with a continuous function - whether that's possible for the human brain is an open question, though we only need a certain fidelity to be useful.
The more useful question is: can the token prediction model scale to the level of a human intelligence within a reasonable power budget compared to a brain? It's comparing apples to oranges right now but the human brain consumes under 20 watts, a tiny fraction of the TDP of a single A100 GP, and the state of the art isn't even close in performance. We've got a long way to go before we can conclusively answer these questions.
This is not the unchallenged, consensus position, though. A competing position within cognitive science is that intelligence requires embodiment, perception, metacognition, curiosity, etc., and that these factors that allow for the emergence of intelligence are indispensable, more or less.
I won't claim to know which is correct, or even if some other alternative is correct; however, this is not settled at all.
I do think it will some day be possible to simulate all of the embodied cognition above, which may truly render this discussion moot, but that LLMs are not doing that at all.
Seriously. How does a bag of random particles have those things?
It cannot, by our own definition.
Hence the metaphysical problem of 'consciousness' as it relates to our variation of scientific materialism.
I suggest the pragmatic approach is along the lines of what the OP said aka 'sufficiently large neural net will be indistinguishable from human' and that's it. We will see things that we can de facto contemplate as 'curiosity' 'perception' 'meta-cognition' if we want to, especially if we start to develop a more meta understanding of these systems, or not, and that's it.
We'll probably be arguing about 'cognition' long, long after we have variation of AI that kind of seem to be AGI. By many measures we are already kind of there Chat GPT will fool humans probably most of the time and that's that.
You know what a book is and can reason about it. As a human you can provide answers that go beyond the knowledge you have ingested.
For example, if you ask ChatGPT if books can be fluorescent it says no. However, as an adult you know someone somewhere has made a book with fluorescent images, as it is a cool thing. You are combining knowledge from two different fields (books + fluorescence) and establishing the likelihood of someone being able to combine them.
GPT-4:
While the term "fluorescent" is typically used to describe substances that can absorb light and then re-emit it, often at a different wavelength, there's no inherent reason why a book couldn't be made with fluorescent properties.
This could be achieved by using fluorescent ink, dyes, or paints on the cover or the pages, or by incorporating fluorescent fibers into the paper itself. When exposed to ultraviolet light (often called "black light"), these materials would glow. This might be used for aesthetic reasons, for practical reasons like aiding reading in low-light conditions, or for interactive elements in children's books or art books.
It's important to note that this is not a common feature for most books, as it would increase production costs and might not be appealing or necessary for all readers or all types of books. Also, long-term exposure to ultraviolet light can be damaging to many types of paper and ink, which could reduce the lifespan of the book.
As of my knowledge cutoff in September 2021, I don't have any specific examples of fluorescent books, but that doesn't mean they don't exist or couldn't be created.
This exactly illustrates the problem of ChatGPT. I asked the same question, phrased slightly differently, and it said no. It also not unlikely my question was used to train CHATGPT further.
Does it, really? I’m pretty sure if you’d ask some people, there are going to be some that’d answer „no“ too.
Besides, you’re shifting the goalposts as you go, altering your arguments as it suits your view. The parent comment just disproved your entire point how LLMs cannot combine different fields - and now you just pick a different angle altogether. Maybe take a step back and reconsider your opinions?
Yet, this doesn't answer my question. Human brain is obviously has so much more in it (visual cortex for starters, grid cells etc), and, in terms of neural network, much more sophisticated architecture. But still, there is a big probability that what we call "knowledge" and "reasoning" and "conciousness" are just a result of this sophisticated architecture. I.e. there is no special magical thing for "reasoning" that next generations of prediction models can't replicate.
There is a faboulous book by Jeff Hawkins "On Intelligence" (2004) that explores this. I think main premise of it still holds true: brain is "just" a highly sophisticated hierarchical tissue whose main job is to extract patterns and make predictions. Fundamentally it doesn't seem very different from what LLMs are.
> LLM are mathematical models that, given your question, return a sequence of words based on a very complex probability model.
I don't think that this is true. LLMs can be modeled with mathematical objects, yes. Functions, sets, numbers, relations, all are very powerful, and can model anything. They can model LLMs, as they can model 'you' for example. Are you a function from the states of the universe to itself?
Why not? I believe we are this close: >< to an AGI, and chatGPT will turn into an AGI soon.
It probably should rely on its neurons less, and use symbolic reasoning more. That, and learn and grow, either maintain a fact database, or retrain its neurons or add more neurons for new facts.
The problem with calling it hype is the broad stroke that's painted about all discussions of its abilities, simply by using that word when it's not all hype. A key part of the definition of hype is the exaggeration of importance and benefits. There are absolutely some out there out there that are exaggerating ChatGPT's abilities, but what are those exaggerations and what are actual abilities that anybody with an OpenAI account can go a verify (true or not) for themselves?
It's an exaggeration to say that GPT-4 can solve all programming problems, but it's easily verifiable that, for example, it's able to solve easy level leetcode problems, and what's impressive on top of that it's able to give an answer to those problems practically before I've finished even reading the problem. That's not hype or a claim by me that you have to take on faith, anybody can copy and paste a problem description and get an answer. It's fair to point out that it's so-so at medium problems, and downright unable to do hard problems, but, well, most humans can't solve hard level problems.
So what's hype, and what's not? It seems to think/reason/understand better than a human child. It's better than some humans with its command of the English language. It has broad knowledge of many subjects beyond what most individual humans know about. It sometimes has poor recall of that knowledge, and some of it is wrong, but it accepts corrections (even if it's unable to commit those corrections back to long-term memory) with far more grace than most humans.
> I have come across people who do programming, legal work, business, accountancy and finance, fashion design, architecture, graphic design, research, teaching, cooking, travel planning, event management etc., all of whom have started using the same tool, ChatGPT
if I found out my lawyer/account/tax advisor/broker had fed my privileged information into ChatGPT I'd be reporting them to their regulator and they'd be struck off
I work in healthcare but have to do a lot of regulatory things and ChatGPT is great for brainstorming arguments and methods and particularly organizing responses. It's like having a mini debate about what things should be discussed and addressed. Often it's been "oh!!! I never thought of that angle!" Basically, it gives me first, very rough drafts to get started and into flow when I'm overthinking things. It's never more detailed than things I would be googling. It's sort of like having an inner monologue that's connected to all the knowledge of the internet. Yes, you often get completely insane, factually wrong responses... but I'm not looking for perfection. It just accelerates the early phases of research and thinking and finding flaws and oversights/blindspots in my ideas.
You can use ChatGPT without feeding a clients personal data to it. You can ask hypothetical questions about a certain situation. Like in coding, you don't have to copy paste in source code be able to get some useful feedback on how to proceed.
This requires care as it may only require a few details or other metadata like when/where one asked to narrow down who they're talking about. Considering ChatGPT leaks and hallucinations I would hope folks in law and medicine are being very careful.
That seems overly risk-averse. Legal privilege does not require such extreme vigilance in op sec. One does not breach privilege by googling keywords relevant to a case then the client's address for example.
In my firm, it is the contrary. We are advised to remove any personal data in our queries and double check the output. We are encouraged to use chatgpt/bing on a daily basis to learn it. Our firm is also planning to purchase an LLM that will work locally.
The market will determine the winner: a few high profile cases where AI enhanced arguments dominate people who are tying their hands behind their back will turn the tide.
I mean, Lexis-Nexis just made their own LLM and trained it with their database. This isn't even about OpenAI anymore. Lexis-Nexis is well-known to legal firms, I'd wager and they have well-established guidelines for what client information can be processed.
I know an associate using it to generate VBA scripts to automate document updates, searches, and email generation. ChatGPT never knows anything about the matter, but it’s really good at writing VBA fast.
You can license the model and run it on your own hardware. I guess a lot of LLM business see this as the profitable end goal (the free version is free but not secure, the paid version is expensive and secure!).
I read a post from someone who got as far as showing up to view an apartment on reservation only to find there was no reservation or available apartment. "Oh, that's our chatbot!"
>LangChain[13] enters the chat! Imagine the horror stories of automated online account blocks and Kafkaesque customer support mazes that are all too common, manifesting at a societal level.
I can imagine this to be a problem. But I can also imagine it to solve a problem. Right now, customer support is often "dehumanised" and Kafkaesque because no single human has the full picture and all the permissions required to resolve a problem.
We're often talking to organisations with "brains" split into a thousand fragments. Having humans in the loop doesn't necessarily help if impersonating a cog is all these humans are allowed to do.
So if an AI can reintegrate this split organisational brain into something resembling human thinking, it might just be an improvement on average.
I agree. 90% of the experiences I've had with modern "support" makes me wish I was talking to GPT-4, instead.
The exceptions are either lucky breaks that feel about as good - and random - as finding a $20 bill on the ground and those experiences I've paid a lot of money for because I'm a relatively wealthy software engineer and have chosen to "opt in" to kinder, more competent service with cash. First class travel and premium banking services come to mind.
AI has the power to make everyone's experience with support more pleasant, which isn't to say that it won't have a cost.
You've brought up an interesting idea in my mind: the infinite patience of AI. No more burned out, jaded customer support people. No more exhaustion answering the same questions over and over. No one feeling motivated today but in a bad mood tomorrow. In some ways, AI will regularize/normalize the CS experience.
I actually believe this will be a huge net benefit.
There is a pop-psychology idea that I recall where three bosses were given to a group of individuals. One boss was always nice, one boss was always mean, the final boss was sometimes nice and sometimes mean. The interesting result was that people disliked the sometimes nice boss more than the always mean boss. The conclusion was that an always mean boss is consistent and therefore they can be planned around. It is the uncertainty of the sometimes nice, sometimes mean boss that causes stress.
Stress is caused by lack of control, in this case due to the inability to predict, and therefore affect, an outcome.
A good example is driving, controlling a vehicle at speed necessitates constantly making accurate predictions. People get characterized as bad drivers largely due to behaving unpredictably.
Yes, these support agents are paid small hourly wages and do the same thing 1000 times a day while dealing with rude or irate customers. Automating their jobs to either eliminate them or change their role to that of bot supervisor will be much better for everyone.
We shouldn’t keep jobs around just to keep people employed. This kind of insane careerism is probably what’s responsible for the current climate of regulations that makes it difficult for many people to find new work and for companies to open more positions. Changing jobs is a good thing, you would think someone on HN would know that.
What are these better jobs that the grunts laid off at the call center will land on? They live paycheck to paycheck so anything handwavy is not going to pay for their food and shelter.
Jobs are created and destroyed every day. They aren’t some permanent unchanging feature of reality. If someone needs people to do something, they will offer money in exchange for that labor. What would you rather they do? Legislate shitty jobs that can never be gotten rid of in some kind of Kafkaesque make work program?
You assume the fragmentation is unintentional- it’s not.
Making Organizational intelligence and knowledge management convoluted and compartmented is one of if not the core way that managers keep power over employees.
So that’s not a problem that a company wants solved.
However it might get solved via this process IF managers start extracting the information from ICs that is then absorbed by a large corporate model - then get rid of the employee
I mean if you think about it, we’re just headed toward capitalists creating the Borg
>Making Organizational intelligence and knowledge management convoluted and compartmented is one of if not the core way that managers keep power over employees. So that’s not a problem that a company wants solved.
Of course they want it solved. They just don't want to pay for it (and neither do customers).
Larger business customers are not exposed to this madness that consumers and small businesses are having to deal with. Large customers get their own account manager who has all the information and all the permissions to fix things. If necessary, they get their technical issues escalated to real specialists.
With these advances in AI, perhaps more of this will become available to everyone. Keeping power over staff (hopefully) won't be a huge problem if "staff" is some AI.
I agree that a lot of structural change will be necessary. But automating stuff and solving the associated societal problems will have to go hand in hand.
Fetishising (forced) human labour is deeply ingrained across all political divides and even in psychology and religion.
The political will to make the necessary changes will only exist once it becomes obvious that we have no other choice.
Geez another article on the AI Spookiness? How many is that this month now?
Most of these articles border on science fiction and involve a lot of projection. While I am concerned in some ways with AI, particularly in mass surveillance and public manipulation via bots, what else can we do other than be reactive to _real_ AI applications when _deployed_? Then we start to discuss proper policy towards AI.
Until we see service sector employees being replaced the way we saw self-checkouts emerge what is the point of these articles other than some form of PR campaign?
> Most of these articles border on science fiction and involve a lot of projection
I understand the fatigue is real but this one does neither of above imo and was fairly grounded in reality. The author(who is CTO of a company) did a GPT integration and realised it could potentially replace 20% of jobs at their company. That is something to be spooked about.
> these articles other than some form of PR campaign
A lot of these are, but as an outside observer of Zerodha I can assure you this isn't. The company has spent zero dollars on marketing in last decade.
It is such a cliche, but the Wayne Gretzky quote is appropriate: "I skate to where the puck is going to be, not where it has been"
In order to stay ahead we have to predict what is coming. Given the rate of change that AI is likely to cause - by the time you've noticed what is happening you will be so far behind as to be completely ineffective.
Very well written article. Author talks about his own journey in this space from usual skepticism to belief and why it feels different this time. But it certainly is very well written. I doubt if a LLM can write like that.
> Most of these articles border on science fiction and involve a lot of projection.
Given that the author stuck mostly to their own experiences of the here-and-now, I'm not sure I understand how the rest of your critique (which, overall, I agree with) applies to this content.
It's a secular religion. The whole thing scans very much the like the man who ambushed me in a public bathhouse a few weeks ago to tell me about how skeptical he was about Jesus his whole life, right up until the moment he found Jesus. And proceeded to expound on it at great length, while I avoided eye contact, fidgeted, stared in the opposite direction, etc.. At some point you have to just get up and walk to the other side of the room from these people, because they show no sign of letting up.
> If a single dumb, stochastic, probabilistic, hallucinating, snake oil LLM with a chat UI offered by one organisation can have such a viral, organic, and widespread adoption ... imagine what better, faster, more “intelligent” systems to follow in the wake of what exists today would be capable of doing.
There are definitely interesting times ahead. What surprised me with ChatGPT is the impact of the interface. The quality of the responses are essential up to some threshold, but the chat interface arguably had an even bigger impact.
I also remember having the same questions in my last job (in logistics) where potential customers wanted to know how we use AI. This sometimes seemed more important than whether our results would enable them to cut costs. Fascinating.
I was hacking around with GPT-3 just a couple months before ChatGPT came out and had this feeling of "omg I found something that the world is largely sleeping on"
Then ChatGPT came out and now that's all anybody ever talks about.
Yeah, absolutely. It’s astounding that Google didn’t come up with a chat-based refinement interface to a search session after all these years, instead forcing us to keep on trying again with a slightly changed query string.
I think it must’ve been an accident. LLMs need a lot to context and the more you talk and narrow your requirements down the better its response gets (in theory). But it really was chatgpt that made it go mainstream. I remember playing with the earlier GPT playgrounds and I also thought neat but not for me
He (and most people on HN) are still underestimating the impact of this.
Everything points at AI soon becoming far more intelligent than any human. That's not science fiction about the remote future in a hundred years. We are talking about something in the order of ten years. We are actually at the brink of the "technological singularity". It's happening.
I would wager in 10 years time we're all looking back in dismay about how AI has just been used to produce an endless stream of fake news, empty-feeling generated content, spam, ads and other forms of monetisation that make our lives miserable.
Some menial administrative jobs that have virtually no value but aren't yet fully automated will be wiped out by conglomerates who offer their white label AI solutions at scale...but that's it.
Think about Google search. Has it really improved much in the last 20 years? Some HN frequents will swear it has gotten worse.
What about your desktop computer? Sure it's about 10x more powerful than 2005, but has having one really changed your life much since then?
> I would wager in 10 years time we're all looking back in dismay about how AI has just been used to produce an endless stream of fake news, empty-feeling generated content, spam, ads and other forms of monetisation that make our lives miserable.
I don't think we will have wait 10 months for that, let alone 10 years.
AI progress has strongly sped up in the last years. We have come a long way since the simple digit recognizers. And there is no end in sight, no AI winter. There are no diminishing returns in intelligence. Strong AI is the last invention.
Nothing points to this I'm afraid. All we have with LLMs is a probabilistic word salad generator that is realistic enough to get people who should / should not know better very excited.
It is better than Google search because it puts the results into a realistic sounding piece of text. It is worse than Google search because it does not provide sources or context. Both will steal the information you feed in.
Exactly no progress has been made in understanding or creating real intelligence in any meaningful sense, and we should have no expectation of any progress anytime soon while everyone is so focused on purely syntactical learning environments. Nobody learns anything by feeding them a dictionary.
The leading neuroscientific theory of the brain says that, at it's core, it predicts experiences. It's called predictive coding. LLM's don't predict real time sensory data, they predict just text, and not a real time stream. But the conceptual similarity is still remarkable, both are at their core predictors.
> Exactly no progress has been made in understanding or creating real intelligence in any meaningful sense
Strong words…
The thing has understanding. It may be alien and imperfect, with strange failure modes and quirks. But it has significant understanding of the real world. At this point, there are countless compelling examples demonstrating this.
The only way the model could so effectively predict the next token of such an immense and diverse collection of texts is by acquiring some level of understanding of the circumstances that gave rise to those texts.
The more I use gpt-4, this clearer this is to me, despite its limitations.
But it is being given only those texts, not the circumstances. All that is can do is associate and surface patterns in the texts - very complex though those patterns may be. It should not be surprising that those patterns are recognizable, and even that they appear 'real', as they are derived from text about our world. However there cannot be any understanding beyond that, just as with the shadows on the wall of Plato's cave.
Are you arguing the humans are not merely looking at the shadows on the cave wall but LLMs are? Or that the shadows that the LLMs sees are fundamentally different / worse than ours?
> All that is can do is associate and surface patterns in the texts - very complex though those patterns may be
One of the more interesting ideas I’ve heard is that text contains embedded world models that are far richer that we previously imagined. We missed this because it is something we take entirely for granted and couldn’t imagine it any other way, like a fish who doesn’t realize they are swimming in “water”. This seems like a very plausible explanation for the performance of gpt-4 and the like.
We humans see and digest much much more than just texts we have been given to read. Indeed, and texts are exactly the shadows on the cave wall that we create and share to describe and remember the world around us - but without direct experience of that world around us they can only be shadows.
Yes quite plausible that extraordinarily complex models are embedded in the text corpus, but these can only relate to the texts themselves, as that is all that exists as far as the LLM is concerned. Again, it should not be a great surprise that they correlate to our real world to some extent, but they cannot go beyond those input texts.
Way less than ten years. You have to also consider the speed of input, thought (output), and communication. GPT-4 is basically human equivalent IQ but probably faster than human. PaLM 2 is not quite human level IQ but is something like 20 times faster than human output.
The input rate of the Claude model is incredible, digesting a book in 20 seconds. Still not quite human level IQ but pretty close.
There is relatively low-hanging fruit for optimizing the models and software and also the hardware for this specific application. We can expect at least another 10 times performance improvement within a couple of years.
And of course as far as IQ that we can measure we can assume it will probably come close to top marks within a few years. But you should scale that based on the incredible speed and other advantages of being digital.
Pure LLMs alone can probably not go vastly beyond human intelligence because they rely on imitating humans (human text). I think there is still one or two breakthroughs missing to get robotics solved.
PaLM aalready includes some LLM-generated training (consensus of different approaches), and these kinds of synthetic self-driven training metrics will only get more sophisticated and effective at improving the capabilities. It’s conceivable that we will start seeing AlphaZero-like improvement curves in reasoning.
I'm not sure how consensus would get you significantly above human baseline. Doesn't that just get you some sort of average?
The basic problem with synthetic self-training is that we need some reward function which tells us whether a given synthetic example is good. In case of AlphaGo Zero, this was a synthetic strategy which won the game, or scored a lot. Which can be automatically detected. But how do we automatically recognize that synthetic text has "high quality"?
One case where it might work is proofs in a formal proof language which can be checked automatically via software. So if a language model is tasked to generate synthetic conjecture/proof pairs, it is possible to automatically recognize the correct ones, and use that for self-training data (unsupervised, supervised, reinforcement, I'm not sure), enabling it to recursively create more complex synthetic proofs.
A very similar approach (with some sort of unit tests instead of proofs) is described here in more detail:
https://arxiv.org/abs/2207.14502
It was a while that I read it, so my description above is kinda fuzzy. It might involve some adversarial step that I missed.
One problem is to get this process off the ground (bootstrapping), which is difficult, since we need some baseline capability first to create any successful synthetic examples, and there aren't a lot of human created formal proofs which can be used as bootstrapping training data.
Another problem is that, even if it worked, this system would just be good at generating proofs. Maybe there is some amount of transfer to natural language intelligence, but I'm not sure about that.
If you have a different idea for creating a reward signal, I would be interested how it could be done.
Yes, but that would require academics to grow a practical bone in their body and realize "understanding" language is just one piece of the "intelligence puzzle."
The hype will die down on LLMs (slowly... all those ML researchers need to justify the sunk-cost of specializing into a very niche, albeit sales-friendly, field).
If it's any consolation, I doubt we'll see progress towards a "technological singularity" until the current crop of career scientists retire into the dirt -- or there is a fundamental change in resource allocation allowing other, more creative types to start experimenting with building out a probabilistic model for:
Human language in -> determination of "what to do"/workflow to run -> run
We're seeing a few startups in this area, but I haven't seen anyone create any useful agent.
I see a ton of buzz around "prompt engineering", which is being able to shape arbitrary inputs into a prompt that will produce high quality results, but the next step is consuming and acting on prompt results programatically and that's an area where I see way less "buzz", but it's likely where the real value is found.
> It is just a glorified probabilistic text generator.
> It is just a “stochastic parrot”.
> It “hallucinates”.
> It is not “intelligent” the way humans are.
> It does not actually “think” like humans.
> It will never get human qualities like humour.[4]
> It is snake oil and is no different from any other
technology that has come before.
> If it causes job losses, new jobs will emerge, like with all previous technological revolutions.
The OP cites all of these as 'moving goalposts' but I'm not sure how, given that they're all true? Like all of the hype I see around LLMs is all the same hype I saw around from Bitcoin. It's by people that haven't used it in a professional environment, with no clue about the actual flaws, limitations or issues. And companies are intent on selling you a solution in search of a problem because they want to create aa product hype cycle.
It feels different because you've bitten onto the bait. Personally given the code output and so forth I've seen from LLMs I'd highly doubt the experience of any software engineer that uses it in their day to day life.
Some (many?) of those statements have been empirically proven false, like understanding humor. Multi-modal LLMs can explain why certain images are funny, for example.
It does not understand humor. LLMs respond affirmatively to whatever input you give it and use the elements of whatever you feed it to try and explain. That's the core mistake the article makes and why that specific point is wrong. Let me demonstrate.
I asked the LLM the following prompt: Can you explain why the joke "Where's the joke?" is funny?
The response: "The humor in the joke "Where's the joke?" is derived from its meta or self-referential nature. The joke plays with the expectation of a traditional joke setup and punchline structure. Typically, when someone tells a joke, there is an expected punchline that delivers the humor. However, in this case, the punchline is intentionally missing, and instead, the question "Where's the joke?" is posed."
All it's doing is the same thing when you ask it to summarize a piece of data. This isn't a joke or meant to be a joke, but by telling it to explain it as a joke/meme will cause the output to differ in and regurgitate a bunch of nonsense about humor. This isn't understanding humor.
Tell you what, I'll go ahead and favorite this response and see what happens in about two years. By then I have a feeling I'll be vindicated and companies will have moved into the next Big Thing. Much like every other radical revolution that's been claimed in the past 10 years (Crypto, NFTs, Metaverse etc).
>AI technologies has probably started and large scale obsoletion of jobs may be around the corner
this has been said with the advent of computerization, the entire internet and yet here's a simple thing to look for: If this is true we will see both skyrocketing unemployment numbers and productivity growth at the same time. Until that is happening you can rest easy. One pretty trivial reason is that deep technology adoption that actually replaces professions wholesale is really slow. A lot of companies still run on ancient Excel versions and fax machines. The average employer is a small business of 15 people where nobody has an idea what Slack is.
Also one of the more sinister and clever strategies in recent times is tech companies framing their crazed hiring-spree corrections (looking at FAANG) as revolutionary AI developments
> It states that no one at Zerodha will lose their job if a technology implementation (AI or non-AI) directly renders their existing responsibilities and tasks obsolete.
This was just written to say that you care. You know well that when push comes to shove, they'll be laid off.
This is very true. There is so much reinvention of processes happening behind the scenes around GPT4 as an API. These advancements will take a few quarters to show impact.
I look forward to Atlas getting a voice interface and responding GPTChat style. That will cement it for most of us, that there's something new under the sun.
Until then I expect the denial and explainers (It's just another technology! Like phones!) will have the floor.
The discussion is healthy because we'd like to anticipate changes before they swamp us. It's important to speculate. The denigration of speculation is less helpful.
I'm happy to watch from the sidelines. But I sure pay more attention to the folks trying to imagine the future, than the folks claiming nothing will change.
Even if you (obviously falsely, in my opinion) believe that nothing gets qualitatively better than GPT-4, we know that the speed will increase by AT LEAST one order of magnitude. PaLM 2 seems marginally less intelligent than GPT-4 but is probably 10 or 20 times faster already.
Given that we have increased computing performance by more than a dozen orders of magnitude already and we are now optimizing for a very specific application, it is almost a given that we will get at least one more order of magnitude performance improvement in the near term.
There are also new approaches like crossbar arrays of memristors that, if we can make them work, promises at least two more orders of magnitude performance increases. Quite possibly more.
Claude can read a whole book and answer a question about it in 20 seconds. Not quite at GPT-4 level but no reason to believe that isn't possible in the near future.
Computing performance and efficiency have consistently increased by orders of magnitude as we leverage previous generations of systems to make improvements and when necessary invent new paradigms.
We are talking about AI here. LLMs are obviously just an intermediate technology. For example, the next "big" model, Google's Gemini, will be multimodal, it will be able to handle other data types besides text. The next step is probably robotics.
I’m a bit more skeptical about robotics though, the human body is a complex marvel of nature and no software will replace the fact that it’s hard to build hardware with the capability of a living musculoskeletal system.
If a Boston Dynamics robot had human level intelligence, it could already do a lot. Their limbs are probably not capable of the agility of humans, but humans also don't have the agility of many animals, which hasn't stopped us.
Yeah, it's different because it's behind a HTTP API and in a year or two, once OpenAI is skint and your entire product is built around their chat API, you'll be paying 50 cents per request ...at which point your product will no longer be profitable.
The competition is only increasing which will drive down the price. Right now users have 3 main providers: OpenAI, Anthromorpic and Google along with dozen open source models.
One of the articles linked here [1] reminded me of the great Ranulph Glanville, and his "Inside Every White Box There Are Two Black Boxes Trying To Get Out"
LLMs are a just a tool. Yes, "just". They're good at certain tasks. Mainly ones that have a lot of training data available and don't require precise output. Images, writing, boilerplate code, etc. For any other task their usefulness falls of sharply.
> What it and its contemporaries have set loose in such a short period of time, and what is to follow on their heels tomorrow, is the real crux of the matter.
What is the idea that LLMs will get significantly better based on? That the technology finally got good enough to be useful is great, but past performance is not a predictor of future performance. Predictions need to be based on the nature of the technology in question.
1. There's only so much usable training data available out there and I suspect most of it is already being used. Once a programming LLM is trained on the code on Github it has basically seen it all.
2. They are incapable of learning anything. Not even trivial concepts. Therefore there's no real way for them to build up their understanding step-by-step like a human would.
You forgot step 1a and 1b (which the article touches on).
1a) say it's incapable of thinking/reasoning/learning/understanding/etc
1b) argue about it's ability to think/reason/learn/understand/etc
In this case:
> They are incapable of learning anything
ChatGPT seems to have learned about plenty of things before 2021. If you ask it about concepts like copyright or gravity or communism, or ask it to "explain quantum computing in simple terms", as the landing page suggests, it displays a fairly good understanding of these concepts.
Human memory is divided into short-term and long-term (along with a bunch more concepts). It's true that ChatGPT lacks the ability to commit information from its short term memory (ie "chats") into it's long-term memory via inference, but GPT-4-Mawr, fine-tuned or re-trained on text including a new concept you invented would display some level of understanding (depending on how much text you had to fine tune it on).
On a per-chat basis, ChatGPT is able to, up to the limits of the context window, know things, and it's entirely fair to point out that a) the context window is fairly limited (8000 tokens, or 32k paid) and b) beyond that, it forgets what it was told. Some people are described as having the memory of a goldfish, and that description applies to ChatGPT. If you can fit your concept into the context window, ChatGPT-4 is able to do some reasoning about it. Thus, it's able to learn and understand new things, but forgets them just as quickly.
Humans sleep every day or so, and during that time the brain gets to do things that it isn't able to do while awake, and it's been suggested that sleep is important for integrating the day's activities and thus learning of advanced concepts. Assuming that's true, then we're just looking at different timescales.
If ChatGPT "sleeps" every couple of months, and learns everything it was told between last time it was trained, it's just operating on different timescales than humans do.
As a non-business person, I imagine that folks would typically want some correctness guarantees, which seem near-impossible with today's LLMs. What kind of business use-cases do they enable? Probably a failure of imagination on my part, but I can't think of many tasks that can be done consistently well by AI that can't also be done with simpler software.
I’m both a business person and an engineer. I recently did a short workshop where a marketing guy took us through using GPT and Midjourney to iterate through new business ideas. In the 4 hours of the workshop, a bunch of mostly non developer people generated about 500 business ideas using GPT, took the top ideas, and turned them into business pitches. A couple of people in the room thought the idea their promoting came up with was good enough to run with.
This sort of stuff is fuzzy, even without AI. In many ways it’s about exploring the knowledge space rather than seeking specific answers. None of this requires correctness because there are no correct answers to begin with.
Even in computing, correctness is not always important. I recently asked GPT to produce a bunch of names from the Austin Powers universe in order to populate a demo database. I asked for them in JSON format and in 20 seconds I had what I needed. There is no wrong answer for that, either.
> The haiku at the end is not a haiku, technically
Yeah, haikus are 17 japanese mora, and so if it doesn't come from the mora region of japan it's not a true haiku, it's just a short poem.
> I imagine that folks would typically want some correctness guarantees
Us programmers really value correctness, but the real world doesn't actually care about it in quite a few cases. In the real world, most data comes from humans, and humans make mistakes all the time, so basically any data might have some amount of error. There's a high tolerance for a few minor mistakes here and there.
In the real world, tasks are often given to interns, and they make small mistakes all the time, but it mostly ends up being okay. If AI manages to do the same, well, that's a huge swath of tasks it can do.
> I can't think of many tasks that can be done consistently well by AI that can't also be done with simpler software
Here's the thing though: "simpler software" probably isn't actually cheaper, available, or usable. It's probably not simpler to operate since, you know, english is simpler than any DSL us programmers have come up with until now.
I've dabbled in comms work, and it's very useful for that. One thing that I despise doing is writing copy and LLMs are really good for that - writing dozens of social media posts is tedious and LLMs can rephrase things so that they sound less repetitive. I'm a good writer but I'm an amazing editor so having something to start with also solves the 'staring at a blank page/doc' problem.
Likewise, generating polite answers to stupid questions. Instead of worrying 'how do I tell an ego-driven exec/politician/candidate that what they want is stupid or not possible?' I can have an LLM spit out the stupid corporate drivel the C-Suite all want from their servants. It's also good for dumbing things down but not making it sound like you're dumbing things down and therefore is good for rewriting information for different audiences.
You don't (right now) use LLMs for generating facts but they have a great deal of utility in massaging language.
Will current developments in generative AI eventually result in a social revolution in human affairs?
Technology has played a role in similar transitions in the past. Mechanized agricultural production mosly eliminated the need for a large agricultural workforce for producing staple crops, a workforce that historically was organized and controlled via systems like serfdom and slavery, which were mostly eliminated as mechanization spread. Domestic household technology (washing machines, vacuums, electricity, etc) ended the need for a household to employ domestic servants and facilitated the entrance of women into the workforce, with associated loosening of societal gender role expectations.
Machine learning systems might play a similar role in enabling automation of the industrial factory system, creating a system in which a few workers manage complex workflows with all basic tasks done by highly adaptable robots. Generative AI appears poised to eliminate a similar amount of basic tedious work in the white-collar world of documentation creation, legal research, all secretarial-type tasks, boilerplate code generation, etc.
The real question in societal terms will be whether or not the fruits of all this labor elimination will end up controlled by a tiny minority of the human population. If so, that could plausibly lead to societal pressure to completely restructure the investment capital system of wealth accumulation and concentration, i.e. AI might cause socialism to become more popular.
However, the notion that AI systems will be making the fundamental choices (going to war, creating a national constitution, writing and voting on legislation, forming a new business entity, etc.) seems farfetched at present.
I’ve lived through the web in 1995. Google in 2000. Web 2.0 in 2005. Smartphones in 2009. Now it’s generative AI in 2023.
Every time, there’s an argument about whether it’s the end of the world or a total nothingburger. Every time, industry leaders are knocked off their perch. Every time, there are surprise developments and innovations, both positive and negative. Every time, within a few years, the new technology goes from seeming magical even in the wealthiest and most advanced locations to commonplace in a rural farm in the developing world.
It’s an incredible, terrifying and fascinating time to be alive.