Assuming it keeps improving at the same rate, which I think we are already seeing not play out. If you compare the first six months when GPT truly hit the mainstream to the previous six months, the improvements are not nearly as evident. That isn’t to say they aren’t noticeable, I could definitely tell it’s improving, but not nearly at the pace it once was.
There’s also the fact that they can’t possibly keep improving frontier models at the same rate (I.e. training investment) when investment starts slowing down. The amount of cash being burned is completely unsustainable and you’re already seeing some pullback.
The issue is that before GPT models basically were useless for any conversation. We are literally in science fiction realm. From a text conversation perspective the gap between where we are at and what’s left to get to is relatively small.
In my opinion, the main thing we need to do is have training happen continuously. And probably more real world data (from sensors).
> what’s left to get to is relatively small
Not necessarily. In many (most?) areas of tech the rate of advancement follows a logarithmic curve. That is to say, the first 90% is achieved quickly but the last 10% takes significantly more time.
The ELIZA effect has been around since 1966. I think lots of folks feel “AI” has advanced much more quickly that it really has because of the nature of its many past boom / bust cycles.
ELIZA has never done well in conversational tests. GPT-4.5, for example, tends to out-human humans. Like I could never ask ELIZA this question and get anything close to a decent response: "Give me three points that convey the impact that 9/11 had on rap music in the 21st century with some good examples?" Asking ChatGPT today gives me an answer that I'd give an A grade to a strong college student. ELIZA's response -- "What do you think?".
On the other hand we keep seeing only marginal generational imorovements in CPU space, yet performance gains over last 10 years in CPUs are very material.
Every new model might not be a leap like it used to be, but give it enough time and improvements add up.
Nobody is disputing that. I specifically said that I can see the improvements from the last six months. What I’m saying is we can’t assume that every two years it will improve at the same rate.
The further we get into this, the more AI feels like 3-D printing. Significantly bigger and will be more widely used for sure. But nowhere near the “new industrial revolution” that all these companies are making it out to be
Do you agree that economic and behaviour shift will be comparable to mobile and we are at the times of Nokia 3310. Does it count as industrial revolution?
I think that’s kind of a strange question/parallel that doesn’t have a concrete answer, partially because even the people making these tools don’t really know where it’s going to land or what the ultimate utility is. Hence why they’re begging all of us to figure out the billion dollar applications for them.
Ultimately they are clearly here to stay but I think they are going to be incredibly important in some industries and minimally present in others (a glorified chatbot/summarizing tool for instance). Whatever form it takes it’s definitely not going to be a model where individuals have subscriptions they pay for monthly.
> even the people making these tools don’t really know where it’s going to land
exactly my point to compare it with pre-iPhone mobile market: wide (and growing fast!) adoption, clear potential (WAP websites, J2ME games), many players in the game, some real market fit discovered already (Blackberry), influx of capitial and tinkerers alike, but still a lot of unknowns where it will ultimately land.
Even if no single improvement was revolutionary (even first iPhone was just a fancy phone without App Store), overall mobile made billion dollar industries possible, for better or worse, and changed the way we live. Counts as industrial revolution, comparable to the Internet itself in my eyes.
What would 3D printing have to do in order for it to be the new industrial revolution to you?
Everyone has one at home spitting out items they need daily/weekly like was promised. I don't know if you remember the 3D printer (somewhat) boom of the 2010's but the hype was crazy when it became more mainstream. Maker spaces popping up in cities everywhere, schools showing off their units, every conference had some talk on them, startups left and right. The AI boom is basically a more-funded version of that. It was hot hot hot and people thought every home was bound to have one.
It took a while, especially because the early 3D printers were a project of calibration unto themselves, but modern printers are fairly trouble free. I accidentally melted the bottom of my blender jug on my toaster oven so I'm printing a replacement one right now. Turns out the critical mass needed is someone else having already done the CAD so I can just hit print from my phone, which makes 3D printing a reality.
it's also worth keeping in mind that alot of the 'improvements' are actually advancements in harnesses and tools.
This is the hot button right here. Most of the advancements have also come at the cost of excess: exponential token use at the expense of marginal gains.
Context is still a large limiting factor, and we have band aids around that area already. And the further along we go the further distributed LLMs get in terms of additional pieces.
As for the original article and sentiment I'm sure AI will be a boon for law. It's going to be much easier for the general consumer / person / small business to represent themselves which feels like a win. The downside is I feel like we're tracking towards a digital hell of "virtual lawyers" that will be at the whim of any org. Consumer laws really need to change now to help avoid this dystopian path we're on.
I'll note that having countless superintelligences handy will accelerate problem-solving e.g. dystopia.
I agree. But notice that you assume that there is a metric with which you can messure improvement. Which is fine if you are measuring against your personal taste.
But it might be that the optimization target itself has a ceiling. If you're training toward human approval ratings from a broad population, you converge toward what median preference selects for. The plateau is baked into what you're measuring against.
It doesn't even need to 'improve' at the same rate to have extraordinary impact in society. Even if the frontier models stayed roughly the same in cost and capability for just 1-2 years, the harnesses and processes built around them would mature. We have not yet metabolized these models. Frankly, a lot of this feels like late 80s early 90s complaints about how office computerization wasn't happening yet--it was, just not at the rate promised by the companies selling computers to businesses. We don't look back at those people in the 80s saying that paper was here to stay as visionaries just because they noticed that propaganda temporarily outran the business environment.
I just wish people would take a step back and think about the timescales here. Language Models are Unsupervised Multitask Learners was in 2019. Here we are seven years later and LOOK AROUND. The landscape is unrecognizable. It's worth thinking about who, in those seven years, had an accurate estimate of the future and whose estimate fundamentally failed. And just as it is valuable to note where propaganda about progress speeds past where we are, we should remember that it is costless to announce that at some unspecified future time all of this will settle down and things will go back to the way they were.