Few have predicted a reasonably-capable text-writing engine or automatic video face replacement, but many have predicted self-driving cars would have been readily available to consumers by now and semi-intelligent helper-robots being around.
Just because unforeseen advancements have been made, does not mean that foreseen advancements come true.
It's about very good general problem solving software that's way beyond the capabilities of humans while not being aligned with human interests. Not because the software is evil, but because aligning values is an unsolved problem (humans aren't even totally aligned - and values also change).
If you have an intelligence that's very good at achieving its goal and you don't have a good way to align its goal with human goals, you can very quickly get into trouble if that intelligence thinks much faster than you do.
https://www.lesswrong.com/posts/mMBTPTjRbsrqbSkZE/sorting-pe...
https://www.lesswrong.com/posts/4ARaTpNX62uaL86j6/the-hidden...
https://www.lesswrong.com/posts/BEtzRE2M5m9YEAQpX/there-s-no...
https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien...
Also, self-driving cars were mostly hyped up by companies, FSD is quite obviously a hard problem, much closer to general intelligence than the average NN application.
When I was a kid, I remember wondering how Soviets were obsessed with faking photos. A few years later, I saw Terminator 2 and realized that faking videos was also a thing. The tools for it would clearly get better and better over time. When I studied ML in the early 2000s, it seemed obvious that pattern recognition tasks such as image manipulation would be "easy" for computers, once we found the right approach and made the ML systems big enough. In the end, I decided not to pursue ML, because jobs were still scarce and I found discrete problems more interesting. That was probably the worst career mistake I've ever made.
Or, watch the latest Veritasium
#2 see linked video, he sits in the backseat, there is no driver and no one to take control.
These are just overhyped drive assist tools that market themselves immorally as something they aren’t.
If you ask it to compute something that would never have been seen on the internet it’s likely to fail. E.g. add 2 extremely large/rare numbers together
A rough calculation, humans can feasibly consume 4-5 TJ/annum of energy, of which a lot is going to go into motion or whatever. And if devoted to mental activity, it has a shelf life of ~70 years before they die.
A distributed computer might theoretically burn TJ/hr and once the weights are known they may as well be in a permanent record. The upper limits of what computers can learn and get good at are much higher than what humans can. They won't need as much implementation trickery as biology to get results.
And a plane is a vastly simpler machine than a bird!
I think this for a couple reasons:
1. The current gap in complexity is so huge. Nodes in an ANN roughly correspond to neurons, and the brain has somewhere on the order of 100 billion of them.
Even if we built an ANN that big, we would only be scratching the surface of the complexity we have in the brain. Each synapse is basically an information processing unit, with behavioral characteristics much more complicated than a simple weight function.
2. The brain is highly specific. The structure and function of the auditory cortex is totally different to that of the motor cortices, to that of the hypothalamus and so on. Some brain regions depend heavily on things like spike timing and ordering to perform their functions. Different brain regions use different mechanisms of plasticity in order to learn.
Currently most ANN's we have are vaguely inspired by the visual cortex (which is probably why a lot of the most interesting things to come out of ML so far have been related to image processing) and use something roughly analogous to net firing frequency for signal processing. I would consider it highly likely that our current ANNs are just structurally incapable of performing some of the types of computation we would consider intrinsically linked to what we think of as general intelligence.
To make the airplane analogy, I believe we're probably closer to Leonardo da Vinci's early sketches of flying machines than we are to the Right Brothers. We might have the basic idea, but I would wager we're still missing some of the key insights required to get AGI off the ground.
edit: it looks like you added some lines while I was typing, so to respond to your last points:
> it’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Few would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure?
I totally agree that it's hard to predict, that technology usually advances faster than we expect, and that tremendous progress is being made. But the road to understanding human intelligence has been characterized by a series of periods of premature optimism followed by setbacks. For instance, in the 20th century, when dyes were getting better, and we were starting to understand how different brain regions had different functions, it may have seemed like we were close to just mapping all the different pieces of the brain, and that completing the resulting puzzle would give a clear insight into the workings of the human mind. Of course it turns out we were quite far from that.
As far as what we can expect in the years leading up to AGI, I suspect it's going to be something that comes on gradually - I think computers will take on more and more tasks that were once reserved for humans over time, and the way we think about interfacing with technology might change so much that the concept of AGI might not seem relevant at some point.
As to whether the current state of things is a failure - I would not characterize it that way. I think we're making real progress, I just also think there is a bit of hubris that we may have "cracked the code" of true machine intelligence. I think we're still a few major revelations away from that.
That's hardly accurate - didn't Musk and Co. promise self-driving cars by 2012? We're in 2020, and the SDC's are great for making youtube videos, but not any good at piloting a vehicle without human intervention.
Since the 90s it has been clear that the only thing holding back what we have today is limited processing power. While there may be some new insights and directions in AI, they are not "general" and they require 3 orders of magnitude more processing power for a lot smaller improvement in performance.
What has been clear since 2010 is that this field has passed the point of diminishing returns already. We throw vastly more computational power at problems that we ever did before, and then call the result an improvement.
Deep blue beat the best human at chess using 11.8 GFLOPS of computational power. Alphago beat the best human at go using 720000 GFLOPS of power. The complexity difference between Chess and Go are within a single order of magnitude - 10x to 99x difference in complexity (https://en.wikipedia.org/wiki/Game_complexity). The difference in AI processing power to beat the best human between Chess and Go is between 4 and 5 orders of magnitude (1000x and 100000x).
This does not look like a success to me - it looks like a brute-force approach. If you spend 10000x more resources for a 10x more benefit, you're at the point of diminishing returns.
Here's a great paper that should be written (but won't be) - plot the improvements in AI and the usage of computational power for AI on the same chart.
From the 90s (https://en.wikipedia.org/wiki/History_of_self-driving_cars#1...): "The robot achieved speeds exceeding 109 miles per hour (175 km/h) on the German Autobahn, with a mean time between human interventions of 5.6 miles (9.0 km), or 95% autonomous driving."
Yup, 95% autonomous. Today we have 95.x% autonomous with roughly 10000x the resource power thrown at the problem.
So, yeah, your assertion that "Few would have predicted the results we’re seeing today in 2010." is wildly off mark, we predicted more than what we see today because we did not expect to hit a point of diminishing returns quite so quickly.
The people who did the 95% SDC in 1997 would have been disbelieving if anyone told them, in 1997, that even with 10000x more processing power thrown at the problem and new sensor hardware that was not available to them, it won't get much better than what they had.
- FSD is one example, but the improvement of computer vision since 2015 has been massive and deep learning approaches to general problem solving too. This wasn’t something people were predicting in 2010.
- The deep blue and stock fish style approaches vs. the alpha go or alpha zero approaches are categorically different - the latter being a lot more interesting and closer to general learning vs. the older approach which is more like brute force.
- GOFAI was a bad approach and the optimism in the 60s was wrong. Today’s looks more promising. Being wrong in the 60s doesn’t necessarily mean people are wrong now. It’s hard to know: https://intelligence.org/2017/10/13/fire-alarm/
For the AGI bit I’d recommend reading some of Eliezer Yudkowsky’s writing or Bostrom’s book (though I find Bostrom’s writing style tedious). There’s a lot of good writing about take offs and AGI/goal alignment that’s worth reading to get a base level understanding of the concepts people have thought through.
AGI doesn’t need to be human like to be dangerous - it can be good at general problem solving with poorly aligned goals and just act much faster. Brains exist everywhere in nature, simpler than human brains. A lot of that computation in training could be an analog of the genetic “pre-training” of evolution for humans that gets our baseline which could be one reasons humans don’t seem to require so much. There was a massive amount of “computation” over time via natural selection to get to our current state.
> plot the improvements in AI and the usage of computational power for AI on the same chart.
Would that be meaningful? I mean, I use an infinity times the computational power for writing a letter than people did 100 years ago, still producing more or less the same results.
A lot of birds don't need to flap their wings either.
The way a plane flies is quite different than the way a bird flies in complexity - they share an underlying mechanism, but planes don't need to flap wings.
It's possible that scaling up does lead to generality and we've seen hints of that.
- https://deepmind.com/blog/article/generally-capable-agents-e...
Also check out GPT-3’s performance on arithmetic tasks in the original paper (https://arxiv.org/abs/2005.14165)
Pages: 21-23, 63
Which shows some generality, the best way to accurately predict an arithmetic answer is to deduce how the mathematical rules work. That paper shows some evidence of that and that’s just from a relatively dumb predict what comes next model.
It’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Few would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure?