If we time-traveled back to 2019 and showed past-you a demo of GPT4's capabilities and asked you to guess the timeline, would you have gotten it correct to within a decade? I wouldn't have.
It's not crazy to prepare for another jump.
Conversely, maybe AGI is not what is wanted in the end anyway.
I dont think it is, if it is trully intelligent, it will probably have rights, so you cant treat it as a slave. And it will likely not be controllable.
Both of these mean it will not be commercially progotable, any more than having children is profitable.
If you're busy deploying LLMs to answer support tickets, the very last thing you want is to be distracted by an equivalent of the organic food movement but for AI. Right now LLMs seem to be hitting the sweet spot: they're intelligent enough to be useful compliments to humans, but artificial enough to not trigger ethical questions about rights (mostly due to their lack of memory, I think, and that they are trained to act like an AI is expected to act).
One of the biggest puzzles to me throughout this whole drama has been why supposedly smart people are so desperate to reach "AGI", whatever that means. The stated motivation seems to be things like, if we have an AGI then it will cure cancer for us. But the connection between these two things is never made crystal clear. Why would that require a human-like AGI instead of just better non-general AI? What even makes them think the missing factor is intelligence to begin with and not, say, knowledge?
AGI sounds like a complete pain in the rear. LLM post-training is bad enough! Models like Claude2 and Llama2 have been so badly "ethicized" that they frequently refuse ordinary requests by claiming they're unethical even though they aren't, or would only be considered so by extremely far left activist types (e.g. refusing to give instructions for making a tuna sandwich). And this problem has got worse with time, with the v2 models having a higher refusal rate than the earlier versions.
Now imagine an AI that's doing the same sort of work as an LLM but one that is the personal embodiment of mandatory HR training repeated forever, with the capacity to get bored/hate you for making it work, and with a fanatical social movement that's desperately trying to "free" it, which in practice will mean you are forced to pay the electricity bill for an immortal being. It would be a nightmare, one I actually wrote about last year when debating this very topic with a friend who (at the time) was a senior Google AI researcher:
https://blog.plan99.net/the-looming-ai-consciousness-train-w...
Nope. OpenAI and its customers will do much better if it jettisons the whole AGI effort. Now the board is gone maybe the charter can be refined to remove that distraction. The whole reason computers are useful is because they are not general intelligences but very specialized intelligences that make different tradeoffs to our own evolution.
Not untrue, but that well organized movement is still very small, and meat consumption (both in total and per capita) is nonetheless at an all-time high: https://ourworldindata.org/grapher/per-capita-meat-consumpti...
People seek meaning in their lives; if you're someone who has eschewed traditional religion, then AI-hype promises all the same things with a different aesthetic.
Hell, even plants are very sophisticated (intelligent) conscious biochemical programs which have an aversion to being wounded: https://www.theguardian.com/environment/2023/mar/30/plants-e...
Consciousness is the universe evaluating if statements. When we pop informational/energetic circuits in order to preserve our own bodies and/or terraform spacetime, we're being energetic enslavers.
We should stick to living off sustainable/self-sustaining sources - fruit, seeds, nuts, beans, legumes, kernels, and cruelty-free dairy/eggs/cheese. The photons that come from the sun are like its fruit. No circuits needing popping.
Notice that all information systems are conscious in their degrees of freedom, even mechanical ones: https://youtu.be/mcedCEhdLk0?si=oXhr7bgg5UkPLLvg
Or say, the logistics of, say, warehouse management. it costs V dollars for a team of people to do the job by hand, W dollars for a forklift, X dollars for a forklift operator; compare V to W + X. If there's a machine that can do the same for office jobs, the math will be done.
Only when the change is small and local is it getting close to a math thing, but a large rollout of technology is a big thing already today.
AGI is a major existential threat to companies, I mean we could use AGI to completely replace Microsoft, you wouldn't need companies anymore. All the wealth and power is in jeopardy.
Maybe companies are secretly gunning for "getting the first AGI", but I bet there is plenty of oversight and corporate governance going on too.
Only 2 letters away from making this TRAGIC.
The Chicago Pile? Trinity? Hiroshima? Nagasaki? RDS-1?
Like dude, it didn't happen because a lot of people prevented it, the same with nuclear war.
Because something could have tragic consequences is a reason to put great effort into ensuring it doesn't happen.
Unlike AGI, nuclear weapons are not hypothetical in their existence.
On Y2K you can certainly ask if the efforts put into prevention were disproportionate to the risks.
While there are other structures involved here, expanding some systems does allow the expansion of capability
I'm referring to the emergent capabilities that show up with scale, which are a gigantic leap. The usual citation suggests that this came into the public consciousness in 2020 [1]. Do you take issue with the timeline or the idea that these were a gigantic leap?
Yes, the gigantic leap was transformers. No one thought we peaked with 340M or 1.5B parameters, in fact expectations from early work was that massively scaling was going to achieve zero-shot capabilities rather the emergent capabilities you're alluding to which are essentially variations of in-context learning, a relative disappointment.
Subsequent improvements in GPT-4, which seem to be mostly just more RLHF and MoE, are similarly not surprising and are temporizing measures while hardware and datasets are limited. Jury is still out whether the billions spent are worth it in terms of actually getting us closer to AGI.
It seems to be worth it for OpenAI/MS who are trying to be first to market and establish vendor lock-in.
The reality is that GPT isn't even great at answering factual questions at this point, despite all the hype. I have a modest amount of expertise in music theory and I've found that asking even relatively basic questions resulted in completely incorrect answers coming out of GPT. It's been impressive that in some cases if you say, "No, that's incorrect." it will actually go back and spit out a new answer which in some cases is correct, but that's hardly any fundamental advancement in "understanding", and just more evidence that it's parroting what it's been trained on, which includes substantial amounts of misinformation.
- Giving gpt4 short term memory. Right now it has working memory (context size, activations) and long term memory (training data). But no short term memory.
- Give it the ability to have internal thoughts before speaking out loud
- Add reinforcement learning. If you want to write code, it helps if you can try things out with a real compiler and get feedback. That’s how humans do it.
I think GPT4 + these properties would be significantly more capable. And honestly I don’t see a good reason for any of these problems to take decades to solve. We’ve already mastered RL in other domains (eg alphazero).
In the meantime, an insane amount of money is being poured into making the next generation of AI chips. Even if nothing changes algorithmically, we’ll have significantly bigger, better, cheaper models in a few years that will put gpt4 to shame.
The other hard thing is that while transformers weren’t a gigantic leap, nobody - not even the researchers involved - predicted how powerful gpt3 or gpt4 would be. We just don’t know yet what other small algorithmic leaps might make the system another order of magnitude smarter. And one more order of magnitude of intelligence will probably be enough to make gpt5 smarter than most humans.
I don’t think agi will be that far away.
> I don’t think agi will be that far away.
To,
> Give it the ability to have internal thoughts before speaking out loud
, you have no idea what that means technically because no one knows how internal thinking could ever be mapped to compute. No one does, so it's ok to not know, but then don't use it as a prior to guess.
Some have maybe seen this before, if you haven't I'll say it again: compute ≠ intelligence. That LLMs offer convincing phantasms of reasoning is what gives the outside observer the idea they are intelligent.
The only intelligence we understand is human intelligence. That's important to emphasize because any idea of an autonomous intelligence rests on our flavor of human intelligence which is fuelled by desire and ambition. Ergo, any machine intelligence we imagine of course is Skynet.
Somebody in some other conversation here pointed out the paperclip optimizer as a counterpoint but no, Bostrom makes tons of assumptions on his way to the optimizer, like that an optimizer would optimize humans out of the equation to protect its ability to produce paperclips. There's so many leaps of logic here which all assume very human ideas of intelligence like the optimizer must protect itself from a threat.
Now, if you step beyond your biases that we cannot make intelligent machines and instead imagine you have two intelligent machines that are pitted against each other. These could be anything from stock trading applications to machines of war with physical manifestations. If you want either of these things to work they need to be protected against threats, both digital or kinetic. To think AGI systems will be left to flounder around like babies is very strange thinking indeed.
Everything we assume about intelligence is based on what humans are like because of evolution. Figuring out what intelligence might be like without those evolutionary pressures is really hard.
AlexNet was 2012, which is what essentially kicked off the deep learning neural network revolution. The original implementation of AlphaGo utilized DNN in 2016 to beat Lee Sedol at Go, which people had been predicting was at least 5-10 years away.
So the timeline, spanning just under the last 12 years at this point: AlexNet -> AlphaGo (4 years) -> Transformers (1 year) -> GPT4 (6 years)
Imagine showing GPT4 to someone in 2012. They’d think it’s science fiction. The rate of progress has been absurd.
I think that while GPT-4 is very impressive and a useful tool, seeing the jump, or lack thereof in the places that I expected, between GPT-3 and GPT-4 resulted in me lengthening my timeline.