It's much more simple, and more deep: most humans believe they are special/unique/non-machine like spiritual beings. Anything suggesting they could be as simple as the result of mechanical matrix multiplications is deeply disturbing and unacceptable.
There is a rich recent anti-AGI literature written by philosophy people which basically boils down to this: "a machine could never be as meaningful and creative as I am, because I am human, while the AGI is just operations on bits".
Life, as it exists on Earth in the form of men, animals and plants, is to be found, let us suppose in a high form in the solar and stellar regions.
Rather than think that so many stars and parts of the heavens are uninhabited and that this earth of ours alone is peopled—and that with beings perhaps of an inferior type—we will suppose that in every region there are inhabitants, differing in nature by rank and all owing their origin to God, who is the center and circumference of all stellar regions.
Of the inhabitants then of worlds other than our own we can know still less having no standards by which to appraise them.
Nicholas of Cusa, c1440; Cardinal and Bishop
The machine learning techniques that were developed and enhanced during the last decade are not magical, like any other machines/software.
But people have different and irrational expectations about AI.
The AI that has taken over google search has made a great product kinda of awful now.
What breakthroughs are you referring to?
I mean Photoshop could do that 10+ years ago without "machine learning" even being a thing people talked about.
Photoshop definitely cannot do that, I know that for a fact.
https://towardsdatascience.com/virtual-background-for-video-...
There's an example article on the subject.
I feel the same principle should apply to software - yes, you've built a mathematical model which can take input and make a decision based on the internal algorithms, if you trained it to detect background in video then that's what it will do.
But it's not intelligence. It's no different than the bacteria deciding what to eat because certain biological receptors were triggered. I think calling it intelligent is one of the biggest lies IT professionals tell themselves and others.
That's not to say the technology isn't impressive - it certainly is. But it's not AI in my opinion.
Take autonomous cars. Sure, Musk is over-hyping, but we are making progress.
I imagine it will go something like:
Step 1) support for drivers (anti sleep or colision).. done?
Step 2) autonomous driving in one area, perfect conditions, using expensive sensors
Step n) gradual iteration removes those qualifications one by one
.. yes, it will take 10/20 years before cars can drive autonomously in chaotic conditions such as "centre of Paris in the rain". But at each of those steps value is created, and at each step people will say "yea but..".
We had real time green screens 10 years ago. I don't think it's that unimaginable.
I've implemented a natural language search using bleeding edge work, the results I can assure you are impressive.
Everything from route planning to spam filtering has seen major upgrades thanks to ML in the last 8 years. Someone mentioned the zoom backgrounds, besides that image generation and the field of image processing in general. Document classification, translation. Recommendations. Malware detection, code completion. I could go on.
No one promised me AGI so idk what you're on about and that certainly wasn't the promise billed to me when things thawed out this time but the results have pretty undeniably changed a lot of tech we use.
"Our long term aim is to solve intelligence, developing more general and capable problem-solving systems, known as artificial general intelligence (AGI)."
Dipping into my own speculation, to your point about how to measure, between our (humanity's) superiority complex and with how we move the baselines right now I don't know if people will acknowledge AGI if and until it's far superior to us. If even an average adult level intelligence is produced I see a bunch of people just treating it poorly and telling the researchers that it's not good enough.
Edit: And maybe I should amend my original statement to say I've never heard a researcher promise me about AGI. That said that statement from DeepMind doesn't really promise anything other than they're working towards it.
https://www.vetta.org/2011/12/goodbye-2011-hello-2012/
He doesn't say so publicly any more, but I think it is due to people's negative reaction. I don't think he changed his opinion about AGI.
You may have implemented something that impressed you but when you move that solution into real use were other's as impressed?
That's what is probably happening with the google search team. A lot of impressive demos, pats on the back, metrics being met but it falls apart in production.
Most people don't think Google's search is good. Most people on Google's team probably think it's better than ever. Those are two different groups.
Spam filtering may have had upgrades but it is not really better for it and in many cases worse.
I used it in real use, the answer was still a hard yes.
My point is - if you don't actually work with state of the art AI research, then yeah, it's easy to see it as nothing more than overhyped garbage, because that's exactly what's being sold to regular consumers.
[1] https://ai.plainenglish.io/how-ai-actually-helped-in-the-dev...
The fact that the researchers were clear about that doesn't absolve the marketing department, CEOs, journalists and pundits from their BS claims that we're doing something like AGI.
Backpropagation has zero creativity. It's an elaborate mechanical parrot, and nothing more. It can never relate to you on a personal level, because it never experiences the world. It has no conception of what the world is.
At least a dog gets hungry.
Not persuaded? Try https://news.ycombinator.com/item?id=23346972
The problem is: it's not really clear how much creativity we have, and how much of it is better explained by highly constrained randomized search and optimization.
> It can never relate to you on a personal level
Well, sure. Even if/once we reach AGI, it's going to be a highly alien creature.
> because it never experiences the world.
Hard to put this on a rigorous setting.
> It has no conception of what the world is.
It has imperfect models of the world it is presented. So do we!
> At least a dog gets hungry.
I don't think "gets hungry" is a very meaningful way to put this. But, yes: higher living beings act with agency in their environment (and most deep learning AIs we build don't, instead having rigorous steps of interaction not forming any memory of the interaction) and have mechanisms to seek novelty in those interactions. I don't view these as impossible barriers to leap over.
AlphaGo is grounded because it experienced Go, and has a very good conception of what Go is. I similarly expect OpenAI's formal math effort to succeed. Doing math (e.g. choosing a problem and posing a conjecture) benefits from real world experience, but proving a theorem really doesn't. Writing a proof does, but it's a separate problem.
I think software engineering requires real world experience, but competitive programming probably doesn't.
You might be using a different definition of "magical" than what others are using in this context.
Of course, when you break down ML techniques, it's all just math running on FETs. So no, it's not extra-dimensional hocus pocus, but absolutely nobody is using that particular definition.
We've seen unexpected superhuman performance from ML, and in many cases, it's been inscrutable to the observer as to how that performance was achieved.
Think move 37 in game #2 of Lee Sedol vs. AlphaGo. This move was shocking to observers, in that it appeared to be "bad", but was ultimately part of a winning strategy for AlphaGo. And this was all done in the backdrop of sudden superhuman performance in a problem domain that was "safe from ML".
When people use the term "magic" in this context, think of "Any sufficiently advanced technology is indistinguishable from magic" mixed with the awe of seeing a machine do something unexpected.
And don't forget, the human brain is just a lump of matter that consumes only 20W of energy to achieve what it does. No magic here either, just physics. Synthetically replicating (and completely surpassing) its functionality is a question of "when", not "if".
Maybe we should start by "humans are the greatest thing ever" and then try to fit our world knowledge to that conclusion. We feel it right in our qualia that we are right, and qualia is ineffable.
That's not my understanding of the 'hard problem of consciousness'. Admittedly, all I know about the subject is what I've heard from D Chalmers in half-a-dozen podcast interviews.
Can you point to a definitive source?
AlphaGo is nothing else than brute force.
And brute force can go a long way, it should not be underestimated.
But so far, this approach has not let to emergent behaviors, the ML blackbox is not giving back more than what was fed.
They used a fantastic amount of compute for their solution, but, as has uniformly been the case for neutral networks, the compute required for both training and inference has dropped rapidly after the initial research result.
This statement is completely false with accepted definitions of "brute force" in the context of computer science.
I think people are completely wrong to pooh pooh the utility of computers being better at search and calculations in an ever wider range of applied fields, but linking computers surpassing humans at more examples of those problems to certainty we'll synthetically replicate brain functionality we barely understand is the sort of stretch which is exactly why AGI-sceptics feel the need to point out that this is just a tool iterating through existing programs and sticking lines of code together until the program outputs the desired output, not evidence of reasoning in a more human-like way.
eg, for speech compression: https://arxiv.org/abs/2107.03312
I presume the amount of hype AI research has been getting for the past 4 decades might be at least part of the reason. I also think AI is terribly named. We are assigning “intelligence” to basically a statistical inference model before philosophers and psychologists have even figured out what “intelligence” is (at least in a non-racist way).
I know that both the quality and (especially) the quantity of inference done with machine learning algorithms is really impressive indeed. But when people are advocating AI research as a step towards some “artificial general intelligence” people (rightly) will raise questions and start poohing you down.
If your plant actually looks like a moss you probably shouldn’t call it a rose. (Even though your moss is actually quite amazing).