that's what I say, people are afraid of robots and AI as if the human being were very reliable, we are in the hands of a race that is capable of enslaving, raping and murdering an entire nation for political interests, but the scary thing is the computer program that talks

It's not that the AI will be in charge. It's that it gives the next tyrant more power.

I also welcome our new artificially intelligent overloads! Hopefully they are a bit more logical than our current overlords. 😄

it wouldn't have a conscience, so bad men would make it do bad things without consequence

It'd be the exact same if programmed to make money for it's creator

I agree but It's not only how good or bad it will be. It's something that you can't kick out and exile. Look at piratebay, you can't get rid of that site.

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Oh u mean something created by us, couldn’t do a worse job than us?

Interesting logic

1. Of course it's possible
2. We have nothing even close to it AI wise yet. Currently it's just inferencing.

Humans are a terrible example of an AGI as evolution is all about 'survival of the fittest'. Human AI creations have all had a specific purpose and a right/wrong answer (knowing the right answer is the only way to train an inferencing AI).

So what is the "right answer" of an AGI? If you don't have that, there's no current way to train one.

ChatGPT isn't learning in real time, but they are definitely using interactions with the users to refine the reinforcement learning / fine-tuning.

I think this is an extremely valid point. We're already seeing this to a degree. It took a matter of days after Chat GPT's release before I saw my first recipe/ script for "suggest high SEO ranking headlines, now write articles to match those articles." There will be, at best, a time lag before search algorithms can respond to this.

Imagine discovering that 90% of Reddit (posts, users, comments) was ai and you've spent months thinking you're part of a community of humans only to find out you've been alone all along...

I already see that the platforms are limiting the information...the powers that be don't want citizens to have access to too much information.

Yes, and these systems are a reflection of humanity as well, carrying with them much of the same potential and biases.

Nice sci-fi story bro

sounds like a case of forbidden chocolate.

Good luck with that. People are already struggling. You think these employers can just cut wages? There's a certain point that you can, and then there's a point where you risk destabilizing the entire economy.

What happens if every employer decides to cut wages? Are you expecting no fallout? Right now with most people not being able to afford rent and food, any reduction on wages would be faced with a backlash not ever seen in this country.

A lot of people who say these things fail to realize that when robots replace the workers, who's going to buy the products? When nobody has a job anymore except the employers, where's your revenue coming from?

.....yes the nanobot swarm graygooing the cities and people is admittedly interested but I'D STILL RATHER WE NEVER MADE IT AND DODGED THE BULLET WHEN WE HAD THE CHANCE

A fundamental computer science principle concerning basic algorithms "you should always expect the worst case scenario" is so under considered in these kinds of discussions that I'm fully expecting us to be doomed.

HOW THE HELL DO YOU COME TO THE CONCLUSION WHEN THE REASON WE MADE THE ROCKETS AND NUKES WAS AS WEAPONS FIRST. WE LITERALLY WANTED TO USE THEM TO KILL WELL BEFORE WE THOUGHT ABOUT ANYTHING ELSE

Not to mention AI is so much worse than any of those largely because the nukes and rockets don't unexpectedly turn on you one day and begin processes of destruction no one properly considered because PEOPLE ASSUME AGI WILL RANDOMLY BE BENEVOLENT

Hey I appreciate the time to engage with the article and provide your thoughts. I'll respond to a few things.

>The first two elements of that is the definition for any model, which is exactly what both AI and deterministic regression algorithms all do.

Yes, under the framework used in the article, an agent using linear regression might be a little intelligent. It can take past state data and use it to make predictions about the future state, and use those predictions to act. That would be more intelligent than an agent which makes random actions.

>I'm not saying it's a bad paper or theory, but that this essay doesn't really justify why it brings it up so much

Yes, that is a fair point. I was worried that spending more time on it would have made it even longer than it already was. But one justification is that it is a good, practical, definition of intelligence that demystifies the process of intelligence to what kind of information processing must be taking place. It is built off of information theory work in information bottlenecks, and is directly related to the motivation for autoencoders.

>The problem is that Schmidhuber 2008 only exists as a preprint and later as a conference paper -- it was never peer-reviewed.

The paper isn't an experiment with data, it was first presented at a conference to put forward an interpretation. It's been cited 189 times. I think it is worth reading, the ideas can be understood pretty easily. But it isn't the only paper that discusses the connection between compression, prediction, and intelligence. Not everyone talks in the language of compression, they may use words like elegance, parameter efficiency, information bottlenecks, or whatever, but we are talking about the same ideas. This paper has some good references, it states, "Several authors [1,5,6,11,7,9] have suggested the relevance of compression to intelligence, especially the inductive inferential (or inductive learning) part of intelligence. M. Hutter even proposed a compression contest (the Hutter prize) which was “motivated by the fact that being able to compress well is closely related to acting intelligently”

>The equation E = mc2 For the newbies out there, this is what's called a red flag.

I was trying to use an example that people would be familiar with. All the example is pointing out is that the equations of physics are highly compressed representations of the data of past physical measurements, that allow us to predict lots of future physical measurements. That could be said of Maxwell's equations or the Standard Model or any successful physical theory. Most physicists like more compressed mathematical descriptions: though they usually would call it more elegant rather than use the language of compression.

>This is completely the wrong way to think about it if you're trying to understand these things, so I hope he actually knows this.

I don't think it is wrong to say that what the transformer "knows" about the images in its dataset has been compressed into its weights. In a very real sense, a transformer is very lossy compression algorithm which takes in a huge dataset and learns weights which represent patterns in the dataset. So no, I'm not saying that literally every image in the dataset was compressed down to 1.2 bytes each. I'm saying that whatever SD learned about the relationships of the pixels in an image to their text labels is stored in 1.2 bytes per dataset image in its weights. And you can actually use those weights as a good image compression codec. The fact that it has to do this in a limited number of parameters is one of the things that forces it to learn higher-level patterns and not rely on memorization or other simpler strategies. Illya Sutskever talks about this, and was part of a team that published on it, basically showing that there is a sweet spot for data/parameter where giving it more parameters improves performance to a point, but there is a point where adding even more decreases performance. His explanation for this is that by limiting the number of parameters, the model is forced to generalize. So in Schmidhubers language, the network is forced to make more compressed representations, so it overfits less and generalizes better.

>First, this is the connectivist problem/fallacy in early AI and cog sci -- the notion that because small neuronal systems could be emulated somewhat with neural nets, and because neural nets could do useful biological-looking things, that then the limiting factor to intelligence/ability is simple scale

My argument about this doesn't come from ML systems mimicking biology. It comes from looking at exponential graphs of cost, performance, model parameters, and so on, and projecting that exponential growth will likely continue for a while. The first airplane didn't fly like a bird, it did something a lot simpler than that. In the same way, I'd bet the first AGI will be a lot simpler than a brain. I could be wrong about that.

But, I'm not even claiming that scaling transformers will lead to AGI, or that AGI will definitely be developed soon. All I'm saying is that there is significant expert uncertainty in when AGI will be developed, and it is possible that it could be developed soon. If it were, that would probably be the most difficult type of AGI to align, which is a concern.

I agree problems of bias and privacy are real and important, but your claim about what anyone in ML believes just isn't true, and the article goes in to some depth about it. Experts in machine learning collectovely give a 50% probability by 2061 of AGI with huge differences in their individual estimates. Almost all of them say it will happen in the next 75 years.

If experts were saying there was a 90% chance an asteroid would hit the earth in the next 75 years, would you claim we shouldn't start working on a solution now?

Are you saying the transformer has brought us no closer to AGI?

This dude references Netflix recommendation system, Amazon recommendations, and Facebook for “what we think true AI is”.

That is so far removed from what many are discussing right now. He doesn’t touch on LLMs at all in that interview. He talks about inference and thinking, and dismisses AI’s capabilities because “all it is in inference”.

It’s a common pushback: “the AI doesn’t actually understand anything.” And my response is, “..so?”

If it gives the illusion of thinking. If it can pass the Turing test to most of the population. If it can eventually get integrated with real-fine data , images, video, and sound — does it honestly matter if it’s “truly thinking as a human does”? Hell, do we even know how HUMANS think?