That’s historically inaccurate

My masters thesis advisor Ben Goertzel popularized the term and has been hosting the AGI conference since 2008:

https://agi-conference.org/

https://goertzel.org/agiri06/%5B1%5D%20Introduction_Nov15_PW...

I had lunch with Yoshua Bengio at AGI 2014 and it was most of the conversation that day

The name AGI (i.e. generalist AI) was originally intended to contrast with narrow AI which is only capable of one, or a few, specific narrow skills. A narrow AI might be able to play chess, or distinguish 20 breeds of dog, but wouldn't be able to play tic tac toe because it wasn't built for that. AGI would be able to learn to do anything, within reason.

The term AGI is obviously used very loosely with little agreement to it's precise definition, but I think a lot of people take it to mean not only generality, but specifically human-level generality, and human-level ability to learn from experience and solve problems.

A large part of the problem with AGI being poorly defined is that intelligence itself is poorly defined. Even if we choose to define AGI as meaning human-level intelligence, what does THAT mean? I think there is a simple reductionist definition of intelligence (as the word is used to refer to human/animal intelligence), but ultimately the meaning of words are derived from their usage, and the word "intelligence" is used in 100 different ways ...

Generalization is a formal concept in machine learning and is measurable.

No, it’s never going to be precise but it’s important to have a good rough definition.

Can we just use Morris et al and move on with our lives?

Position: Levels of AGI for Operationalizing Progress on the Path to AGI: https://arxiv.org/html/2311.02462v4

There are generational policy and societal shifts that need to be addressed somewhere around true Competent AGI (50% of knowledge work tasks automatable). Just like climate change, we need a shared lexicon to refer to this continuum. You can argue for different values of X but the crucial point is if X% of knowledge work is automated within a decade, then there are obvious risks we need to think about.

So much of the discourse is stuck at “we will never get to X=99” when we could agree to disagree on that and move on to considering the x=25 case. Or predict our timelines for X and then actually be held accountable for our falsifiable predictions, instead of the current vide based discussions.

The error in this argument is that electricity is real.

Can you give an example how it would be transformative compared to specialized AI?

> It would seem our own generalized intelligence is an emergent property of many, _many_ specialized processes

You can say that about other animals, but about humans it is not so sure. No animal can be taught as general set of skills as a human can, they might have some better specialized skills but clearly there is something special that makes humans so much more versatile.

So it seems there was this simple little thing humans got that makes them general, while for example our very close relatives the monkeys are not.

Yesterday my dad, in his late 70's, used Gemini with a video stream to program the thermostat. He then called me to tell me this, rather then call me to come stop by and program the thermostat.

You can call this hype, maybe it is all hype until LLMs can work on 10M LOC codebases, but recognize that LLMs are a shift that is totally incomparable to any previous AI advancement.

Yeah “AI” tools (such a loose term but largely applicable) have been involved in audio production for a very long time. They have actually made huge strides with noise removal/voice isolation, auto transcription/captioning, and “enhancement” in the last five years in particular.

I hate Adobe, I don’t like to give them credit for anything. But their audio enhance tool is actual sorcery. Every competitor isn’t even close. You can take garbage zoom audio and make it sound like it was borderline recorded in a treated room/studio. I’ve been in production for almost 15 years and it would take me half a day or more of tweaking a voice track with multiple tools that cost me hundreds of dollars to get it 50% as good as what they accomplish in a minute with the click of a button.

Bitter lesson applies here as well though. Generalized models will beat specialized models given enough time and compute. How much bespoke NLP is there anymore? Generalized foundational models will subsume all of it eventually.

Yeah I agree with it. There is a lot of hype, but there is some potentials there.

"just"

It also wasn't even remotely close to learning Dota 2 proper. They ran a massively simplified version of the game where the AI and humans alternated between playing one of two pre-defined team compositions, meaning >90% of the games characters and >99.999999% of the possible compositions and matchups weren't even on the table, plus other standard mechanics were also changed or disabled altogether for the sake of the AI team.

Saying you've solved Dota after stripping out nearly all of its complexity is like saying you've solved Chess, but on a version where the back row is all Bishops.

To be fair humans have had quite a few million years across a growing population to gather all of the knowledge that we have.

As we're learning with LLMs, the dataset is what matters - and what's awesome is that you can see that in us, as well! I've read that our evolution is comparatively slow to the rate of knowledge accumulation in the information age - and that what this means is that you can essentially take a caveman, raise them in our modern environment and they'll be just as intelligent as the average human today.

But the core of our intelligence is logic/problem solving. We just have to solve higher order problems today, like figuring out how to make that chart in excel do the thing you want, but in days past it was figuring out how to keep the fire lit when it's raining. When you look at it, we've possessed the very core of that problem solving ability for quite a while now. I think that is the key to why we are human, and our close ancestors monkeys are...still just monkeys.

It's that problem solving ability that we need to figure out how to produce within ML models, then we'll be cooking with gas!