Past Performance is Not Indicative of Future Results (2020)

I've heard this airplane argument before, and while I do consider it plausible that AGI might be achievable with some system which is fundamentally much different than the human brain, I still don't think it can be achieved using simple scaling and optimization of the techniques in use today.

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.

I'm curious how the physics of light is termed racial bias, it's skin-colour bias if anything -- you can be "black" and be lighter skinned than a "white" person, for example -- but surely it's a consequence of how cameras/light works rather than a bias.

Of course if you don't take account of the difficulties that come with using the tool then you might be acting with racial bias, but that's different. Or, all cameras/eyes/visual imaging means are "racist".

Everyone I've ever spoken to who has insisted that materialism is a dead end, has never been able to provide a compelling explanation for why they believe that. It's not as if materialistic progress in neuroscience and ML/AI has stalled. If anything, it's accelerating.

I have no doubt that Kurzweil's timelines and outcomes are wrong, as have the predictions of just about every prior futurist. I don't see what that has to do with materialism being a dead end.

I think you're misunderstanding Kuhn slightly. He invented the term paradigm shift. What he means by normal science with intertwined spurts of revolution is more provocative. He means that in order to observe periods of revolution, the "dogma" of normal science must be cast aside and new normal must move in to replace it. Normal science hits a wall, gets stuck in a "rut" as Kuhn describes it.

I think, in a way, Doctorow is making that same argument for the current state of ML: "I don't think that we're remotely close or even on the right path in any way". In other words, general thinking that ML will lead to AGI is stuck in a rut and needs a new approach and no amount of progressive improvement on ML will lead to AGI. I don't think Doctorow's opinion here is especially insightful, he's just a writer so he commits thoughts to words and has an audience. I don't even know wether I agree or not. But I do think this piece comes off as more in the spirit of Kuhn than you're suggesting.

And of course you can interpret Kuhn however you want. I don't think Kuhn was saying you shouldn't use/apply the tools built by normal science to everyday life. But he, subtly, argues that some level of casting off entrenched dogmatic theories, in the academic domain, is a requirement for revolutionary progress. Kuhn agrees that rationalism is a good framework for approaching reality, but also equates phases of normal science to phases of religious domination that predated it. Essentially truly free thought is really really hard because society invents normals (dogma) and makes it hard to deviate. Academia is no exception. Science, during periods of normals, is (or can become) essentially over-calibrated and over-dependent on its own contemporary zeitgeist. If some contemporary theory that everyone bases progressive research off of is not quite right, it kinda spoils the derivative research. Not always true because sometimes the theories are correct.

The first AI winter came after we realized that the AI of the time, the high level logic, reasoning and planning algorithms we had implemented, were useless in the face of the fuzziness of the real world. Basically we had tried to skip straight to modeling our own intellect, without bothering to first model the reptile brain that supplies it with a model of the world on which to operate. Being able to make a plan to ferry a wolf, sheep and cabbage across the river in a tiny boat without any of them getting eaten doesn't help much if you're unable to tell apart a wolf, sheep and cabbage, let alone steer a boat.

That's what makes me excited about our recent advances in ML. Finally, we are getting around to modeling the lower levels of our cognitive system, the fuzzy pattern recognition part that supplies our consciousness with something recognizable to reason about, and gives us learned skills to perform in the world.

We still don't know how to wire all that up. Maybe a single ML model can achieve AGI if it is adaptable enough in its architecture. Maybe a group of specialized ML models need to make up subsystems for a centralized AGI ML-model (like a human's visual and language centers). Maybe we need several middle layers to aggregate and coordinate the submodules before they hook into the central unit. Maybe we can even use the logic, planning or expert system approach from before the AI winter for the central "consciousness" unit. Who knows?

But to me it feels like we've finally got one of the most important building blocks to work with in modern ML. Maybe it's the only one we'll need, maybe it's only a step of the way. But the fact that we have in a handful of years not managed to go from "model a corner of a reptile brain" to "model a full human brain" is no reason to call this a failure or predict another winter just yet. We've got a great new building block, and all we've really done with it so far is basically to prod it with a stick, to see what it can do on its own. Maybe figuring out the next steps toward AGI will be another winter. But the advances we've made with ML have convinced me that we'll get there eventually, and that when we do, ML will be part of it some extent. Frankly I'm super excited just to see people try.