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.