Actually Intel did not really "find a way around the problem". CPU speeds have barely improved since then, instead it's all about multi-core.
If passively parallel was all it took, we could have AI today, but we can't. And it doesn't look like CPU's are going to get any faster.
The curve did exactly what the linked article said: it turned into an S curve.
Moore's law still works because that's a measure of cost, but if you plot a single core, you get an S curve - which means no more singularity.
Plus, even if you had a super fast computer, you'd still not get a singularity since no one knows how to program AI - even if you gave them the fastest computer in the world.
I don't want to jump to conclusions, but the way you throw around the term "CPU speeds," (how do you even measure that?) you sound to me like a programmer for whom computation is a magical process composed of functions, arguments, processes and files.
Moore's Law is only indirectly related to CPU speed. Instead, it predicts the MOST ECONOMICALLY PROFITABLE minimum feature size of a semiconductor manufacturing process.
To a lot of people, those two things are one and the same, but in reality, computing power tends to grow because of innovations in processor architecture. In other words, material and device engineers will wring lots of improvements out of a given process, giving the architecture guys more transistors to implement bigger caches, longer pipelines, branch prediction, and the like.
So while "Moore's Law" continues apace, "CPU speeds" (however you measure those) have stalled a bit. This is because the current slate of architectural improvements has been exhausted, and there's a lot of uncertainty surrounding how to implement the Next Big Thing (core-level parallelism). This shouldn't be terribly worrisome to us, as it's happened before.
From the 1970s to the early 1990s, CPU manufacturers focused on "bit-level" parallelism, basically throwing in bigger registers and more instructions to burn through growing hardware budgets. When it became obvious that this approach wasn't improving performance any more, we got tghe RISC processors that enabled pipelining, upclocking, and caching.
If you didn't already know all of this -- and a lot more background besides -- your opinions about "programming an AI" are worse than useless. You're contributing zero information, and adding a little more noise (in the form of unsubstantiated certainty) to a field that's already debated too hotly.
Use of the term "the singularity", particularly your use here sets my teeth on edge. It's not about technology, it is about our models.
The North Pole is a singularity - coordinates converge there to become a single point (singular). They only do this because we chose a coordinate system that makes this happen. It is silly to talk about how you coils go there and then not be able to go any further north, as if that means it is the edge of the world.
Likewise "the singularity" is not a magical threshold of AI and immortality or transhumanism, it's where a given model of the future predicts nonsense because you have pushed it too far. Pick a different predictive model, get different results.
To claim, then, that the singularity may never happen is to claim either that change with feedback will stop, or a perfect model.
Better to claim that AI or genetically enhanced people, or neural prostheses, or smart matter, or superconductivity, or antigerones, or quantum computing, or virtual reality or whatever game changing technology won't happen - if that's what you mean.
(I for one think good PDA keyboards will never happen. :( hurry up Gunilla Alsio and senseboard, and that new t9 thing)
Do you think no one will ever figure this out? There's a bunch of stuff that no one used to know how to do but is common now. Why would AI be any different?
I don't know what all, or any, general intelligence algorithms would look like but they only one we have currently available, the human brain, appears to be intrinsically parallel.
I wish I could find where, but I remember Don Knuth saying that he didn't think that the multicore was really the only answer, only that its almost lazyness or hubris that has stopped innovation in other areas (he used nicer wording then that). Perhaps as a result of there being really no competition in architectures anymore?
Or maybe I dreamed it all up, and multicore it is !
Right now not many people have enough calculation power available to really experiment much with AI. I'm only working with game AI where the bots run around in a simplified model world with a simplified set of available actions. But even with those constrains the by far biggest challenge is is the available processing time. You still have to pre-calculate much stuff that people can work out in a second.
The current state of AI feels to me similar to the state the 3D graphics felt to me in Amiga times. You can already do a few nice effects in real-time, but for real cool stuff you still have to run the computer 3 days. The basics are already mostly there even if they might be used in new compositions in 20 years.
If passively parallel was all it took, we could have AI today, but we can't. And it doesn't look like CPU's are going to get any faster.
The curve did exactly what the linked article said: it turned into an S curve.
Moore's law still works because that's a measure of cost, but if you plot a single core, you get an S curve - which means no more singularity.
Plus, even if you had a super fast computer, you'd still not get a singularity since no one knows how to program AI - even if you gave them the fastest computer in the world.