I don't blame you for saying "the singularity" rather than "a singularity", as that was in the title of the linked post. However, I think it is bad mental hygiene and wish you hadn't said "the singularity" in the title of your post.
I do blame you for introducing "won't be happening on schedule". As far as I can tell, more commenters here believe in a God of the Bible than believe in Kurzweil's schedule.
As far as I can tell, more commenters here believe in a God of the Bible than believe in Kurzweil's schedule.
I wish I could upvote this twice: one for being a good use of hyperbole to put the point across, and once for not really being hyperbole.
I don't put much stock in the specifics of Kurzweil's schedule, but I'm skeptical of the relevance of some of Allen's objections. I agree with the first comment on the article, that he's too focused on replication of human intelligence as a prerequisite for a singularity.
Existing AI may be pretty brittle, but I suspect that by the time we can create AI that can, to use his example, infer that a tightrope walker would have excellent balance, Strong AI is likely not to be far off.
Robin Hanson addressed this same thing today, but found faults in Allen's reasoning. I made a comment there too but I don't know if it has posted yet, so I included it below:
The connectomics project between Harvard and MIT is a particular place where a useful approximate link between specific technologies and ability to emulate brains may be calculable in the short term.
FWIW, I am studying in this course this semester and I am working on some research that uses connectomics to provide plausible complexity bounds on some brain operations, for the purpose of arguing against Ian Parberry’s analysis and conclusion that human cognitive resources are prohibitively difficult to emulate without better abstract knowledge of cognitive science.
There is other recent evidence that suggests an algorithmic approach to brain activity will at the very least give us short term access to replicating certain specific human cognitive functionality. I think this is a case where Leo Breiman’s distinction between “the two cultures” of data analysis is pretty apt.
Perhaps you can answer this: does Hanson start from a factually wrong claim about sufficient conditions for brain emulation? Would we need to know the strength of the connections within the brain, as this link claims, and does this pose more difficult problems?
I am not a brain scientist myself. I think that knowing strength of connection as well as plasticity are both very important. In fact, I think that plasticity considerations are one of the main things that Penrose correctly addresses in The Emperor's New MInd. However, from a graph-theoretic and machine learning point of view, this does not strike me as intractable. Just yesterday I witnessed some new results in the connectomics project in which they can essentially (95+% accuracy on training data sets) reconstruct the wiring diagram of non-trivial volumes of a mouse brain (10^(-7) m^3) in less than 5 minutes using Gibbs sampling methods.
The current problem is getting enough resolution to make the accuracy much higher than 95%. No engineer involved with the project believes this will be difficult to accomplish in a ~10 year time span. The next detail will be imaging at that resolution in a video of a functioning brain and they are already discussing ways to achieve this too. I agree there are some real difficulties in understanding neurotransmitter functionality. But nothing suggests it will be prohibitively difficult for engineering in the next 25 years... at least that's my opinion if we are to just brute force directly store detailed videos of neural activities at the 250 nm resolution level. If we make any breakthrough whatsoever about abstraction and principles in neural physics that allow us to discard some of that brute force data resolution, it will only make the problem easier and less expensive.
(95+% accuracy on training data sets)
I have only very limited knowledge in this area, so I could be misreading you. But doesn't "in training data sets" mean that the process had been developed using that specific data? That could mean that you have a program really good at reconstructing that piece of mouse brain, but not at reconstructing mouse brain in general. We had this problem in the last research project I worked on, where we'd use a gene expression data set to predict mood in bipolar subjects. We had to test the predictions on a separate data set from the one used in development to make sure it wasn't overfit to the training data. Is the same thing the case for your work, or am I misunderstanding your use of "training data"?
It is a good insight to notice that this is a potential problem, which is generally referred to as a generalization error. If you train a classifier or compute a regression on some data, there is always a chance that when you are given new data, it will perform poorly because of unforeseen larger-scale patterns that were poorly represented in the training data.
However, the scientists performing this work as also aware of this. This is why algorithmic learning theory, like machine learning methods, is so successful. You can derive tight bounds on generalization error. The process you refer to with the gene expression -- testing on additional labeled data to see that you are not overfitting and that your parameters give good predictive power -- is called cross-validation, and it's definitely a huge part of the connectomics project.
You might enjoy this paper by Leo Breiman, which talks about this exact distinction between merely fitting data vs. algorithmic data analysis. Many statisticians are still stuck believing that it is good to assume underlying analytic models for nature and then use goodness-of-fit tests to determine which underlying models are best. This is a categorically bad way to analyze data except in some special cases. Algorithmic data analysis instead uses cross-validation to measure accuracy and seeks to model the data formation process algorithmically rather than generatively.
Most computer scientists are not even aware of this distinction because the algorithmic approach (usually through machine learning) is the only one they have ever even been taught.
Thanks for the response and the paper link. I'm confident that the connectomics project does use cross-validation. I'm just wondering, is the 95+% accuracy you mentioned on the training data or the test data?
Summary:
"The human brain is really complicated, so I don't think we'll be able to do AI any time soon, because we need 'exponential' increases in software - also 'complexity brake'" (whatever those mean)
[edit] Changed TL:DR to Summary, which I should have used (or similar) in the first place.
From your link:
But concepts are not useful or useless of themselves. Only usages are correct or incorrect.
In this particular case they are going into some detail to explain what they mean.
It's hard to tell the difference between the very first little bit of an exponential curve and a linear one.
And a logistic curve is even harder before it starts slowing. I think the various arguments for superhuman AI are far more significant than the projected curves.
The "logistic growth curve" charge, as voiced by John Wilkins:
First of all it occurs to me that people who expect the Singularity to occur simply do not get the logistic growth curve.
I didn't mean it as a "charge", I was simply pointing out that until and unless it hits an inflection point there is simply no way of knowing which it is (unless there is a known limitation to growth, like the speed of light). Which is one of the reasons that I consider the arguments for superhuman AI far more significant than plotting curves.
Here's a great article by Paul Allen about why the singularity won't happen anytime soon. Basically a lot of the things we do are just not amenable to awesome looking exponential graphs.