It's Closer Than You Think
I think that true AI will arrive in the world much sooner than most people expect. I believe it may happen sometime in 2015. I have many reasons but I will mention just a few important ones in this article. I have argued some of these points elsewhere.
Time Is the Only Teacher
There is something truly groundbreaking that a number of people in the AI research community (e.g., Jeff Hawkins, Andrew Ng, and others) have figured out in the last decade or so. They have come to realize that intelligence is entirely based on the relative timing of discrete sensory and motor signals. It turns out that there are only two kinds of temporal relationships: signals can be either concurrent or sequential. This realization simplifies things tremendously because it gives us a way to do unsupervised learning and invariant object recognition just by observing signal timing. Time is the only supervisor in perceptual learning. No labeled examples are necessary. I believe this to be a breakthrough of enormous importance. It goes without saying that the supervised deep learning models that are currently the rage in AI circles will fall by the wayside.
We Don't Need So Many Neurons
Many have argued that we will need super powerful computers in order to emulate the tens of billions of neurons in the human brain. A critic may ask, do we really need that many neurons and such vast computing power to demonstrate true intelligence? I personally don't think so. My research into cortical columns and sequence recognition has convinced me that we will need at least two orders of magnitude fewer neurons to emulate a mammalian cortex than we thought. I have come to the conclusion that the brain is forced to use parallelism in its cortical columns in order to compensate for the slow speed of its neurons. There is good reason to suppose that the hundred or so minicolumns that comprise a macrocolumn are just individual speed recognizers for a given sequence. They can be emulated in a computer with a single minicolumn and a couple of variables.
In this vein, one can also argue that once the basic principles of intelligence are fully understood, there really is no need to emulate all the billions of neurons in a brain in order to demonstrate very powerful intelligent behavior. A million or so neurons combined with the right model will perform wonders. Bees and wasps can do amazing things with a million neurons.
It gets better. The requirement for massive computational resources becomes even less of a problem when you consider that only a fraction of the brain's cortex is awake at any one time. It may come at a surprise to many that over 90% of the cortex is essentially asleep even when we are fully awake. This is because only a very small part of the cortex, the part we are focusing on, is active at one time.
The Bayesian Red Herring
True AI could have happened decades ago if only we knew how it worked. Obviously, there is something about intelligence that still escapes researchers in the field. I am convinced that one of the reasons it did not happen years ago (other than the aberration that was symbolic AI or GOFAI) is that AI researchers have fallen in love with probabilistic approaches to intelligence such as Bayesian statistics. This, too, is a major waste of time in my opinion. I say this because, contrary to conventional wisdom, the brain does not compute probabilities.
The probabilistic AI model assumes that the world is inherently uncertain and that the job of an intelligent system is to compute the probabilities. The correct model, in my view, assumes that the world is perfectly consistent and that the job of the intelligent system is to discover this perfection. The two models are polar opposites. I believe that once researchers realize that the brain uses a non-probabilistic, winner-take-all approach to recognition, AI will be upon us like a tsunami.
"People are not probability thinkers but cause-effect thinkers." These words were spoken by none other than Dr. Judea Pearl during a 2012 Cambridge University Press interview. Pearl, an early champion of the Bayesian approach to AI, apparently had a complete change of heart. In my opinion, this should have been a wake-up call for the AI community but Pearl's words seem to have fallen on deaf ears. This is regrettable because the probabilistic approach to AI is one of the main impediments to progress in this field. Getting rid of it will simplify our task by orders of magnitude. Fortunately, a number of people are fast moving in this direction.
There are other reasons that true AI is closer than most of us think, including a few that I will reveal when I release the Rebel Speech demo (hang in there). Perceptual learning and knowledge representation are at the heart of intelligence. Once we fully solve the problem of perception and memory, everything else will be child's play in comparison, even things like motor learning, motivation and adaptation. The future is almost at the door.