A Turing machine is Alan Turing’s original model of a basic computer. A Turing machine can model any computational process. In the 1930’s, Kurt Godel proved that there were certain mathematical problems that could not be proved. Later that decade Alan Turing and Alonzo Church both published papers suggesting that certain problems were unsolvable by a Turing machine (the Church-Turing thesis). Since a Turing machine can model any computer, these problems are unsolvable by any computer. It can also be interpreted that since a Turing machine cannot solve such problems, neither can a human. Since the human brain and body follow the laws of physics and can eventually be modelled by a computer or Turing machine, the same rule must apply to them. So the question is, are there certain problems that humans cannot solve?

One thing that is mentioned in “The Singularity is Near” is the theory that universe is ultimately a piece of software.  This is a theory fist proposed by Edward Fredkin in the 1980s.  If you look at physics as basically an information process, with all interactions really just changing the “information” in different elements (such as their spin, charge, etc.), then universe is full of an enormous amount of information and is performing an incredible number of calculations all the time.  Of course, “there is no shortcut to finding out what… [the program] …will lead to.”

Here’s a quote from Robert Wright talking about Fredkin:

“Fredkin believes the universe is very literally a computer and that it is being used by someone, or something, to solve a problem.  It sounds like a good-news/bad-news joke: the good news is that our lives have purpose; the bad news is that their purpose is to help some remote hacker estimate pi to nine jillion decimal places.”

It’s certainly an interesting idea, it was also mentioned at the EDGE talk I went to last week by Seth Lloyd (he’s releasing a a book on this soon).  But it makes you wonder, “what is the universe calculating?”, “why is it calculating it?”, “who designed/programmed the universe?”

I’ve been reading the book “The Singularity is Near” by Ray Kurzweil, which Holly and Rick gave me for Christmas. It’s all about the accelerating rate of technology and what will happen when that rate approaches infinity (the singularity). This is something I discussed in one of my old rants from 2003 (Part 1, Part 2). I speculated that this infinite rate could be reached when we can manipulate and travel in time. Since an actual rate of infinity seems impossible, would we be able to differentiate between close to infinity and really really close to infinity? Would our perception of time change as technologies are developed at such an absurd rate?

In the book, Kurzweil suggests that the singularity will occur when when we develop powerful enough computers with good enough AI that they can design newer better computers. These intelligent computers would redesign themselves, continually designing better and better computers and other technology. With the computer power available increasing exponentially and the amount of money we spend on computers each year also increasing exponentially, we’ll be buying enough computing power to match all of life on Earth by the 2040’s, so this is when Kurzweil suggests the singularity will occur. We will all be augmented by computers in our brains and also have rapidly accelerating intelligence to go along with the artificially intelligent computers.

Here’s a quote from the book (p. 22):

“Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an “intelligence explosion,” and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make.”

~Irving John Good, “Speculations Concerning the First Ultraintelligent Machine,” 1965.

When I was visiting the University of Texas in Austin, Prof. Stone gave a software demonstration of automated cars driving through an intersection. Since they were controlled by computers and could communicate wirelessly, there was no reason for stoplights and they were all zipping through pretty fast.

It will be cool when they have automated parking lots where you can pull up to the door and your car goes off and finds a spot. Then when you check out it sends a signal to your car to come back to the door.

I also imagine that cars will no longer be restricted to the right side of the road. Depending on traffic, cars could drive on the left side of the road and easily merge back over if another car was coming. Roads could be optimized for traffic in this way. I imagine it would be like walking on a sidewalk. Other than the occasional dance step that occurs when you start walking towards someone head on, humans can share the same sidewalk going both directions pretty easily.

I was thinking the other day how much different kids grew up that are just 5 or 10 years younger than me. For me, computers got big in late elementary school/jr high, dial-up internet in high school, cell phones at the end of high school, and broadband internet in college. Kids now have grown up their whole lives with cell phones and broadband internet, myspace and instant messenger. They’re always connected no matter where they go. They’re never separate from their friends at night, chatting online. And they’ve grown up using these tools, which I’m sure will make them the most tech savvy generation yet.

It will be interesting to see how different this generation will be when they are graduating college and entering the work force in a few years. They should relate socially different than our generation and be much more technologically savvy.

South Korea plans to get robots into every home, and even patrolling the streets, in the next few years:

http://www.nytimes.com/2006/04/02/world/asia/02robot.html

I was thinking yesterday about the difference between the way things are computed/processed in digital electronics and the way they work in the brain. What are the differences? What are the advantages of each? How would a computer work with neural networks and how would a brain work with digital circuits?

Digital Logic:

In a computer, everything is built out of transistors. All signals are discrete, either on or off, 0 or 1. Latches and flops are built out of transistors, and nand, nor, and not gates are built out of transistors. From these come all the processing in a computer. Each of these gates takes some number of inputs, which are all defined as either 0 or 1. Then depending on the type of gate, it has some defined rules, and there will be an output of either 0 or 1. Everything in digital logic is deterministic: if you know what the inputs are, you can always tell what the output will be.

Neural networks:

In a neural network, everything is arranged from neurons rather than transistors. The neurons connect to each other through synapses. One neuron can have many others connected to it. Each of these neurons may fire and send a signal through their synapses to the next neuron. If one neuron has enough neurons sending signals to it then it will eventually reach a point where it will fire and send a signal through its synapses. It could fire if one neuron sends a particularly strong signal to it, or if many neurons together send small signals that add up to a significant one. Basically it sums the incoming signals over time and space and if they cross some threshold, this neuron will be activated. The thresholds change and adapt depending on how often each synapse fires. As the firings decrease, the neuron becomes more sensitive to it, and as it fires more the neuron becomes less sensitive to that synapse. Neurons can also grow new connections to other neurons. The neural network is non-deterministic, the same inputs may not always result in the same outputs because the thresholds at different synapses may have changed or completely new neuronal connections may have been created.

Differences/Benefits:

One main difference is that neural networks can adapt and change. Another difference is that neural networks have more values than just on and off. A neuron could be not firing at all, it could be firing a little, it could be firing a lot, or somewhere in between. A neuron could fire so often that other neurons become insensitive to its input. Instead of a simple OR gate in digital logic, where the gate turns on when either input is on, neurons would be less discrete. The neuron may turn on some if either input firing a lot, or if both are firing some, and the neuron may fire a lot if both inputs are firing a lot.

How could a computer be designed using neural networks?

Well, to start, it wouldnt be too hard to design a chip that could act like a neuron, summing inputs over time and measuring them against some changing threshold. I’m not sure how new neural connections would work since these connections would seemingly be hard wired. But all the logic used for computer processing would have to be changed since it is all set up for digital design. Instead of AND/OR gates you might have neurons that require a strong input to activate and neurons that require a small input to activate. And how their adaptations would affect the computing, I have no idea.

How would a brain work with digital circuitry?

Any “brain” that uses digital circuits could not really be considered a brain. There would be no adaptation or change in the brain and so it doesn’t seem much like a brain to me.

This brings up another good question: what is it that gives us our humanity? our consienceness and intelligence? Is it the fact that the brain adapts and changes and develops? Or is it something inherent in the circuitry of the brain? What separates our brains from monkeys? What separates our brains from computers? What makes us who we are?

I would love to be able to design a computer program that was smart and actually had some intelligence. I have designed the AI for my 3D connect four program and I’ve worked on a chatbot. The real sign of intelligence would be if it could learn. In fact, I have thought about making my connect four program be able to learn from its losses. The end result of all this AI work would be to simulate the human mind. There would be 2 ways of doing this. The first would be to understand the thought process of everyone and write some code to simulate it. This, of course, is impossible. The second way would be to somehow analyze the physics and chemistry of the brain and try to replicate the actual electronic signals or cells and see what happens when we connect it all together. I doubt that we can ever really replicate the brain as a computer; even though computing power keeps increasing exponentially, I don’t think it will reach the computing power of the human brain at any point.