Let's build a brain!
How hard would it be to build a human-level AI?
One interesting way of looking at this problem is to examine how hard it would be to run a computer simulation of a human brain.
AI requires hardware (which entails both computational capacity and storage capacity), and software. Let's look at each of these in order.
1. Computational capacity
We now know enough about how the brain works to take a reasonable guess at its total computational capacity:
- The average human brain contains about 100 billion neurons
- Each neuron has about 1,000 synapses (this number is a little uncertain, but is within a factor of 10)
- To a first approximation, each neuron can cycle about 200 times per second
Doing a little math (and assuming that one neuron-synapse cycle = 1 flop):
- 100 billion neurons x 1,000 synapses x 200 cycles / second = 20 petaflops
So, to simulate a human brain running in real-time, we need about 20 petaflops of computational capacity. Let's see how current hardware stacks up:
Computer: Recently announced Japanese successor to Earth Simulator (due in 2011)
Capacity: 10 petaflops (2 needed to model a brain)
Cost: $893 million
Cost to model a brain: $1.8 billion
Computer: Blue Gene/L (when finished)
Capacity: 360 teraflops (56 needed to model a brain)
Cost: $100 million
Cost to model a brain: $5.6 billion
Computer: 2005 personal computer
Capacity: 4 gigaflops (5,000,000 needed to model a brain)
Cost: $1,000
Cost to model a brain: $5 billion
Obviously, these numbers are only very approximate. Nonetheless, it's clear that simulating a human brain in real-time is currently within computational reach, although it's a major undertaking.
2. Storage capacity
In addition to needing a lot of raw computational power, an artificial brain will need to store a lot of state. The three main elements for which we'll need to store state are the neurons, the synapses, and the glial cells. Let's take a look at how much state each of these requires:
- The brain contains about 100 billion neurons, each of which has a fair amount of state. Let's give each one a very generous 10K of storage. That comes to a total of 1,000 TB.
- There are about 20 glial cells for each neuron. It's not clear how much meaningful state is associated with each one, but let's be generous: we'll give each one 10K also. That comes to a total of 20,000 TB.
- Most of the action is in the synapses. We're figuring about 1,000 synapses per neuron, each of which will need some state. Let's allow 100 bytes per synapse. That comes to a total of 10,000 TB.
Our very generous grand total for storing all the state in the human brain is a mere 31,000 TB. That's a very manageable amount: disk storage is currently running about $1,000 per terabyte. That means that at today's prices, we can store a complete human brain for only $31 million.
3. Software size
On the face of it, people seem pretty complicated. You'd think that it takes a lot of data to make a person, but you'd be wrong.
The whole human genome consists of about 3.2 billion base pairs. Each base pair contains two bits of information (A, C, G, or T), meaning that the entire genome only contains 800 MB of information. You can fit the complete blueprints for a person on a decent-sized thumb drive.
It actually gets even better. There's some ambiguity about the exact number, but as far as we can tell, between 95% and 98% of the genome is completely unused "junk DNA." That means that the complete DNA blueprints for building a person contain no more than 40 MB of real information.
By comparison, the code portion (ie, the .text fork) of Microsoft Word is about 11 MB, or about 1/4th the size of the entire functioning human genome. The whole of Microsoft Office is probably about 5 times that size, or roughly the size of the entire functioning human genome.
It's not clear what the true information density of the genome is (it certainly contains a non-trivial amount of redundant information), nor what percentage of it is remotely related to digestion. It's interesting to note that the human genome is 98.4% identical to that of chimpanzees (ie, only 640 KB of information separates us from chimps), and that well over 90% of all mouse genes have very similar counterparts in humans.