Whilst I am not a raging advocate of ‘sitting back and ‘waiting’ for the Singularity to happen (I prefer to get excited about the technologies that underlie the concept of it), I feel that I have a responsibility to defend the poor meme in the case where an argument against is is actually very wrong, such as in this article from Science Not Fiction:
Genomics Has Bad News For The Singularity
The basic argument that the article puts forward is that the cost of sequencing the human genome has fallen following a super-exponential trend over the past 10 years. And yet, we do not have amazing breakthroughs in drug treatment and designer therapies. So how could we expect to have “genuine artificial intelligence, self-replicating nanorobots, and human-machine hybrids” even though Moore’s law is ensuring that the cost of processing power is falling? And it is falling at a much slower rate than genome sequencing costs!
The article states:
“In less than a decade, genomics has shown that improvements in the cost and speed of a technology do not guarantee real-world applications or immediate paradigm shifts in how we live our day-to-day lives.”
I feel however, that the article is somewhat comparing apples and oranges. I have two arguments against the comparison:
The first is that sequencing the genome just gives us data. There’s no algorithmic component. We still have little idea of how most of the code is actually implemented in the making of an organism. We don’t have the protein algorithmics. It’s like having the source code for an AGI without a compiler. But we do have reasonable physical and algorithmic models for neurons (and even entire brains!), we just lack the computational power to simulate billions of neurons in a highly connected structure. We can simulate larger and larger neural networks as hardware increases in speed, connectivity, and efficiency. And given that the algorithm is ‘captured’ in the very structure of the neural net, the algorithm advances as the hardware improves. This is not the case in genome sequencing.
The second argument is that sequencing genomes is not a process that can be bootstrapped. The very process of knowing a genome sequence isn’t going to help us sequence genomes faster or help you engineer designer drugs. But building smart AI systems – or “genuine artificial intelligence” as the article states – CAN enable you to bootstrap the process, as you will have access to copyable capital for almost no cost: Intelligent machines which can be put to the task of designing more intelligent machines. If we can build AIs that pass a particular threshold in terms of being able to design improved algorithmic versions of themselves, why should this be limited by hardware requirements at all? Moore’s law really just gives us an upper bound on the resources necessary to build intelligent systems if we approach the problem using a brute-force method.
We still need people working on the algorithmic side of things in AI – just as we need people working on how genes are actually expressed and give rise to characteristics in organisms. But in the case of AI, we already have an existence proof for such an object – the human brain, and so even with no algorithmic advances, we should be able to build one in-silico. Applications for genomics do not have such a clearly defined goal based on something that exists naturally (though harnessing effetcs like the way in which cancer cells avoid apoptosis might be a good place to start).
I’d be interested in hearing people thoughts on this.