How much innate knowledge can the genome encode?

In a recent debate between Gary Marcus and Yoshua Bengio about the future of Artificial Intelligence, the question came up of how much information the genome can encode. This relates to the idea of how much innate or prior “knowledge” human beings are really born with, versus what we learn through experience. This is a hot topic in AI these days as people debate how much prior knowledge needs to be pre-wired into AI systems, in order to get them to achieve something more akin to natural intelligence. 

Bengio (like Yann leCun) argues for putting as little prior knowledge into the system as we can get away with – mainly in the form of meta-learning rules, rather than specific details about specific things in the environment – such that the system that emerges through deep learning from the data supplied to it will be maximally capable of generalisation. (In his view, more detailed priors give a more specialised, but a more limited and possibly more biased machine). Marcus argues for more…

Is your future income written in your DNA?

A newly published paper makes the claim that variation in people’s income can be partly traced to variations in their genes. Indeed, it identifies over a hundred specific genetic variants that are statistically associated with income in a large sample of people derived from the UK Biobank. To some, this idea is frankly preposterous – a naïve and outrageous over-reach of genetic determinism and reductionism, with strains of social Darwinism. To others, it is completely expected – not trivial, in terms of the work involved, but certainly not at all surprising and not so earth-shattering in terms of social implications.
The devil is in the details, of course, of the methodology and the results, and, importantly, the way they are presented and interpreted.
The idea that something like a person’s income could be partly heritable – that is, that variation in income across the population could be partly attributable to genetic differences between people – is in fact, not new at all and reall…

Beyond reductionism – systems biology gets dynamic

Is biology just complicated physics? Can we understand living things as complex machines, with different parts dedicated to specific functions? Or can we finally move to investigating them as complex, integrative, and dynamic systems?
For many decades, mechanistic and reductionist approaches have dominated biology, for a number of compelling reasons. First, they seem more legitimately scientific than holistic alternatives – more precise, more rigorous, closer to the pure objectivity of physics. Second, they work, up to a point at least – they have given us powerful insights into the logic of biological systems, yielding new power to predict and manipulate. And third, they were all we had – studying entire systems was just too difficult. All of that is changing, as illustrated by a flurry of recent papers that are using new technology to revive some old theories and neglected philosophies.
The central method of biological reductionism is to use controlled manipulation of individual comp…