If a machine-based process can outperform a human being is it significant? That weighty question hung in the background as I reviewed Jürgen Schmidhuber’s work on traffic sign classification. Similar results have emerged from IBM’s Watson competition and even on the TOEFL test. In each case, machines beat people.
But is that fact significant? There are a couple of ways we can look at these kinds of comparisons. First, we can draw analogies to other capabilities that were not accessible by mechanical aid and show that the fact that they outperformed humans was not overly profound. The wheel quickly outperformed human legs for moving heavy objects. The cup outperformed the hands for drinking water. This then invites the realization that the extension of these physical comparisons leads to extraordinary juxtapositions: the airline really outperformed human legs for transport, etc. And this, in turn, justifies the claim that since we are now just outperforming human mental processes, we can only expect exponential improvements moving forward.
But this may be a category mistake in more than the obvious differentiator of the mental and the physical. Instead, the category mismatch is between levels of complexity. The number of parts in a Boeing 747 is 6 million versus one moving human as the baseline (we could enumerate the cells and organelles, etc., but then we would need to enumerate the crystal lattices of the aircraft steel, so that level of granularity is a wash). The number of memory addresses in a big server computer is 64 x 10^9 or higher, with disk storage in the TBs (10^12). Meanwhile, the human brain has 100 x 10^9 neurons and 10^14 connections. So, with just 2 orders of magnitude between computers and brains versus 6 between humans and planes, we find ourselves approaching Kurzweil’s argument that we have to wait until 2040. I’m more pessimistic and figure 2080, but then no one expects the Inquisition, either, to quote the esteemed philosophers, Monty Python.
We might move that back even further, though, because we still lack a theory of the large scale control of the collected software modules needed to operate on that massive neural simulation. At least Schmidhuber’s work used an artifical neural network. The others were looser about any affiliation to actual human information processing, though the LSI work is mathematically similar to some kinds of ANNs in terms of outcomes.
So if analogies only serve to support a mild kind of techno-optimism, we still can think about the problem in other ways by inverting the comparisons or emphasizing the risk of superintelligent machines. Thus is born the existential risk school of technological singularities. But such concerns and planning doesn’t really address the question of whether superintelligent machines are actually possible, or whether current achievements are significant.
And that brings us to the third perspective: the focus on competitive outcomes in AI research leads to only mild advances in the state-of-the-art, but does lead to important social outcomes. These are Apollo moon shots, in other words. Regardless of significant scientific advances, they stir the mind and the soul. It may transform the mild techno-optimism into moderate techo-optimism. And that’s OK, because the alternative is stationary fear.