The potential for improved human intelligence is enormous. Cognitive ability is influenced by thousands of genetic loci, each of small effect. If all were simultaneously improved, it would be possible to achieve, very roughly, about 100 standard deviations of improvement, corresponding to an IQ of over 1,000. We can’t imagine what capabilities this level of intelligence represents, but we can be sure it is far beyond our own. Cognitive engineering, via direct edits to embryonic human DNA, will eventually produce individuals who are well beyond all historical figures in cognitive ability. By 2050, this process will likely have begun.
These two threads—smarter people and smarter machines—will inevitably intersect. Just as machines will be much smarter in 2050, we can expect that the humans who design, build, and program them will also be smarter. Naively, one would expect the rate of advance of machine intelligence to outstrip that of biological intelligence. Tinkering with a machine seems easier than modifying a living species, one generation at a time. But advances in genomics—both in our ability to relate complex traits to the underlying genetic codes, and the ability to make direct edits to genomes—will allow rapid advances in biologically-based cognition. Also, once machines reach human levels of intelligence, our ability to tinker starts to be limited by ethical considerations. Rebooting an operating system is one thing, but what about a sentient being with memories and a sense of free will?
It is easy to forget that the computer revolution was led by a handful of geniuses: individuals with truly unusual cognitive ability. Alan Turing and John von Neumann both contributed to the realization of computers whose program is stored in memory and can be modified during execution. This idea appeared originally in the form of the Turing Machine, and was given practical realization in the so-called von Neumann architecture of the first electronic computers, such as the EDVAC. While this computing design seems natural, even obvious, to us now, it was at the time a significant conceptual leap.
Turing and von Neumann were special, and far beyond peers of their era. Both played an essential role in the Allied victory in WWII. Turing famously broke the German Enigma codes, but not before conceptualizing the notion of “mechanized thought” in his Turing Machine, which was to become the main theoretical construct in modern computer science. Before the war, von Neumann placed the new quantum theory on a rigorous mathematical foundation.
AI research also pushes even very bright humans to their limits. The frontier machine intelligence architecture of the moment uses deep neural nets: multilayered networks of simulated neurons inspired by their biological counterparts. Silicon brains of this kind, running on huge clusters of GPUs (graphical processor units made cheap by research and development and economies of scale in the video game industry), have recently surpassed human performance on a number of narrowly defined tasks, such as image or character recognition. We are learning how to tune deep neural nets using large samples of training data, but the resulting structures are mysterious to us.
The detailed inner workings of a complex machine intelligence (or of a biological brain) may turn out to be incomprehensible to our human minds—or at least the human minds of today. While one can imagine a researcher “getting lucky” by stumbling on an architecture or design whose performance surpasses her own capability to understand it, it is hard to imagine systematic improvements without deeper comprehension.
Perhaps we will experience a positive feedback loop: Better human minds invent better machine learning methods, which in turn accelerate our ability to improve human DNA and create even better minds.
The feedback loop between algorithms and genomes will result in a rich and complex world, with myriad types of intelligences at play: the ordinary human (rapidly losing the ability to comprehend what is going on around them); the enhanced human (the driver of change over the next 100 years, but perhaps eventually surpassed); and all around them vast machine intellects, some alien (evolved completely in silico) and some strangely familiar (hybrids). Rather than the standard science-fiction scenario of relatively unchanged, familiar humans interacting with ever-improving computer minds, we will experience a future with a diversity of both human and machine intelligences.
There will also be many kinds of quantum computers. Currently there are over dozen approaches to quantum computing.
There will be many kinds of neuromorphic machines.
There will be optical computers.
Many different approaches to computing will be useful for different kinds of problems.
Superintelligence is not required to develop molecular nanotechnology. There is already advanced DNA nanotechnology and there has been some experiments proving the controlled movement of molecules. The fact that molecular nanotechnology has been underfunded for a couple of decades does not mean superintelligence is required to solve it or make it happen.
Superintelligence is not required to solve climate change or air pollution. France has cleaner air than most other countries. They have 80% of their electricity from nuclear power. Europe also has stringent standards on their car engines which reduces the amount of particulates and air pollution.
The dynamics and interaction of people is allowing problems to remain unsolved.
This can be seen in the disfunction of the US political system. The solutions used in other countries for many problems could be adopted or emulated and improved upon. They also show that solutions exist.
If we see the emergence of significantly superior superintelligent humans and superintelligent machines, it will be interesting to see what true surprises will be developed.
SOURCE – Nautilus, Infoproc