Transcript:
Hi. I'm Tim Tyler and this is a video about memetic algorithms.
While social scientists have mostly ignored or misunderstood memetics, the topic has received much warmer welcome from computer scientists. Where social scientists lamented their complex, integrated cultures being atomized into isolated memes, the computer scientists shrugged and went on to use the idea to solve their engineering problems.
Computer scientists had previously employed genetic algorithms as a means of finding solutions to their problems. These involve virtual creatures who are rewarded for producing better solutions to whatever optimization problem their creators are trying to solve. The virtual creatures are iteratively modified using mutation operators, and are maintained in a breeding pool - allowing for recombination to take place.
Cultural evolution appears to have dramatically increased the rate of development of human civilization. If culture evolves faster than DNA does, perhaps we can improve the power of our computer optimization strategies by drawing of some aspects of cultural evolution. This led to the idea of memetic algorithms.
Memetic algorithms are similar to genetic algorithms - but in addition to mutation, recombination and selection, they make use of individual learning, social learning and teaching. Where genetic algorithms were inspired by organic evolution, memetic algorithms draw inspiration from cultural evolution.
Evolution took billions of years to produce creatures sophisticated enough to produce an open-ended type of cultural evolution based on behavioural imitation. This appears to have been because observing the actions of another and then recreating them is not a trivial task. Detailed imitation of behaviour is technically quite a difficult reverse-engineering task, which requires complex cognition to perform.
However, fortunately, we do not have to wait until we have human-level machine intelligence before machines can make use of social learning techniques. We can engineer virtual environments in which even the relatively dumb artificial agents that we can build today can enjoy the benefits of cultural transmission.
Rather than passing on cultural information by a clumsy process based on behavioural imitation, memetic algorithms can work with creatures which have been designed for direct thought transfer. Agents can can record their sense data and replay them for the benefit of other agents. Behaviours can be encoded in a portable format and ported directly between agents. Memories can be directly downloaded from one agent and uploaded into another. Cultural transmission is pretty easy for machines - it doesn't require advanced intelligence.
Memetic algorithms may include a genetic component - and thus may exhibit meme-gene coevolution. Not DNA genes - of course - but rather the same kinds of genes that are used with genetic algorithms. In cases where both memes and genes are involved, there is a tradeoff between the resources expended on each type of evolution.
So far memetic algorithms have become quite a popular technique, with many books and papers on the topic and a string of international conferences dealing with the subject.
Memetic algorithms lie directly on the path which leads towards machine intelligence. Cultural evolution in humans illustrates the process machines will need to master to continue their growth. Machines are masters of mind-to-mind thought transfer, which makes it easy for them to engage in social learning. If you have a partly-intelligent machine, one obvious thing you can do to boost its power is to network it together with other similar machines - and let them cooperate with each other. The dense environment provided inside modern data centers provides an ideal environment for machines to engage in social activity with each other.
Overall, it seems highly likely that memetic algorithms will play a key role in the development of machine intelligence. Memetic algorithms will thus probably prove to be the most important application of memetics - in the long term.
Enjoy,
References
- Tyler, Tim (2013) Memetic algorithms
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