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How to Make Artificial Intelligence More Democratic

a study from Western University suggests, there has been a "de-democratization" in AI: the number of researchers able to contribute to cutting-edge developments is shrinking. This narrows the pool of people who are able to define the research directions for this pivotal technology, which has social implications. It may even be contributing to some of the ethical challenges facing AI development, including privacy invasion, bias and the environmental impact of large models.

To combat these problems, researchers are trying to figure out how to do more with less. One such recent advance is called “less than one”–shot learning (LO-shot learning), developed by Ilia Sucholutsky and Matthias Schonlau from the University of Waterloo.[Office1] [RK2]  The principle behind LO-shot learning is that it should be possible for an AI to learn about objects in the world without being fed an example of each one.

Allowing AIs to learn with considerably less data is important for several reasons. First, it better encapsulates the actual process of learning by forcing the system to generalize to classes it has not seen. By building in abstractions that capture the relationships between objects, this technique also reduces the potential for bias. 

Next, the less extensive the data one needs to use, the less incentive exists to surveil people to build better algorithms. For example, soft distillation techniques have already impacted medical AI research, which trains its models using sensitive health information. In one recent paper, researchers used soft distillation in diagnostic x-ray imagery based on a small, privacy-preserving data set.

Finally, allowing AIs to learn with less plentiful data helps to democratize the field of artificial intelligence. With smaller AIs, academia can remain relevant and avoid the risk of professors being poached by industry. 

See the full story here: https://www.scientificamerican.com/article/how-to-make-artificial-intelligence-more-democratic/