TECH
WOMEN in AI
Women are imperative in the tech industry, bringing about some of the biggest advancements for the field and the world. At the Montreal Artificial Intelligence Symposium, with their inspiring keynotes, Melanie Mitchell and Margaret Mitchell proved just how important women are to the industry. By Rebecca Kahn
Women are instrumental to the monotonous work in technology (think of women being the first human “computers” while men went to fight in wars), in addition to heading initiatives to craft up ingenious ideas. Today, women are creating the next big advancement in artificial intelligence (AI), bringing us into the 21st century with AI that is more reliable, understanding, and fair.
Intelligence without understanding
People see artificial intelligence as the key to all of our problems, but there are still some sizable limitations and vulnerabilities. Melanie Mitchell’s research exposes these shortcomings, with a particular focus on AI’s lack of human-level understanding. Melanie is the author or editor of five books and numerous papers as well as a programmer, currently working on “Situate,” which extends Copycat to interpret and make analogies between real-world visual situations. We had the pleasure of catching up with her after her presentation, Artificial Intelligence and the “Barrier of Meaning,” at the Montreal AI Symposium.
68
Her presentation began with an overview of the historical instances (of which there were many) when experts vastly underestimated the amount of time that certain AI milestones would be reached. Melanie elaborates on this phenomenon: “They see machines doing something really impressive, like playing Go or playing chess and beating the world champion, and think, ‘Oh, they have to be incredibly intelligent to be able to do that, so how far could human intelligence really be?’. I think people underestimate how hard it is and how complex human intelligence is.” In particular, understanding is preventing us from reaching human-level AI. Other things missing from the AI picture are reliability and robustness. A lot of the deeplearning architecture relies on getting better overtime and learning from examples in order to make better classifications – think Google Photos, image captioning, translation services, etc. However, unreliability pops up quickly, including problems with generalization, biases, abstraction, transfer learning, lack of common sense, and vulnerability.
