What I want to talk to you about is this idea of machines governing things. And one particular thing that I see governed by the machines is the radio spectrum. That’s an example where I see this kind of regulation can work, and I guess the most radical thing about it is that I think a robotic regulator can actually bring more human values to its regulation.
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BJ Copeland states that a strong AI machine would be one, built in the form of a man; two, have the same sensory perception as a human; and three, go through the same education and learning processes as a human child. With these three attributes, similar to human development, the mind of the machine would be born as a child and will eventually mature as an adult.
We are here to talk about fucking machines. In London, on a foggy evening, on a Tuesday, for yet another debate about fucking machines. Another curated discussion underlined by our own human insecurity about versions of us in silica. Fucking anthropomorphic fucking machines. Machines that fuck us. And let’s face it, machines are already fucking us, or so we seem to be told.
The big concerns that I have about artificial intelligence are really not about the Singularity, which frankly computer scientists say is…if it’s possible at all it’s hundreds of years away. I’m actually much more interested in the effects that we are seeing of AI now.
I’m interested in data and discrimination, in the things that have come to make us uniquely who we are, how we look, where we are from, our personal and demographic identities, what languages we speak. These things are effectively incomprehensible to machines. What is generally celebrated as human diversity and experience is transformed by machine reading into something absurd, something that marks us as different.
I am profoundly envious of people who get to write about settled domains or sort of settled states of affairs in human events. For me, I was dealing with a set of technologies which are either recently emerged or still in the process of emerging. And so it was a continual Red Queen’s race to keep up with these things as they announce themselves to us and try and wrap my head around them, understand what it was that they were proposing, understand what their effects were when deployed in the world.
The question is what are we doing in the industry, or what is the machine learning research community doing, to combat instances of algorithmic bias? So I think there is a certain amount of good news, and it’s the good news that I wanted to focus on in my talk today.