#202: Artificial Intelligence

Tech Policy Podcast

TechFreedom

Technology

4.8 • 45 Ratings

🗓️ 30 October 2017

⏱️ 32 minutes

🔗️ Recording | iTunes | RSS

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Summary

Artificial intelligence is already transforming our lives in many ways, and it has the potential to do so much more. But it seems like news headlines only focus on potential job loss and the end of the world than increased productivity and social benefits. Is this because our mental imagery of AI is so influenced by dystopian sci-fi novels and movies like Terminator? Or have policymakers not done enough to communicate honestly about the disruptions we face with AI? What can listeners of this podcast reasonably expect to see in the coming years? Is there even a single definition of AI that everyone can agree on? Evan is joined by Elizabeth Hudson, Senior Research Scientist of Machine Learning at Symbotic, an industrial robotics company, and Austin Carson, Executive Director of TechFreedom.

Transcript

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0:00.0	Welcome to the tech policy podcast. I'm Evan Schwarger. On today's show, artificial intelligence, it's hard to believe we've done basically 200 episodes, and this is the first one where we're exploring this very exciting topic. And while artificial intelligence does have the potential to revolutionize our society in many ways and benefit people, increase productivity. There's clearly a lot of anxiety about potential job
0:25.5	loss and even the end of civilization as we know it, depending on who you ask. So we're going to
0:29.8	try to unpack this very complex and deep topic in 20 minutes, but of course we'll be doing
0:35.1	some follow-up episodes after this, as this will be a topic of
0:38.7	conversation for many, many years to come.
0:41.1	Joining me to discuss this is Elizabeth Clark Polner, director of artificial intelligence
0:45.7	at Symbolic, a industrial robotics company.
0:49.0	Thank you for having me.
0:50.0	And Hawaiian shirt enthusiast and tech freedom executive director, Austin Carson, is also
0:53.7	joining the show.
0:54.3	Austin, thanks for joining.
0:55.4	It feels really weird that I'm wearing a dress shirt right now, but I'm excited to be here.
0:59.5	All right.
1:00.1	So to start off, I mean, one of the big issues in this discussion is do we even agree on what the hell it is we're talking about?
1:07.0	It seems like depending on who you ask, the definitions are all over the map.
1:10.5	So let's just start out by defining what is AI. talking about. It seems like depending on who you ask, the definitions are all over the map.
1:13.9	So let's just start out by defining what is AI.
1:20.3	So people do use different definitions to refer to AI, but it's generally understood to be pattern recognition that's automated. So traditional big data, data science stipulates that you create a model.
1:28.6	You say, I think that this is the data that I have, and I think that this is how it should be put together in order to predict a response.
1:35.3	Humans do that pretty well, but we're biased.
1:39.1	And so AI tries to address those biases and says, all right, let's try all the different ways to combine the data that we have in order to make a prediction. Instead of you having to define a priori,
1:48.2	I think this is how the problem should be solved. It makes those decisions for you.
	...

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