Mimicking human decision making with algorithms w/Adjunct Professor Harri Ketamo

Harri talks about AI and how he aims to mimic human decision making with algorithms. Harri has done a lot of AI for computer games to create opponents that are entertaining to play against. It is easy to develop a very bad or a very good opponent, but designing an opponent that behaves like a human, is entertaining to play against and that you can beat is quite hard. He talks about how AI in computer games is a very important story telling tool and an important part of making a game entertaining to play. This work led him into other parts of the AI field. Harri thinks that we sometimes have a problem separating what is real from what is the type of story telling he knows from gaming AI. He calls for critical analysis of AI and says that data has to be used to verify AI decisions and results. We also hear about the current use of AI in among other things sports games, where the challenge is to make a believable computer copy of a real-world player to make the games feel more real. We then get to hear about his current work in developing AI systems that create mind-maps from texts to make the computer able to determine the context. By doing this we can derive better sentiment and meaning. This AI system is trained using a large amount of reliable data from scientific papers and CNN. One application of this that Harri is working on, is in the labor market matching needs for talent with available people. This will help companies find staff and people find job opportunities. This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate. Learn more at Wevolver.com Promote your company in our podcast? If you are interested in sponsoring the podcast, you can contact us at richard@wevolver.com

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Mimicking human decision making with algorithms w/Adjunct Professor Harri Ketamo is an episode from Wevolver Robots in Depth by Richard Hulskes.

This episode is 00:33:00 long.

This episode was published on Feb 5, 2020.

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