02:21 - Julie’s Superpower: Working Really Hard and Maintaining Focused Attention on Things for a Long Period of Time
04:25 - Robotics and Working in Artificial Intelligence (AI)
* What To Expect When You're Expecting Robots: The Future of Human-Robot Collaboration (https://www.amazon.com/What-Expect-Youre-Expecting-Robots/dp/1541699114) (Julie and Laura Major’s book)
11:10 - Structuring and Optimizing the World for Machines, AI, and Robots
* The Turing Test (https://en.wikipedia.org/wiki/Turing_test)
* Labeled Data
* Teslas vs Airplanes
* Mode Confusion
* Ten challenges for making automation a "team player" in joint human-agent activity (https://ieeexplore.ieee.org/document/1363742)
26:10 - Understanding Output and Building Calibrated Trust
* Mental Models
33:39 - Robots and Humans in Public Spaces
* Predictability
* Directability
* Standardization
* Infrastructure
* Safety Imperatives
* Future of Work Implications
* Joint Activity
* The Shannon Model (https://en.wikipedia.org/wiki/Shannon%E2%80%93Weaver_model)
51:41 - What To Expect When You're Expecting Robots: The Future of Human-Robot Collaboration (https://www.amazon.com/What-Expect-Youre-Expecting-Robots/dp/1541699114) (Book Discussion)
54:40 - More on Human/Machine Collaboration:
* Girl Decoded: A Scientist's Quest to Reclaim Our Humanity by Bringing Emotional Intelligence to Technology (https://www.amazon.com/Girl-Decoded-Scientists-Intelligence-Technology-ebook/dp/B07VF1SKPV)
* Reinforcement learning with human teachers: Evidence of feedback and guidance with implications for learning performance (https://www.cc.gatech.edu/~athomaz/papers/Sophie-Guidance.pdf)
* Evaluating fluency in human–robot collaboration (https://ieeexplore.ieee.org/document/8678448)
Reflections:
Rein: There may be a sense in which AI or ML systems are categorically different from the sorts of systems we’ve tried to control in the past because you can’t characterize the variety of the system anymore just by observing its inputs and outputs.
Damien: Artificial intelligence is not human intelligence, nor should it be. The goals are making systems and human lives better; not making the computer better.
Julie: Aim for mediocrity!
This episode was brought to you by @therubyrep (https://twitter.com/therubyrep) of DevReps, LLC (http://www.devreps.com/). To pledge your support and to join our awesome Slack community, visit patreon.com/greaterthancode (https://www.patreon.com/greaterthancode)
To make a one-time donation so that we can continue to bring you more content and transcripts like this, please do so at paypal.me/devreps (https://www.paypal.me/devreps). You will also get an invitation to our Slack community this way as well.
Special Guest: Julie Shah.

02:21 - Julie’s Superpower: Working Really Hard and Maintaining Focused Attention on Things for a Long Period of Time

04:25 - Robotics and Working in Artificial Intelligence (AI)

What To Expect When You're Expecting Robots: The Future of Human-Robot Collaboration (Julie and Laura Major’s book)

11:10 - Structuring and Optimizing the World for Machines, AI, and Robots

The Turing Test
Labeled Data
Teslas vs Airplanes
Mode Confusion
Ten challenges for making automation a "team player" in joint human-agent activity

26:10 - Understanding Output and Building Calibrated Trust

Mental Models

33:39 - Robots and Humans in Public Spaces

Predictability
Directability
Standardization
Infrastructure
Safety Imperatives
Future of Work Implications
Joint Activity

The Shannon Model

51:41 - What To Expect When You're Expecting Robots: The Future of Human-Robot Collaboration (Book Discussion)

54:40 - More on Human/Machine Collaboration:

Girl Decoded: A Scientist's Quest to Reclaim Our Humanity by Bringing Emotional Intelligence to Technology
Reinforcement learning with human teachers: Evidence of feedback and guidance with implications for learning performance
Evaluating fluency in human–robot collaboration

Reflections:

Rein: There may be a sense in which AI or ML systems are categorically different from the sorts of systems we’ve tried to control in the past because you can’t characterize the variety of the system anymore just by observing its inputs and outputs.

Damien: Artificial intelligence is not human intelligence, nor should it be. The goals are making systems and human lives better; not making the computer better.

Julie: Aim for mediocrity!

This episode was brought to you by @therubyrep of DevReps, LLC. To pledge your support and to join our awesome Slack community, visit patreon.com/greaterthancode

To make a one-time donation so that we can continue to bring you more content and transcripts like this, please do so at paypal.me/devreps. You will also get an invitation to our Slack community this way as well.

Special Guest: Julie Shah.

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