People worry that machines are going to get too smart. The bigger problem right now is that they aren’t smart enough. Daryl Weir, mathematician and data scientist joins us to talk about machine learning and AI.
We discuss the impacts of seeding systems with biased data, how our designs and algorithms evolve and impact beyond what we had originally envisaged. We also talk about the new automation revolution and the importance of awareness, self-regulation and care.
“Everything that is currently referred to as AI is basically statistics from the 1950s“
(Listening time: 37 minutes)
Episode 215: Machine learning with @darylweir.— UX Podcast (@uxpodcast) July 19, 2019
Mathematician and data scientist Daryl Weir joins us to pick apart data bias, automation and the meaning of intelligence in AI and machine learning. And how we should manage the impact.https://t.co/3vHrrT6Htr #ux #podcast
- Futurice’s Intelligence Augmentation Design Toolkit
- What Artificial Intelligence Can and Can’t Do Right Now by Andrew Ng
- Episode 166: Oblivious design
- Send us your thoughts at: firstname.lastname@example.org
- Backstage mailing list: Sign up here
- Enjoy the episode? Become a patron of UX Podcast