3x17: Why the Heck Even Use Deep Learning?

Although it’s a powerful tool, deep learning is perhaps over-used in modern applications. In this episode of the Utilizing AI podcast, Rich Harang joins Chris Grundemann and Stephen Foskett to discuss the various reasons people use AI, both good and bad. In a November Twitter thread, Rich posited that the following conditions were required to use AI for real: The cost of errors must be extremely low, the decision needs to be possible but expensive, there needs to be the same kind of decision frequently, there needs to be a benefit and be better than a simple rule, you have to not care how it got the answer, the base rate must be close to even, you need a steady stream of data for training, and you must match the size and cost of the model to the application. On the other hand, these same considerations can point us to problem sets that make a great match for DL, and we should focus on using the right tool for the job.

Three Questions:

• Chris Grundemann: Are there any jobs that will be completely eliminated by AI in the next five years?
• Stephen Foskett: How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
• Adam Probst of ZenML: What percentage of companies will be using ML in five years?

Links:

• Rich’s Twitter Thread: https://twitter.com/rharang/status/1465340190919217153
• Sara Hooker’s Paper, “The Hardware Lottery”: https://hardwarelottery.github.io

Gests and Hosts

• Rich Harang, Senior Technical Lead at Duo Security. Connect with Rich on Twitter at @RHarang.
• Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
• Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.

Date: 1/11/2022 Tags: @RHarang, @SFoskett, @ChrisGrundemann