Are you optimistic about our future with ARTIFICIAL INTELLIGENCE? Avi Goldfarb (professor and author of “Prediction Machines”) shares a brief history of AI, various AI applications that are being used in the marketplace, and specific reasons why we should be optimistic about our future with AI.

SHOWNOTES

Contents

* Key Learnings

* References & Links

* Andrea’s Commentary

* Interview Transcript

* Conclusion

 

3 Key Learnings

1.The A.I. Hierarchy: Definitions

COMPUTER SCIENCE

Top level umbrella term

ARTIFICIAL INTELLIGENCE (AI)

Subset of computer science

“Machines that can do what normally requires human intelligence.”

AI is a moving target. Larry Tesler says, “AI is whatever hasn’t been done yet.”

MACHINE LEARNING

Subset of AI

Prediction technology

DEEP LEARNING

Subset of machine learning

Artificial neural networks that learn from large quantities of data.

Also called deep neural learning or deep neural network

 (see also References & Links below)

2. Reasons we can be optimistic about our future with AI

* Children & Technology – There is reason to believe our kids’ obsession with video games and other technology might not be as bad as we thought (see danah boyd below).

* Disaster Management – AI can help us predict weather disasters and play a significant role in disaster relief management – from real-time mapping of disaster sites and to post-disaster communication

* Making life more pleasant – AI can help us by doing the things we don’t want to do (think dangerous, think dirty, think annoying). Then we can focus on doing the stuff we want to do.

* Productivity – AI can make us more productive, and wealthier – as a whole. (How that wealth is distributed is a whole other Q.) Research demonstrates that AI can encourage and enable communication, business (commerce) and travel.

* Jobs – Yes, AI will affect the job market. But there’s a lot of work for us humans! Many skills will become even more valuable, particularly the ability to take actions,  to make judgment and to build…

3. Examples of AI

* Simple prediction technology (autofill, purchase suggestions based on browsing and/or past purchases

* Siri and Alexa

* Visual perception (exactly what it sounds like – predicting an image)

* Speech recognition

* Translation

* Weather forecasting

* Geographical mapping

* Driverless cars

* Netflix library …

References & Links

Avi Goldfarb

* Rotman UofT – http://www.rotman.utoronto.ca/FacultyAndResearch/Faculty/FacultyBios/Goldfarb.aspx

* BOOK: “Prediction Machines: The Simple Economics of Artificial Intelligence” – https://amzn.to/2GoX6Ac

* LinkedIn – https://www.linkedin.com/in/avi-goldfarb-46a7473/

* Twitter – https://twitter.com/avicgoldfarb

The Organizations

* Creative Destruction Lab – https://www.creativedestructionlab.com/

* Marketing Science – https://pubsonline.informs.org/page/mksc/editorial-board

* NBER – National Bureau of Economic Research – https://www.nber.org/

* AtomWise – https://www.atomwise.com/

* OpenAI – https://openai.com/