How to Reimagine Education and Society in a Post-Pandemic World with Alberto Todeschini

HumAIn Podcast - Artificial Intelligence, Data Science, Developer Tools, and Technical Education

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Alberto Todeschini is a Faculty director, consultant and lecturer in artificial intelligence. He has supervised over 150 projects covering a wide variety of industries and techniques, with a special focus on sustainability in energy and water. He also works with the University of California, Berkeley, GetSmarter, and aivancity.

Episode Links:

Alberto Todeschini's LinkedIn: https://www.linkedin.com/in/atodeschini/

Alberto Todeschini's Twitter:

Alberto Todeschini's Website: https://www.ischool.berkeley.edu/people/alberto-todeschini

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Outline:

Here’s the timestamps for the episode:

(00:00) – Introduction

(01:49) – It has been interesting because in the last few years, a lot of this is about the environment, about energy and about agriculture having been penetrated by data science. I'm pretty optimistic actually, coming out of this big dark cloud. First half of 2022 will be some good news.

(03:56) – Newer energy technologies have been around for a while, but they really have become mainstream recently, such as wind and solar. They are intrinsically data-driven. So you need to squeeze every last percent of energy out of this massively capital intensive works.

(06:22) – With COVID, we've been forced essentially to experiment. We will see more experimentation around the livable cities for instance. There's a lot of appetite for resilience, for community resilience, maybe at the city level, but also at the regional level and national level.

(09:00) – We've seen the investment moving elsewhere to renewable, which is certainly more future proof. if you talk to the epidemiologists, they'll say, well, there will be another pandemic. As a matter of fact, it could be a lot deadlier. So it will be nice to have this distributed way of storing large amounts of essential items.

(12:40) – 5G enables this distributed system and the ability to communicate incredibly quickly and also to do, technically speaking, inference on the edge.

(17:08) - The market in Europe is pretty fragmented. Partially that has to do with language. So, pretty much most European countries would speak reasonable English, but that's not absolutely not true for the entire population. One of the things that maybe has changed with COVID is the sense of locality.

(20:25) – There's a huge amount of work that needs to be done postmortem, in the real meaning of the term, to understand what went wrong with the data collection. So that next time, collect it better. What went wrong with communication between health authorities and political authorities and the general population.

(24:49) – Cultivated areas are very interesting because agriculture consumes the majority of fresh workers and about half of agriculture. Currently it is not sustainable. Purely from the point of view of water. And we're not talking about deforestation, we're not talking about runoff of chemicals into the ocean, purely just the water.

7:05) – Some of the main carbon capture technology is very water-intensive. As we increase both the data collection, as well as the predictions, which are two of the main things that we can do with machine learning, we can just use water better.

(28:45) – These companies that are, from day one, data-driven companies, are all thriving and they're becoming ever more unmatchable.

(38:45) – Let’s use technology to figure out how to improve life in the city or make places where we enjoy walking. We like walking, and we enjoy local restaurants. We enjoy going out. We like biking around the same city, livable cities. So maybe that is something we can think about and work towards.

(41:31) – It's been awful. It still is awful, but I'm optimistic. Look around your neighborhood and think of things that you want to stay with us. We've been given a great opportunity to reset a lot of our habits.

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