Introduction

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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

(01:59) – Cultural difference in cultural attitudes about privacy and concerns that exist in some locations in Asia

(02:51) – Difference in attitudes to freedom of speech in Europe and in the US: Americans value freedom of speech less, and it's something similar with privacy. To take privacy seriously on a global level we need to talk to people from around the world and understand that population density, economic growth, and other factors are very important

(05:27) – One of the challenges about AI being merged into business is interpretability. If you can't interpret and explain your algorithms to your investors, you may have a hard time. You may choose something that works a little less well, but it is a lot easier to interpret

(10:13) – There's a certain technical proficiency that doesn't have to be extraordinary. Involving the experts to solve real problems

(14:56) – The dangers of AI. Weaknesses in classification systems susceptible to attacks, either by misfire or potentially more vicious. We are moving into a world with hundreds of millions, billions of gadgets that do machine learning in houses, in hospitals, on army bases

(21:01) – Generative Adversarial Networks and a way to be protected from attacks

(25:45) – Fake News.We are not going to be able to trust our unaided human consensus with anything that comes to us digitally

(29:43) – There's more time and ability allowing us it's increasingly feed different types of data into a single system. That's why the overall system works better to feed different modalities of texts into data, into our algorithms and for users, it will get richer products and richer experiences

(34:03) – Augmented human intelligence. Creating experiences where data science and machine-driven learning is able to augment user experience and create better solutions for society