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Gianluca Mauro is the CEO of AI Academy, which he founded with the mission of helping people understand what artificial intelligence is and its place in their organizations and their career. Gianluca is the author of the book "Zero to AI - A nontechnical, hype-free guide to prospering in AI era" 

Over the years, Gianluca and his team have done both technical consulting and training workshops, working with companies like P&G, Merck, Brunello Cucinelli, Daikin, Fater, Bayer, and EIT Innoenergy 

Gianluca teaches Artificial Intelligence to people without a tech background, without any code or math. Why? Because he believes, the future of artificial intelligence is in the hands of people who can find use cases in their organizations, and then define and run AI projects. 

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Episode Links:  

Gianluca Mauro LinkedIn: https://www.linkedin.com/in/gianlucamauro/ 

Gianluca Mauro Twitter: https://twitter.com/gianlucahmd 

Gianluca Mauro Website: academy.com">https://ai-academy.com 

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Outline: 

Here’s the timestamps for the episode: 

(04:15)-Sometimes it's not a concept that people are familiar with. It sounds weird to anybody who works in tech. But, a lot of companies, in these industries, are still struggling with the cloud. So, when you go to these companies and start talking about this technology, they are excited. They're like, this sounds amazing, but you have to keep into account the reality of where they are, they're not in a place where they can invest in hiring a full-blown data science team, because then nobody knows how to interact with them. 

(09:29)- So, having the right governance for how to use the data, how to keep it in the right shape, and making sure that the quality is what we need, and then actually bring into the laptops of the data scientists that they can make tests and run experiments and make graphs. So, I always like to say it doesn't really matter how good your technology is. How good is your data warehouse or whatever kind of stock you use if using that data is not easy. If using that data it's not straightforward for a data scientist. 

(17:32)- And in the same way, if we want to use AI for marketing, you need to give tools to the marketers that understand the problem to use AI on their data for their problems. When I talk about sales, well, I understand sales data set and takes me a lot of time to understand the logics of sales, have a sales team of the data that its Sales team works with to a sales team who really understands this data, the right tools to, they don't have to be able to do everything but the list to get started, well, then they know much better than me the data.  

(18:17)- So, it's kind of a paradox, because the most important thing of the app is the recommender system. But the reason why that works is not because of the tech, but because of how the UX feeds the tech. And if you think about this, think about this concept, well, then your UX designers, they need to understand this, they need to understand what it means to feed an algorithm with the right data.  

(23:40)- And so we have seen cases where these things went wrong. And I may start from the stuff that everybody knows about, the elections in 2016, fake news and all this stuff up until more niche, let's say topics that maybe not a lot of people aren't aware of. But that actually had a strong impact on people. An example is AI in hiring. There was a very interesting research made by MIT Technology Review about how a lot of companies that sell software for hiring and leverage AI are actually biased. 

(31:01)- And it has been amazing, honestly, because then you'll have people coming from all sorts of backgrounds. I give them the tools and the foundational knowledge that they need to talk about these topics in a way that is productive and they bring the wrong perspectives. They bring their own experience. And I had to say, I've been amazed by the insights that we were able to get from these conversations.