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Welcome to our newest season of HumAIn podcast in 2021. HumAIn is your first look at the startups and industry titans that are leading and disrupting ML and AI, data science, developer tools, and technical education. I am your host David Yakobovitch, and this is HumAIn. If you like this episode, remember to subscribe and leave a review. Now onto our show.
Welcome listeners to the HumAIn Podcast where we discuss all things AI data science and developer tools. Today we’re featuring John Gianluca Mauro from Copenhagen, Denmark. Gianluca is a thought leader in artificial intelligence. He runs the AI Academy, is the author of Zero to AI, and is working on some exciting new projects. Gianluca. Thanks for joining us on the show.
Thank you for having me.
Well really excited for today’s conversation. You and I are both educators at heart working a lot to accelerate learning and training in the new economy. Can you tell us about the AI Academy?
Sure. So, I started AI Academy with my co-founder, Nick Canova Leiji, when we came back from Silicon Valley, which was 2016, roughly. So, I was in Silicon Valley, which is a project that was sponsored by the Italian Ministry for Economic Development. And the goal was really to take a bunch of people that seemed promising, send it to Silicon Valley, try to absorb as much as we could, and then come back to Italy and give back to Italy, in Europe in general.
And I remember when I went there, this was for the technical people listening, this was when the first image classifications models started, actually working. 2015 was when the first model achieved superhuman accuracy on image net, so more than less than 5% error rate. So, everybody in Silicon Valley was talking about it. And I didn’t know much about this topic, I just knew a little bit of the technical stuff. But I didn’t really appreciate the opportunity until I went to Silicon Valley.
So, when I came back, I thought together with Nikola my co-founder, hey, we need to talk about what we saw. It doesn’t scale to send a bunch of people from Italy to Silicon Valley. And the only thing that we can do is to try to explain to people what we’ve seen and try to make them see the same opportunity that we saw. So, we started this company called AI academy that was focused from day one on education. And then a beginning also on technical consulting.
I was not super experienced but Nico was, my co-founder was really good. He already had worked for a bunch of companies in Silicon Valley doing AI and research and development mostly. And so we started doing some technical consulting as well. And then in the last couple of years, we kind of transitioned to doing just education, and more of a strategic consulting, because we’ve seen technology becoming more and more democratic, easier to use.
And what companies that were working with us needed was more of a direction, rather than a bunch of pilot scripts, what they needed was having a vision, having a strategy to implement all these projects. And so we started focusing more on longer strategic consulting.
That’s super fantastic. I remember when I was here in New York, working with the General Assembly and Galvanize, we saw something similar here in the States that companies could say, ‘teach me fishing, teach me Python, teach me R, teach me SQL, wait, but how do I fish this project’? And how do you go from development to production to build those end end systems, which, as you mentioned, Gianluca back in 2015, there really wasn’t much of that? Now, there’s been so many new developer tools and data tools on the market, the industry’s continuing to evolve.
Exactly. And to give you an idea of what kind of people that I talk to, I mostly work with traditional large organizations. Some of my clients are Procter & Gamble, Fatah, which is a joint venture that produces the Pampers diapers brand in Italy, and the Linus tampons brand, in Italy, Brunello Cucinelli is a fashion brand, a bunch of energy companies. So, a lot of organizations that are not tech companies.
So, in their hearts, in their vision, technology has never been a huge party and has never played a big role. So, when you talk to companies like this image in 2015, and you have to explain to them, what is TensorFlow, how to code, a neural network and what the hell does it mean to do online training and even just the cloud?
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.
Nobody knows how to interact with these people. So, you need to meet them where they are and these people right now or not, it’s a little bit better. But like five years ago, they mostly needed to have an understanding of what the hell we were talking about, what is AI? What can they do with it? What is the true opportunity? It would, it’s the snake oil that certain companies are selling them. And that’s how we started.
Today, what I’m seeing is, companies have a little bit more of a sophisticated understanding of what is the opportunity, really, what kind of value they can extract from these technologies. They have the tools, maybe they have, some of them also have the people, but they really struggle in the strategy and implementation.
What do they do as a first step? How do we validate their ideas? How do they deploy them? How do they make sure that they can test them and continuously check that the return investment they’re getting is the one that they expected? So these are the topics that today are very important for the kind of clients that I work with.
The key thing that you share there Gianluca, which is an agreement with what I saw, at Galvanize, we have a thesis, every company is a technology company, or every company can be a technology company, though technology is not good enough, you could have the best algorithm, the best dataset, but what is your plan to business commercialization? And that’s the key that a lot of companies have been looking at, especially now post-pandemic, how do you bridge that gap of technology and business? And it sounds like that is something that you focus on with your team to help business teams succeed?
So, the metaphor that I always use is that AI and tech in general, to me, is a tool. It’s like a toolbox, you open your toolbox, and you have a hammer, you have a screwdriver, you may have a bunch of other tools. And five years ago, companies were telling me, hey, what’s the best hammer that I can buy? Or what’s the best screwdriver that I can buy? And I was like, wait a minute, can we focus first on what you actually need, which is, a hole in the wall, or something like this, and then we talk about the tool.
Today, they have these, as I was saying before, they’re a little bit more sophisticated. So they start thinking about the tools a little bit later. They need to understand first what their goal is and what they want to do with it, especially because the tools, the hammers, and screwdrivers of today are a little bit easier to use.
So, it’s not as complicated together. Proper hammer working. I totally agree with you just to make them understand, what’s the vision? What’s the purpose? What do they want, they want a hole in the wall, and then find the right tool for it. But a lot of people had even reversed five years ago.
A lot of these tools today are becoming very no code and low code, we’re seeing a lot of new data developer tools coming out. There’s been a lot of reports saying that the 2020s is the decade of data, there’s the evolution of the modern data stack, thinking back to software engineering, the last 20 years, everything was getting into this great toolkit, or toolbox to have all these different programming languages and frameworks. And now we’re seeing the modernization of that, in the data science, machine learning, and data analysis industry. What’s your take Gianluca on some of this modern data stack and the decade of data?
A lot of companies are understanding that the real value is in the data and not in your algorithms, the algorithms are disposable. There’s Andrew Yang who is talking about his data-centric approach to artificial intelligence and data science. And I strongly agree with that, and I’ve seen it in all the projects that I’ve carried with these companies, most of the time the value that you get out of your AI projects come from having the right data for your project.
So, in that case, if the value is in the data, then the question becomes, how do I make sure that they have the right data available, that the quality of your data, it’s, appropriate, and that these data, it’s actually usable? So, that data scientists can use it to test new ideas and try new projects out? So, in this case, a lot of people are still focusing on the tools, having the right data stack, but I have found that the biggest problem in companies is governance.
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 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.
I have a funny story about this. There’s a friend of mine who works for a large Italian company in a team of 60 data scientists, it’s a pretty large organization. And he told me that when they started their AI team, five or six years ago, they hired people from the best organizations from Amazon, from Google, very senior, very skilled data scientists, they all promised them to work on very interesting projects to use this very interesting data. They showed them the amazing infrastructure they were going to use, then they hired these people. And then half of them quit in the first six months.
Because just to get a data set on their machines to run some simple exploration, it was a nightmare of asking the legal department asking your boss and his boss to your to his boss than it was to his boss, it was a nightmare. And so these people got bored. And so when people talk to me about, data stuck with the country to us, I always ask first, are you sure that you have the right culture and the right governance, to take full advantage of this technology is not really useful to have the best tech if your organization is not ready to make the stack work for what it’s supposed to do, which is to make data scientists work in a more agile and faster way. What do you think about it?
It’s interesting that today at Single Store, I had an innovation conference in conversation with our Chief Innovation Officer Oliver, he originally came from SAS, and we were talking about the teams that they built out there. And so a few years ago, it’s exactly what you just described on Gianluca, they’d hire a team of 40 or 50 algorithm engineers, these data scientists that are, tuning models and making a specific use case. But today, that’s not as needed anymore, there’s a lot more work that should be done to validate data early on or work with more modern frameworks. So, there is a transition with the teams.
Myself today also, as an investor in this space with Data Power Ventures, we’re investing a lot into data developer tools, one of the companies that we recently came into, and they pre seed round Retable AI, is building a system that augments DBT and Fivetran. And their thesis is, the data developer tools have actually been underserved, mostly with the data analysts.
Everyone’s been building for data scientists, machine learning engineers, the AI specialist, but how about the data analyst? My career started as an actuary and became a data analyst and a business intelligence analyst. And those people in those fields are often doing the hard work.
To stage and prep the data so that data scientists can be successful. And so this company is one that’s building a tool that’s no code that has predictive insights, to make it easier to move through those workflows. And the key is thinking about the modern data stack, their thesis is, you can hire a team of five, six people for a few $100,000, or have a tool, SAS subscription, a few $1,000 a month, and you have a data analyst that manages that.
And you save a lot, because let’s go counterintuitive to what I shared before, not every company is a technology company. And not every company has the budget to hire a full army of data scientists and ML engineers. So, it’s definitely both sides of the coin that we’re seeing with new and emerging tools.
And there’s also another point to this, which is you need to give the tool to the person that actually needs it and actually uses it. So he made a good example, where he said , the data analysts are the people that do a lot of the work, because they’re the ones crunching the numbers and gaining insights and doing all this kind of stuff. But if the tools are made for data scientists, then you have data scientists building for data analysts, who are then going to use the tools to do the work.
And so a lot of the time, it’s hard to know what an analyst needs if you don’t do his job. And this reminds me of a conversation I had with my friend, Jim Gao, who was the person at Google who built a system that allowed Google to save 40% of the energy-saving of their data centers. That’s Ford zero. Right now, he opened a new startup called Phaidra. And this startup, it’s trying to give you tools, and to engineers to do the job that he did at Google as a data scientist, basically, so basically give all the tools to engineers working in industrial departments of large organizations to build these kinds of models, because they are the ones that understand the system.
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.
So, they know much more than me what it’s possible to do. It’s kind of like, we’re in a moment where we’re trying to create a middle ground between the technical people and the domain experts. So, there are some tasks and some projects that right now are starting to become more accessible to domain experts, they don’t have technical expertise. Whereas before 100% of the AI projects of the data science projects were all in the hands of technical people. So, there has been this kind of shift enabled by these tools.
And there was for a brief period of time, there was a title that was emerging in the space, but McKinsey and others were talking about the new take of the data translator. This role has the hybrid of the data analysts, data scientists, I don’t think that title ever really stuck. I’ve seen a lot of startups emerging. It’s pretty much-become data analysts, data scientists, if you’re a more specialized team, maybe there’s the machine learning engineer, the AI specialists, and data engineer. Are you seeing any other titles emerging or dynamics with the teams?
Yes, there’s a friend of mine, he is an AI evangelist, pharmaceutical company, what does it mean? Well, basically, he was a data scientist. And then he transitioned into this role where he goes to the business people, and he tries to listen to their needs and tries to explain to them, hey, this kind of challenge that you have may become an AI project. And it tries to kind of pull from the business to identify potential opportunities for AI. And so his title is AI Evangelist. I’ve met people who, on the business cards, they have an AI product manager, there’s a bunch of different titles, really.
But to me, it sounds like there’s not really a well-defined name to this role. But there is a need that people have been talking about. And it’s pretty clear, which is the need of somebody who bridges, technical capabilities, what all these tools can do. And the business needs the name of this person? I don’t know, and I don’t think everybody knows right now. McKinsey tried to push data translator, the name that sounds really well.
So it didn’t really stick but, maybe it’s gonna be called AI evangelist. I don’t know. But it doesn’t matter the name, I don’t know, what matters is that there is a need for somebody that understands both worlds. Maybe in the future, that’s not going to be the need and everybody’s going to, quote-unquote, speak data science or speak data or speak AI whatever you want to, you want to focus on.
I like both of the points that you share Gianluca because the evangelists are so important. At Single Store, we have technical evangelists and developer evangelists. But these are generally like all-purpose around the tool, and specializing in data and AI with an AI evangelist. I really like that title. I was recently at an AI summit in New York City, which is one of the leading AI conferences as well on technology and the business. So actually, one of the executives at Fiddler AI was presenting and their title is AI Evangelist at Fiddler AI.
So, we’re definitely seeing that as the merging title. We should see more of that in the next couple of years. And that fits directly into the PMMs in the product marketing organization. On the product manager side, I agree with you also, I did a training delivery. And last year that was remote as a result of the pandemic and I taught 30 AI product managers how to incorporate AI into their workflows and workstreams and product roadmaps. So, a PM is a PM, but they can specialize as well. So, definitely, why not have a software PM, why not have a data PM, why not have an AI PM. So it’s really exciting. Those titles have a little bit more weight to them. So I’m looking forward to seeing those emerging in the market.
Absolutely. Because at the end of the day, AI data science, it’s software, but it’s different enough from building an app that you may actually have a need for a specialized role. And I was actually training a user experience team in Rome last week. And I thought about how important it is for a UX team to know how AI works. If they need to, embed touch points during the user experience where you get the right data from the user so that you can feed AI models.
Think about Tik Tok for a moment. The success of Tick Tok is because of these recommender systems that work really well. But if you think about it, the actual difference between Tik Tok and every other social media. It’s not the algorithm probably, but it’s in how the user experience feeds the algorithm. So, the fact that you have a single video at every point in time and there’s nothing else that distracts you is the perfect way to collect the right data about how much you like the video?
How much time do you spend on the video? There was a Wall Street Journal investigation that tried to make some bots and try to understand what the algorithm is measuring. And it looks like the only thing that matters is really how much time is spent on a video. So, the UX was thought out to get the right data for an algorithm to recommend videos.
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.
And so they need to design the user experience around the needs of the algorithm and of the human, obviously. But you see what I’m going for you cannot possibly come up with Tik Tok just with a team of data scientists without the help of a UX designer that understands AI and designs a user experience that takes into consideration what the AI needs. Does that make sense?
And thinking about all these insights for all these emerging platforms, whether they’re the Tik Toks of the world, or the applications that helped me with my everyday needs, like lemonade for insurance around the apartment or lofts, there’s so many use cases that we’re seeing data and AI today. And you as well, Gianluca, you’re the author of the book Zero to AI, which is about helping everyone learn and be more successful with AI. Can you share with us a little bit more about your book and why you decided to become an author?
Sure. So, let’s start with why I wrote a book. So, since I came back to Europe, I’ve been kind of obsessed with this idea of making what I’ve seen in Silicon Valley. So, the knowledge that I acquired is a little bit more democratic, so it makes it easier for people to understand technology and envision what technology can do for them from the organization. Because it’s, it’s not something you can push to people, you cannot go to a marketeer and tell him, hey, this is how AI is gonna change marketing? No, a better approach would be to explain AI to this person and give him the tools to foresee how it’s going to change his industry.
So, this is what I wanted to do. And I realized, well, I’m doing a lot of consulting and training for large corporations, but how can I take this knowledge? How can I take this framework that I have for explaining AI to non-technical people, and make it scale? And I thought our book made sense.
So, I wrote this book that, takes basically everything that I’ve been sharing with executives, managers in large corporations and packages it in roughly 200 pages, so that people that have no idea about what is AI but are interested in how that can shape their business, carry the book, and get a good understanding of how the technology works, and how they could potentially implement it in their organization.
So, starting from finding opportunities to framing them as machine learning projects, to finding the right data to what it means to have the right data to how you can deploy it or how you can, start building some prototypes. That was really the goal of the book. And the book has been pretty successful, because a lot of the people that read the book came to me and said, ‘Hey, I got inspired to do Project X or to do Project Y’.
And that’s exactly what I want to do. Obviously, you’re not going to read a book and then make a trillion-dollar company AI. But that’s a good start to build this foundation of knowledge that you need as a non-technical person to find the right opportunities for AI. So, I’m pretty happy about the success of the book so to speak.
Exciting that is really great to hear about how you’re helping bridge the gap, but both technologists and business leaders with Zero to AI, and from your experience, both in consulting and business and leaving as educators, author, and consultants, we’ve been seeing a lot of trends, especially the last couple years. I was recently at the AI summit in New York. And the big word this year, you could see through all the keynotes, was trustworthy, responsible, ethical. That’s all coming to light now and I know Gianluca, you, and I’ve talked over the years that this is not something new. People have been talking about ethics and responsible AI like 5, 6 years ago, but now it seems to be coming to light to hear more of your take on ethics around data and AI.
Absolutely. So, if you think about where AI started from instead of from Silicon Valley, that’s where, basically, all the tech companies that really push the boundaries of AI are from and what’s the mindset of these people? Well, the infamous quote from Mark Zuckerberg, move fast and break things. And I see how it can serve someone having this mindset. But if you think about it, once you have technology that with a click, you can deploy it to a billion people, then you can break things for a billion people.
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.
And they tend to either favor men for tech roles as a classic example or to discriminate against people of color. So, there’s a lot of issues around not just the tech that everybody talks about. So again, social media, but also around more like nature, applications, like AI for hiring. So, that’s the scenario. That’s where we are today. There’s a lot of people building technology very fast, but then having a lot of issues that have just not been checked. And the public opinion, in the last couple of years, has become a little bit more careful about these things, more skeptical of technology, and there’s less trust for technology companies.
And that’s fueled by again, what happened with Facebook, when the elections in 2016, about all the different topics, I don’t want to get into the details and saying who’s right and who’s wrong. But a fact is that people don’t trust technology anymore. And as I said before, they have good reasons to do that.
So, there’s this huge trend of ethical AI. The point is, what does it mean to build ethical AI? A lot of times companies are trying to balance the need for ethical AI, with the mindset that they have to build fast, move fast and break things. The two things are non necessarily comparable, a lot of times you actually need to slow down when you are deploying technologies that can actually impact a billion people. And that’s where technology companies are, how do they keep the speed and the momentum that made them rich, while keeping an eye open for the problems that may cost to society. And in the meantime, you have regulators that are trying to catch up.
And the European Union, for instance, published the AI act in April, and that’s a first step is not perfect, but regulators are trying to set up some boundaries to at least, ethical AI, it’s one thing lawful AI, it’s another thing today, everything that you do with AI, it’s by definition, within the law, because there’s no law that restricts what you can and cannot do when it comes to artificial intelligence. So, you have tech companies, you have the government trying to regulate it. But what nobody has to forget and always have to keep in mind is the public opinion.
So, people simply don’t trust technology anymore as they were used to. I see these all the time, whenever there’s a new technology coming up. Yesterday, I had a deep dive with some of my customers about the metaverse, and everybody, the first thing that they said is I am scared. So it’s not anymore, hey, I’m excited. There’s a new iPhone, it’s hated. I’m scared. This new technology may have these issues. This other issue may make us even more dependent on technology, it may create disparities, you may do a bunch of different things. But people first question the utility of these technologies.
And then they start thinking whether they can actually bring a positive impact. And that’s something that we don’t have to forget, we have to keep in mind. People don’t trust technology as much as they were used to. And so tech companies need to adapt if they want to keep pushing this technology and keep making this technology more used. What do you think about it? Does it make sense?
It’s absolutely essential as part of my investment thesis at Data Power Ventures. We’ve created the industry’s first data rider. So, when you think about diversity, just the last few years, everyone says that they’re coming out in support of BIPOC for women in tech for LGBTQ for all diversity. But the diversity ride that was started over 10 years ago, there were different companies championing for diversity when no one was listening in the room. And now everyone, after the George Floyd shootings in the United States, everyone comes out supporting diversity.
The same thing is starting to happen with data and AI. When you look at that parallel for many years, people like perhaps yourself and myself, have been saying, we have to be responsible, we have to be ethical. No one cared. No one listened. And just now after we’ve seen some issues like ClearView AI and selling the data to the police departments in the United States, and other data breaches in Europe around GDPR, where there was non-compliance, now people are starting to listen.
And this is why I’ve also created this data writer to say, look, if we’re going to make investments in venture-backed companies, we need investors, we need founders, we need a community to be aligned on being responsible social stewards, with the fair use of data, not only for the cap table with investments but among all of our constituents.
It’s to the point that everyone at the company, it is their fiduciary duty to be fair and responsible with data, it should no longer be just the right of the SRE to say, Oh, the AWS endpoint is not open to the world. So our data is secure, it’s everyone’s responsibility to ensure they’re building resilient systems, and thinking about society as a whole. So, that’s absolutely paramount. I’m excited that the EU has taken a stance with the Artificial Intelligence Act, the US is lagging behind as we historically do with privacy and rights.
So, it’s great that the US has taken that stance, I know that the Biden administration has brought about a new AI commission. So, we’ll see if the US has something similar in the next couple of years. Taking this all in hand, we’ve covered a lot of great topics today on the show. Gianluca you talked about moving forward, talk about this decade of data. What do you see on the horizon? Or what are you excited about? As we’re moving into the new year?
So, from a general perspective, I’m excited about the topic that we just talked about. I’m excited about data ethics and how this is going to change. I hope that we’re going to get to a point where ethics is not going to be an optional, where ethics is going to be embedded into the workflow of data scientists. And we’re really at the beginning of this change and really looking forward to seeing a word is going to go and hopefully to contribute. That’s probably the thing that excites me the most on a, let’s say from a general point of view when it comes to my company, my programs, there’s one thing that I’ve been researching for the last three months.
I realized AI is very interesting for sure. Super powerful, but a lot of people in non-technical organizations miss the context so they can understand AI, but they maybe don’t know about how data flows between Apple and Apple devices, or they don’t know how Facebook operates. They hear about the metaverse. And they’re confused about what that is. They don’t know what NFTs are. And so you’re giving them a tool, but they don’t necessarily understand the technological context that they can use this tool. So I started this program where I do a bi-weekly deep dive on a topic.
We talked about the metaverse yesterday. We talked about self-driving cars two weeks ago, and I explained the topic for roughly 40 minutes, and then we had a little discussion or saw you divide the audience into breakup rims, and they are able to. Exchange ideas and talk between each other about these topics. And then they come back to me and then we discuss what they found out.
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.
So next year, I want to expand this program. Because again, it’s important to bring knowledge, to the masses to be, make more democratic there’s access to general technology knowledge, not just artificial intelligence, because our division agents need to work in a context that makes sense. It needs to have the right context.
We get the right data, the right platforms to be used when it comes to, building apps and this kind of, final applications. And so I’m excited for this. This is the BERT a lot broader than in most exciting.
Well, I’m excited as well. And Gianluca, can’t wait to see where that ends up. Perhaps you have a new platform yourself that you’ll be revealing next year to our listeners. So thanks so much. This has been an episode on HumAIn with Gianluca Mauro, who is the founder of AI academy and the author of Zero to AI. Thanks so much for joining us on the show today.
Thank you, David.
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