On being humAIn

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Welcome to Practical AI. This is Daniel Whitenack and I'm joined by my co host Chris Benson, who is an AI strategist at Lockheed Martin. How you doing Chris?

Doing great.

How's it going today Daniel?

Oh, it's going pretty good. I'm packing up and getting ready to do a very non AI data science thing which is going going backpacking for a bit. So I'm gonna.

Oh that sounds great.

Gonna be out of touch next week and hopefully away from any sort of cell phone signal and that sort of thing. So actually I'm pretty, pretty excited.

Fantastic. What area you gonna be in?

I'm going up to Minnesota to the Superior Hiking Trail which goes along Lake Superior. So it should be should be a good time. It'll be be a new one for me and I have no doubt that being in isolation a little bit will give my mind some time to think about all of those AI problems that I am trying to solve as well. So looking forward to that.

Sounds good.

Yeah. Well, today we're very privileged to be joined by David Jacobovich, who is a principal data scientist at Galvanize and also a fellow podcast host. He's the host of the Humane podcast. So humane. Like with the. The AI emphasized. And we're really happy to have you here, David.

Thanks so much, guys. It's a pleasure.

Yeah. First off, why don't you kick things off by giving us a little bit of your background and how you got interested in AI data science things and ended up where you're at now.

Sure. So ever since I was young, I loved math competition and I competed both in the state and national level in the US and went to college actually for applied mathematics and physics and doing theoretical proofs and quickly realized the industry was changing from research to applied. So I started moving in the direction of code and applied research, which led me down the path of actuarial Science back in 2010.

Gotcha. Yeah. So this is kind of maybe just the start of a lot of the data science hype. Maybe.

That's right. Right. Big data wasn't even a word till 2012. You know, the AI revival was only kicking off around then, so I. In 2012, I first learned C. I was also playing with Fortran and Cobol, because that's what the company I worked for had. So I was picking up some of those old languages. Right. But Cobol's never going away.

I don't think Fortran either, as far as I can tell.

Yeah. Especially with financial services. So I've seen that reemerge over the years. And although that's a tangent, but I think that's interesting because as everyone's moving to cloud, you know, it's still, how do we maintain these systems with these languages? But I love it because I'll go back into the background. But when I teach a lot today, I tell the students, hey, if you want to work in Jupyter, if you want to work in an ide, guess what? It supports FORTRAN and cobol, so you can always pick up those old languages.

Yeah. It's something that would be definitely a fun exercise. And I've kind of done this a little bit with not those languages, but kind of trying to implement things side by side in different notebooks and see. See how they look and experiment that way is a fun thing to do.

Didn't you do bindings for go, Daniel, if I recall?

Yeah. So I worked originally on one of the first GO kernels for Jupyter, which is now maintained by other people who are doing great things with it. But, yeah, there's a lot of fun times to be had with Jupyter and languages other than Python.

I would say that's super cool. And it's amazing how the bindings have evolved, the technology and you know, when I was getting involved in actuarial science, not much of that existed. Even APIs were just emerging in a certain aspect. So, you know, back in 2010, it was around the water cooler, literally at the office, in person, before remote work was even happening. Teams were saying, hey, we're thinking about getting on the cloud. Hey, we're thinking about getting these servers. And people were talking about Python in this language. And yeah, Python's been around since the 1990s, but it was just getting into the financial services back then. So I said, I'm going to pick it up and started learning it. And before you know it, the last eight years have been involved with different financial services companies implementing data solutions with Python and helping them build everything from analytics and dashboards to predictive models and setting up data strategy as well as building out centers of excellence. And, you know, that led me to not only learn how to teach and how to code, but then how to help others take over processes. I think having worked with a lot of companies over the years, one of the biggest flaws we always see is not enough things are documented. And it's really challenging for those not coming from tech to pick up tech skills. So I've always been that go to person around the water cooler to teach you how to use Excel and SQL and Python. And it just became a natural fit in the past few years where I got into learning and development, pedagogy and training.

So that is a perfect segue into first question I have for you and that is tell us about Galvanize and what do you do and how did that come into your life here?

Sure. So Galvanize was founded in 2012. We're one of the boot camp providers for software engineering and data science in the United States. We have three segments of the business, a consumer, a remote and an enterprise. I'm on the enterprise corporate arm and that plays a lot to my previous skill set of helping other individuals learn tech in corporate. Prior to being at Galvanize, I was at General assembly doing the same thing in our enterprise side. Working with financial clients, scaling hundreds of individuals in organizations to reskill and upskill in the Python programming language, in Jupyter, in working with return on investment projects for their divisions. And you know, at Galvanize we have all those divisions as well. We are both consumer and enterprise facing and we're all over the US And I think what's most exciting is there's been so much growth happening in 2019 and we're seeing that even into the next three years, predominantly because everyone is wanting to reskill and upskill and code is now the first thing that people are picking up.

Yeah.

And as you kind of got into that training side of things, I mean, it sounds like you got into sort of data science training pretty early in terms of these, when these programs were coming out and that sort of thing. What really motivated you to see that need for better data data science training? Or was it kind of a personal thing on your side where you really kind of developed some passion for teaching or found out you were good at it, or what led you down that path?

So for me, it's very mission driven, even since middle school. And the math competitions, because we would have math competitions where you not only competed individually, but you had team assessments. And that's where you would have to solve four questions between 30 to 60 seconds and come up with a group answer. It's incredible how fast paced it was, both statewide, nationally and internationally. And so if you had the weakest link on your team, you had to get them up to speed so that you can successfully perform. So I've always been interested in helping everyone rise to the occasion. But beyond that, I've noticed how technology has transformed so quickly. So my father actually was an entrepreneur and owned a business that worked at the schematic level to repair TVs, VCRs, DVDs, and all sort of electronic gadgets in South Florida, you know, all throughout the 80s and 90s. At one point, this company grew to over 20 people. They had three locations, they were doing millions of dollars of business a year. And then before you know it, the whole industry changed, right? All these new SM TVs appeared, products disappeared, and it was so challenging to keep up with the times in technology. And before you know it, that whole servicing industry and warranty industry started to evaporate. And fortunately for our family, my dad was already in his 60s when that started. So he went into an early retirement. But then I started thinking, hey, how could someone like my dad learn to code? And he really wanted to. And he had that capacity because he had that technical mind, working with, fixing electronics with capacitors and all these gadgets. And it was interesting because I in essence mentored my dad as he was picking up Python through some of these platforms and coaching him. And at the end of the day, what I realized is he didn't want to learn Python for data analysis. He knew at 63 years old that he wasn't going to become a data analyst at the Fortune 500 company. But he knew if he could take the work that he did in RPA and robotics and apply Python there, it would make a lot more sense. So what did my dad naturally do? He bought Arduino boards and Raspberry PI boards and connected sensors to refract light off the walls in the living room and sound waves when the dogs moved and sensed actually position locations for where movement was occurring, because he was always sensing movement with audio and for visual on those TVs and the soundboards. So it was so interesting for me as a takeaway to realize to make code and to make languages stick, you have to make it relatable for the learners and you have to provide them capstones that they can take back for their portfolio and they're having a fun time learning as well.

I gotta say, that sounds. Your dad sounds super cool there, getting into that. I certainly think there's a lesson to be learned there about always kind of looking for the new thing, no matter what age you're at, and staying engaged and diving in to broaden it a little bit. As we look at data science training, both in industry and in academia and kind of, you know, it's evolved so quickly over the last few years. Where is it lacking? What is industry doing well and not so well? Where could they improve? And the same for academia and are we really doing a good job preparing data scientists for getting out there in the world at this point?

So it's super interesting because at our organization, I do work a lot with the New York City government on their different programs, with the Small Business Administration and training programs. So I sit down with politicians and local leaders and talk about how are we serving constituents who are making $18,000 a year and get them up to $85,000 a year. And the truth is, most programs are really rushing into industry without full preparation. So we haven't seen the best results all throughout. Many programs say, hey, you know, our average GR graduate makes $78,000 and they get a job within six months of graduation. But that's not always true for us. At Galvanize, we are on both course report and switch up. And we have everything that's peer reviewed and checked through the industry to make sure that we're giving you the real facts on how our students do and perform. But even then, for us, we're constantly having to innovate on the curriculum. You see now all the universities are launching data science programs and a lot of them are getting into AI programs as well as whether you're looking at the first ones like Berkeley and Columbia, or other ones popping up all around the country, I wouldn't say any of them have won the game per se, because the technology is changing so fast. I think when someone's thinking about going into learning through a data science training program, whether it's a university or a bootcamp, it depends on the goal you're looking to achieve. If you're going directly into an undergrad or a master's program, it makes sense to tack it on so you have that extra skill set that's going to help future proof yourself in whatever role that you move into in your career. But if you're going straight into a bootcamp without any other prior experience, it's often a struggle. Because those boot camps, if you're doing the full time, which is 60 to 100 hours a week for three months, and then you're expecting to get a job afterwards, there's a reality check I have to share with most students. I tell them that you need to have a basis there. The biggest students who have great success going through boot camps are those who are early software engineers or have a PhD, and that's a very limited pool, right? So if you're coming from a liberal arts movement, you can be successful in a boot camp. However, you're going to have to put in a lot of time and work to see those results. And the classic example I share with students is if you're someone who already is a software engineer and you only study two hours a week and look to get that job, but you're someone who's a liberal arts, but you spend 10 hours a week, you're going to ramp up a lot quicker than the software engineer, just not in the beginning. So it is all about time, output and thinking smarter. Is there a program that's better or worse? There's so many out there. And I like to say that we have some of the best programs in the industry, but they're constantly evolving. And I think when you choose a program you want to be involved in, you want to make sure that that institution or that bootcamp has full time curriculum, people who are constantly innovating and improving and to be willing to ask them, yeah, what's the tech stack? What are we going to learn? You know, are we just learning Python? But what packages and what databases and you know, what projects and feel free to ask those big tough questions because that's going to serve you best down the road.

So I mean, I hear a little bit of what you're saying in terms of like the helping people understand where they really want to get to where they're coming from. Do you feel like as a industry we've crystallized that all in terms of what like a data scientist is? It seems like for so long and maybe still to some, in many ways defining what data science is is just like so varied that it almost, it almost loses meaning in some sense because it could be like, oh, you know, you're doing tensorflow and deep learning all the way to sort of analytics things to like big data as kind of distributed processing things. Do you think that we've kind of crystall around that, around that term at all? I've noticed like recently a lot more effort in terms of kind of specialized job role titles like, you know, like machine learning engineer or you know, even like things like data science engineer or data. Of course, data engineer has been around for a while now, like AI engineer. It seems like a lot of people are kind of shifting to the side of like, oh, we need to add like engineer in the name because like these data science people coming through don't really know how to build anything.

Right.

So I don't know what is your sense of that as you kind of survey people coming through these sorts of programs, the types of positions that they're looking for, the types of things industry is looking for. What is your perspective on that?

Right. So if I look at an ML engineer, that's someone who has software experience in building applications, and a data engineer is someone who could already work with cloud systems or distributed systems, and often the boot camps and the master's programs just don't give enough there. Right. And that's why you want to look at capstones for that. But I think the challenge is there's so much information to cover and pack into it that data science has just become this term that encompasses the industry. And how I look at it is, simply put, what used to be big data became predictive analytics became data science, which is now the AI industry. It's constantly evolving. But the truth is when you look at data science roles, 60 to 80% of the work is still in the data. It's cleaning data, it's labeling data, it's getting it all set up. I featured actually just at the beginning of August on the Humane podcast, Mark Sears, who runs Cloud Factory, which is one of the big data labeling companies between Europe and Africa, and they have 10,000 people just labeling data. And I think the reality that a lot of data scientists don't know until they join the company is you're not playing with algorithms all day, maybe you might, but even an ML engineer, you're going to be working with APIs and setting up pipelines and systems before you get to start training and testing and working with other teams to see those results. So I definitely see a specialization occurring in the field. In fact, I'm calling now a new site subfield emerging in data science, which we're starting to see in some trend reports called Data Science as a service. So similar to how we saw infrastructure as a service with things from like Hashicorp with Ansible and Terraform and a lot of deployment options for the cloud, we're going to start seeing that and we already are in data science as a service. So we're seeing companies like Neptune and Spell and Weights and Bias and other ones which have all just recently raised their series as that are helping deploy systems. You even see the founders of Anaconda, a couple of them branched off and launched Saturn Cloud which is launched these systems and Docker containers and whoop, now do your data science. Paperspace also in Brooklyn, got notorious for that and has been doing a phenomenal job partnering with companies like Fast, AI and Insight Data Science Fellowship as well.

Kind of as a follow up to that, it kind of feels like our industry is starting to grow up. I'm older than the two of you guys and I was around when the Internet that first kind of exploded and went mainstream and you know, in the early 90s and it was you went from a very few job descriptions that were then kind of fragmented as it exploded outward and had, you know, many dozens of job descriptions very soon. It feels to me very much like the data science world and the AI world are kind of starting to do that now where instead of every role being tied to a data scientist, you're seeing lots of specialization and the and therefore even data scientist is becoming a little bit specialized in terms of what activities it does in that ecosystem. Do you have any thoughts on that?

That makes complete sense. A lot of companies now in consulting are actually hiring staff data scientists. These are data scientists who are supporting many teams. But then some teams hire a data scientist to help with just that division. So I think you're going to see that where there are those who are both cross functional and those focused only on a product. And actually for those who are for data science and AI jobs now, next time you're checking out a company and you're looking at their job boards, whether that's on their website or indeed or LinkedIn, I encourage you to see in the job description, what are they talking about? Right. Is it the product, is it the team, is it the whole company? And in fact, when you're looking at that for a role, it's important to look at the requirements and the recommendations. So often people choose to apply or not apply for jobs based on if they. They check every single box on there. But having interviewed and worked with on hiring teams to bring in a lot of data scientists at Galvanize and other companies, it's not every box that has to be checked. You totally agree. Yeah, I mean, the requirements, generally, most of those boxes should be checked. So you do want to make sure you're covering 75% or more. But think of the requirements as must haves. You generally should have that, but not necessarily everything. And sometimes that tech stack is specific to a company. So if you're looking at 10 data scientist positions between startups and Fortune 500, every requirements could be different. Someone uses Python, someone uses r, someone uses TensorFlow, someone uses MXNet. My goodness, should you learn them all just until you get a job? I don't think so. Right. Go with what you got and then start applying that. And in the interview, companies are very flexible with that. Right. If you're like, oh, I'm amazing at R, but I'm just, just picking up Python, sure. I'm going to do the code interview in R. Let's see what you can do. And if you do it, great, you know what? Phenomenal. And there's so many integrations happening there. I also think this is such a tangent, but I think R is playing catch up. You know, R fell in the rankings to only the 9th most desirable language this past year. They used to be top five for a few years and now they're playing catch up. There's some really cool new packages coming out, so I wouldn't be surprised if R climbs back up the leaderboard next couple years.

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So, David, we already mentioned that you have your own AI podcast called the Humane Podcast. It's great to have another podcaster on the show with us. It's the first time we've done this and we're really excited to kind of help bridge the gap between some of these different people creating content. So it's really great to have that opportunity. I was wondering if you could just share kind of the premise behind the Humane Podcast and why you decided to start creating.

Sure. Thanks, guys. You know, I think technology is moving at such a blistering pace in what we're now coining the fourth Industrial Revolution. And as you mentioned, the gap is continuing to grow, especially between humans and machines. And all these new products are coming out, all these new companies are coming out which are supposed to improve our lives, but a lot of our jobs are at risk. So I created the Humane Podcast to bridge the gap between humans and machines. And in the fourth Industrial Revolution, I feature in an interview format conversations with chief data scientists, AI advisors and leaders who advance AI for all to help everyone learn more about humans and their processes in AI, which is called Human Centered AI, Empathetic Design, which is how we can build better processes for humans, and other topics like AI for social good, AI governance, and AI research. I think for me, coming up with the Humane Podcast was so natural with all the training and deliveries I've been doing, as well as seeing my dad and his own journey from going in robotics to code and then saying, all right, David, you're the one who's going to be the next generation. And so I wanted to make sure these conversations could be heard and for a broader audience, because so many people, whether they're working today or they'll be working tomorrow, are concerned about these trends and how their jobs will be impacted. So that's a little bit about humane. Another thing about humane, just fun fact, not many people know humane in its spelling in French actually means to be human. So it's a little play in words, throw the AI in there. But it's a great podcast I've had a lot of fun with. Been going on for about 10 months. And thanks so much for letting me talk a little bit about this.

That sure. I love the focus on how you're addressing some of the hard questions. And I know that Daniel and I are always out there doing talks and meeting people in different events and stuff, and those same questions come up all the time in conversations. And so I think it's really great to just address them head on and sort through the problems. I'm really kind of curious. Could you kind of share with us over the last 10 months, maybe some of the highlights of the various episodes or interviews that you've done? I'm really curious, like, what are some of the peaks of content that you've had over that time?

Yeah, so it's super interesting. Because I always take a different theme to the podcast. And so I reach out to people who I'd love to invite to my dinner table and talk about the industry. You know, one who I had on was about synthetic data. I had Jeremy Kaufman from Scale Venture Partners in Foster City, and we talked about how startups like Keep Truckin and Convoy have scaled into billion dollar ventures by using synthetic data. I had the opportunity to talk with Kristen Carrer, who is a female founder who works in data science training as well and is based in Boston. And we talked about how the industry has changed from research to applied. I've also spoken with one of my good friends, Noel Larcherte, who used to be an early employee at the Alexa team in Voice and now just got named the number one voice advocate of 2019. And we recently sat on a panel at the Voice conference in Newark talking about how Microsoft and Amazon are working together to create a universal audio bot. So what I really love to do is that's just three examples of conversations that we've had on the podcast. But it's to talk about different themes, talk about trends, and speak about how it's relatable to each and every one. So whether someone is a data scientist today, they're a business executive, or there's someone who just wants to get into the industry, it's a little bit of entertainment and education for all.

So, yeah, thanks for sharing those. And I actually can't wait to listen to some of those that you mentioned. But I want to try to kind of play devil's advocate a little bit here and kind of give you a chance to say, like, if I am out there and I'm thinking, oh, well, the gap between humans and machines is, is, is widening basically. What, why, why should I care? You know, if Gmail is able to kind of complete my, complete my sentences and it's convenient for me, why should I care? That I don't really understand that, or I don't understand my data that's being used for that, or how it's being used, or it's just a convenience for me. Why, why is the gap between humans and machines such a concern? Why should we care?

I like to place it to something that's personal for each and every person. So one of our presidential candidates right now, Andrew Yang from the State of New York, is talking about being the humanity first presidential candidate. And the reason he's taken that stance is because there are things that we see every day in our life that are being automated. For example, the self checkout lines at the grocery store used to be run by staff and people who had jobs whether they were improving their life or getting a steady paycheck. And we've now seen several presidential candidates just in the past few weeks actually talk about do they support self checkout lines or not. And it seems like this should not be something controversial. But the truth is there's at least a couple million people in the United States who work in grocery stores. And whether that's as assistant managers or helping with transactions or better customer experience, the truth is those are jobs that like that can go away. We look at places like gas stations, right? If we're in the Northeast, in New Jersey, that's one of the few states that still has human service gas stations. But everywhere else it's self service that's been around for many years. There's no AI there. But that shows how the jobs were eliminated. And I think we're going to continue to see that happening. What we're looking at today in customer service experiences, even with when you call into a company to have a check in on what you're doing with your current flight or for paying a bill, it's been completely automated away and the service has gotten even better in the last three years. And we don't see it often because we don't see these people day to day. They're not eating at our dinner table, we're not talking to them about their life. But the truth is those jobs are going away. Which means, means it's not just impacting them, but it's also impacting you and it soon could impact your career as well. And I don't like to play pessimist, I know we're playing devil's advocate here, but there is the opportunity where AI's intention, right? Like what is the big goal on AI? Like why should we even use AI and all this automation. It's for efficiency, efficient markets, right? And in big cities they're notorious for being efficient system. And if you can make something more efficient, you will make it more efficient because you can drive down costs or increase revenue. And the challenge is every industry is going to be impacted there. Earlier this year in April, Deloitte actually had their human Capital management report and said we're moving into a world of super jobs. Super jobs are jobs 3.0, which means the analyst at Goldman Sachs who used to work on their Excel reports and create a PowerPoint and present an investment banking manager and determine that this is the next investment that's going to help them make a Lot of money. No longer is that same job. That was what they did in 2003. But today in 2019, they have NLP systems that help auto generate reports, automatically create dashboards, and then the analysts are offering some oversight as well as some customization with the higher cognitive tasks. And then they'll also maybe help with that person presentation. But that itself has eliminated the need for a lot of investment banking analysts where you used to have 200 traders or analysts on the floor, Goldman Sachs today in 2019, you can do that with five to 10 people. So there has been a constant evolution. It's going to keep happening. And I tell people it's not that you need to care about it, it's just that if you don't care about it, well then you're putting yourself at risk. And ultimately when you are optimizing for anything in life, you should be minimizing your risk, maybe not for investments. Right. So there could be a little play on words there. But in general you should be creating a better process if you come from that mental model for your moral hazard.

Yeah, I just saw an article, I'm looking at the date, it was August 7th, where JP Morgan has now apparently is experimenting with an AI copywriter that apparently at least in some cases can write better ads than, than humans can. So I think this is a, is a similar trend to, to what you're talking about. And do you think that the main piece of this that really comes into play is, is automation that's the main player there or is it also kind of a logical gap where people are, are less sure about what their technology is doing or how it's operating for them to produce convenience and that sort of thing.

Well, I think the three big industries that we're going to see rapid onset of automation in the next 10 years are data and AI, connected devices and Internet of Things and robotics and all three of those industries are rapidly advancing? In automation, particularly there, what's happening is the products we're using today are no longer being developed by humans. And the example that you just mentioned with JP Morgan with their copywriting AI, sure, a little bit of it is public relations and talking about what's out here. So they get first to market, but in fact they're not first to market. Bloomberg and other financial companies have been creating articles with automated systems for years. If you're someone who invests in the stock market, next time you go onto any website like Seeking Alpha or Bloomberg or Reuters or any of these, and you look at the general news of the day. And the article says this stock has gone up 10% and the EPS is XYZ and the dollars are Y. That could all be written by a machine. And in fact, it probably already is. That's why earlier in 2019, there were over 100,000 media and copywriting jobs eliminated in New York City from companies like buzzfeed, Vice and others. Because all that is starting to be automated and teams are realizing, well, do we need 100 copywriters if instead we can have so many generated stories from a system and then we have a copywriter supervisor who checks through them and see which is most plausible and does some refinements there. The challenging thought is this. In a capitalist society like the United States, everything runs by money. And if money is not being made, automation is the first thought to come to mind. When we look at media companies like the New York Times and Washington Post, who now run their businesses with digital subscriptions, they have more in 2019 than they had print subscriptions in 2001. But when you look at other publications like the Los Angeles Times in California, the Sun Sentinel in South Florida, the Houston Chronicle, all of these ones are struggling. They don't have as many subscriptions, so they don't have as much revenue. And the truth is, revenue is driven by how much business you can bring in. And if the business is declining, the first thought that comes to mind is how automation can help solve. So I really think that's why companies like JP Morgan are looking at AI copywriters and Bloomberg and Reuters and Vice and buzzfeed have already started getting in on those trends. I don't think ever all humans will be replaced. I think there's something to say with the sentiment of how we each think uniquely with our mental models and our perspective that a lot of people like to read and learn about. And I think that's one of the reasons why Substack recently raised, I think it was $100 million round for human based newsletters.

So I'd like to tie a few of the threads that we've discussed together. We've been talking about kind of the relationship between human and machine. And you pointed out that you kind of have this kind of three points that are kind of coming together between kind of AI and data on one, connected devices on another, and robotics on a third. And you know, and you've talked about some use cases here, and it feels like we're almost dancing around a particular term that we're all talking about these days anyway, which is human centered AI and where it is Augmenting humans. It's allowing for the collaboration between humans and machine and in many cases where, you know, kind of the human is sort of orchestrating a symphony of AI collaborators that might be working together to get something done for a company. And so I know you like to talk a lot about human centered AI and could you tell us a little bit more about what it is and why it's growing so fast and what you think the implications are going forward?

Sure. So human centered AI as a term just became big in the market in the last year. It became big because Stanford said we're going to launch the High Institute, their own human Centered AI Institute, with Fei, Fei Li, who's been a professor at Stanford for many years and ran Google Labs for a few years as well. And the intention is really thinking about the future because Stanford and even other major institutions like MIT with CSAIL are thinking about what is going to happen when technology is everywhere. The elimination is already happening with jobs, and it's not just in the us. I recently had a colleague traveling in Shenzhen, China and they stayed at the JW Marriott, a premium ultra luxury hotel, part of the Marriott Bonvoy brand. And when at the hotel, that individual wanted to get room service. And what did the JW Marriott do there now? They have robotic butlers that drop off the latest Diet Coke that you like or your meal. They no longer have humans going from rooms. Those robotic butlers have computer vision. They can press the elevator button, go in, ring your doorbell, drop the food off and they don't have to wait so that it provides greater access for on Demand Service 24, 7. So I mentioned that because that's why I think Stanford and MIT and other institutions have moved in on this human centered AI movement where look, we're moving to AI, we all get it. That is the future. And sure there's some hype. It's going to be slower and faster in certain segments than we think or expect. But if we don't start placing diverse opinions into these processes early on, thinking about bias and how we can make sure the systems work for all people, then we're going to slip behind. So by thinking about that, you can say, when I design a process, does it work for someone who's 75 years old and someone who's 7 years old? Am I designing a process that can move in different terrains? Is the product going to be one that works across multiple languages? Anything that is non accessible needs to be accessible with AI. And the reason is because otherwise you're excluding different Cultures. Today we serve all cultures primarily by hiring people who speak different languages. If you travel to Disney World or Disneyland, you get that they have hundreds of fantastic park service individuals who speak different languages and support you as tour guides throughout your journey. But in the future, that could just be a sound piece with different languages. The challenge is we have to make sure we're being accessible for all and starting to design technology that's enabling humans at the onset. One example that's been a failure that has been quite prominent in the news is how Apple with Siri, their audio enabled assistant, never had an Icelandic language for Iceland. So when you were a kid, right now, as an alpha generation or generation Z growing up, you were using Siri, the human language of English, to communicate with Siri. Right. So you're speaking in English but not Icelandic, because Icelandic didn't exist for Siri. And what that meant is they've shown now that the Icelandic language is becoming extinct in Iceland because kids do not want to learn it and therefore parents are not going to teach it. And before you know it, we're having the diaspora of culture appearing again as a result of technology. You know, I like to think back to one of my favorite authors, Jared Diamond. You know, he's written Guns Germ Steel, Colossal. Yep, I've read it now. Upheaval, so all his really interesting books and how culture and society change and you know, I think we're now entering this new wave. And whether we call it the third Industrial Revolution, the Fourth Industrial Revolution, you know, whatever name you want to give it, I think the next 30 to 40 years is going to be a generation defined by connecting systems with Internet everywhere, data everywhere, listening everywhere. And once that's complete, if we're not thinking about the human at the onset, a lot of jobs are no longer going to be here. And that might mean we need something like conditional basic income or we need to have stronger governance to protect our societies. Of course that depends on your mental model. But we can just look at facial recognition and see how cities all across the US are banning facial recognition, both in schools and in cities, because of the concern of jobs going away and the concerns of privacy.

So, David, I really appreciate how you brought up this idea of kind of how technology makes an impact on culture. I think that's actually really important. I think of things like, you know, everybody kind of thinks of Google Translate as being, you know, so great, which, which it is in, in many ways and is. It is an amazing accomplishment. But, you know, it supports, I forget how many languages now around around 100, and some of them better than others. But there's, you know, over 7,000 languages being spoken in the world right now. So it's, it's kind of a drop in the bucket. And, you know, over 6,000 of those languages are spoken by 25% of the world's population, which means that those are basically marginalized communities. Right? And so when technology has been made available, like you were talking about in certain languages, all it tends to do is kind of further marginalize communities. And what I'm wondering is, you know, what do you think is a way that we could incentivize sort of creating technology for these marginalized, or using financial terms, emerging markets or whatever you want to call them, do you think that there's a role there to be played by regulation? Is that needed? Or is there a way that we as AI practitioners can kind of have adjust our practices and our workflows to better orient ourselves in terms of the technology that we're building?

I think it could be a combination of both. I hope it's more that we as developers, reorient ourself and our technology. You know, think about if you are a developer building a product, you don't want your product to break, right? So whatever the user input is, you're designing it so that if that's a number, the number gets fed into the system, but if it's text, the text gets converted to a number, so it still gets fed into the system. So I think just like data scientists and engineers today who are developing their APIs and their systems not to break based on inputs, the same thing should be that are we thinking about humans first in these systems? We're starting to see AI guidelines. I know the European Union recently launched their own AI ethics standards that came out a few months ago. There's similar initiatives going on in the US as well on ethics and about integrating these systems for all, but, you know, it's really about having the conversation and then making sure you take action. So just like you mentioned about the languages, I had the opportunity to sit down on a panel, an advisory session with one of the leading candidates for the New York City mayoral election in 2021. And that exact topic you brought up, we brought up as well. So, you know, you mentioned 6,000, 7,000 plus languages. And in fact, in New York city, yes, over 20% of the people do not speak English, and 10% of that right is about 800,000 people of the 8 million people in New York. So what if you take some of those languages just the top 10 other languages that are not English or Spanish is about a quarter percent, sorry, 25% of the population. That's enough to have enough votes to win an election. Not that the goal should be just to win an election, but it should be serving all constituents. And when you're in an accessible city like New York City, it should not just be translating services for eight languages, but how about, you know, at least 100 of them or more. And I think when we start thinking about processes, we need to do a lot more competitive intelligence, a lot of research on who our constituents are and then to best serve them. And if that means rolling out a feature in tranches, that's totally fine. But as long as you have the goals there and you're thinking about excessively on the onset, it's paving you in the right direction.

So one of the things that I've heard talked about a lot lately in kind of almost as an extension maybe of human centered AI is I've heard about empathetic technology and empathetic AI and kind of where we need to go with that. And how do you connect those two from a relationship standpoint where, where is empathetic meet human centered and you know, are there other components there that I'm missing?

Yeah.

So I think bridging the gap on human centered AI and empathetic design, there's a great story that came out in California, I think it was with Kaiser just a few months ago, where there was this grandparent in his 70 year old years old on his deathbed at the hospital and they sent the robot in to say there's no other treatment we can do for you, you're going to die, go home. Something like that. I'm paraphrasing of course, but you know that robot, not empathetic design. Now what could be empathetic design a robot that's serving as a nurse to actually clean a wound when you can't always have a nurse on call because they're working safe 16 hour shifts, or a robot that could have infrared computer vision to detect where your vein is. So to better help, inject your medication or the insulin or whichever treatment you need. So that could be very empathetic. There are ways to do that. And it starts with design thinking. In fact, on the Humane podcast, I talked with Chris Butler from Ipsoft just about that design thinking and, and all these questions that are critical because you need to think about what is the customer experience or the consumer experience. And that's this whole new field that's been coined in 2019, CX. So now it's the CX industry about making sure. It is about humans, it's about customers, and it's about empathy. I think we as a society have the moral obligation to provide the best possible customer service. And by doing that, you build loyalty and you keep customers and create more revenue. But if you don't do that, you create the risk of not only alienating your customers, but also losing those who are most valuable. One classic case of this is a lot of the cell phone companies now have been using data to understand what is your threshold of getting angry to switch from a service provider. And they've been using that data to see how many times they could push back on you before giving you a discount or giving you a change in your service.

I knew it, right?

It's real. Come on. We all like to think it's not happening, but I call it hacking AI, which people have done for a while. But when you're talking to one of these companies, Verizon, sprint, T mobile, AT&T on the phone, you better believe that in real time, they're taking your audio, it's converting to text, they're getting the sentiment, and based on that, they might be making some different decisions if you sound more angry or you sound more calm.

So, you know, here on the Practical AI podcast, of course, we like to keep things practical. And as you're talking through all these things, of course, you know, it's probably easier for me to see practically some of those hacks like you're talking about now, like hacking to optimize or to, you know, make things more efficient or to automate or whatever those things are, just because of my, like, technical mind. But as you have gone through and you've of course advised a lot of different companies, you've worked with a lot of different students and teams as, as they're gearing up, what are some practical sorts of ways that, like me as an AI practitioner, are there some kind of practical steps I could take to start modifying my workflow such that I am becoming, you know, maybe a little bit more empathetic or human centered in the way that I design the systems that I'm, that I'm building? Have you found anything that kind of consistently helps or is practical in that sense?

So if you're building systems today and you're thinking, I want to integrate AI, I want to bring in automation, but I want to make sure I best serve my customers, it's important to first decide, are you going to go with a system you're building from the ground up with code and engineers where you can control each and every step in the process. Or are you going to take a pre baked solution that one of these data science as a service companies or one of the cloud providers like Google, Amazon, Microsoft, Microsoft or IBM or others have available for you? The reason I say that is because if you take a pre baked solution, that bias and that inherent potentially inaccuracy that exists in the systems will be present in your products. Could be easier to implement it, but you may not be able to customize it as much as you want. So you definitely want to consider first whether your organization or your team has the technical chops and availability and bandwidth to do some coding or use a pre baked solution. I think secondly then it's always thinking about your customer. So I think design guidelines are only starting to emerge right now. There's more human guidelines. There's been ones recently on cybersecurity that have come out in the last few weeks. But right now I think think it's still a very nascent industry on determining the exact guidelines. I think just starting to ask questions such as thinking of like Business Model Canvas. Right. Or Lean Model Canvas. Who are my customers? How am I serving them? I think that's a great starting point because in fact most of the times these questions that we ask as engineers, where do all those answers lie in our mind? We never write them down. Once you start writing it down, it looks completely different. And that's why I think it's important that if you're building a product or changing a product that's putting humans first, think about partnering with someone who is a strategist in business or someone who might come from the liberal arts background because they're going to add that unique vantage that could help you think about each and every person.

I love how you kind of came full circle right there and kind of got back. As I know we talked a little bit about those different backgrounds early in the call. And so if somebody they've gotten a little taste of kind of what human centered AI and design thinking and empathetic technologies are in this conversation. But if they want to dig into that, do you have any resources that you think are particularly good or you can point them to that listeners can go and kind of of see more about it, understand more and learn more in this effort?

Yeah. One of my favorite trend reports that's been talking a lot about these sub industries is from one of my mentors in New York who I went through her teaching Training Fellowship. Amy Webb so Amy Webb has this new book, the Big Nine. She talks a lot about technology and trends and teaches at nyu, but she has a tech trend report that is almost, almost 400 pages this year. So it is definitely a long read. Wow. But they're all broken out by all these sub trends. Some are AI, some is, you know, human based, some is cyber and you can start to see what companies are working there and what those products look like. I think also Matt Turk from Firstmark Capital in New York really like him and he has every year he creates a big dashboard of all the AI data science companies and what they're doing in the industry. That's really where the data science as a service has been emerging. And he's also looking at companies that are starting to think about ethics and I think some of those were shown this year, but I think starting next year we're going to see a lot of those companies emerging into the trends.

Awesome. Well, thank you so much for sharing those things. I know. I'm going to take a look right afterwards, but really been great to have you on the podcast, David. It's been awesome to hear about your perspective on human centered AI, to hear a little bit about galvanize and data science learning and all sorts of things. So I really appreciate you being on the show and really recommend to our listeners to go check out the Humane podcast and take a listen, see some of that great content that was mentioned. But thank you so much for, for for joining us.

David, thanks so much. Daniel, thanks so much. Chris. Thank you.

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