The conversation this week is with Neil Sahota. Neil is an IBM Master Inventor, United Nations AI advisor, Chief Innovation Officer, and globally recognized speaker and author of the award-winning Best Business Book of 2019. Own the AI Revolution. Neil is a founding member of the UN's AI For Good initiative, and is actively helping them build out their ecosystem of strategic partnerships. Additionally, through his work with the Global Fortune 500 companies as a change maker, he created a disruptive thinking framework to show people how you can think differently.
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Resources and Topics Mentioned in this Episode
Neil Sahota 0:00
This is a great example of what a lot of us call hybrid intelligence where we're complementing human abilities with machine capabilities. So while the AI is not directly like teaching us how to be more creative, it's creating this kind of environment and set of dynamic and ever changing situations that stimulate this part of our brain development. And so as a result, AI is helping us become more creative.
AI Announcer 0:30
Welcome to the conversations on applied AI podcast where Justin grumman's and the team at emerging technologies know of talk with experts in the fields of artificial intelligence and deep learning. In each episode, we cut through the hype and dive into how these technologies are being applied to real world problems today. We hope that you find this episode educational and applicable to your industry and connect with us to learn more about our organization at applied ai.mn. Enjoy.
Justin Grammens 1:01
Welcome everyone to the conversations on applied AI Podcast. Today. Our guest is Neil Sahota. Neil is an IBM Master Inventor, United Nations AI advisor, Chief Innovation Officer and globally recognized speaker and author of the award winning Best Business Book of 2019, Own the AI revolution. Neil is a founding member of the UN's AI for good initiative, and is actively helping them build out their ecosystem of strategic partnerships. Additionally, through his work with the Global Fortune 500 companies as a change maker, he created a disruptive thinking framework to show people how you can think differently. Welcome to the show, Neil.
Neil Sahota 1:35
Hey, thanks for having me, Justin.
Justin Grammens 1:37
Well, I gave a little bit of a brief description with regards to where you are today, just kind of curious to know, kind of how you got into technology and maybe the trajectory of your career from when you started to where you are now,
Neil Sahota 1:48
I'd say it's probably a series of happy accidents that got me here. The truth be told my first foray to technology, I was like an eight year old kid, and love playing sports. I remember my mom saying that you can't just play sports, you have to do something different to the kid or Hearthstone to be learning a musical instrument. And I didn't really want to do that. And I remember she was bringing it up again, while we're at some shopping center, and was walking by there was a place where they were teaching new computer programming. And so my mom's like, you have to pick something. So that will work computers. And she's like, looked at me looked at me, she's like, okay, took me in and signed me up. And so I'm going to class with like, you know, people in their early 20s. learning basic programming.
Justin Grammens 2:33
Sure. Like the old Apple two stuff like that.
Neil Sahota 2:36
That's exactly hello world.
Justin Grammens 2:39
Exactly. Yeah. print statements and go to statements. Yeah, all that early stuff. That was fun.
Neil Sahota 2:44
I actually really enjoyed it that I wasn't expecting. That's kind of how I got my foray into computer science. And, you know, I kept pursuing that. The honest truth is that I never sat around thinking that I'm going to pioneer AI or do any other things. I just kept looking for opportunities. And but 17-18 years ago, when business intelligence really started picking up and I was working with some, you know, very large companies and had people say like, it's amazing what computers are telling us, I thought to myself, like, well, they're not really telling us anything, right? We have these tools to collect lots of data and store it and slice and dice it make nice looking reports. But as long as you myself, could a computer look at data and actually analyze it like a person could. And that set me down this path of developing. So when I was calling enterprise intelligence, which we now call machine learning. And so I had some patterns that are things that got the attention of IBM R&D, got a call, they said, Hey, we want to talk to you about some of your work because we're working on a secret project. Just got started. I think you saw some problems for us. The next thing I know, they asked me to join the secret project called Watson back then.
Justin Grammens 3:59
Oh, wow. Okay, so you were early in on that Watson stuff huh?
Neil Sahota 4:04
Early and part of the original team and try to figure out, can computer play Jeopardy?
Justin Grammens 4:11
Uh huh. That was like deep blue. Right? Was that was that one of the early ones too deep, too late
Neil Sahota 4:16
1997. That was the computer that could play chess. But that was brute force calculation. Right? That's not that quite the same as AI. It was calculated every possible move and scenario and making the statistically best choice. Sure, Jeopardy was a different challenge. Because think about the natural language Justin that we use. We're talking about a game show where there's some slang and jargon and everything is phrased the form of an answer to figure out the question. We didn't know if we could actually do this. And we reached out the Jeopardy folks that have a two year commitment in advance because they actually take things out. That far out and The BDS setup and all these things. And so I still remember like Santa Paul was, like, Are you sure you guys can do this? And of course, we're like, Yeah, sure. We had no idea we could or not, like, okay.
Justin Grammens 5:12
That's that's the world of technology just say yes, all the time, you know, you'll figure it out.
Neil Sahota 5:16
And the truth is, it's no secret now that it was 5050, the night of the challenge that Watson would work. And if you watch like the old clips on YouTube, you might see some was there. Watson didn't actually start out. Well, it missed like six months for seven questions. And you can see us back then our BlackBerry's going, like, Hey, I won't be looking for any job. But surprisingly, turn that turn it around. And one. So he was like, This is amazing. This is a game changer. You know, what are you guys going to do next? That's true, because we hadn't thought that far out. We ever thought, bash Jeopardy. And it's like, Oh, you guys spent a lot of money and a lot of time, what are you doing with this? And so we said, we'll get back to you. And we have a bit of an internal holy war about the next step. Because historically, IBM was smart people, history of trying to do things that are kind of our own and build out the solutions and products. And I was one of the people advocating like, this is just too big of a game changer, that let's open up the platform for other people to use. It can be applied anywhere. Let's give people that not just here's some software tools, and API's are some free around that. Let's help people actually ideate and create their own products and services, essentially a new venture, whether it's entrepreneur or intrapreneurial, right, because it's it's a different model of computing that ultimately won out.
Justin Grammens 6:49
That's awesome. So that was the early days of basic creating an AI platform. Right. And and IBM, I mean, were you guys kind of the first first to market?
Neil Sahota 6:57
I think for something like that. Yes, I think it really showed the potential of what could be you back back then? No, this was 2011. Probably the only real things we would call AI at that point, were Watson and Palantir. Palantir was locked away because of all their military contracts at the time. And I think it got a lot of people kind of one, frightened. Unfortunately, we weren't getting death threats and things like that. But it got a lot of people kind of thinking about what's actually possible. This is logic, okay, the natural language and the machine learning on being able to answer the questions it was teaching, watching the strategy of the game, right, there was actually a lot of these other things that have come together, being a great player, that people started realizing we could probably use this to solve some of the bigger challenges that we've had. And so when we created the ecosystem, well, the first places we actually went into was healthcare, because lots of pain points, lots of data. So could we do something I was advocating? Let's start off small, let's build some maybe simple diagnostic tools for doctors and nurses, the marketing guys went out, say, like, we're gonna cure cancer. Cancer is really common. There's different forms, you can stage all these things that have to click all the students I like, we've come up with a cure for cancer and a couple of years. But I think that's where, unfortunately, where the hype of the newness really kind of kicked in, and people's expectations throughout the AI capabilities just skyrocketed.
Justin Grammens 8:33
Sure. And I mean, I think we maybe we've gone through another, maybe two, I don't know, AI winters, I guess, between what happened then. And now it seems like every 10 years, everyone has these huge expectations that it's going to completely change the world. And it's kind of drifted back, but it feels like it's maybe here to stay now with some of the new deep learning techniques. I don't know what your view is of that.
Neil Sahota 8:53
I absolutely agree. Just and I think that people have kind of reset their expectations a couple of times. But I think there's much better understanding and awareness of what AI can do and what it can't do. The challenges just with do we have good data is that data actually available for training that gets put a tamper on? I'll call it the pie in the sky ideas. But I think people are still very impressed and some degrees about surprise. But we can actually teach an AI system to do because a lot of people, especially the early days, thought about automation, because that's what we used to do faster, cheaper or less errors. We're talking about the third generation of computers now there's, there's a whole new set of capabilities here. And the fact that you have a machine that can actually take on activities that require some level of cognition opens up the new areas because now you looking at what we're doing today. Well, we're using AI to help depressed suicidal teenagers. This whole field of artificial empathy, and communication coaches, some of the thing things that we thought that machines would never really be good at. Surprisingly, they are because we found a way commoditize that training and actually teach them how to assist us humans and doing some of these activities, even the soft skill ones. That's fascinating.
Justin Grammens 10:15
Yeah, totally. This kind of the bias, like, oh, yeah, compute, I'll never be able to do that now. Probably debate around if it actually feels anything, but it can certainly act like it does, right. And at the end of the day, as long as it's improving humans lives, that's really all all that matters. In a lot of these cases,
Neil Sahota 10:32
For 1% Yes. And I think it's surprising that things will be realistic, it's good, and there's value. But I also know that there's some people that their freight is not the right word there, I think they feel like their place in the universe has become a little bit more muted that some of the things that we thought made a special that only humans could do, some of that's been kind of taken away that kind of diminished our place in the universe, if you will. So maybe it's a blow to the ego.
Justin Grammens 10:58
Yeah, that's a good way to put it. What was the next thing that you moved on to? Because I know, you know, you've been a part of the United Nations AI advisor, Chief Innovation Officer at UC Irvine, kind of where did you move next, I guess, after leaving IBM,
Neil Sahota 11:11
so hoping to build out that ecosystem got me the attention of the United Nations, so that was invited to come give a big speech at an event they do every four years with all the world leaders and ambassadors, and I was warned in advance that they thought AI is Terminator time, it's gonna rise up, conquer the world, eradicate humanity. So no pressure. So I went in, and I had a 30 minute speech, and I was a little bit more optimistic than the Terminator scenario. But I did just talk about what is AI? And how do we regulate it, I actually focused some half of my speech talking about how we're already using AI for public service. And I spent some time talking about how we can use AI to help fulfill the goals of the SDGs, the sustainable development goals. And the speech was very well received. Like, I got a nice standing ovations, I was really appreciative. But that evening at the reception, the Secretary General sought me out. And he was like, Neil, we never thought about using the technology. We're worried about regulating and misuse. But there's an opportunity here. And there's a lot of momentum a lot of people were excited about you were talking about, and I want to do something healthy helped me figure something out, like, well, of course. And so I want a meeting with him and his team the next week, and we were kind of talking about what we could do. And this was essentially the birth of the AI for good initiative. So solution based projects, not just policy papers, or talking points, but we actually build real things that help real people. And to do that they needed help with, okay, how do we structure everything? How do we assess project ideas? How do we, you know, manage a portfolio that in one of my two dues as the advisor for the UN, but the other piece was actually the partnership piece, because historically, the UN is a collection of different agencies that never actually work together, they all have their own little domains. And so one of my big things was to break that culture, because there was no way they're really going to understand and adopt AI, let alone champion the use of it otherwise. And so I actually helped them build their own ecosystem around the I forget what agencies work together. And they worked with other nonprofits, NGOs, private industry, and academia. So this was whole new world for them to do this. But it was this conglomerate of people coming together to essentially, you know, volunteer their time volunteer their resources, that really spurred action into the whole Africa Initiative, which is why today we've completed probably about close to 300 projects. We have 170 active projects going on right now.
Justin Grammens 14:12
That's phenomenal. And and you know, looking at some of the other things, it seems like you're personally invested, being on board of directors for several nonprofit organizations. And he seems like you'd like to give back I guess, a lot and a lot of different ways. I commend you for that. It's really cool.
Neil Sahota 14:27
Well, I appreciate that, Justin. I really have to give a lot of credit to my sense of community service to my parents and growing up because I'm actually originally from New York. And it was a bit of a rough neighborhood. But it was a really tight knit community. So the roles try to see how you can help each other out. And that became really part of my DNA. So I'm big believer, and we could all do something to help people and planet. We just sometimes they'll look for those opportunities in the normal stuff we do day to day.
Justin Grammens 14:56
Yeah, well, it's good that people like you in the world. Thank you for giving back. I can do some really, really cool stuff here. So the UN, that's gotta be an awesome experience. I mean, I'm sure you, your network was very connected with people from all over the world. And then when did you start thinking about wanting to write a book, I mean, that's just another feels to me like a pretty a pretty big milestone, I think to be able to achieve that.
Neil Sahota 15:16
It was and write a book was a interesting experience. I actually had talked about doing it for two years before I actually started doing it. I just never had the time. And it was one of the reasons I finally decided I want to leave IBM, because I wanted to make the time to help people because IBM work, the UN works when we do other things. What I found is a lot of people have the same questions. And in particular, were two questions everyone had, which was, I know, I need to be doing something with AI. How do I figure that out? And then second, if I figured that out, how do I actually get started, because a lot of lot of people thought, again, it's like the previous computing, I got a lot of smart programmers and data scientists and stuff, they'll let me know what I can do. But with AI, it's because it's different capabilities. And the way it gets used as train is different. Most technologists don't know the domain that we're working in, or the industry, they're working in well enough to know what the pain points actually are. And so is the some of the best solutions I've seen actually come from, you know, the business or domain side of the world, I wanted to find a way to help people get that basic understanding, without all the technical jargon without the fear mongering, and be able to see through some of the stories, a case lays on my blog about just regular non technical people creating these amazing AI solutions. And so the goal of the book was to give them that and answer those two questions. How do I figure out what I should be doing? And how do I get started?
Justin Grammens 16:54
Yeah, cool. It's called on the AI revolution. We'll have liner notes here, when we publish the podcast here. And I'll make sure to include links off to the book and links off to the United Nations AI advisor, all that sort of stuff. I love the approach of sort of starting with the problem set, you find, at least personally, I find a lot of people walking around with AI as the technology. It's like the hammer looking for the nail, right? And, and it's like, yeah, we can do all this amazing stuff. But like, ultimately, what business problem is trying to solve so kind of flipping it around? And taking a look at what is AI? And I guess the title is interesting, you know, on the AI revolution, where did that idea come from? I guess, you know, you're looking to have somebody actually step up and take ownership.
Neil Sahota 17:35
Yeah. And I feel like each one of us actually has the ability to do something, lipstick have a beautiful idea. The title is got a bit of a story behind it, Justin. So if you don't mind, indulge me for a second. That was not the original title of the book, I actually wanted to call it Uber yourself before you get caught act. So all my good buddies is Peter Diamandis, he used that phrase a couple of times, and he was like, Neil, you could go ahead and use that for your book, don't worry about it. But my publisher hated it. absolutely hated the title, they wanted to call the book, AI or die. That was kind of against the, you know, hopeful message, I'm trying to get the people. But my publisher was nice enough to try and work on this. And so we kind of went back and forth discussing what made the sense. And, you know, basic came out that look, to the Watson work, we've kind of, we've kind of started the wave of this AI. And we're going through now this fourth industrial revolution, basically this gigantic digital transformation that's being powered by AI. And a lot of people kind of feel helpless or hopeless about it, that Oh, of AI is going to take my job or you know, I'm going to be reliant on the big tech companies to create these tools and push things out. And so we realized that only a revolution made a lot of sense, because each one of us could actually be a driver in this revolution, we would have to be the passenger. Here's how you do that. So that's how it kind of came, came together from the title.
Justin Grammens 19:09
I love it. That's great. Good for you to just sort of push back on the publisher to you know, as well. And now I'd have them completely drive the decisions because yeah, thinking about them, they probably don't know the space as well, as you do. Right? Most most, most publishers, Matt might be publishing a bunch of different stuff. But you as the author, he should probably have ultimate say with regards to what it's going to go out as. And, you know, I was thinking about like, just disruption in general, when you mentioned Peter Diamandis. I mean, he's well known for sort of being this disruptive innovator in the space for many, many years. And, you know, you've you've created this framework I think you and I were talking about before we started the podcast here, and I'd like you to talk a little bit, maybe about sort of how it works. I mentioned it during the intro, sort of a thinking framework of how people can think differently and it feels like you've sort of done this over your career. How does this work? Like is this woven into the book, I guess, as well,
Neil Sahota 19:58
I guess I've done some amazing things about My career maybe more than I realized since I was on the inside. But people like how do you think of these things? Right? Because I always say like, there's an innovator all of us. It's not the domain of just Elon Musk, or Jeff Bezos. You know, it was always like, well, it starts by thinking differently. And everyone rolls their eyes like, great, Neil. That's what everybody says, think differently. Think outside the box. How do you do that? And so I finally a couple years ago said, How did I do that? And I look back at my career and stuff. And I realized, I actually did some steps, I learned some techniques that I actually applied to do this. And so I put this together, it's what they call tuck, bow. And tuck, will the the letter stand for the different kind of stages, the first stage is T, think different. And this is really that, how do you really ideate and so you know, challenge assumptions different. There's different techniques I've learned. So you can actually come up with this kind of innovative idea. But once you have the idea, you have to go to you, which is understand different. So how do I know this idea is actually valuable? How do I actually know that it's, you know, solving for a need. And so this becomes kind of an alignment thing, because and refine the idea to see, is there value and meaning here? If there is then you go to see which is create different? So it's how do you actually implement this? How do you get the right people the right skills, and actually build it, roll it out to deployment? And then you're going to be, which is be different. So it's not enough to have a great product? Or even a great idea? How do you drive adoption? How do you help people understand the value, they're going to realize from this, and then in tangent with that as the Oh, which is own different, to actually building the infrastructure around this to support so your innovation actually can be successful. And it's the Oh, where I find a lot of people or a lot of organizations really stumble around with tea, of course, as well. But the Oh, like, I always like to use Tesla as an example. And that they weren't the first EV, electric vehicle company out there. And it's not like they revolutionized battery technology. And I know they have some sleek design. But that wasn't really enough to suddenly spurred the whole growth they they achieved, they actually tackle the biggest issue I think most people had was, I'm worried about ran out of power. And so they actually went out and build the infrastructure of supercharging stations, they built an app. So you can find the stations negotiated with the business parks, the retailers, and so forth, to build these stations, all these places. They provide an infrastructure that facilitated the adoption, facilitated their success.
Justin Grammens 22:50
And so yeah, it's it's sort of following this framework that you've been successful at that you're letting people know about, have you. And again, we'll put links off to your off to the nilsa jota.com. website, like where do people learn to find out more about this sort of step by step program.
Neil Sahota 23:04
So interestingly enough, there's a little bit of an in my book on the AI revolution, I'm actually 90% done with my second book, which is actually just about tuck bow, and how you actually put this out. And so I'm trying to put more information about taco and some of the steps onto my website. And I'm sharing some stories through my social media, but you look at things like, you know, as humans, we don't think about audio data as much as well as we're listening to a podcast or something like that. But thanks to taco, we have organizations like rainforest connection, that are now have figured out we can use audio data to identify people that are doing illegal forestation, or illegal poaching.
Justin Grammens 23:47
Yeah, Have you have you worked with those guys before Topher White was was the guy I actually met at that organization years ago?
Neil Sahota 23:53
Yeah, early on, and help help facilitate to some of the UN stuff. But their work is just amazing, right? It's not something we need to really think about where people are kept thinking about, can we use satellite imagery? Can we use video feeds, the whole audio thing was just a stroke of brilliance that that was a great example of challenging some key assumptions through my Topo framework, so I love what those guys are doing. Very cool.
Justin Grammens 24:20
When you said, you spoke originally about AI, and people are all sort of worried about coming to take my job and all that type of stuff. I mean, you just got to look around you and just see all the benefits, especially around like voice assistants now, you know, that are just everywhere. While the AI term can scare people, it's kind of already here. And if you just start pointing out situations, self driving cars, you know, as self self flying airplanes, and a lot of ways, right, I just flew back from a business trip. And you know, I know that the pilots there, they just kind of set autopilot pretty much most of the time and you know, their plane can fly itself much better than they ever could. So it's really all around us and it's doing some really, really good things are there Are there any applications that are that you're kind of blown away by, I guess, that you've seen over and over the past couple of years that you really like to use as references.
Neil Sahota 25:08
There's two that I'm really passionate about. And I've seen a lot of advancements for. So first is really around the whole area of artificial empathy. Even though the machines don't feel the emotions, we're good at detecting and responding. And it's this has become more than just even the body language. But this whole now use of neurolinguistics, that your language is like a fingerprint. And based on just the word choice, your your, you know, you normally use, we can decode a lot about okay, what's your commitment level? How do you actually best learn? What were your values, and if I want to communicate with you, or engage you, were the right words, the right things to even focus on, it's, you know, it's become an amazing tool. This just started from work with depressed suicidal teenagers. So giving people come to these safe outlets, not a replacement for human relationships, the safe outlets where they can build their confidence up and try and then connect meaningfully with with people. So artificial empathy, one big area. The second, this is surprising, even to myself is actually augmenting human creativity. We haven't figured out a way to teach AI to be imaginative or creative yet, but what we found out is we can actually complement our own abilities through the use of AI, the metaverse and cognitive science. So this whole notion about complex problem solving using digital twins, has been great scenario planning, war risk, because hey, it's like you're in a Doctor Strange bear universe. Whatever I do here, it doesn't impact the real world. But we found over time is that the people that were doing this actually sustained some growth in some of these areas, particularly creative thinking, they're able to actually now achieve that kind of state of flow much faster, and actually maintain it much longer. And this is a great example of what a lot of us call hybrid intelligence, where we're complementing a human abilities with machine capabilities. So while the AI is not directly like teaching us how to be more creative, it's creating this kind of environment and set of dynamic, you know, an ever changing situations that stimulate this part of our brain development. So as a result, AI is helping us become more creative.
Justin Grammens 27:29
That's fascinating. One of the beauties that I think about in the reason I started this podcast, frankly, is just AI can be applied in so many different areas, I, I feel like I'm never going to run out of guests to talk to this, because just the moment, I feel like, Oh, I'm kind of running at the end of this stuff, then somebody comes up with some new solution, in terms of how they're using artificial intelligence is just a really, really awesome, ongoing piece of technology. I think that's just going to continue to morph from for decades to come.
Neil Sahota 27:56
Sky's the limit. Justin, right.
Justin Grammens 27:59
Exactly, exactly. What's the one thing I like to ask people to like, what's sort of a day in the life of a person in your role?
Neil Sahota 28:06
I don't think I have lucky, regular day. Every day brings something new. So I mean, it's obviously I can't escape the meetings. I haven't figured that part out yet. So if everybody audience knows, hit me up. But every day, it's really about some new stuff. You do projects with the UN some new ideas. Just yesterday, I was I was talking to a guy I hadn't spoken to him since the start of the pandemic. And he used this downtime to create an AI tutor for underserved schoolchildren. So I was like that, that's amazing. And he created like, you know, like a nice little avatar, like on the stock and nice, just wants to roll it out to school. So he's looking for what's the best way to do that, and not to hook into some of the UN initiatives. So a lot of is part of is helping people kind of refined their ideas. Part was just a lot of the execution that adoption with the tough work we got to do. But it's never the same day twice. That's that's actually what I love about what I do. It's it's always a new set of challenges and new sort of hopes.
Justin Grammens 29:08
Yeah, that's awesome. I think I saw that you are sort of part of a lack of a better term, I guess, like an angel Advisor Group. Is that right?
Neil Sahota 29:16
Yeah. I've been very active in advising investors and angels as well as participating in some of these funds. Yes, truth is Justin, the best innovative ideas come from the entrepreneurs in the startup companies. I think we just create a corporate culture now where the big companies one, they don't want to rock the boat, and they don't want to make the big investment for their own money and time and they're more than happy now, because they all have venture arms to place a small bet they're ready to get this, you know, aspiring set of entrepreneurs a couple of million dollars and some other types of equipment and help them put 50 million of their own time in and 1000s of hours of their own people's time. I'm here to see if they can do something. And so I want to help fuel that, because I think that's going to become the main source of innovation as we actually move forward. And if you think about drug discovery, the big pharma companies are all moving away from drug developed, or drug drug discovery to molecule development. They're putting that in the hands of the smaller companies. And if they can come up with something, they're more than happy to help with manufacturing, sales and distribution, and they get a pretty large revenue share out of that, which means that it's really on us to all be innovators to figure out well, what's the next big thing you could do?
Justin Grammens 30:37
That's fabulous. Yeah, that's good. That's good. How do people reach out to you now I guess, I'd say, where are you best found on? Again, Twitter?
Neil Sahota 30:46
Yeah, I'm very active on LinkedIn, Twitter, Instagram. So if you're wondering what I'm up to you are some of the new trends are, please do follow me, I post a lot of information on my website, which is just by name. He also has a.com. So if you want to just check out what's going on, or you have an idea, or you're looking to help like the UN for the AI for good, just drop me a message. And I'm happy to help any way I can.
Justin Grammens 31:11
Yeah, that's awesome. I mean, I wonder if I was somebody coming out of college today, just knowing what you know, how would you suggest? What are some courses, some books, I guess, some organizations, can they just reach out to the UN and say, I'm here to help? What would you suggest people do?
Neil Sahota 31:26
Well, the UN never has that volunteer. So if you want to help that, they'll definitely try and make the best use of whatever you're willing to give in terms of your time. So I definitely encourage that in terms of you're in college, or you're going to start your career. There's a few things out there that even if you're not a technologist or engineer, just learn some of the foundational capabilities of like AI and machine learning, you don't need to be a data scientist, you don't need to become an ML programmer. Just learn what the tools offer so that you have the ability to apply those capabilities in your work. The other thing I'll tell you is that you do have to think differently. Most companies today, they want people that are critical and creative thinkers. I found this surprising when I saw the stats a couple years ago, but most companies prefer to hire MFAs, Master of Fine Arts over MBAs because he liked that creative thinking. So flex, flex that brain muscle, take some classes that's kind of stimulate that cortex and develop those creative thinking skills. And then the best thing I can tell you is, don't be afraid to take risks. Risk is not a bad word, right? Risk is just uncertainty, it can be positive risk, which creates benefits, it can be a negative risk, which has threats. But if you're not really ever failing, you're not taking enough risk. And if you're not really thinking about how you can do something differently, you're definitely not taking enough risk. And it's not lip service from all these companies, whether you're a big, big startup big established for Fortune 500 company, or a small entrepreneurial startup. Everybody wants people that are willing to be risk takers these days. So take advantage.
Justin Grammens 33:09
I love it. That is perfect. That's perfect. was great. It's been a great, great conversation. Is there anything else that you maybe wanted to talk about? I guess that I didn't bring up during our chat here?
Neil Sahota 33:20
Oh, there's probably a lot of things we could we could talk about just that it's such a right field. But I'll just kind of maybe end with this thought for everybody. Because I know, look, we all watch movies and TV. We all read the books, the magazines, and it's always human versus a machine, right? And he was always without in the end because there's something intrinsically special about us? Well, I don't think that's the right mindset, there are things that humans do better than machines, and vice versa. This is not human versus machine. This is human and machine. As soon as we flip that script in our minds, this is where we actually start to understand the opportunities that we have with AI. And so if there's one thing you could do right now, is kind of reteach yourself human and machine.
Justin Grammens 34:10
Well said, well said, Yeah. As I have been on my journey with regards to interviewing, just thought leaders like yourself and a bunch of really, really smart people in the space, people have been talking about augmentation, right? It's really about using AI in the technology to augment your life and make things easier, basically get rid of all the mundane stuff, and the stuff that humans aren't good at, that you actually don't want to do. Those are the things that are basically primed for AI. And I think that was kind of the crux of what you said there I guess is is the and you know, and so how can we use AI technology to become an agent and augment what we do instead of a complete replacement? I love it. I love it that goes exactly along with what I've been hearing and learning and trying to put myself my my mindset and and my frame of reference, I guess into those same words. Well, thank you, Neil. I appreciate the time today and Again, you can say, well, we'll be putting all of this information, all your contact information online, links to the book, links to your website in all of the notes. And we'd love to have you back on in the future. You know, we can talk about how things have changed in the coming years, maybe, you know, we'll can reconnect next year and and see see how things go. But it's a fascinating field. And it's been really, really great having you on the show. So I appreciate your time today.
Neil Sahota 35:20
Oh, my pleasure. And I would love to come back. I think we'll year from now Justin will have a whole new set of things to talk about. Absolutely.
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