Future of The Tech Industry Within The Scope of AI - Ascent Conference Future of The Tech Industry Within The Scope of AI - Ascent Conference

Future of The Tech Industry Within The Scope of AI

Falon Fatemi, Founder & CEO @ Node

Ascent Conference 2019

[00:00:06] My name is Falon Fatemi. I’m the founder and CEO of an air company called Node. I also have a column in Forbes where I talk about how I is impacting the future of work. Today, we’re going to talk a little bit about what the future holds with artificial intelligence, what it means. We’ll use my company a little bit as an example and then talk a little bit about how it might affect all of you. So A.I. is affecting how we live, what we watch, how we travel, sleep. Work and lead, and it’s not just a flash in the pan trend, it’s absolutely here to stay. Some numbers, seven point four billion invested as of Q2. That’s a lot of dough. 18 companies in twenty eighteen valued at over a billion. And this is compared to two in twenty sixteen. So it’s really starting to accelerate. But even with that, we’re still scratching the surface. There’s a ton of opportunity and we are really in the inception of this entire evolution.

[00:01:10] And in terms of the opportunities, there’s definitely like there is going to be as pervasive as electricity, so being able to apply it from consumer use cases, deleveraging A.I. for drug discovery, for brain diseases, a real companies doing that. There are applications everywhere in terms of the big companies, the big behemoths. The innovation is not just reserved to them. There are a massive amount of what some might view as mundane solutions that are actually big market opportunities. For example, I just came across a company the other week who’s using A.I. to help with importing CVS into any application and being able to automatically cleanse the information and adjust it. I mean, that sounds like a small problem, but it’s actually a massive one and a huge market opportunity. And then also it’s important to understand the context of how is being leveraged for certain problem spaces, especially where there’s a lot of regulation and, you know, legal considerations, for example, in the financial services industry or in the insurance industry or the regulators have not caught up with cutting edge. I they still require explain ability of models and making decisions. And so leveraging cutting edge techniques like deep learning isn’t going to be the right approach yet. So if we can all agree that A.I. is here to stay, well, then why hasn’t every organization adopted it yet? Well, the reality is it’s hard. And why is it hard? The dirty little secret in AI is that it’s not about the algorithms, it’s all about the data. So you hire these data scientists that are super expensive in which there’s never enough of. And what they end up doing is spending the millions in years acquiring data, normalizing data cleansing data, setting up data architecture. So by that point, your millions and years into finally being able to start to experiment, if you get it right the first time, it’s it’s not technically feasible or risk from a risk perspective feasible for many companies. And what we’re seeing is it’s absolutely accelerating the pace of innovation, but as a result, it’s totally declining the rate for businesses that frankly can’t keep up. And some stats here, in the last 20 years, over 50 percent of the Fortune 500 have disappeared and life expectancy has decreased from 75 years in 1950, five to 15 years in 2015 and 2015. We’re not even talking about guy playing a part. So imagine where we’re going to be in 20, 25. A.I. is going to be a wrecking ball to the enterprises that don’t adopt it. And this is where actually, from my point of view, I think the next wave of transformation is not going to come from consumer applications that are here today and gone tomorrow. It’s going to come from the enterprise because we are literally in a situation where we have to innovate or die. And when we talk about A.I., it’s important that we really understand what it means because there’s a lot of marketing out there. I talked to CEO last week who said, yeah, we don’t have any AI in our product, even though it’s all over their website. So when most people are saying I on the left side, what they’re doing is kind of an older approach to A.I. It’s taking a complex problem, like I want to find more customers like my best customers, where best is extremely hard to codify and articulate into a set of rules. And what you end up with is actually fancy data analysis that’s called A.I. that ultimately might actually limit the possibilities and the markets that you could go after it based on what we as humans can see in terms of patterns in the data.

[00:04:45] Now, deep learning, how many people know deep learning? Raise your hand.

[00:04:50] Great, I can’t wait till next year, everyone’s hands up so deep learning, it has found a lot of success in the field of computer vision and self-driving cars. And the power of this technology is that it can learn from the data and interactions themselves and identify signals both implicit and explicit, that we could never as humans dimensional lies, but are extremely important signals for driving true intuitive predictions.

[00:05:16] And from my perspective, deep learning is absolutely the future for these complex business problems.

[00:05:25] So using my company as an example, this is actually the problem that we’re set up to solve. We’re focused on using cutting edge, deep learning to enable organizations and frankly, any developer, not just data scientists, to be able to, within a matter of seconds, put the power of prediction into their applications or apply it to solve the most critical business problems. The technology that we’ve invented is what we call artificial intuition, and this is defined as a proprietary, deep learning platform that analyzes people and company data and turns it into use case specific predictions that help organizations be able to identify which prospects will become customers, which customers are likely to leave before they leave, or which key talent is likely to leave before they leave. And we have customers that use our technology as the prediction infrastructure within their applications. And then we also have companies that are leveraging this directly. Now, here are examples of how technology, like deep learning can transform an application, a workflow application that we use daily as an example of connecting cell, which is a dialing solution, they enable sales, sales reps to frankly cold call all of us. And but with Noad, it can be smarter and more personalized and actually adjust strategy on the fly based on what’s working and what’s not working personalized to every sales rep, which not only leads to the ultimate outcomes that this entire application is aiming towards, which is more meetings booked, but actually helps uncover entire new markets of opportunity that were previously untapped through these more deterministic based approaches. Another example is a company called Guess Where he tracks a lot of email interactions between sales reps and prospects. Now, based on the first three email interactions, our system can accurately predict the likelihood that a prospect will become a customer or not become a customer, which is going to increase win rates for the client base by 10 to 15 percent.

[00:07:15] Imagine knowing that in the first three emails where to spend your time. Think how many dollars you’re going to save and make.

[00:07:22] And then analytics, big analytics company tracking billions of YNAB interactions. Imagine that with 80 percent accuracy. You can know based on how people are using your application and having a better understanding of who they are, whether they’re going to basically become promotors and evangelize your product or actually churn and be able to do something about that and build a better product as a result. And the system here is able to do this not by analyzing traditional risk factors, but it’s able to do this by actually learning which signals actually drive towards a product working better for certain types of customers, which is pretty powerful. With a model like this, you could actually apply it to a new market. You’re thinking about going into and see into the future in terms of a longevity of future customers in that market. So what’s the future going to look like for all of us individuals that are operating in the tech space with A.I.? Well, it’s going to be extremely important that we become even more specialized in terms of our skills. And again, a lot of the focus around A.I. Everyone loves the sexy algorithms. It’s not about the algorithms. It’s all about the data and making sure that you have strong domain expertize around the right data that you eliminate biases where you can’t. This is absolutely critical to be able to enable a human centered world of AI in terms of A.I. thinkers and innovators. I mean, this is where we really need to be looking at gaps and opportunities in applying A.I. in ways that we haven’t even thought of. In terms of trends in the bigger picture, I mean, there’s also the negative aspects of how many people in the audience know about Deep Fixx. Yeah, so if you don’t know what you will buy the 2020 election, it’s my prediction, but basically the fix is deep learning technology that’s used to create fake content, both visual as well as written, that frankly, we as humans cannot identify is fake or real. And the genie is out of the bottle. The Internet’s already I mean, I already assume everything I read is fake. Now, I think it’s turning the Internet into a magazine stand. But this kind of technology, this is where we really need to understand where is are these are are these types of technologies being applied in ways that are not constructive to society? And what can we do to actually combat these kinds of bad actors? Because power, this technology, it’s can be used for good, but also for evil. Now, in terms of automating what needs to be automated, so the power of prediction in a variety of of markets will actually completely change business models. So today, Amazon, you shop and then they ship. They’re only added maybe in the near future. There’s a book called Prediction Machines actually talks about this where in the future they’re going to actually ship to you because they already know what you’re likely to want and then you’re going to purchase it.

[00:10:10] I mean, that’s a crazy world. No more shopping required. It just comes to you.

[00:10:16] That world is here, another example is as we start to look at actually connecting our operational infrastructure in cities and communities, a world with no traffic is absolutely possible, especially with self-driving cars.

[00:10:31] Think about that, the future is near as well.

[00:10:36] And a lot of people freak out thinking that A is going to take our jobs or Elon Musk says that robots are going to save us all. I don’t believe in any of that. I think the big opportunity here is it’s going to actually shift all of our roles from manual work and research to decision making and judgment and actually focus humans on the types of tasks that we are really good at, building relationships or making decisions in terms of, you know, being empowered by these types of, you know, predictions and technology.

[00:11:06] Thanks, everyone.

 

 

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