PAW 2019 Podcast Series

PAW 2019 Conference Series – Jeff Todd – Wolfram Research

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Jeff Todd, Senior Account Executive at Wolfram Research.  

Find Jeff Online:

Email: jtodd@wolfram.com

LinkedIn

Wolfram


[00:02]

This is Jamin, and you’re listening to the Happy Market Research Podcast live today at Predictive Analytics World.  I have Jeff Todd, the Senior Technology Expert at Wolfram Research, Inc. Jeff, thank very much for being on the podcast today.

[00:14]  

Thanks for having me, Jamin.

[00:18]  

What do you think about the show?

[00:20]  

I think it’s great.  There’s a collaborative spirit that I feel like a lot of the people here…  As an industry, I think people are all trying to solve a lot of hard problems now.  For a while, I think they were trying to get the data all into one place. Now that they’ve got it there, they kind of the real problem ahead of them.  So I think everyone is up against the same wall. And so, rather than trying to everyone get their own competitive edge and find a way to outsmart the other people, they’re all kind of just here to learn and figure out the same problems, which is really exciting.      

[00:50]  

It definitely feels like the rising-tide principle’s applying here, where it’s a lot less cut-throat and a lot more collaborative, recognizing the fact that we’re at the very early stages of massive growth inside of major and I call it minor (not in a negative way) but smaller organizations.  At the end of the day, whether it’s optimizing your production line or creating outstanding customer experiences, data plays a key part in the entire ecosystem for success. And the proportion of the decisions that are happening in the organization are still uninformed. In that way I’m saying we’ve got an outsized opportunity in front of us for growth as it relates with insights inside of companies. 

[01:40]

Yeah, I think that’s exactly right.  I think too we see many organizations that are trying to achieve automation for a variety of tasks that have traditionally been manual tasks.  And that’ll divert a lot of human resources. And you certainly hear about machine learning, neural nets, AI as a fear of interruption to folks like cab drivers and truck drivers and replacing them, and the question of what do these people do.  Obviously, there’s going to be a place for those people to go. It always evolves and expands, but I think it’s very exciting that you have both the type of AI and machine learning outcomes that you have where we’ll be able to get more insights out of the data to be able to make new decisions, make new discoveries, new innovations and kind of push things past and have that growth that you’re describing.  At the same time, I think we’ll see a lot of our world start to change a little bit as things that we’ve traditionally been used to interacting with humans become less and less so, which we already see at the shopping markets, grocery stores interact less and less everyday people on the way out.

[02:39]   

Yeah, yeah.  Let’s back up a little bit.  You’ve been in the industry for a while.  What do you see as one of the megatrends? How are we going to be different in three to five years

[02:53]

Well, I think autonomous driving is going to be probably one of the first major things that most of us see a huge change in our regular day-to-day world.  I think that it’s already happening. Cars are already on the road as we can tell. I was just riding along with a friend of mine, who had a Tesla, marveling at the ingenuity of speeding down the highway

[03:09]  

The autopilot is crazy!

[03:12]

It’s amazing.  I was actually just transfixed on the screen watching it interpret all the cars around, and, in fact, asking my friend how his experience was.  Mentioned that it actually reacts faster than he reacts when someone is about to do something dangerous.     

[03:27]

So, I drove from Fresno to Las Vegas as opposed to fly.  It’s a six-hour drive, one-hour flight. But, by the time you’re done messing around and I had to take some stuff (this equipment and things like that); so, “I’m just going to drive, enjoy it.”  I used autopilot about 30% of the way. I wound up getting a flat because I didn’t use autopilot. And I thought I would speed and I hit a bad spot and yadi yadi yada. Anyway, dang it, why did I take over?  I should have let the machine do it? 

[03:59]

Right, I think you’ll find that’s going to be…  Even people who have fears of the future and issues potentially with it…  I know my friend was ready to embrace that technology; his wife was not. She was very scared to get into the car.  

[04:12]

My wife’s the same.

[04:13]

Yeah, and use that.  But she took a long trip (several hours), and that just completely changed her mind:  the fact that she could kind of just sit back and relax to some degree while the car did the work.  I think people are going to come around to the use of automation to not only vehicles but other parts of their lives.  I will be surprised if we don’t see a little bit of a backlash at first from that as well as maybe even a return or a renaissance of human interaction.  As we get more and more separated from that experience, I think people actually in certain areas want to come back to that.

[04:47]

I think that’s a really good example.  You’re seeing that right now with a little bit more investment for Amazon into brick and mortar bookstores, which is hilarious.  I think about the grocery disruption, as you already pointed out, this happening in the grocer space where now you have fully automated (you just load up your cart and leave) frameworks.  But there’s some interactions in the grocery store that I really like. So I can see that, after the pendulum sort of swings all the way over, I can see like you having a human butcher or that kind person you can talk to and ask questions and still get that connection.  To your earlier point, it’s not going away; it’s going to create higher value opportunities for us as opposed to worrying about the ones and zeros side of life. It’s an exciting time.    

[05:42]  

I agree.  

[05:43]

I checked out Wolfram a little while ago, and I’ve been on Wolfram Alpha for about the last 20 minutes because WolframAlpha.com, it’s like a Google-like search engine, but it uses an LP (now this is a lay person telling you what you do).

[06:04]

You’re doing great.  Keep going.

[08:04]

It’s all humility at this point.  Uses an LP to and a bunch of fancy math to…  You can ask it a question and it will search many, many different data sources after it interprets your question and then it answers it in a way that looks like a human being put a report together for you.  And then it also incidentally has all the reference points so you can see what data’s been used in the context it’s been gathered, etc., etc. So WolframAlpha is a really neat resource for anybody that wants to get more information about a certain subject, which is crazy that I just now found out about it. I feel like I’ve been behind, but we can bring that to light now with this podcast.  Maybe you can tell us a little bit about Wolfram and what it is you guys do.

[06:54]

Sure.  So, Wolfram Research is a 30-year-old technology company.  We’ve been around a long time.

[06:59]

That’s 30 years, folks.

[07:00]

30 years, 31 probably here in June.  Actually, so as the brain child of Stephen Wolfram…  Dr. Stephen Wolfram was kind of the run-of-the-mill physics genius.

[07:10]   

20-year-old Ph.D.

[07:12]

15.

[07:13]      

15, I’m sorry.  My bad.

[07:15]

No problem.

[07:15]

That’s a big difference.

[07:16

It is a big difference.  It is a big difference. So, in his research, he found that he was growing frustrated.  That if I wanted to pursue statistics and probability, I had to learn one language. If I wanted to do machine learning, I had to learn a whole new language.  If I wanted to do image processing, yet another language. And I had to make them all work together. I believe the belief was that that was not an ideal situation.  There should be one language, a uniform approach to any kind of computation; it should be high level; it should be intelligent; it should be automated; it should be integrated.  There should be a lot of intelligence put into it so that I, the end-user, have the quickest route from my question to my answer with the least amount of coding.  

And so, that’s what we’ve been doing over the past 30 years.  And that’s one of the reasons why Wolfram Alpha can do what it does as well as why you haven’t seen another Wolfram Alpha from any other competitor come out in that time because there just isn’t a way to replicate that.  You’d have to replicate the 20 years of that work that led up to the ability to create something like that. So, we work with all kinds of industries. We don’t have any one particular segment or market that we’re heavy in.  We work with anyone who needs to do computation, which is pretty much everybody these days. 

[08:18] 

Yeah, to say the least.  The programming language that you developed, that was in the 80s, right?

[08:25]

That’s right.

[08:25]         

Yeah, so, I mean a long time.  There’s been a lot of heritage, I guess.  I’m really surprised I haven’t heard more about Wolfram.  I mean I was doing stats through the 90s, using SPSS predominantly.  Intuitively, from what I’ve gathered so far, it looks like I could get similar outcomes easier had I been using Wolfram.   

[08:51]

Yeah, there definitely has been a progression of features that have been added into the language over time.  I think, in the beginning, a lot of the symbolic math was predominantly what people knew it for and used it for.  So, it got a heavy presence in academia, and I think then, as it matures, most people maybe had the idea that it was this symbolic package.  That’s what we started with. It certainly could do numerics, but in its day, that wasn’t the focus. Later down the road, we began implementing just continuous and huge amounts of functionality, entire sets of functionality that would equate to a third-party program, several third-party programs each release.  And so, we began to become this extremely comprehensive system that, I think, as you say not as many people knew about. And so, they would come and revisit. And I think I have yet to have someone visit the booth or come and talk to in person where their jaw doesn’t hit the floor as least at some point in time where we show them in one function what they remember taking them half a week and 100 lines of code to try to figure out.    

[09:48]  

So, Wolfram Alpha’s free.  How do you make money?

[09:52]  

So, Wolfram Alpha is free.  There are some Pro Features that you can subscribe to.  So, a lot of students are customers of ours, and they will go in and they’ll put in all their kind of difficult symbolic integrals and differential equations, even calculus and algebra, as they’re trying to learn.  And one of the nice things about the Pro Subscription, it’ll actually allow you to show steps on every one of the problems. I certainly wish I had that when I was in college because I had my teacher for about one hour a day and then I had the back of the book with all the answers, but I didn’t have how to solve everything for the rest of the time.  And so, Wolfram Alpha to some degree has actually been a substitute teacher for many people in helping them work through that. We also have a custom version of Wolfram Alpha…

[10:31]  

Really quick before you talk about the custom version.  So, have you seen Incredibles 2?  

[10:34]

Yes.

[10:35]

So, like that scene where he’s like, “New math!”  That’s like right there thinking, “That’s awesome.  I can’t wait to get home and show my kids how smart I am.”

[10:45]   

That’s right.  Well, you know and I’ve had so many people say, “Thank God.  It saved my life in college. You guys were the reason I graduated.”  Every time I have a… meet somebody, I say, “Do you have kids?” And they say, “Yeah, I’ve got some people who are just about heading into middle school or high school.”  I say, “Go find Wolfram Alpha. It’s going to save your life when they come home and show you the math they’ve never seen before. It’s going to help you out.”      

[11:07]

All right.  You’re saying Pro.

[11:09]  

Yeah, so we actually, as a means of making money, so we actually have the ability to take the technology of Wolfram Alpha, the free site, and everything that we’ve put into that and layer that on top of organizational data.  So, imagine being in your corporation and you – the CEO, CFO, manager, employees – being able to ask natural English questions of your own data and being able to get that back, as you said, as a report like a human gave to you ad hoc with no pre-scripting, with no business intelligence, no IT guy that’s gone off and made this one-off thing for you.  It has the intelligence kind of baked into it. So there’s an AI sits between the person asking the question, all of the data, and all the algorithms.  You ask a question. It goes out, finds the data, finds the appropriate algorithms, applies it, supplies you back with the reports, the answers in real time.  So you can continue to ask questions, rather than wait two weeks for Report No. 1, wait two weeks for Report No. 3, and then forget, “What did I even ask for?”      

[11:59]

What kind of data is it able to query?  I’m thinking about at an organizational level.  Like are you guys pulling in stuff from Excel files or…? 

[12:09]

Yeah, it could be any.  It supports 180 different file formats.  So that could be Excel files, CSV, text files; it could be JPEGs, GIFs; it could be a wide range (HDF5); it could be a wide range of file types.  It could be any kind of database; it could be streaming. For example, we have financial data in the free version. You can ask it, “What was Microsoft’s last 30 days closing price?”  That will change: if you ask it throughout the day, that will change as it updates ‘cause it’s a streaming service that we’ll pull down from. So there’s a variety of different data sources that you can feed into that.  It could be images. You could perform image-processing techniques in natural language on images you have somewhere in the company. So, it’s a variety of things that they would want. And it could be any department: sales could be looking at sales figures; HR could see what team member is under which manager; they could see what projects people are working on; who has a birthday today.  There’s all kinds of variety of things that they could use as well as engineers could have entire specs of their models of their machines that they might want to ask, “Does this bolt fit this specification?” And we have connectivity, and we’ve worked with Amazon Alexa; we’ve worked with Apple Siri. So there’s actually ways that you could ask a question live. And I’ve actually worked with folks like Dow Chemical where they’ve got their hands in the gloves; they’re doing chemical experiments and they would like to ask a question.  Maybe, they need to know the melting point of methanol right now.          

[13:34]

It seems important to me right now.  I’ve never thought of it until just now.

[13:38]

That’s right.

[13:38]

The most important thing in the world.

[13:39]

The most important thing in the world.  So they want to ask that question. Instead of having to stop the experiment, pull their hands out of the gloves, walk 15 minutes back to their office, ask the question, come back, they could just ask, “Alexa, ask Wolfram for the melting point of methanol.”  Get that and then continue the experiment.

[13:52]  

That’s really cool.  Is part of the challenge in the market, it’s so broad?

[13:58]

Absolutely.  It’s a really hard thing to have a little bit to help every single person.  People often ask, “Which market do you guys market to? Which vertical are you guys in?  What solution space do you guys fill?” And I often have to just show them like a list of literally every other technical program that exists and say, “This is our list that we compete with.  This is our list that we have to know a little bit about” because in a given week I might talk to Morgan Stanley; I might talk to Pfizer; I might talk to Disney; I might talk to Nationwide Insurance and all about different things.  They’re all doing computation, working with data, but they’re all after different industries and different markets. And we kind of have to be able to know a little about everything, which can be difficult.    

[14:42]

Yeah, for sure.  Well, the good news math is math regardless of the sector.  So the applications… The context is important though, to your point, and you do need to be like a subject matter expert, so to speak, in every freakin’ sector. 

[14:54]

It can be tough, it can be tough.

[14:57]

As you…  You think about the success that you’ve had over the last few years, do you have a specific customer story that resonates with you?  Like, “Gosh, this was such a great outcome” or example of them applying Wolfram to their business, then getting some benefit.   

[15:14]

I think any time you can be part of a technology or integrate a technology that you use every day or that you can point toward.  I think a lot of times our technology gets used in a way that get obfuscated. It was in the research, in the R&D step, or it was by some engineer.  You never get to see the eventual outcome. People don’t necessarily know that you were part of it. Obviously, it’s nice for me to know that when people are using iPhones or when I’m at home talking to my Amazon Alexa, there’s a part of Wolfram in the back-end maybe helping with that.  On a personal note though, I was talking with someone here from NIH about a potential project with them. We worked with a company called Christy Health. Christy was actually a person that had a really hard life. They were, I think, the first heart and liver transplant in the western U.S.  They also had a kidney transplant last year. And so, Christy was the wife, partner of the person that ran Christy Health.  

In doing so, he found this huge issue with reporting medical data.  So, as he would do dialysis and as he would work with her on all these things, he found that there was just… things were getting scrawled on notes; things were not getting reported back correctly; and it made it really difficult for him and for Christy to be able to advance and to be able to get better and be treated correctly and for doctors to understand what they have gone through.  And so, he actually used our technology to stand up services that other people like him and like Christy could actually download themselves to better manage their own health because it’s so important. And so, when I get a chance to work with organizations or work with people who are really making a difference in our day-to-day lives for things that really matter like people’s lives and their health and their love for each other, that’s actually a really great thing to be a part of.    

[16:53]

Oh, for sure.  That’s awesome.  I love that story.  You’ve seen the industry evolution.  Looking forward three to five years, how are we going to different? 

[17:03]

That’s a tough question.  

[17:04]

It’s actually really tough.  

[17:07]

Yeah, and usually I think we rely on Stephen Wolfram to look five years in the future for us.  

[17:12]

Right, yeah, there you go.

[17:13]

That’ll be out.  So, maybe, I’ll just look and see what’s going to be in Mathmatica 13 and I can tell you what the next three to five years is going to be.  

[17:19]

The Nostradamus of our day.  

[17:21]

Interesting story, right?  Today, I think you’ll see in the news Python and Jupiter are just discovering and touting the idea of notebooks as an interface for programming.  And many people are saying, “Oh, this is great. Now I can marry my text in line with my code in line with my results. And it’s an awesome way to show people all these results.”  And we always get a little bit of chuckle because we actually had the notebook 30 years ago, and that’s been our main way of working in Mathmatica for all that time. So, talk about living in the future, I mean that was started 30 years ago, and it’s just now kind of becoming “the thing.” 

[17:53]

Yeah, it’s funny, huh?  Like all these old school practices, like even the cloud, we were doing cloud computing back in 2000, but it was not called that. Everybody launched their cloud solutions and it took me like two years to figure out I needed to start calling what we’re doing the cloud.  It’s funny. 

[18:11]

Terminology changes, I think we’re going to see a lot more…  It’s interesting because every, I think, four to five years there seems to be trend.  Before all the machine learning, neural networks, and AI, which is like the huge trend right now, it was all Big Data;  it was “How do I process all this data and where do I put it?” and “I need to set up a data lake.” and “I need to figure out where we can have a strategy for that.”  And before that, there was probably GPU computing. And before that, it was grids and clusters and “How do I get more out of stuff?” These days, I don’t hear about grids and clusters anymore.  I don’t even hear that much about GPU even though it’s still a great part of the competitional landscape. So I don’t what the next four- or five-year thing is going to be where everyone goes crazy and goes after it, and it’s going to be the next big, huge topic that we have a conference around.          

[18:55]

Yeah, that’s right.  That’s awesome, that’s awesome.  So, conference has been interesting:  great speakers, great attendees, lead generations.  All that going pretty well for you guys?    

[19:05]

Yeah.  You know I’ve been at shows that have had 1,000 people; I’ve had shows that have been 50,000 people.  And it’s really about whether you’re at a show where your message and your technology is relevant to the people that are there.  And I’ve been to shows where we get 1,000 leads, and not a single one of them is really worth anything because it was just not anyone that relevant to what we do.  And we’ve been at a show like today where almost every single person I talk to has an extremely relevant problem that we can help to solve. So certainly, I’d much rather have a discussion with those people every day all day than just be at a show and just chug numbers all day.    

[19:38]

They are my favorite type of people.  Jeff, if somebody wants to get in contact with you or Wolfram, how would they do that?  

[19:45]

You can email me at JTodd@Wolfram.com.  Certainly, you can just call in Wolfram direct line:  1-800-WOLFRAM and ask for me personally. I’d be happy to talk with you.  Or you can come to our booth here at Predictive Analytics World if anyone’s listening and come on down and we’ll be happy to walk you through a demo.  

[20:04]

Of course, we’ll include that information in the show notes for those of you who are listening.  Jeff, thanks very much for being on the Happy Market Research Podcast.  

[20:10]

Thank you so much for having me.

[20:12]

Those of you who have found value in this episode, if you please take the time, screenshot, share on social media (LinkedIn, Twitter), I’d greatly appreciate it.  Have a wonderful rest of your day.