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Speaker 1: Get in touch with technology with tech Stuff from how

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Speaker 1: stuff works dot com. Hey there, everyone, and welcome to

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Speaker 1: tech Stuff. I'm your host, Jonathan Strickland. I'm an executive

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Speaker 1: producer with How Stuff Works, and I heart radio and

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Speaker 1: I love all things tech. And I'm still in San

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Speaker 1: Francisco at the two thousand nineteen IBM THINK Conference. IBM

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Speaker 1: was kind enough to invite me out here to dive

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Speaker 1: into their world and see what sort of technologies, products

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Speaker 1: and services the company has on offer to help push

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Speaker 1: the stuff that makes your stuff work into the future.

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Speaker 1: And so this is my perspective on the things that

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Speaker 1: I encountered here at the conference, and there were a

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Speaker 1: lot of really interesting elements here. And in a previous episode,

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Speaker 1: I talked about the struggle between taking an open source

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Speaker 1: approach and a proprietary approach in developing technology. So in

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Speaker 1: this episode, I'm really going to focus more on some

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Speaker 1: of these sessions and stories about open source and the

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Speaker 1: benefits of going with that approach that I saw here

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Speaker 1: at IBM THINK two thousand nineteen, and some of these

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Speaker 1: stories are really impactful, I think. Now a quick refresher,

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Speaker 1: the open source approach is one in which the development

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Speaker 1: process for a technology is open for a community to

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Speaker 1: work on. Often, it also allows for people in the

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Speaker 1: general public, or at least the developers and engineers out there,

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Speaker 1: to actively tweak, adapt, modify, or upgrade that technology in

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Speaker 1: their own way. One of the results of this approach

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Speaker 1: is that you have this enormous community that can help

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Speaker 1: make the technology better. In software and means that developers

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Speaker 1: can identify and patch vulnerabilities, they can extend features, they

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Speaker 1: can improve the way software works by applying their own

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Speaker 1: ideas and expertise to the overall project. It's kind of

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Speaker 1: like having a development team made up of millions of people. Now,

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Speaker 1: in theory, the good ideas stick around, the okay I

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Speaker 1: ideas can be massaged and tweaked to become good ideas,

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Speaker 1: and the bad ideas end up being abandoned. It all

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Speaker 1: happens really quickly, with potential iterations arriving much faster than

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Speaker 1: you would see with a proprietary closed approach. With the

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Speaker 1: proprietary approach, any changes to code rely upon a dedicated

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Speaker 1: and thus, by its very nature, a limited team of developers. Now,

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Speaker 1: on the positive side, with proprietary you can work to

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Speaker 1: create the experience you want people to have when they

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Speaker 1: use your software. You can have a very defined vision,

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Speaker 1: but on the flip side, the path of development is

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Speaker 1: completely dependent upon one group or one company's vision, and

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Speaker 1: there may not be room to incorporate valid ideas that

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Speaker 1: originate from outside that entity. One of the other big

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Speaker 1: differentiators between open source and proprietary approaches has to do

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Speaker 1: with adaptability. Sometimes markets change in ways that companies didn't anticipate,

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Speaker 1: and if you're running a really big company that has

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Speaker 1: tech processes that depend upon proprietary software and then the

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Speaker 1: market changes, you're stuck waiting on your provider to recognize

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Speaker 1: that change to the market and then to respond to it,

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Speaker 1: and you can't really do much in the meantime, and

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Speaker 1: if things are getting worse, you're kind of stuck. The

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Speaker 1: open source community can continuously work on software, changing it

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Speaker 1: as the market itself changes, in fact, anticipating changes and

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Speaker 1: building in new features to help mitigate any negative effects

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Speaker 1: of that change. So in an ideal case, you get

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Speaker 1: changes in the market and changes in software going hand

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Speaker 1: in hand with all everything else, and that leads to

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Speaker 1: a minimum of a gap between what the market is

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Speaker 1: doing and how businesses can respond to it. And then

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Speaker 1: There's a related problem with going with proprietary approaches. That's

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Speaker 1: the companies that do so limit the talent they can

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Speaker 1: rely upon to run that part of the business. Open

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Speaker 1: source software creates a lot of opportunities to find and

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Speaker 1: bring on new talent. This is particularly important for companies

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Speaker 1: located in places that don't have a really deep tech

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Speaker 1: talent population. So if you run a tech company in

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Speaker 1: San Francisco or maybe New York, you probably don't have

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Speaker 1: a shortage of talent to draw upon. There's probably plenty

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Speaker 1: of people who are qualified and you would want them

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Speaker 1: on your team. But maybe you're someplace else like Omaha, Nebraska,

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Speaker 1: and no shakes on Omaha. It's a great city, but

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Speaker 1: you may not have quite as large a talent pool

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Speaker 1: to pull from. You might have to try and give

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Speaker 1: incentives for people to move there instead. So open source

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Speaker 1: opens up a lot more opportunities. Otherwise, you have to

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Speaker 1: look for people who specialize in that proprietary software. It

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Speaker 1: might at first be surprising to hear that an organization

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Speaker 1: like IBM, which is an enormous multibillion dollar company it

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Speaker 1: does busy us all around the world, that such an

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Speaker 1: entity embraces open source but in truth, it's a strategy

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Speaker 1: that best serves the company's interests. Open source isn't just

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Speaker 1: about rapid evolution. It's also about establishing what the standards

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Speaker 1: are for a given process or approach. A developer might

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Speaker 1: have one idea for those standards, and then the community

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Speaker 1: might have a slightly different idea, but over time it

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Speaker 1: tends to shake out, and because of the open nature

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Speaker 1: of the code, it can find its way into many

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Speaker 1: different products, services and companies and other implementations, and it

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Speaker 1: helps cut down on the proprietary approach that by necessity

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Speaker 1: limits what the end user can do with the technology.

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Speaker 1: IBM really showed how interested it was in the open

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Speaker 1: source philosophy in when it announced plans to acquire the

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Speaker 1: company red Hat. So let's have a quick rundown on

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Speaker 1: what red Hat is. It's a company that began in

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Speaker 1: its earliest phases you could argue around, and it was

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Speaker 1: around the concept of a version of the Linux operating

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Speaker 1: system tweaked by a hacker named Mark Ewing. Now, there

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Speaker 1: are a lot of different versions of Linux, or maybe

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Speaker 1: I should say that there are a lot of distributions

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Speaker 1: or distros of Linux. That's the proper way to refer

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Speaker 1: to it. At the heart of any Linux distro is

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Speaker 1: the Linux kernel itself, and it's an example of free

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Speaker 1: and open software. And while Lena's Torvald's created and released

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Speaker 1: the first version of the Linux kernel way back in

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Speaker 1: thousands of programmers have made additions, improvements, and tweaks to

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Speaker 1: that kernel in the nearly three decades that followed. Linux

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Speaker 1: distro typically includes several other elements in addition to the

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Speaker 1: kernel in order to create a fully fleshed out operating system.

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Speaker 1: That operating system might be for a personal computer, it

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Speaker 1: could be for a mobile device, it might be for

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Speaker 1: a web server or even a supercomputer. And you can

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Speaker 1: also find it on stuff like set top boxes and routers.

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Speaker 1: It is an incredibly versatile operating system all right back

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Speaker 1: to the founding of Red Hat, Ewing used to wear

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Speaker 1: a red Cornell lacrosse hat that his grandfather had owned,

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Speaker 1: and he had become known as the guy in the

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Speaker 1: red hat while he was working in a computer lab

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Speaker 1: at Carnegie Melon, So he decided to use Red Hat

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Speaker 1: as the name of his Linux distro, and later on

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Speaker 1: Ewing with joint forces with a businessman named Bob Young.

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Speaker 1: Bob Young had been buying copies of red Hat from

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Speaker 1: Ewing and then selling them, and he had been selling

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Speaker 1: them about as fast as he could get them. So

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Speaker 1: they decided this was a very valuable product and they

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Speaker 1: formed red Hat Software in n Now, this is not

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Speaker 1: an episode about red Hat, so we're gonna skip ahead

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Speaker 1: to the company was successful at turning a profit despite

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Speaker 1: being centered on an open source and effectively free software package.

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Speaker 1: So if you're making something and offering it up for free,

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Speaker 1: how do you make money from that? How did red

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Speaker 1: Hat generate revenue? Well, the company sells professional services and

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Speaker 1: has maintenance and support plans that customers can use to

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Speaker 1: get tech support for their products. Red Hat developers still

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Speaker 1: make major contributions to the software, which is largely shaped

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Speaker 1: by the red Hat community of users and developers, but

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Speaker 1: the revenue comes from providing the supporting services around the product,

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Speaker 1: rather than from the product directly. In October, IBM announced

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Speaker 1: that it was making a move to acquire red Hat.

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Speaker 1: The deal would involve IBM buying all red Hat shares

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Speaker 1: for one dollars per share, which means this is a

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Speaker 1: thirty four billion dollar deal. Red Hat investors approved the

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Speaker 1: deal in January nineteen. Now, at the time of think

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Speaker 1: twenty nineteen when I'm recording this episode, that deal is

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Speaker 1: still going through the regulatory entities of the government for approval.

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Speaker 1: But the Red Hat acquisition plays a crucial role in

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Speaker 1: ibm strategy to lead in the cloud computing space in particular. Now,

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Speaker 1: as I mentioned in my earlier episode, one of the

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Speaker 1: big barriers of entry to cloud computing is a fear

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Speaker 1: of lock in going with a proprietary approach with a

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Speaker 1: specific vendor, and that means you've hitched your wagon to

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Speaker 1: that provider's proverbial horse. And if that horse would I

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Speaker 1: don't know. I gotta stick with this metaphor, I would

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Speaker 1: say it throws a shoe or something. You're stuck. The

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Speaker 1: open source approach allows for more options, and options sound

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Speaker 1: a whole lot better than only having a single choice.

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Speaker 1: But while all these are the business arguments for open source,

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Speaker 1: IBM has also pulled another lever. This one has to

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Speaker 1: do with the power of the open source approach to

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Speaker 1: do good in the world by engaging programmers and hackers

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Speaker 1: to build code to meet specific challenges and help people

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Speaker 1: who are most in need. I'll explain more in just

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Speaker 1: a moment, but first let's take a quick break. In eighteen,

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Speaker 1: the David Clark Cause proposed an initiative called Call for Code,

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Speaker 1: which would establish a particular challenge each year for coders

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Speaker 1: to tackle, and it would be a challenge that would

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Speaker 1: have a real world impact and could help people who

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Speaker 1: need it most. IBM joined on as a founding partner,

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Speaker 1: committing millions of dollars and making available much of the

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Speaker 1: company's technology and resources that it had developed for its clients,

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Speaker 1: so participants got access to these incredibly powerful tools for

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Speaker 1: their projects. The theme for the first Call for Code

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Speaker 1: was natural disasters, so they decided to go big really

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Speaker 1: early on. And natural disasters are an enormous threat to

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Speaker 1: millions of people around the world. Flooding, fires, hurricanes, earthquakes, tornadoes,

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Speaker 1: blizzards and more are all potential threats. They can wreak

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Speaker 1: havoc on massive scale seals and mounting an effective response

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Speaker 1: or mitigating the effects of the disasters that are hitting

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Speaker 1: different areas, those are those are types of businesses. We

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Speaker 1: just haven't seen much innovation around. We haven't seen a

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Speaker 1: lot of movement there. We've definitely had advancements in tech

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Speaker 1: and technique for dealing with disasters, but it's not the

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Speaker 1: sort of traditional problem that attracts programmers or companies. The

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Speaker 1: Call for Code was designed to be an incentive to

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Speaker 1: help change this, and to use the open source approach

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Speaker 1: to speed up the process. In the United States, the

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Speaker 1: need for solutions to create more effective disaster response efforts

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Speaker 1: was evident and keenly felt by eighteen. Puerto Rico, as

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Speaker 1: well as many islands in the Caribbean, had been hit

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Speaker 1: by a Hurricane Maria in September. The effects were absolutely devastating,

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Speaker 1: and the recovery has been slow, to say the least.

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Speaker 1: In fact, it's still going on in twenty nineteen. By

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Speaker 1: January of twenty eighteen, months after the hurricane had hit,

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Speaker 1: nearly half a million people on Puerto Rico were still

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Speaker 1: without electricity, and power outages due to failures in the

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Speaker 1: infrastructure exacerbated the problem. Hundreds of thousands more would find

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Speaker 1: themselves without power, at least on a temporary basis, and

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Speaker 1: by temporary I mean it would last days. The lack

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Speaker 1: of electricity obviously creates additional dangers. People can't use modern

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Speaker 1: communications to alert others to their needs, and first responders

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Speaker 1: have trouble knowing where they are needed. The Call for

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Speaker 1: Code initiative ended up getting a lot of momentum really quickly.

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Speaker 1: The announcement came out in May eighteen, and within six months,

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Speaker 1: thousands of coders from a hundred fifty six countries had

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Speaker 1: been hard at work and a community had formed around

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Speaker 1: this challenge. There were online discussions using tools like Slack

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Speaker 1: to facilitate communication. Some teams even found their fellow team

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Speaker 1: members through these channels, which connected people who were otherwise

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Speaker 1: living so far apart from one another that they probably

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Speaker 1: never would have had any connection otherwise. There were more

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Speaker 1: than three hundred Call for Code City challenges that took

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Speaker 1: place in cities all over the world to prepare the

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Speaker 1: coders for the grand challenge itself, and the prize was

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Speaker 1: a prestigious one. The winning team would take home two

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Speaker 1: thousand dollars, but more importantly, they would also get the

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Speaker 1: opportunity to actually deploy their solution with IBM S support.

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Speaker 1: Now that meant the submission wasn't just an interesting idea

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Speaker 1: that would win a prize. It would be put into

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Speaker 1: practice in the real world and real people could stand

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Speaker 1: to benefit from it. The winning team would also get

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Speaker 1: long term support from the Linux Foundation and an opportunity

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Speaker 1: to pitch their project to a venture capital firm. Now

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Speaker 1: teams really did embrace the open source philosophy. They also

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Speaker 1: were able to make use of many of the IBM

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Speaker 1: technologies that likewise depend on open source, such as the

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Speaker 1: company's cloud services. They got to work designing software and hardware,

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Speaker 1: and collectively the competitors built more than two thousand five

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Speaker 1: applications and there were more than one hundred thousand developers participating.

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Speaker 1: The finalists all had intriguing ideas, and I'm gonna run

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Speaker 1: down some of the finalists. I'll talk about the winner

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Speaker 1: in just a moment, but here are just some of

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Speaker 1: the ideas that made it to the final rounds. One

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Speaker 1: team proposed a system called the Post Disaster Rapid Response

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Speaker 1: retro Fit or p D three R. Their approach realized

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Speaker 1: on using artificial intelligence to assess damage done to houses

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Speaker 1: in the wake of a disaster like an earthquake, to

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Speaker 1: determine if and how the structures can be repaired effectively,

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Speaker 1: with designs meant to better withstand future earthquakes. So the

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Speaker 1: team used IBM s Watson platform in its design, both

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Speaker 1: to train computer models and to help recognize the extent

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Speaker 1: of damage in homes. So you're using computer vision, you're

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Speaker 1: using artificial intelligence to make design decisions. It was a

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Speaker 1: really inventive approach to this and to try and improve

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Speaker 1: communities in the future so that they could better withstand

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Speaker 1: these disasters that might hit at perhaps not regular intervals,

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Speaker 1: but often enough for it to be a concern. Another

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Speaker 1: team created what was called the Lolly Wildfire Detection System.

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Speaker 1: Their methodology was to deploy heat sensors over a wide

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Speaker 1: area that would be able to update the overall system

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Speaker 1: on the back end with real time analytics. So the

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Speaker 1: back end of the system used Watson to look at

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Speaker 1: changes and identify any markers that would indicate that a

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Speaker 1: fire might be moving into the area. Further, they used

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Speaker 1: artificial intelligence that would help figure out the features of

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Speaker 1: the fire. It could determine what the intensity of the

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Speaker 1: fire was, what the shape of it was, and it

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Speaker 1: could pair this with other information such as weather information

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Speaker 1: find out what the likely direction the fire might move

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Speaker 1: in would be. And this system could help responders plan

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Speaker 1: where they should dedicate firefighting resources. It could give residents

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Speaker 1: and early warning to evacuate before it becomes a life

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Speaker 1: threatening endeavor in the United States. The wildfires that happened

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Speaker 1: in the western part of the country in ten were

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Speaker 1: still very fresh in the minds of people, which I'm

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Speaker 1: sure made this particular pitch really compelling. United Aid Net,

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Speaker 1: which was another finalist, wanting to create a way for

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Speaker 1: those affected by natural disasters to get funds quickly. The

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Speaker 1: premise behind you a N is that you might have

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Speaker 1: family members who live in areas that are vulnerable to

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Speaker 1: natural disasters, and they might need access to money immediately

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Speaker 1: following a natural disaster, and it can be hard to

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Speaker 1: get money to people who need it. So you a

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Speaker 1: N has two interconnected networks. One is with various financial

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Speaker 1: institutions and the other is a network for family members.

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Speaker 1: So you can sign up those family members to be

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Speaker 1: part of this system and they can become beneficiaries so

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Speaker 1: that they can, in the wake of a disaster, draw

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Speaker 1: funds from an account you've set up and shared with them.

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Speaker 1: In some parts of the world, money transfers can take

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Speaker 1: as long as a few days, and according to U

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Speaker 1: a N, their approach decreases this to a couple of hours.

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Speaker 1: They proposed using blockchain to track the transactions, and they

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Speaker 1: relied upon facial recognition technology and again machine vision to

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Speaker 1: authorize withdrawals, leaning heavily on Watson for the horsepower to

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Speaker 1: do so. IBM had some internal teams that competed in

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Speaker 1: this as well. One of them freed to created a

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Speaker 1: sort of Internet connected earthquake sensor and a back end

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Speaker 1: to send alerts to mobile phones. So their focus was

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Speaker 1: primarily on schools, using some low cost hardware which they

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Speaker 1: actually housed in cardboard cases, so that really kept the

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Speaker 1: cost slow. They could put those sensors into schools and

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Speaker 1: monitor the schools for signs of earthquakes, you know, looking

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Speaker 1: for any sort of vibrations that would indicate an earthquake.

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Speaker 1: Is they're sort of like a seismograph, and it can

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Speaker 1: also estimate the magnitude of an earthquake once as detected.

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Speaker 1: It so not just that there is an earthquake, but

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Speaker 1: how wrong is it? You know, how how serious is

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Speaker 1: this problem? And it can send out alerts to mobile

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Speaker 1: phones that belong to students, parents, and teachers, as well

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Speaker 1: as first responders and other members of the community, bringing

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Speaker 1: the sensors to potentially vulnerable sites, as opposed to relying

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Speaker 1: on a sensing station that could potentially be a thousand

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Speaker 1: miles away means that you can get hyperlocal results and

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Speaker 1: that you can mobilize a response very quickly. And I'm

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Speaker 1: not just talking about a physical response about you know,

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Speaker 1: rescue workers going to the site, but kick starting the

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Speaker 1: bureaucratic response, the government response needed to h to supply

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Speaker 1: aid and other services to the area after the disaster.

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Speaker 1: It can get that started earlier so that you don't

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Speaker 1: have this long gap of time between when people need

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Speaker 1: services and when they can actually get them. Another IBM

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Speaker 1: project was help Chain, which took aim to address one

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Speaker 1: of the most challenging aspects of disaster relief, which is

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Speaker 1: making a case for donations. It can be a key

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Speaker 1: to ask people to donate money. The team cited a

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Speaker 1: figure about Puerto Rico and Hurricane Maria that is heartbreaking

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Speaker 1: and infuriating. Around ten thousand shipping containers full of food

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Speaker 1: and water never reached the point of distribution to the

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Speaker 1: people of Puerto Rico. That was reported by NPR. Now,

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Speaker 1: when you hear stories like that, you start to believe

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Speaker 1: that any donation you make would just be a waste

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Speaker 1: of money. Maybe you feel that the charities are taking

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Speaker 1: too much money for administrative costs and very little if

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Speaker 1: any of that money makes its way to the people

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Speaker 1: who actually need it. Or maybe you hear stories of

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Speaker 1: mismanagement and figure that any donation you make would be

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Speaker 1: like throwing money out the window. The help Chain team

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Speaker 1: used blockchain technology to help donators track a donation throughout

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Speaker 1: its life cycle so that they could see exactly how

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Speaker 1: much of their donation actually made it to the people

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Speaker 1: who needed it. They could see each stage of that

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Speaker 1: that transaction, and the transparency puts pressure on organizations to

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Speaker 1: deliver upon the promise they made to help those in need.

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Speaker 1: When everyone can see clearly how much money is going

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Speaker 1: toward administrative overhead, you can't expect to be extravagant with

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Speaker 1: those costs and still have people donate to your your cause. Now,

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Speaker 1: when we come back, I'll tell you about the winning

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Speaker 1: team of the team Call for Code and the follow

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Speaker 1: up effort called Code and Response. But first let's take

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Speaker 1: another quick break. The winning team for the two thousand

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Speaker 1: eight teen Call for Code was Project OWL O w L,

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Speaker 1: and that stands for Organization, Whereabouts and Logistics. I had

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Speaker 1: a chance to sit down with Brian Canals, one of

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Speaker 1: the members of the Project Owl team to talk about

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Speaker 1: this at the IBM think table, and here's what he

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Speaker 1: had to say. I'm here with Brian Canals of Project Owl,

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Speaker 1: which is a phenomenal project that marries hardware and software

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Speaker 1: for disaster relief in an application that I think is

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Speaker 1: absolutely fascinating. Please, Brian, can you give us kind of

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Speaker 1: the the big picture look at what Project Owl is

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Speaker 1: all about? Thank you. So. Project Owl, as you mentioned,

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Speaker 1: is a combination of two key ideas um and during

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Speaker 1: the Call for Code competition, we i a dated this

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Speaker 1: technology to help prepare for and recover from natural disasters

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Speaker 1: through an application of a lot of the technology that

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Speaker 1: IBM supports our our solution with. So we built two things.

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Speaker 1: The first is a software incident management system that's a

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Speaker 1: lot of buzzwords. It's really just dots on a map,

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Speaker 1: a way to see, uh, what things and resources and

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Speaker 1: people are where at what time to get a really

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Speaker 1: clear operating picture of an incident, which in some scenarios

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Speaker 1: if we're talking about a hurricane, chaos and misinformation is pervasive.

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Speaker 1: But there's still a problem in the worst disasters where

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Speaker 1: this type of data analytics capability would be really useful

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Speaker 1: to really large chaotic disasters, these are the ones where

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Speaker 1: infrastructure connectivity electricity is most likely to be offline. So

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Speaker 1: we built a a network of IoT devices we call

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Speaker 1: duck links, and these duck links generate a WiFi network

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Speaker 1: that anyone can connect to with their cell phone or

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Speaker 1: their laptop, and we can drop these duck links really

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Speaker 1: quickly through commercial drones and a cluster that we call

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Speaker 1: a cluster duck. And by dropping these cluster duck networks

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Speaker 1: were able to distribute OWL incident management software two locations

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Speaker 1: that may have no electricity or communications in a cost effective,

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Speaker 1: fast manner. So from what I understand, you only need

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Speaker 1: five of these ducks to cover a square mile, so

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Speaker 1: just a broad deployment can restore connectivity to a pretty

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Speaker 1: massive area in a short amount of time. And on

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Speaker 1: the software, I would imagine that having all that information

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Speaker 1: at your fingertips, you know important details such as what's

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Speaker 1: the weather going to do next, which obviously is going

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Speaker 1: to affect any kind of recovery efforts, and also who

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Speaker 1: is where so that you're not duplicating efforts, that you're

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Speaker 1: making the best use of your resources. Is that an

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Speaker 1: accurate assessment? Yeah, and you touched on one other aspect

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Speaker 1: that I didn't mention, we leveraged multiple APIs from the

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Speaker 1: Weather Company, so we're able to get real time local

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Speaker 1: climates in an area, which is really useful for first

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Speaker 1: responders operating during a disaster. UM but in conjunction with

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Speaker 1: that and using all the Watson APIs, we can get

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Speaker 1: a really crisp clear look on the ground. And as

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Speaker 1: you said, deploying these ducks UM is a unique way

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Speaker 1: to bring coms in an area that traditionally this is

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Speaker 1: a really complicated, expensive thing to do. And on top

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Speaker 1: of that, not everyone can carry a VSAT in their backpack.

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Speaker 1: These are hard that they require training, they're complicated and

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Speaker 1: time consuming to set up, not to mention expense, and

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Speaker 1: you have to align them to a particular geostationary satellite

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Speaker 1: in the sky that's at a very particular coordinate and

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Speaker 1: latitude and longitude. It's very hard to use. So our

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Speaker 1: ducks were designed to be almost, you know, idiot proof

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Speaker 1: in the sense that you just kind of throw a

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Speaker 1: bunch of these out and they just kind of turn

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Speaker 1: on an Internet in a place that doesn't have anything.

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Speaker 1: And I understand they even can float, so if you're

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Speaker 1: in a situation like a flooded area in the wake

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Speaker 1: of a hurricane. They still work there too. Yeah, especially

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Speaker 1: calling them ducks. It was imperative that they could float,

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Speaker 1: they could sit on the ground, and that we're thinking

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Speaker 1: about ways that maybe we'll make them fly to Oh fascinating.

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Speaker 1: So I'm curious. I I read a little bit about

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Speaker 1: the the actual genesis of the group. Can you talk

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Speaker 1: a little bit about how you all came together? So, yeah,

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Speaker 1: our team is just made up of technologists who and

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Speaker 1: I be personally here. I just love building things and

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Speaker 1: that has led me to some interesting communities and especially

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Speaker 1: software and technology competitions. I've been doing this for a while,

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Speaker 1: um and I think that's a great environment to find

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Speaker 1: people who are really passionate about building technology. Um So,

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Speaker 1: a couple of the guys on our team I had

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Speaker 1: met through previous competitions and then we've you know, worked

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Speaker 1: together on other projects. One gentleman in particular, Mangus Pereira

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Speaker 1: on our team from Greenville, North Carolina. We met each

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Speaker 1: other very early on in the competition through Slack, and

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Speaker 1: I distinctly remember we had one conversation one phone call

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Speaker 1: after that, and his passion and interest and creativity. And

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Speaker 1: he comes from the background of being self taught that

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Speaker 1: his desire to just be there doing things was self

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Speaker 1: evident from the very beginning. These are the people I

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Speaker 1: want to work with. These are the passionate minds I

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Speaker 1: want to be around. So through kind of these different communities,

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Speaker 1: through the Call for Code Slack, we're able to in

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Speaker 1: this team of people who just want to be they're

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Speaker 1: doing good things. And clearly you have impressed a lot

437
00:26:06,280 --> 00:26:09,760
Speaker 1: of people for you one call for code, So what

438
00:26:10,560 --> 00:26:12,879
Speaker 1: is next? What do you plan to do next after

439
00:26:13,240 --> 00:26:18,040
Speaker 1: after winning that prestigious or yeah, thank you? Um. So

440
00:26:18,119 --> 00:26:22,280
Speaker 1: when we won the Call for Code that was October Halloween,

441
00:26:22,320 --> 00:26:27,040
Speaker 1: I believe, and basically the next day we had set

442
00:26:27,040 --> 00:26:29,520
Speaker 1: a goal post in the ground that you know, this

443
00:26:29,600 --> 00:26:32,800
Speaker 1: wasn't just about winning a competition or building technology, as

444
00:26:32,840 --> 00:26:35,920
Speaker 1: fun as that is. This is about making a positive impact,

445
00:26:36,160 --> 00:26:39,800
Speaker 1: making tech for good. And we set a goal post

446
00:26:39,880 --> 00:26:41,679
Speaker 1: that we're gonna go to Puerto Rico and do a

447
00:26:41,720 --> 00:26:44,359
Speaker 1: stress test before a hurricane hits, and we're gonna deploy

448
00:26:44,359 --> 00:26:46,240
Speaker 1: a bunch of these and really see what can our

449
00:26:46,240 --> 00:26:50,440
Speaker 1: technology actually do. That test is happening in two weeks

450
00:26:50,920 --> 00:26:54,920
Speaker 1: in Puerto Rico. We're gonna go to three different regions,

451
00:26:55,000 --> 00:26:58,280
Speaker 1: an urban region in San Juan, a mountainous region, and

452
00:26:58,320 --> 00:27:02,080
Speaker 1: a coastal region, and we're basically going to start a

453
00:27:02,160 --> 00:27:05,760
Speaker 1: stop watch, deploy a hundred ducks, cover about twenty square

454
00:27:05,760 --> 00:27:10,840
Speaker 1: miles and see how quickly, how effectively, and how much

455
00:27:10,880 --> 00:27:13,040
Speaker 1: does it cost for a few people with a bunch

456
00:27:13,080 --> 00:27:15,679
Speaker 1: of ducks to get an Internet and Comm's network up

457
00:27:15,680 --> 00:27:17,840
Speaker 1: and running. And you know, can we do this in

458
00:27:17,880 --> 00:27:21,239
Speaker 1: a couple hours? Fascinating? Well, I have to say that

459
00:27:21,280 --> 00:27:24,919
Speaker 1: your story is absolutely inspiring on multiple fronts, from just

460
00:27:25,000 --> 00:27:29,520
Speaker 1: being passionate about tech and building things and this area

461
00:27:29,520 --> 00:27:33,119
Speaker 1: of experimentation that I find fascinating, to the desire to

462
00:27:33,200 --> 00:27:35,879
Speaker 1: actually make a positive impact and to help people who

463
00:27:35,920 --> 00:27:39,399
Speaker 1: are at the most vulnerable who need that help at

464
00:27:39,440 --> 00:27:43,479
Speaker 1: a critical time through an easy user interface where they

465
00:27:43,480 --> 00:27:46,480
Speaker 1: can connect, they can find out how everyone is doing,

466
00:27:46,480 --> 00:27:50,359
Speaker 1: they know what to do next. This is a wonderful story.

467
00:27:50,560 --> 00:27:54,440
Speaker 1: I am very honored to have been able to talk

468
00:27:54,520 --> 00:27:57,840
Speaker 1: with you about it. I greatly appreciate the opportunity and

469
00:27:58,000 --> 00:28:01,200
Speaker 1: I wish you the best of success us in Project

470
00:28:01,240 --> 00:28:04,040
Speaker 1: al in the in the years to come. Thank you,

471
00:28:04,119 --> 00:28:07,600
Speaker 1: so much. Um, you know, but like I said, we

472
00:28:07,720 --> 00:28:10,440
Speaker 1: just were really inspired to build things and do good.

473
00:28:10,480 --> 00:28:13,400
Speaker 1: I think, um, one of the challenges. You're very familiar

474
00:28:13,400 --> 00:28:16,000
Speaker 1: with technology and the landscape. I think there's been a

475
00:28:16,119 --> 00:28:19,720
Speaker 1: challenge with technology and that we're not certain a lot

476
00:28:19,760 --> 00:28:22,600
Speaker 1: of these things are actually helping us. Some people might

477
00:28:22,640 --> 00:28:25,560
Speaker 1: be getting rich, but does this really help us with

478
00:28:25,720 --> 00:28:28,600
Speaker 1: what's going on? So I think you know that the

479
00:28:28,680 --> 00:28:31,040
Speaker 1: holy grail of what we're trying to do as a

480
00:28:31,080 --> 00:28:34,440
Speaker 1: company is to marry the ability to build a business

481
00:28:34,600 --> 00:28:37,919
Speaker 1: and build a company with the ability to do good

482
00:28:37,960 --> 00:28:41,640
Speaker 1: and make a tangible, positive social impact. It really sounds

483
00:28:41,640 --> 00:28:44,640
Speaker 1: to me like you've made great strides towards that goal.

484
00:28:44,880 --> 00:28:46,920
Speaker 1: So I wish you the best. Thank you so much,

485
00:28:46,920 --> 00:28:50,400
Speaker 1: thank you. I really like Project Owl's approach here. The

486
00:28:50,480 --> 00:28:54,680
Speaker 1: hardware is both simple and innovative. Housing the mesh network

487
00:28:54,760 --> 00:28:58,480
Speaker 1: nodes in rubberized buoyant cases and then using a drone

488
00:28:58,520 --> 00:29:01,680
Speaker 1: delivery system makes deployment much less of a challenge than

489
00:29:01,680 --> 00:29:05,160
Speaker 1: it would be if you were using cell phone towers

490
00:29:05,240 --> 00:29:09,280
Speaker 1: or some other more traditional means of deployment, and using

491
00:29:09,320 --> 00:29:12,600
Speaker 1: cloud services to pull together different pieces of data to

492
00:29:12,680 --> 00:29:15,600
Speaker 1: provide to first responders and to help them plan effectively

493
00:29:15,880 --> 00:29:18,720
Speaker 1: is really exciting as well. It could help responders focus

494
00:29:18,840 --> 00:29:22,680
Speaker 1: specific efforts on particular regions rather than going in blind

495
00:29:22,760 --> 00:29:25,760
Speaker 1: with a wide but shallow response. You know, if you

496
00:29:25,760 --> 00:29:29,200
Speaker 1: have to prepare for anything, you probably don't have the

497
00:29:29,200 --> 00:29:32,959
Speaker 1: capacity to have a very deep response to any one

498
00:29:33,080 --> 00:29:36,920
Speaker 1: given need because you're trying to prepare to provide for

499
00:29:37,000 --> 00:29:41,040
Speaker 1: any need. Well, this would give responders more tools to

500
00:29:41,120 --> 00:29:45,800
Speaker 1: plan a specific approach and a more effective one. It

501
00:29:45,840 --> 00:29:49,040
Speaker 1: also helps them not duplicate those responses. So the code

502
00:29:49,040 --> 00:29:52,719
Speaker 1: in Response phase of this whole initiative is now ramping up.

503
00:29:52,760 --> 00:29:56,760
Speaker 1: IBM calls it a twenty five million dollar four your

504
00:29:56,800 --> 00:30:01,120
Speaker 1: initiative that will build, fortify, test, and laun open technology

505
00:30:01,160 --> 00:30:05,040
Speaker 1: solutions that helped communities needing critical aid. So this is

506
00:30:05,080 --> 00:30:08,480
Speaker 1: the opportunity to actually put into place the proposed solution

507
00:30:08,880 --> 00:30:11,200
Speaker 1: and see if it works in the real world, to

508
00:30:11,280 --> 00:30:15,080
Speaker 1: take it from the development team, from the code and

509
00:30:15,280 --> 00:30:19,080
Speaker 1: the prototypes and actually put it out there for real

510
00:30:19,200 --> 00:30:23,800
Speaker 1: zes well. In addition, collaboration is continuing beyond the call

511
00:30:23,920 --> 00:30:28,560
Speaker 1: for code competition, carrying forward that open source philosophy beyond

512
00:30:28,600 --> 00:30:32,240
Speaker 1: the event itself. Pedro Cruz who headed up another call

513
00:30:32,280 --> 00:30:35,840
Speaker 1: for code project called Drone Aide, maybe working with Project

514
00:30:35,880 --> 00:30:39,600
Speaker 1: Owl to combine his ideas with THEIRS into a new

515
00:30:39,640 --> 00:30:43,200
Speaker 1: implementation that could really help people. Drone Aide grew out

516
00:30:43,240 --> 00:30:45,760
Speaker 1: of a real world experience that Pedro Cruz had in

517
00:30:45,800 --> 00:30:49,120
Speaker 1: the wake of Hurricane Maria and Puerto Rico. Because getting

518
00:30:49,120 --> 00:30:52,960
Speaker 1: around the island was nearly impossible due to the massive

519
00:30:53,040 --> 00:30:57,760
Speaker 1: destruction and because communication systems were down, Cruz wasn't sure

520
00:30:58,040 --> 00:31:01,040
Speaker 1: how his family was doing, so he went and grabbed

521
00:31:01,040 --> 00:31:03,040
Speaker 1: a drone that he owned as a camera on it,

522
00:31:03,080 --> 00:31:05,040
Speaker 1: and he decided to fly his drone around to check

523
00:31:05,080 --> 00:31:07,560
Speaker 1: on his family members make sure they were okay. And

524
00:31:07,600 --> 00:31:10,000
Speaker 1: while he was doing that, he noticed that in some

525
00:31:10,120 --> 00:31:12,640
Speaker 1: of the areas around Puerto Rico, some of the damaged

526
00:31:12,640 --> 00:31:16,800
Speaker 1: areas that people had written messages on the ground, you know,

527
00:31:16,920 --> 00:31:20,400
Speaker 1: using chalk on pavement, and some of them were messages

528
00:31:20,440 --> 00:31:23,479
Speaker 1: to alert other people in the community about their family

529
00:31:23,520 --> 00:31:27,040
Speaker 1: members and their status. Others were requests for help. There

530
00:31:27,040 --> 00:31:29,560
Speaker 1: are a lot of requests for fresh water, for example,

531
00:31:29,840 --> 00:31:33,840
Speaker 1: and CRUs thought that a solution using object character recognition

532
00:31:34,240 --> 00:31:36,840
Speaker 1: could be paired with a drone to do scans of

533
00:31:36,840 --> 00:31:39,960
Speaker 1: an area and search for these messages and then you

534
00:31:40,000 --> 00:31:42,320
Speaker 1: could geo tag them so you know exactly where those

535
00:31:42,360 --> 00:31:45,280
Speaker 1: messages are and you know where people are and what

536
00:31:45,520 --> 00:31:47,800
Speaker 1: they need, and you can send that information to first

537
00:31:47,840 --> 00:31:51,640
Speaker 1: responders so they know immediately who needs what and where

538
00:31:51,640 --> 00:31:55,320
Speaker 1: they are. I think twenty nineteen I attended a session

539
00:31:55,320 --> 00:31:59,480
Speaker 1: in which Canals and Crews spoke about this collaboration, combining

540
00:31:59,520 --> 00:32:02,720
Speaker 1: the drone approach with the dashboard software that OWL built

541
00:32:02,840 --> 00:32:06,040
Speaker 1: to add more information for first responders, giving them a

542
00:32:06,160 --> 00:32:09,400
Speaker 1: much more complete picture of what's going on. And by

543
00:32:09,560 --> 00:32:13,760
Speaker 1: using object character recognition and automated processes to identify places

544
00:32:13,760 --> 00:32:16,120
Speaker 1: where people need help, you take the need for a

545
00:32:16,240 --> 00:32:20,200
Speaker 1: human to review hours of footage, which is an enormous

546
00:32:20,240 --> 00:32:24,120
Speaker 1: time constraint. These stories are really inspiring and I think

547
00:32:24,160 --> 00:32:27,840
Speaker 1: it serves IBM well in many ways. It's not just

548
00:32:28,000 --> 00:32:32,000
Speaker 1: good PR, although there's no denying it's good PR. It

549
00:32:32,080 --> 00:32:35,320
Speaker 1: also is encouraging more people to get into coding. It's

550
00:32:35,360 --> 00:32:38,800
Speaker 1: tapping into the desire to make a real positive difference,

551
00:32:38,800 --> 00:32:40,440
Speaker 1: and a lot of people want to do that and

552
00:32:40,520 --> 00:32:45,160
Speaker 1: aren't sure how so designing this program gives people an

553
00:32:45,200 --> 00:32:47,720
Speaker 1: avenue to actually do that. So it's not just to

554
00:32:48,040 --> 00:32:50,000
Speaker 1: build an app or a piece of software, but to

555
00:32:50,040 --> 00:32:53,520
Speaker 1: actually improve or even save lives. That's going to lead

556
00:32:53,560 --> 00:32:57,040
Speaker 1: to more people getting into the field, which will benefit

557
00:32:57,080 --> 00:33:00,120
Speaker 1: tech companies like IBM when they're seeking out talent. And

558
00:33:00,200 --> 00:33:03,719
Speaker 1: obviously the use of IBM S technologies raises awareness of

559
00:33:03,760 --> 00:33:06,760
Speaker 1: what those technologies are and how they can make an impact.

560
00:33:07,160 --> 00:33:10,720
Speaker 1: But throughout Think twenty nineteen, the message of using technology

561
00:33:10,760 --> 00:33:14,480
Speaker 1: to make a positive change was repeated. Jenny Romedi's opening

562
00:33:14,560 --> 00:33:18,240
Speaker 1: keynote even featured a video titled Dear Tech, and that

563
00:33:18,400 --> 00:33:21,720
Speaker 1: was an open letter to technology and to developers about

564
00:33:21,840 --> 00:33:24,760
Speaker 1: using these tools to transform the world in positive ways

565
00:33:24,840 --> 00:33:28,440
Speaker 1: and making that positive impact. I'll be following up on

566
00:33:28,520 --> 00:33:30,920
Speaker 1: Project al in the future. I want to speak to

567
00:33:30,960 --> 00:33:33,840
Speaker 1: the team to find out how the deployment tests turn out.

568
00:33:34,120 --> 00:33:36,720
Speaker 1: My hope is that they get the support to scale

569
00:33:36,800 --> 00:33:39,240
Speaker 1: up their efforts so that they can actually put this

570
00:33:39,480 --> 00:33:42,000
Speaker 1: into practice in the real world in in the wake

571
00:33:42,080 --> 00:33:45,400
Speaker 1: of real world disasters. Now you never want to need

572
00:33:45,560 --> 00:33:50,640
Speaker 1: a natural disaster response plan, but you absolutely should have one.

573
00:33:51,400 --> 00:33:55,280
Speaker 1: But back to open source. Open source projects cover every

574
00:33:55,320 --> 00:33:57,719
Speaker 1: area of coding. You can think of the community of

575
00:33:57,760 --> 00:34:01,800
Speaker 1: developers that grows around. Open source tends to be passionate.

576
00:34:02,120 --> 00:34:06,800
Speaker 1: Members can be really opinionated. People can disagree over implementations

577
00:34:06,920 --> 00:34:11,880
Speaker 1: or changes. Ideally, through the process of contributing code, the

578
00:34:11,920 --> 00:34:15,439
Speaker 1: best options survive and the others evolved or they fall

579
00:34:15,480 --> 00:34:19,680
Speaker 1: to the side. And there are thousands of workshops, hackathons,

580
00:34:19,840 --> 00:34:23,200
Speaker 1: meet ups, and seminars about coding. And from what I've seen,

581
00:34:23,239 --> 00:34:25,959
Speaker 1: the community tends to be really eager to welcome new

582
00:34:26,000 --> 00:34:30,239
Speaker 1: people into their world. After all, new people bring new ideas,

583
00:34:30,440 --> 00:34:34,600
Speaker 1: new perspectives, and new solutions, and that's really what open

584
00:34:34,600 --> 00:34:38,320
Speaker 1: source is all about. Well, that wraps up this episode

585
00:34:38,360 --> 00:34:41,120
Speaker 1: from Think two thousand nineteen. I want to thank IBM

586
00:34:41,160 --> 00:34:44,920
Speaker 1: again for the opportunity to kind of explore this world

587
00:34:44,960 --> 00:34:47,359
Speaker 1: and to talk with so many people who are really

588
00:34:47,360 --> 00:34:51,040
Speaker 1: passionate about open source. It was really a cool experience

589
00:34:51,080 --> 00:34:53,640
Speaker 1: for me. If you guys have any suggestions for future

590
00:34:53,719 --> 00:34:56,520
Speaker 1: episodes of tech Stuff, send me a note. The email

591
00:34:56,520 --> 00:34:59,480
Speaker 1: addresses tech Stuff at how stuff works dot com, or

592
00:34:59,520 --> 00:35:03,200
Speaker 1: you can visit our website that's tech Stuff podcast dot com.

593
00:35:03,600 --> 00:35:05,759
Speaker 1: You can find links to our social media there. You

594
00:35:05,760 --> 00:35:08,919
Speaker 1: can find links to our merchandise store. Check those out

595
00:35:09,120 --> 00:35:17,160
Speaker 1: and I'll talk to you again really soon. For more

596
00:35:17,239 --> 00:35:19,520
Speaker 1: on this and thousands of other topics. Is that how

597
00:35:19,560 --> 00:35:30,320
Speaker 1: stuff works dot com