Speaker 1: Get in touch with technology with tech Stuff from how
Speaker 1: stuff works dot Com. Hey there, and welcome to tech Stuff.
Speaker 1: I'm Jonathan Strickland. I'm the executive producer of this here podcast,
Speaker 1: and I worked with how Stuff Works and my Heart
Speaker 1: Radio and love all things tech. And yes, I'm once
Speaker 1: again recording from my hotel room in San Francisco to
Speaker 1: talk about what I've seen and learned at the IBM
Speaker 1: Think two thousand nineteen conference. Thanks again to IBM for
Speaker 1: bringing me out here to really dive into all the
Speaker 1: cool stuff going on. And this is my particular point
Speaker 1: of view of what I saw. I'm really excited about
Speaker 1: this particular topic. If you listened to the episodes I
Speaker 1: recorded last year at IBM Think two thousand and eighteen,
Speaker 1: which was in Las Vegas, Nevada, you heard my two
Speaker 1: part series about the five and five presentation, which is
Speaker 1: a session in which IBM researchers presents some of the
Speaker 1: cool things they're working on and the results of some
Speaker 1: high tech bleeding edge research. This year, IBM changed things
Speaker 1: up a little bit by focusing the presentation on one
Speaker 1: broad topic, and it's one of my favorites. Food. I
Speaker 1: love food, particularly if there's hot sauce involved, but the
Speaker 1: IBM research was a bit more ambitious than finding the
Speaker 1: right condiment to make my burrito zing. Rather, the presentations
Speaker 1: I saw brought the audience on a journey for the
Speaker 1: entire ecosystem of our food, from growing it to distributing it,
Speaker 1: to figuring out what to do with plastic waste that's
Speaker 1: generated afterward. The subject is a really important one, so
Speaker 1: when you combine the threats of climate change with the
Speaker 1: growing population, you quickly come to the conclusion that feeding
Speaker 1: the planet is just going to get more challenging than
Speaker 1: it already is, and that managing resources and making food
Speaker 1: available will be absolutely critical. Each presenter focused on a
Speaker 1: particular topic, which led pretty smoothly into the next one.
Speaker 1: So I'm going to give you a rundown on what
Speaker 1: those presentations were and the related technologies. First up was
Speaker 1: Juliet Mutahi, a software engineer from Nairobi, Kenya. Her main
Speaker 1: focus was on the food chain, the food supply chain.
Speaker 1: She personalized her story by telling the audience of her
Speaker 1: background as the daughter of a coffee farmer in Kenya.
Speaker 1: Her father's coffee farm is part of a cooperative or
Speaker 1: a co op, and that's an association of business owners
Speaker 1: who worked together for the common benefit of the members.
Speaker 1: They can coordinate to negotiate the best prices for their products,
Speaker 1: for example, and make sure that no one is failing
Speaker 1: to get his or her fair share. And co ops
Speaker 1: help establish best practices like fair pricing and labor and
Speaker 1: they can negotiate long term contracts with buyers on a
Speaker 1: level that an individual farm owner might not be able
Speaker 1: to manage. In Kenya, there are twelve thousand members who
Speaker 1: are part of these co ops and they contribute more
Speaker 1: and half of Kenya's coffee production. Cooperatives work because their
Speaker 1: members share information across the value chain. They do it
Speaker 1: through spoken word. If one farm produces high quality coffee,
Speaker 1: the cooperative would negotiate to get a fitting price, a
Speaker 1: premium price for that coffee. So how can we use
Speaker 1: technology to sort of achieve the same sort of things
Speaker 1: that have been going on in cooperatives. Well, technology is
Speaker 1: allowing for the next evolution of this model, and like
Speaker 1: pretty much every digital solution you can think of, it
Speaker 1: all revolves around data. How can farmers collect more information
Speaker 1: about their land, their soil and make reliable predictions of
Speaker 1: future harvests? To better anticipate contract negotiations or better manage
Speaker 1: their farms. She spoke of an approach in which a
Speaker 1: farmer would use a tractor with embedded sensors in it
Speaker 1: to till the land. The tractor would be doing its
Speaker 1: normal tractory duties while simultaneously creating a digital map of
Speaker 1: the farmland itself, so that now there's a digital representation
Speaker 1: of the farm. Feeding the information to IBM S Watson
Speaker 1: for Agriculture Platform would allow for meaningful use of that data.
Speaker 1: Watson can take that information and combine it with other
Speaker 1: sources to make predictions about future yields and give farmers
Speaker 1: ideas about the conditions of their land. It can also
Speaker 1: analyze the data to estimate what past yields might have been.
Speaker 1: And you might wonder, why would you ever need to
Speaker 1: know what has already happened. Why would you need to
Speaker 1: estimate a past yield? It becomes really important in cases
Speaker 1: where a farmer has to file an insurance claim. It
Speaker 1: helps justify that insurance claim. If the farmer says that,
Speaker 1: due to whatever reason, the yield was a certain size
Speaker 1: and the data backs that up, it can help the
Speaker 1: farmer get that insurance claim and it also helps build
Speaker 1: out models that will increase efficiency further down the supply chain.
Speaker 1: Mutahi also talked about a cool analytical tool called the
Speaker 1: IBM Argo Pod. This is a great example of an
Speaker 1: Internet of Things device. It's about the size and shape
Speaker 1: of a regular business card. Embedded in the card are
Speaker 1: sensors that can do soil analysis, and a farmer just
Speaker 1: needs to put a small sample of soil on the
Speaker 1: card and chemical reactions will tell the sensor everything it
Speaker 1: needs to know in just ten seconds, and then the
Speaker 1: farmer can take a picture of the card using a
Speaker 1: smartphone and use a related app to store the information
Speaker 1: into a blockchain record. This doesn't just tell the farmer
Speaker 1: of the conditions on the farm, it can also help
Speaker 1: the farmer establish credit because a bank could extend credit
Speaker 1: to a farm against a predicted harvest that's based on
Speaker 1: this collected information. So for farmers all over the world,
Speaker 1: this could be an enormous help. Mutahi then handed the
Speaker 1: stage over to Shri ram rug Hoven. He is the
Speaker 1: Vice president and chief Technical Officer of IBM Research in India.
Speaker 1: He shared a pretty staggering pair of facts. One third
Speaker 1: of all food produced and nearly half of all fruits
Speaker 1: and vegetables never get assumed. It just goes to waste.
Speaker 1: So imagine if half of your work was immediately dismissed.
Speaker 1: You still had to do the work, but you know
Speaker 1: that half of it wouldn't count. That would be frustrating
Speaker 1: for most jobs, but when it comes to food production,
Speaker 1: it can lead to waste management problems at best, and
Speaker 1: at worst you could be facing starvation issues. So what
Speaker 1: is the challenge here? Is this just a case of
Speaker 1: some places having more food than the population can consume. Well,
Speaker 1: it's actually a lot more complicated than that. The food
Speaker 1: supply chain is a big issue. There are many points
Speaker 1: along a supply chain, and at every stage spoilage can
Speaker 1: and does occur, So there's a decent chance that a
Speaker 1: lot of food will be spoiled before it can ever
Speaker 1: find its way onto a market shelf. From the fields
Speaker 1: to the storage facilities, to transportation vehicles to distribution centers,
Speaker 1: to processors to shops to the home, there are a
Speaker 1: lot of stops along the way, and presently this is
Speaker 1: largely a dumb system, meaning there's no real way to
Speaker 1: gather information and share it along the supply chain. So
Speaker 1: if you receive a creative apples in a distribution center
Speaker 1: that are maybe two days away from being at peak ripeness,
Speaker 1: but you have no way of knowing that information. When
Speaker 1: you've got the crate, you might put that create on
Speaker 1: a truck that's going across the country, and by the
Speaker 1: time the apples get to their destination, a large number
Speaker 1: of them have already passed their prime and they just
Speaker 1: get thrown out. So what's the solution to this problem.
Speaker 1: The proposal we heard is that you would take a
Speaker 1: combination of technologies, and that includes the Internet of things, blockchain,
Speaker 1: and artificial intelligence in order to keep track of everything
Speaker 1: and make decisions. The Internet of things and the blockchain
Speaker 1: combined could log each stage of the journey the food
Speaker 1: takes from field to the market and keep track on
Speaker 1: the freshness of the food. There would be a record
Speaker 1: for each crate or palette or whatever unit of food
Speaker 1: that could record when it arrived and when it each point,
Speaker 1: and you could understand quickly how much time had passed
Speaker 1: since it was harvested. AI could help guide the decision
Speaker 1: making process when it comes to deciding where do you
Speaker 1: send this next. So the example we heard in the
Speaker 1: presentation involved oranges, and here's how it goes. Let's say
Speaker 1: that you run a distribution center in Florida near where
Speaker 1: oranges are grown. So farmers oranges come into your distribution center,
Speaker 1: and it's your job to send those oranges out to
Speaker 1: UH stores in various cities. And the two main cities
Speaker 1: under your responsibility are Atlanta and Chicago. And as it
Speaker 1: turns out, in Atlanta, people are hog wild for these oranges.
Speaker 1: And I can confirm that because at least from this
Speaker 1: Atlanta's point of view, this is accurate. But in Chicago,
Speaker 1: oranges for some reason just aren't moving nearly so fast
Speaker 1: from the produce section. With access to this information, the
Speaker 1: AI can make decisions. Oranges that still have a really
Speaker 1: long time to ripen could be sent to Chicago because
Speaker 1: they could sit in refrigeration a bit longer. They could
Speaker 1: remain fresh while the inhabitants of the Windy City decide
Speaker 1: they finally want to fight off scurvy. Oranges that are
Speaker 1: close to passing prime ripeness can make the shorter trip
Speaker 1: to Atlanta, where they are more likely to be purchased
Speaker 1: and consumed quickly. The distributor can maximize efficiency and minimize waste.
Speaker 1: Now next we're going to hear about a proposal that
Speaker 1: is all about food safety. But first let's take a
Speaker 1: quick break. The third presenter in the five and five
Speaker 1: event was Gerroud Dubois, director of IBM research at Almondon.
Speaker 1: Dubois began his presentation by talking about a catastrophe in
Speaker 1: China in two thousand and eight. A shipment of baby
Speaker 1: formula had been contaminated with a chemical called Melman. This
Speaker 1: is an industrial chemical that manufacturers used to treat stuff
Speaker 1: like ceramics and plastics. It is used in glue, in
Speaker 1: flame retardants, and laminates. It is not, as you might imagine,
Speaker 1: meant for consumption. Parents who fed their babies this formula
Speaker 1: quickly became alarmed when the babies began to get sick.
Speaker 1: Melman can cause kidney damage and Chinese hospitals had to
Speaker 1: admit more than fifty thousand babies. And even darker part
Speaker 1: of the story is that the addition of the chemical
Speaker 1: was likely intentional. The chemical makes it appear that the formula,
Speaker 1: when combined with milk, is more protein rich than it
Speaker 1: actually is, so it was an attempt at deception. So
Speaker 1: that's even more disturbing. Well, du Bois proposed that we
Speaker 1: rely upon microbes as a sort of microscopic canary in
Speaker 1: a coal mine, or as he called it, a microscopic
Speaker 1: c I a agent, and it would detect when contaminants
Speaker 1: are present in food products. And a microbe is a
Speaker 1: micro organism. Bacteria are a type of microbe, and you've
Speaker 1: likely used stuff like antimicrobial products to help sanitize stuff,
Speaker 1: and this could give you the implication that microbes are
Speaker 1: bad and some are definitely harmful to us, but some
Speaker 1: microbes are beneficial. And the human body is host to
Speaker 1: almost actually slightly more microbial cells than human cells. The
Speaker 1: ratio appears to be somewhere around one point three bacterial
Speaker 1: cells to every human cell, so you could say that
Speaker 1: you're more bacteria than person, though that's not really the point.
Speaker 1: Our microbial biome or biota is actually part of us,
Speaker 1: so I would say we are collectively made of both
Speaker 1: bacteria and human cells. We are bored, I guess. Anyway,
Speaker 1: Not all microbes are bad. Some are very helpful and
Speaker 1: they aid us in digesting certain materials, so some are
Speaker 1: good and we should rely upon those. Do Boa proposes
Speaker 1: we could use microbes to detect the presence of contaminants
Speaker 1: and food. So for a long time, scientists thought that
Speaker 1: the behaviors of microbes were too complex and unpredictable to
Speaker 1: reveal meaningful information, that it was almost random. But by
Speaker 1: studying them at a genetic level, looking at the d
Speaker 1: n A and the RNA of microbes, we've gathered a
Speaker 1: lot more information that reveals microbes react in different ways
Speaker 1: to different conditions. To make that determination, we had to
Speaker 1: use a big data approach. Dubai used a helpful analogy
Speaker 1: to describe how challenging this process really was. So imagine
Speaker 1: you have ten thousand boxes of jigsaw puzzles. They're different puzzles,
Speaker 1: and you dump all the pieces out into one enormous pile.
Speaker 1: You mix them up real good, then you throw the
Speaker 1: boxes away, so you don't even have the record of
Speaker 1: what the pictures look like, and now it's your job
Speaker 1: to put together those ten thousand puzzles. That's sort of
Speaker 1: the scale of the job we had to do to
Speaker 1: suss out how microbes behave given different conditions. At IBM,
Speaker 1: the research team created a database containing all the microbial
Speaker 1: genomes the scientific world has mapped so far. The genomes
Speaker 1: and the analysis of microbial behavior represents around five hundred
Speaker 1: terabytes of data. But the process was successful and that
Speaker 1: there are microbial behaviors that can indicate the presence of
Speaker 1: contaminants in our food. You have to know which microbes
Speaker 1: you're looking for and what behaviors indicates safe versus unsafe food.
Speaker 1: But it could add a new method for food safety
Speaker 1: inspectors to use to guarantee that the products that make
Speaker 1: it to consumers are actually safe to consume. Du Bois
Speaker 1: then introduced us to Donna Dillenberger and IBM Fellow at
Speaker 1: the Thomas J. Watson Research Center. Now, I mentioned in
Speaker 1: an earlier episode that the title of IBM Fellow is
Speaker 1: the highest honor IBM bestows on distinguished researchers, scientists, engineers,
Speaker 1: and the like. Dillenberger also talked about food safety. She
Speaker 1: cited a CDC report that I tracked down. The report
Speaker 1: is the Emerging Infectious Diseases Report, and the section Dillenberger
Speaker 1: was specifically referencing is chapter five of that report titled
Speaker 1: food Related Illness and Death in the United States. Now,
Speaker 1: I'm going to quote the abstract of that report verbatim.
Speaker 1: Here's part of that abstract. We estimate that food born
Speaker 1: diseases cause approximately seventy six million illnesses, three hundred twenty
Speaker 1: five thousand hospitalizations, and five thousand deaths in the United
Speaker 1: States each year. Known pathogens account for an estimated fourteen
Speaker 1: million illnesses, sixty thousand hospitalizations, and eighteen hundred deaths. Three
Speaker 1: pathogens Salmonella, listeria, and toxoplasma, are responsible for fifteen hundred
Speaker 1: deaths each year, more than seventy of those caused by
Speaker 1: known pathogens, while unknown agents account for the remaining sixty
Speaker 1: two million illnesses, two hundred sixty five thousand hospitalizations, and
Speaker 1: three thousand, two hundred deaths. Overall, food born diseases appear
Speaker 1: to cause more illnesses but fewer deaths than previously estimated.
Speaker 1: End quote. Now, fewer deaths is a good thing, but
Speaker 1: even fewer would be better, And the huge number of
Speaker 1: illnesses represents not just discomfort, stress, anxiety, at a or
Speaker 1: quality of life. There's a ripple effect. The hits society
Speaker 1: as a whole in ways like lost productivity or additional
Speaker 1: burden on the medical industry. So how can we tell
Speaker 1: if the food we buy is safe to eat? The
Speaker 1: bacteria responsible are microscopic, As I mentioned, just a moment ago.
Speaker 1: We're not likely to have a lab set up in
Speaker 1: our kitchens. Dyllenberger introduced a hardware and software tool that
Speaker 1: could actually help. The hardware component is a smartphone peripheral.
Speaker 1: It's essentially a microscope that plugs into your smartphone and
Speaker 1: feeds a magnified image to the phone's camera sensor. The
Speaker 1: software side is an analysis tool using AI and image
Speaker 1: recognition strategies to identify bacteria present in food. The results
Speaker 1: of the analysis pop up on the screen as shapes
Speaker 1: around the bacteria, and the type and color of shape
Speaker 1: indicates which kind of bacteria might be present. It can
Speaker 1: scan down to the micron scale and let you know
Speaker 1: if the food you have is safe to eat or
Speaker 1: if it is host to say, a colony of eke
Speaker 1: to lie. Dillenberger also revealed that the same technology could
Speaker 1: be used to help discover food counterfeits, and yes, that
Speaker 1: is a thing. In fact, it's a big thing. Dillenberger
Speaker 1: referred to a study that found in the United States,
Speaker 1: olive oil producers weren't always being totally honest, or at
Speaker 1: least they weren't meeting the right standards. Because a study
Speaker 1: in two found that seventy of the bottles of labeled
Speaker 1: extra virgin olive oil actually failed to meet the standards
Speaker 1: to qualify as extra virgin. You wouldn't be able to
Speaker 1: tell the casual glance the difference between regular olive oil
Speaker 1: and good old e v O O, but the scanner
Speaker 1: and paired software can analyze the reflected light from a
Speaker 1: sample of olive oil and determine whether or not it
Speaker 1: matches the real deal or not. The same tech can
Speaker 1: help you figure out if a label is legitimate or
Speaker 1: if it's a clever fake. And it doesn't stop with food,
Speaker 1: though that was the primary focus of the five and
Speaker 1: five presentation, the scanner could also be used to sort
Speaker 1: out the real McCoy from fakers and all sorts of products,
Speaker 1: from fashion accessories to prescription drugs. Moreover, Dylan Berger said
Speaker 1: that while the present implementation uses a smartphone, future versions
Speaker 1: could see embedded sensors integrated into common kitchen tools like
Speaker 1: cutting boards or knives or measuring cups or bowls, and
Speaker 1: by looking for not just authenticity, but for the presence
Speaker 1: of potential pathogens, we could improve our fine dining experience
Speaker 1: and also prevent cases of food poisoning. Next, we're gonna
Speaker 1: learn a little bit about the conclusion of the five
Speaker 1: and five event and what we might do with some
Speaker 1: of the plastic waste we generate. But first let's take
Speaker 1: a quick break. The final presenter at five and five
Speaker 1: was Jeanette Garcia, who has one of the most kick
Speaker 1: ass titles I have ever seen. It's Master Inventor at
Speaker 1: IBM Research Almada. She helped put into perspective exactly how
Speaker 1: big a problem plastic waste has become, and it is
Speaker 1: a huge problem. The world produces around three hundred million
Speaker 1: tons of plastic every year, and about half of that
Speaker 1: plastic is in the form of stuff that's intended for
Speaker 1: single use, meaning we just toss it out afterward. Lots
Speaker 1: of communities are now recycling, but sadly, only a small fraction,
Speaker 1: about ten per cent of plastic gets recycled. So why
Speaker 1: is that, Well, there are a few reasons. One is
Speaker 1: that there are different types of plastics and they can't
Speaker 1: all be processed in the same way, which means they
Speaker 1: have to be sorted into different categories. That takes effort
Speaker 1: and time, which also means it costs money. There are
Speaker 1: contaminants that can be a problem particularly food waste, and
Speaker 1: Garcia mentioned that we often don't know what the rules
Speaker 1: are are we supposed to rinse off the plastic. First,
Speaker 1: some types of plastic aren't recyclable at all, And then
Speaker 1: there's the problem of economics. Pastic is pretty easy stuff
Speaker 1: to manufacture. If it costs more money to recycle plastic
Speaker 1: than it does to just make new plastic, you have
Speaker 1: to come up with some other economic incentives to encourage recycling.
Speaker 1: At the same time, plastic is undeniably useful stuff. We
Speaker 1: rely on it for lots of things, not the least
Speaker 1: of which is food packaging. It's hard to dismiss how
Speaker 1: important plastic is in keeping our foods fresher longer, which
Speaker 1: goes back to cutting down food waste. So how do
Speaker 1: we resolve the problem. The specific type of plastic Garcia
Speaker 1: talked about was pet or pet plastic, which is found
Speaker 1: not only in packaging but also stuff like polyester clothing.
Speaker 1: The seventies, this is the type of plastic used in
Speaker 1: stuff like water and soda bottles. If you look at
Speaker 1: a bottle made from pet, you'll see the chasing arrows
Speaker 1: sign with the number one at the center. It makes
Speaker 1: up a lot of plastic, but I should point out
Speaker 1: that it's also the most recycled type of plastic in
Speaker 1: the world. PET is completely recy cyclable. The United States
Speaker 1: lags behind Europe when it comes to recycling PET plastic.
Speaker 1: We in the US recycle a little more than of
Speaker 1: PT plastic, while Europe is over the line, but we
Speaker 1: can always do better. One of the methods we rely
Speaker 1: upon to recycle this type of plastic is to wash
Speaker 1: the plastic and then melt it down. Another version uses
Speaker 1: chemicals to depolymerize the plastic. Polymer is a long chain
Speaker 1: of molecules, so this approach effectively breaks those chains down.
Speaker 1: But the melting process has a zero tolerance for contamination,
Speaker 1: meaning the only stuff that can get melted down is
Speaker 1: the plastic itself. Any other stuff will foul the melted
Speaker 1: mixture and make it unusable. It also only works with
Speaker 1: clear bottles. The chemical approach has its own drawbacks, a
Speaker 1: big one being the cost of the process, which creates
Speaker 1: that economic barrier I mentioned earlier. IBM solution is called
Speaker 1: volcat which stands for volatile catalyst. So what the heck
Speaker 1: does that mean? A catalyst is a substance that facility.
Speaker 1: It's a chemical reaction. You would typically use a catalyst
Speaker 1: to speed up a process that would otherwise take much
Speaker 1: longer to complete. Catalysts do this without undergoing permanent chemical
Speaker 1: changes themselves. They're incredibly useful in hundreds of different industrial applications.
Speaker 1: With the volecat approach, IBM could grind up pet plastic
Speaker 1: into flakes, and it wouldn't matter if the plastic was
Speaker 1: clear or had color added to it, or if it
Speaker 1: was clean or if it was dirty. With volcat, IBM
Speaker 1: takes that plastic and puts it into a heating chamber.
Speaker 1: The chamber heats the plastic up to about a hundred
Speaker 1: nine degrees celsius or three seventy four degrees fahrenheit. At
Speaker 1: that point researchers add the catalyst. Then they cool the
Speaker 1: mixture down to get below one hundred degrees celsius or
Speaker 1: two hundred twelve degrees fahrenheit. That's the temperature which water
Speaker 1: boils under one atmosphere of pressure. The plastic polymers break
Speaker 1: down into a more basic molecular form called B H
Speaker 1: E T, which is a monomer. And you can think
Speaker 1: of a monomer as a molecule that forms the basic
Speaker 1: component of a polymer identical monomers linked together to form polymers.
Speaker 1: So now you've got what is effectively the building blocks
Speaker 1: of PET. The volatile catalyst is completely recoverable and IBM
Speaker 1: engineers can filter the b ET from any other stuff
Speaker 1: that was in the mix. The b h g T
Speaker 1: can go on to form new pet containers, So we
Speaker 1: could keep using the same plastic over and over again
Speaker 1: without the need to make more of the stuff, and
Speaker 1: should our demand for plastic exceed that amount, we could
Speaker 1: literally mine the Pacific garbage patch and landfills for more material.
Speaker 1: The process sounds incredibly promising and increasing recycling rates, particularly
Speaker 1: for plastics that are dirty or have color added to
Speaker 1: the plastic material. Since the process removes the need to
Speaker 1: clean the plastic first, it could reduce the recycling workload.
Speaker 1: If it is scalable, it could be a viable alternative
Speaker 1: to the present approaches to plastic recycling. These research projects
Speaker 1: were all really interesting to me, and they are all
Speaker 1: trying to make big changes in the entire food life cycle.
Speaker 1: It's a truly enormous challenge and there's still more to
Speaker 1: work on developing technologies to reduce water waste, improve transportation
Speaker 1: and distribution deal with other types of waste, and guaranteeing
Speaker 1: everyone in the world has access to healthy and safe
Speaker 1: food is a monumental effort. These research projects are just
Speaker 1: a few of the ways innovators are looking into making
Speaker 1: a big difference. Will we see each of these ideas
Speaker 1: implemented in the real world. What's a bit early to say,
Speaker 1: but all of those five presenters made a prediction of
Speaker 1: what the world will be like in five years if
Speaker 1: we do further develop, scale and deploy those technologies, and
Speaker 1: it was a pretty good start to fixing some incredibly
Speaker 1: tough problems. Research will continue to play a huge part
Speaker 1: at IBM. It's ingrained in the company's culture. Much of
Speaker 1: it focuses on the company's core businesses, which is to
Speaker 1: be expected. You can't stay in business if you never
Speaker 1: focus on your services and products. But a lot of
Speaker 1: that work has applications beyond the enterprise world, and it's
Speaker 1: this drive to leverage powerful technologies to make real changes
Speaker 1: out there that I find so interesting. I'd like to
Speaker 1: thank IBM for bringing me out here. It was great
Speaker 1: once again getting to explore and experience the Think Conference.
Speaker 1: I got to see a lot of cool presentations about
Speaker 1: technologies that I was aware of but didn't know very
Speaker 1: much about. And I also really am thankful for the
Speaker 1: opportunity to talk to so many incredible people here at
Speaker 1: the conference and get their perspectives on technology and how
Speaker 1: we might apply it in the future. If you guys
Speaker 1: have any suggestions for future episodes of tech Stuff, contact
Speaker 1: me the email addresses tech Stuff at how stuff works
Speaker 1: dot com, or go to our website that's tech Stuff
Speaker 1: podcast dot com and you can find the ways to
Speaker 1: get in touch with me through social media. There. You
Speaker 1: can also find the link to our merchandise store and
Speaker 1: I will talk to you again really soon for more
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