Jonathan Attends IBM Think 2018

they usually do. That is because I am not currently in the massive, wonderful, underground, super secret studio at how stuff Works. Instead, I am on the road. I'm actually in Las Vegas, Nevada, where I am attending the inaugural IBM Think Conference. It's taking place between March nineteenth and March ten and it's kind of a big industry conference for people who are in the text space, who work with computers and software and hardware. Kind of a place for them to meet, to network and to learn a

lot more about some bleeding edge technologies. They have lots of different activities going on all week, and I am going to be here attending some of the events, talking to people, getting more information, and recording special episodes for you, my beloved listeners. So I hope you enjoy this special series and I can't wait to really dive in and learn more about these topics. It looks really interesting. Now.

One thing I will say before I jump in and give you kind of an overview of what I have to expect this week is that it's crazy busy, y'all. I mean, the conference has got so many people attending. I haven't seen any numbers about how many people are actually at this thing. But it's taking place at Mandalay Bay as one of the resorts and casinos here at Las Vegas, and it has a very large convention center. During ce S, you may have heard me do several

episodes about ce S over the years. This is the place where the press events happened before the show floor opens. At the Las Vegas Convention Center at Mandalaid Bay, you would have all the different rooms booked up for the various press conferences that companies would use to unveil their latest and greatest products they're coming out over the next

year or two. Well, at IBM think that's what is being used as the meeting space and the keynote space, presentation space for all sorts of different I T computer topics and activities. It's kind of overwhelming, to be honest. In fact, I am now kind of unwinding in my hotel room before I have to go back out and jump right back into I'm going to go to a session where they're going to talk about five big topics in science and technology. So I'm really looking forward to it.

There's some very interesting people who are here presenting, and I can't wait to learn more. Some of the areas that we're going to talk about this week will include blockchain. Now, if you've been listening to text stuff over the past few months, you've probably heard a recent episode I did about blockchain. Blockchain is the technology that underlies cryptocurrencies like bitcoin, and in fact, I would argue bitcoin is probably the most famous use of blockchain technology, but blockchain is not

only good for bitcoins and cryptocurrencies. That's one use for it, but not the only one. In fact, a lot of people argue that blockchain is going to be the next um next evolution of the web. So you might remember people were talking about web two point oh more than a decade ago, and blockchain would be like the next step. So what does that even mean? Well, what one point oh was the sort of websites you first saw, primarily when the web first launched, that would be websites that

were pretty much static. They did not change, They were not really interactive. They did not have any capacity to have users add to or change things in any meaningful way. And so it was kind of like looking at a newspaper or a magazine, something that's in a fixed format that doesn't have any real um, any real, any real

special thing about it. Right, there's nothing that gives it any sort of uh sense that you your presence matters, or that that's even registering at all, unless there was one of those little helpful counters, as was often the case in early websites, where it would tell you how many visitors had been to that website above. From that, there really wasn't anything that indicated that you mattered at all.

So that was web one point. Oh and a lot of those websites ended up kind of just being uh momentary. They didn't stick around because there was no point in going back and visiting after you've seen them once. They weren't ever going to change. If they did change, you didn't necessarily know about it, and so you would just have to go back and visit and see if anything had changed since the last time you were there. It

was kind of inconvenient. Web two point oh arguably was when websites started to incorporate interactive features where users could come into the website and things would change, they'd be dynamic. Sometimes this meant that there were animated aspects to the web page. Sometimes it just meant that there was a

way for users to leave feedback. For example, some people point to Amazon as being an early web dew point oh style web page because you could leave user reviews on the site, so you could actually impact what happened

with products on Amazon dot Com. And then the course later on, Amazon was also incorporating things like recommendation engines that would try to engage users and and encourage them to spend more money and to buy more things, and that was also and indication that was web two point oh. It was something that was more than just visit website,

read some information and leave. Blockchain is supposedly going to be the next step, and really it comes down to the very nature of blockchain itself, which is a peer to peer technology within a network. So you're not necessarily talking about Internet wide. It's a network within the Internet. Now it might be a network that spans multip pole networks, because it's all peers that are connecting to one another

and using a method that creates blocks of data. Thus the block in the block chain, and each block is sequentially added to a chain of other blocks, and it all dates back to the very first block in the chain, and there's information within each block that can be traced

all the way back to that first one. Uh. The transactions that happen within a block are recorded within that block, and as other computers inside the peer to peer network verify and validate those transactions, whatever they may be, they then are able to create the next block in the blockchain. With cryptocurrency, you get a reward for this. That's what's

called mining. It's when in bitcoin your computer participates in solving a particularly tough math problem essentially, and if you do end that ends up being the validation for previous transactions. The transactions all are codified as a block added to the end of the chain, and everyone in the peer to peer network has a has access to a ledger that is updated across the entire network. So the ledger is the full record of all transactions dating back to

the very first one. They're going to be very deep dives on blockchain technology here this week, as people talk about the different ways to use it beyond cryptocurrencies, and how it could serve as a backbone to future web interactions. Honestly, I'm very eager to hear more about that, because while I've just described kind of a very high level of what blockchain is, I I am really curious about how this is going to be used to actually, uh form

the the spine of the web in the future. Was it going to be besides a way of keeping track of transactions, like whether it's currencies or property or anything along those lines, what can it do beyond that? And I honestly don't fully understand that, so I look forward to learning more while I'm here. That's just one of the aspects that will be covered during this conference. Another one, a big one, is artificial intelligence. Now I've talked about

that a lot on tech stuff as well. Artificial intelligence A lot of people when they hear that, I think they immediately go to the idea of a machine that can quote unquote think like a person. It is mimicking the way brains think, or it somehow possesses human like intelligence, or perhaps even superhuman intelligence, where it's able to think even better than humans are. But artificial intelligence is a much more broad category than that. That would be a

very specific niche definition of what artificial intelligence is. Artificial intelligence encapsulates multiple disciplines, and it is also uh something that involves lots and lots of different parts of intelligence, not just this cognition that we think of, but think of things like image recognition with computers. Teaching a computer to identify a specific kind of thing to extrapolate from knowledge, it's tricky. You might be able to teach a computer, for example, that a mug is a mug like a

coffee mug. You've got a red coffee mug has got sort of a curved cut mug. It's not, you know, just straight up and down. It's got kind of kind of bowls out a little bit. And you take images of it from a certain angle with certain lighting, and you feed it to a computer and you let them You have your computer breakdown the image so it can I identify where the all the borders are, what are what is the mug versus, what is the background versus what is the platform it's on? And you Essentially, you

teach the computer, hey, this is a mug. Computers don't magically then understand that all other containers that have that same basic sort of shape um are mugs. If you showed another picture of that same mug with different lighting, the computer might not be able to tell you that that's a mug. Or it's at a different angle. Maybe you turn it so that the handle is facing a different way, and then you showed the computer the image, it may not be able to figure out that that

is also a mug. If it's a different color, if it's a different shape. All of these things are tricky for machines. Like as human beings, we can see an example or two of something and then we're pretty good. We then can say, all right, I now I get the general idea of the things that that are the

constitute a mug. And when I encounter something else, even if it is of a different shape, a different color, different lighting, on a different surface, maybe it's even got a different type of liquid inside of it, I still can figure out that that thing is a mug. Because I can extrapolate. I can take what I've learned and extend it beyond just the few instances that I have encountered.

And then if I walk into a mug store, I don't look at just one and say that's a mug, but I have no idea what the rest of these things are. I can actually say, oh, these are all different types of mugs and different sizes and shapes and colors. Computers are not good at that generally speaking. This is one of the reasons why you may have heard about that story of a computer being fed thousands upon thousands of images of cat pictures so that the computer could

learn what a cat is. Because without all of that, without the this huge body of examples, the computer simply can't extrapolate and figure out what is is not a cat. And even after all of those images were fed to the computer, it's still had some issues. It wasn't like it had magically understood what a cat was. It did not have an innate grasp of cat nous, and by that I mean the qualities that make a cat not

a character from Hunger games. So artificial intelligence, again is very much a multidisciplinary thing, image recognition being just one tiny part of it. There are numerous other aspects to artificial intelligence, including things like voice recognition and natural language processing. Natural language processing, of course, is where a computer starts to learn what we mean when we say things a

certain way. Now, again, just like with our ability to extrapolate based upon the limited examples we have seen of any one object, with image recognition, natural language, we can have different ways of saying the same thing and still mean the same meaning. So I might say it's raining, or it's pouring outside, or it's coming down like cats and dogs. Those are all different ways of me saying that water is falling from the sky in the form of precipitation, and you will get wet if you walk outside.

And people who have had just a limited amount of exposure to these sort of ideas can pick up on that, but computers, again do not unless you expressly tell them. So. The way we say things, the word order we choose, the syntax, the the accent we may speak with, the dialect if you prefer that we might speak with, the emphasis we would place on different words, the speed at which we speak. All of these different factors make it

very challenging for computers to understand us. That being said, voice recognition and natural language processing has come a far away in a short amount of time. Over the last couple of decades, it has really advanced quite a bit, which is why we see all of these digital personal assistance that we can talk to like Siri and Alexa and Google Assistant, and they have become pretty good and

understanding us. They're not perfect, but they're pretty good at figuring out what we want, even if we say things in different ways. But that's another aspect of artificial intelligence. All of these sort of things are the kind of topics that will be talked about during the IBM Think Conference, And again they're gonna go into much greater detail and talk about real world applications for AI, not just theory, not just how far we've come, but how we can

put it to use. There's more to come about the IBM Think Conference, but before I get much further into it, let's take a quick break to and our sponsor IBM Watson. You may have heard of it as the computer that defeated former Jeopardy champions. Well, it's much more than that. Although that definitely was a big publicity stunt. You could argue it was the equivalent of when Deep Blue went

up against Kasparov in the chess competitions. But IBM s Watson is sort of in that natural language processing and understanding what people are saying and being able to pull data based on that. That is sort of an example of that. So in Jeopardy it was really put to the test because with Jeopardy, you don't just get clues and then you have to come up with the right answer.

Sometimes those clues are puns or rhymes, or they are very circumspect, like it's very circuitous the kind of logic you have to use to figure out what the actual answer is in the form of a question, and Watson did really well with it. You may remember that the way Watson worked was that it had a massive database. It was not connected to the Internet for the purposes of Jeopardy. It just had a big database of information

and it would process whatever the clue was. It would reference its database, and it would look for potential answers and assign each potential answer a probability, sort of a confidence probability. If Watson's confidence probability probability was high enough, and as I recall, it was somewhere in the eight percentile range, like it had to be at least sure that that was the right answer. It would then put

forward that as its answer in jeopardy. If none of the answers that came up with met that level of confidence, Watson would not answer. It would not attempt to see if perhaps it could take a wild guess and at the right answer, and it turned out to work. Watson ended up winning that game of Jeopardy. But of course, Watson isn't just about showing up former champions. It's about all sorts of stuff IBM SUH. Early applications of Watson were to include it with medical procedures so that it

could help doctors diagnose and treat patients. It was not replacing doctors. It was not meant to be a robo doctor that would treat you based on your symptoms and then you just go to the robo pharmacist. But rather it was acting as an assistant to help either confirm what a doctor was thinking or perhaps narrowed down some options of what the cause of any particular symptoms might be. And it has seen a lot of use in that field. It's gone well beyond that, though some of the other

applications have been a little silly. You may remember if you listen to the Forward Thinking podcast We did an episode about Chef Watson that was an application that IBM created where Watson would design a meal for you based off of ingredients you told it. We're at your disposal. So you might say, hey, I have chicken, and I've got rosemary, and i have some potatoes, and I've got some rice, and i've got some green beans. What can

I make with these? And it would say, all right, well, let me come up with a recipe for you, and would generate a recipe and it probably includes some ingredients that you might not have on hand. It would be stuff that you would have to go shop for. But the interesting thing was it was dynamically creating these recipes. It wasn't accessing a database of recipes and pulling from

the ones that included the ingredients you mentioned. Instead, it would look at how flavors had been combined in the past by looking at a huge library of recipes that

had been fed to Chef Watson. So just imagine a library is worth of cookbooks fed to this computer, and the computer says, well, based upon what I can see from all these recipes, roseberry and chicken do pair well together, So we're going to make a recipe that uses those two ingredients, and based upon all the different ways I've seen to prepare chicken, I think a baked chicken dish would be the perfect one to go with, and so on and so forth. But it's not pulling a recipe,

it was creating one based upon its knowledge. It's like a chef, not a cook. Uh. This did not always necessarily work out great. If you listen to that episode of Forward Thinking, you'll hear some of the experiences we had as we were testing out Chef Watson. I remember a cauliflower fricacy recipe in which the cauliflower was listed

as optional, which I thought was a bit unusual. Also, Uh, the other interesting thing was that you could feed the exact same ingredients into Chef Watson multiple times and you'll get different results each time. Again because the recipes are created dynamically, so it's not again not not looking at a recipe it's already written. It's writing a new one for you each time you ask it. It's kind of interesting.

There was also a an example recently where Watson was being used as a platform for the Weather Company, where if you use the Weather Company's app, Watson was helping power that so it's kind of like an an A p I. So different developers can use Watson as a platform to build different applications, and it all depends on what your application needs whether or not Watson is a good fit. It doesn't necessarily mean that every single application

is going to benefit from using Watson. If you want to create a game that's like Angry Birds, Watson may not be of much use. But if it's anything where you have someone asking questions or asking for data, then Watson might end up being helpful. And so that Obviously, there's a lot of different roundtable discussions and breakout sessions

all about IBM Watson and how to use it. One thing that I think is gonna be particularly interesting in a very sensitive subject given extremely recent news, is the idea of self driving cars. There are some sessions that

are supposed to be about self driving cars. The reason I say sensitive is because the day I'm recording this, on March nineteen, two thousand eighteen, UH there was also very tragic news out of Arizona, and that news was that Uber uh has a fleet of self driving vehicles that they've been testing in different markets, including in Arizona, and one of those self driving vehicles and SUV ended up striking and killing a pedestrian, Elaine Hertzberg. She was

trying to cross the street. She was walking a bicycle across the street in Tempe, Arizona, on the night of March eighteen, two thousand eighteen, and she was struck by this SUV. And the SUV was an autonomous mode. There was a driver in the vehicle because while Uber has been testing this autonomous vehicle technology, they have been asked to keep an operator behind the wheel of the car to take over just in case something goes wrong and

the vehicle is unable to cope with it. And I'm as of the recording of this podcast, I'm not sure exactly what happened, whether or not the driver was aware of what was about to happen, was unable to respond in time. I don't know what the sequence of events was, specifically in regards to that operator. But the tragic story is that this autonomous car, the self driving car, struck and killed a pedestrian. And as far as I can determine, it is the first fatality due to an autonomous car.

Obviously not the first accident. There have been a few others and a few that have been attributed specifically to the autonomous cars and not to human drivers. But this is a terrible, terrible story, and obviously that's going to affect the conversations that go on here, So I hope to attend some of those and hear what experts in the field have to say about this and what is the best course of action. Obviously, you want to be respectful of the victim and her family, and you want

to be realistic. You do not want to dismiss this. Obviously that would be horrible, it'd be unthinkable. But how do you move forward when there's so much momentum technologically speaking behind the movement to go to autonomous cars? And I'm I want to find out what people's thoughts are on this. It may very well be that there aren't any prepared sessions to cover this because the news is so recent, but I'm sure there will be questions about it.

There's a bit more I expect to see here at the IBM Think Conference, and I'll tell you about it in just a minute, but I've got to take a quick break to thank our sponsor. Another area that is going to get a lot of coverage here at the conference is all about cloud and data issues, and oh boy, there's a lot to talk about there too, because again, recent news has been pretty rough in regards to data mining.

So I guess I should talk quickly about what this is the first place cloud computing In case you were not aware, is this this model of computing in which you have powerful computers connected to a network that are doing computation for you, more specifically, for your computer. It might be just computation, it might be storage, It might just be storage, it might be both. But the idea is that instead of your computer doing all the work, a computer on a network is doing all the work

for you and sending you the results. So your computer is just receiving some information. It's not having to crunch any numbers. This could be really useful if you wanted to do something that was well beyond your computers processing abilities. It's also a great way to distribute work across a network of computers instead of depending on just one processor. Even if it's a massive, multi core processor, it can still be more efficient to distribute that workload cross a

network of computers. An example of this would be the many at home projects like SETI at home. These are projects where computers uh in a centralized location received massive amounts of data. So with SETI, it's data about radio frequencies radio waves, and it's generally mostly noise. The vast majority of that information is mostly noise, but there could

be some signal in that noise. That's the whole point at SETI at Home is to look for any potential signal that could have originated away from us, extraterrestrial in nature, aliens, and other words. But to do that, you have to siphon through an awful lot of radio frequencies that either came from Earth and just bounced around, so we're just picking up stuff that we sent out, or they came from natural occurring phenomena like pulsars or some other celestial body.

And to do that would just take a regular computer way too much time. We're constantly gathering more of this information, so you would fall behind very quickly, and you would never be able to catch up because every time you're solving a little bit of that problem, you're getting a hundred times over more information every minute, so you would

never be able to keep up with it. Steady at Home divides that data into chunks and sends it out across its network to people's computers, and their computers will work on parts of those problems while the processor would otherwise be idle. So let's say that you've got your

computer on your CPU is working at capacity. Well, if you had one of these programs on it, you could dedicate a lot of that unused CPU processing power to solving these problems, and you would only be solving a teeny tiny fraction of the overall work, and other computers would be working on the same stuff and sending all that data back to the main center of computers, which would verify by the results and then continue dividing up

the job and sending it out to other computers. This is kind of a method of grid computing or cloud computing. Cloud storage is very similar. You probably have used it. If you haven't used it on your computer, you've definitely used on your smartphone. This is where you store information

on servers that belong to someone else. So you might have photo albums that are sitting on someone else's computer by someone else, I usually mean a corporation like Apple or a Google or Facebook or something like that, and you have instances of those images, perhaps on your smartphone but they also exist on other computers. That's cloud storage, and it's very useful if you want to be able to store more stuff than what your device can hold. That's fantastic. It it's great to be able to turn

to that. But it's also somewhat limited because um, someone else has your your your file, your work, your images, and that means that if they change their policies, you may no longer have access to it, or you may not have full control over it. You may have surrendered control over the things that you generated to the entity that is now storing it. You might be compromising your own privacy. Uh. It is a tricky situation. It's it's got a lot of factors to it, and it's a

big big deal here at IBM. Think recently there was a big news story and I'm going to do a full episode about this later, but there was a big news story about a company called Cambridge Analytica, which used an enormous amount of data that it mind from primarily Facebook, in order to influence elections, to get information about voters and potential voters, and to help push them in a

specific direction when it came to elections. It is an enormous story and developing scandal really, and because of that story, I feel like that's going to end up generating some questions here at the conference as well, not just about the viability of cloud services and data mining, but the

ethics of it. What is ethical, what is not? And how should we codify that, How should we define those ethics and how how do we hold ourselves accountable to ethical standards to make sure that the technologies that we have at our disposal are used in a responsible manner, because some would argue that so far that has not happened,

that we have had multiple instances of violations of privacy security. Uh. Another example of of that sort of thing is all the different data breaches we have seen over the years where companies have not done a good job at protecting customer data. And since that data is very much important to us as individuals, this is a big concern. In fact, that's another area at IBM THINK. It's all about data security. How do we keep that data safe? How do we

protect against cyber attackers? And I'm sure I will see a lot of information about that. Another big area of discussion at IBM THINK is all about infrastructure. How do you incorporate this technology into existing infrastructures. How do you design new infrastructures with this technology? And that could be anything. It could be anything from the infrastructure of a building to a city, to a country, to all sorts of stuff.

But there are a lot of interesting discussions that are on the schedule about the Internet of Things, about integrating this technology into buildings and cities and making it a seamless part of the infrastructure, not an overlay, but an integral part. So when we talk about things like smart homes and smart cities, that's what this is all about.

How can this technology actually improve things, not just be a gimmick, but be something that ends up becoming absolutely necessary, so that it is so seamlessly entwined with our infrastructure that it is, uh, we can't imagine our lives without it moving forward. Obviously, that also raises other questions, mostly pertaining to things that I've already talked about, like privacy

and security. How can you make sure that once you have this infrastructure, it's safe from bad actors who would perhaps try to damage or otherwise leverage that information in malicious ways. So there's a lot to talk about their Internet of Things has brought up a lot of interesting questions about security. You may remember when we had Shannon

Morse on the show. She talked a bit about this, how there are a lot of companies out there that are springing to market with these Internet of Things products that maybe haven't been fully fleshed out, especially when security comes into play. And there's also quantum computing. That's another discussion that's going on here at IBM. Think there's talk about quantum computing emerging from labs and going into practical use. Quantum computers are interesting things. They make use of cubits.

Cubits are quantum bits. A bit obviously for those who have been listening you know all about this. Bits are the basic units of information. They can either be a zero or a one, and that is you can think of as a note or a yes, or an off and an on, and using it's and chaining bits together, you can represent all sorts of different types of information. Ultimately,

computers are processing information in bits. A cubit. A quantum bit can be in superposition, which means it can inhabit all possible states, which means it can be both a zero and a one and everything Technically in between simultaneously. Now that does not necessarily mean anything for every single type of application, but for certain types of computational work,

that would make it much easier to process information rapidly. Specifically, any anything that was using parallel processing, cubits would be pretty good for that. Not all the computational problems would benefit from quantum computing, but the ones that would. The processing would take a fraction of the amount of time. I mean that a fraction of a fraction of the

amount of time that classical computer would take. One of the big things that that cubits could do is make decryption really easy, which is kind of terrifying because encryption is how we keep a lot of data safe. Basically, the way you're you're your base level encryption works is that you you take a really really really big prime number. That meaning that it's a number that is only divisible by itself. You it doesn't have any other factors. You cannot uh find anything else in there too too, uh

divide it by and get a whole integer. So, for example, the number five, you can't divide five by anything other than itself and get a whole integer. Except instead of using the number five, you would use a digit that are a number that is maybe you know, hundreds of digits long, but still prime number. Then you take another incredibly long prime number, a really really really really really long one, and you multiply both of those together, and then you have a product. That product ends up being

the crux of your encryption. If someone tries to break your encryption, they can look at that product and they see what the product is, but they don't know which two numbers you use to multiply together to get that product, so they have to start trying to figure it out, and they do this by going through all the known prime numbers to see if you can divide the product by that prime number, and if so, is the other factor also a prime number. This can take a really

long time. For a classical computer, it takes ages because it has to go through every single possible solution before it can try and, you know, find the right one, or at least every possible one leading up to the right one. With a quantum computer, because you can have cubits in superposition, they can process this information much more quickly.

It's like they're doing numerous processes, numerous solutions simultaneously, because all the cubits can be both zero and one at the same time, So as long as you have enough cubits enough to be able to process the request you have in theory, you could do that kind of computational problem in an instant as opposed to perhaps years or decades or centuries, depending upon the complexity of the computational problem. Now, again, that's only for a specific set of computational problems. For

that set, quantum computers will be amazing. But if you wanted to play a game on a quantum computer, it wouldn't necessarily run any better. In fact, we probably run worse than on a classical computer because you have to have enough cubits two at least equal what the classical computer could do. Cubits also are very tricky keeping bits

in superposition. Keeping anything in a quantum state is tricky because the slightest thing can cause it to decohere too for the whole system to sort of fall apart and then just become a classical computer. And since most quantum computers have a relatively small number of cubits, they end up becoming very dumb computers. If you were to disturb your typical quantum computer and reverted to classical computer status. It would probably be less powerful than your average smart watch.

But there's gonna be a lot of discussion here at IBM think about quantum computing and how it will start to become a practical thing and not just something that's been worked on in laboratories and research facilities. There are a lot of interesting speakers here as well. Obviously IBM has a lot of their experts here on things like cognitive machine learning, artificial intelligence, virtual reality, augmented reality. That

both of those subjects are also represented here at the conference. Uh. They're going to be doing breakout sessions all week long, and I hope to talk to some of them this week. But they're also representatives from other companies that are taking on sessions, uh, Folks from like American Airlines or Nvidia,

or even companies like ticket Master. There's also some celebrities here, uh, some people who are famous for their commentary on science and their contributions to science, like astrophysicist Neil deGrasse Tyson is here, futurist Michio Kaku is here. So they will be giving keynote presentations as well on various subject matters.

I can't wait to hear some of those. I probably won't be able to talk to them because they're booked pretty solid, but I do hope to talk to at least some of the experts in these various fields and get their insight everything from what do they think is cool about their area of study, what are some of the most recent developments that have them excited, How did they get into their field in the first place, And if someone else is interested in that field, what should

they do, how should they pursue it. I want to ask all those sorts of questions. So I hope to present to you guys several bonus episodes this week, all about this conference and the people I talked to and the things I encounter and learn, and hopefully that will all be useful to you guys and you'll enjoy it. And uh, the regular episodes will also publish, so we're going to have a whole bunch of tech stuffs in

a short amount of time. But I also hope to do more of these in the future, where I go to certain events and create special episodes just for those experiences, so I can bring you some more current events and and cool news on top of the normal tech Stuff episodes, so the show is not changing. We haven't completely revamped it. This is just sort of a mini series of specialness,

so you're just getting more of what you love. I hope now I'm gonna focus really heavily on IBM Think this week, but I'll be back also in the office next week doing my regular tech stuff stick, which means I need to hear from you guys. If you have suggestions for topics that you really want to hear more about, send me a message. You can get in touch with me via email. The address is tech Stuff at how stuff works dot com, or you can drop me a

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Wednesdays and Fridays. I stream my recording sessions live so you can watch as I record an episode of tech Stuff and watch as I make silly mistakes and have to stop myself and go back and fix it, and you can even chat with me. There's a chat room in there, and I welcome all people who want to chat and tell me about their favorite episodes of tech stuff or things they would like me to cover. I

welcome that. I hope to see you in there, and I will talk to you again really soon for more on this and bathos of other topics because it how staff works dot com