Speaker 1: Get in touch with technology with tech Stuff from how
Speaker 1: stuff Works dot com. Hey there, this is Jonathan Strickland
Speaker 1: and you are listening to tech Stuff. I am an
Speaker 1: executive producer with How Stuff Works and I love all
Speaker 1: things tech, and today we're going to rejoin the overview
Speaker 1: of cloud computing. What exactly is cloud computing and how
Speaker 1: does it work. We talked a little bit about the
Speaker 1: history of cloud computing in the last episode and kind
Speaker 1: of give some definitions of it. So if you haven't
Speaker 1: listened to that, go check that out. It is a
Speaker 1: bonus episode. This is also a bonus episode. I am
Speaker 1: in Las Vegas, Nevada. Since that's the correct way it's pronounced,
Speaker 1: I've learned, and I am covering Sweet World eighteen. It's
Speaker 1: a big cloud computing conference held by net Sweet and
Speaker 1: so as part of that, I thought I would do
Speaker 1: a couple of episodes about cloud computing, and in this one,
Speaker 1: we're going to look a little bit more into what
Speaker 1: cloud computing can do these days. Turns out it is
Speaker 1: a big, big business, a multibillion dollar business these days,
Speaker 1: and I'll talk a little bit more about that and
Speaker 1: uh enjoy So the ability to connect computers together also
Speaker 1: gave rise to computing models similar to cloud computing. For example,
Speaker 1: there's cluster computing. That's where you couple lots of different
Speaker 1: machines together to work as a unified system. Typically, I
Speaker 1: mean the coupling can be loose or tight. It doesn't
Speaker 1: really it doesn't necessitate tightly coupling machines together, but frequently
Speaker 1: that is the way it's done. And often these machines
Speaker 1: are all in the same location, so they may all
Speaker 1: be in the same data center. Clusters just tend to
Speaker 1: be more tightly connected than other computer models. So there's
Speaker 1: also grid computing. Grid computing is less tightly coupled than
Speaker 1: cluster computing. You may have computers that are part of
Speaker 1: this grid in remote locations there and nowhere close to
Speaker 1: each other, and they're all working together to solve various problems.
Speaker 1: But typically they're working kind of the way a multi
Speaker 1: core processor works. And by that I mean that the
Speaker 1: various computers in the grid are working on different parts
Speaker 1: of a problem. You could have the problem divide it
Speaker 1: up into various segments, and the various grid computers are
Speaker 1: working on each of them are working on a segment
Speaker 1: of that problem. This you can see examples of this,
Speaker 1: and things like the various at home projects like folding
Speaker 1: at Home or set at Home that use user computers
Speaker 1: as uh as units in a grid computing system to
Speaker 1: solve very difficult problems that would normally take a supercomputer
Speaker 1: ages to complete if it were working on it just
Speaker 1: by itself. So both cluster computers and grid computers, both
Speaker 1: of those models are sort of like virtual supercomputers because
Speaker 1: they are grouping the assets of various regular powered machines
Speaker 1: together to make it more than what it was, so
Speaker 1: you can create kind of a virtual supercomputer now. In
Speaker 1: Professor Ramna Chilapa at Emory University here in Atlanta described
Speaker 1: cloud computing as a method that would be defined not
Speaker 1: by technological limits, but rather by economic factors. So, in
Speaker 1: other words, the professor was saying that the costs associated
Speaker 1: with scaling processes would make it imperative for businesses to
Speaker 1: offload that burden by making use of cloud computing providers,
Speaker 1: and that this would in turn create the opportunity for
Speaker 1: such providers to kind of coalesce and become the partner
Speaker 1: these companies needed to grow and do work. In other words,
Speaker 1: he was saying the economic environment out there is such
Speaker 1: that there is a need for services that can provide
Speaker 1: these sort of cloud computing processes and storage. Uh So,
Speaker 1: because there's a need, sooner or later, they're going to
Speaker 1: be businesses filling that need. It's it's like nature of
Speaker 1: whorring a vacuum kind of concept. So cloud computing isn't
Speaker 1: just about technology but also the bottom line, and that
Speaker 1: should not come as a surprise since many factors that
Speaker 1: we frequently associate with technological advances are actually tied closely
Speaker 1: to economic factors. For example, Moore's law, which we frequently
Speaker 1: simplify by saying processing power doubles every two years or so,
Speaker 1: was originally an observation about how economic factors would drive
Speaker 1: the necessity to develop a means to double the number
Speaker 1: of transistors on a square inch of silicon substrate. In
Speaker 1: other words, money makes the world go round. In Evan
Speaker 1: Goldberg founded a company called net Ledger. The company was
Speaker 1: offering up software as a service, which means it's a
Speaker 1: good time to tackle a few common buzzwords associated with
Speaker 1: cloud computing. I'll get back to Mr Goldberg in just
Speaker 1: a minute. Now. For one thing, you'll find a lot
Speaker 1: of as a service buzz terms associated with cloud computing.
Speaker 1: Software as a service is a big one that is
Speaker 1: also known as S A A S, with both s
Speaker 1: as capitalized in the lower A the as in lower
Speaker 1: case UM. And this model, you don't sell software packages
Speaker 1: to a customer. Instead, you set up essentially a subscription
Speaker 1: service so that the customer can access the software. You
Speaker 1: maintain ownership of the software itself. You as the provider.
Speaker 1: You own the software. You're not selling instances of it
Speaker 1: to people. You provide access to the software in return
Speaker 1: for money. Then there's platform as a service that's P
Speaker 1: A A S. Again the P and the S or
Speaker 1: upper case the a's or lower case. That's the case
Speaker 1: for all of these as a service buzzwords. With platform
Speaker 1: as a service, you create a virtual platform for the
Speaker 1: customer for the purposes of developing programs or apps. So
Speaker 1: developers use the virtual platform them when creating whatever app
Speaker 1: they're working on at the time, and it can serve
Speaker 1: as a place where developers share tools and processes to
Speaker 1: help speed up development. There's a variant of this called
Speaker 1: mobile back end as a service that, as the name suggests,
Speaker 1: performs the task of being the back end operations of
Speaker 1: a mobile application. But this is all about creating the
Speaker 1: tools the developer needs in order to create and test
Speaker 1: and then deploy the software they are making. Next, you've
Speaker 1: got the infrastructure as a service or i a a S.
Speaker 1: This is even more robust than the platform as a service,
Speaker 1: and it's really meant for businesses for enterprises. So let's
Speaker 1: imagine that you start your own business and you're making toys,
Speaker 1: and you start out small, maybe you're even working out
Speaker 1: of your own home, and you're doing all of this
Speaker 1: by hand. But your business grows, your demand increases, and
Speaker 1: you are physically incapable of meaning that demand all by yourself.
Speaker 1: So you need to scale up operations. And this kind
Speaker 1: of starts slowly. You know, at first it's not too hectic.
Speaker 1: You can hire on some people and take on that responsibility.
Speaker 1: But if demand continues to increase, you have to take
Speaker 1: the next step. You have to start forming relationships with
Speaker 1: factories to make your toys. And this is both to
Speaker 1: increase the supply as well as to reduce the cost.
Speaker 1: By producing them in bulk, you can reduce the cost
Speaker 1: on a per unit basis to produce and also still
Speaker 1: meet the demand that your customers have. You have to
Speaker 1: manage supply chains. Though you have to make sure all
Speaker 1: the components you need to make your toys are getting
Speaker 1: to the factories. You have to figure out how to
Speaker 1: get the manufactured toys to retailers or to customers. You
Speaker 1: have to manage all the money that's involved in those transactions,
Speaker 1: whether it's payments to services like the factories or accepting
Speaker 1: payments from customers. And for some businesses, this becomes a
Speaker 1: barrier to growth because it's so overwhelming. The transformation of
Speaker 1: small company to mid size company or mid size company
Speaker 1: to large company can be really daunting. So the lure
Speaker 1: of infrastructure as a service is that companies that have
Speaker 1: already designed systems, usually suites of programs that streamlined various processes,
Speaker 1: will share that knowledge and capability with you for a price.
Speaker 1: So you pay a subscription and then you get all
Speaker 1: access to all these tools that may help you do
Speaker 1: things like track your supply chain, to track production and
Speaker 1: even see how things have changed over time. Like there
Speaker 1: are some very sophisticated programs out there that allow you
Speaker 1: to get very granular with data. Data analysis is a
Speaker 1: huge part of cloud computing services because it provides another
Speaker 1: value to the customer if you can say, not only
Speaker 1: will we show you what the UH you know where
Speaker 1: you are at any given time, will show you trends
Speaker 1: and will even start to predict risks that might be
Speaker 1: in place, like maybe we see that because of this
Speaker 1: one element in the supply chain, you're going to have
Speaker 1: a bottleneck in the manufacturing process, which might mean you
Speaker 1: need to be able to communicate out to customers that
Speaker 1: there's currently a shortage of whatever the product is and
Speaker 1: that you will be turning this around as soon as
Speaker 1: you can. Or it might even be something about how
Speaker 1: producing UH your product in one region might be more
Speaker 1: economically viable than another region, even within the same country,
Speaker 1: so that you can get your products to market with
Speaker 1: a lower cost, not just economic, but environmental. It could
Speaker 1: be something as as UH intrinsic as how many miles
Speaker 1: have to be traveled in order to get your product
Speaker 1: to market. So it gets really really complicated, and you
Speaker 1: can see how if you're running your own business, once
Speaker 1: you start layering on these different considerations, it rapidly gets
Speaker 1: outside of most people's comfort zone, so that that's kind
Speaker 1: of the selling point for infrastructure as a service. There's
Speaker 1: a more recent off ring that's called function as a
Speaker 1: service or f a a S. This creates a level
Speaker 1: of abstraction between the developer and virtual machines, so that
Speaker 1: developers only have to worry about creating very narrowly functional
Speaker 1: blocks of code, and essentially they're just creating the instructions
Speaker 1: that sit on top of this service, and the service
Speaker 1: does everything else. Like it removes the necessity to program
Speaker 1: for a specific stack of technology, so the developer just
Speaker 1: has to worry about the code that he or she
Speaker 1: needs to do whatever it is they want to do.
Speaker 1: And typically this kind of service executes upon a real
Speaker 1: world event happening. So something happens in the real world
Speaker 1: that triggers the function, and the function then gets executed
Speaker 1: by this service. So an example it would be, let's
Speaker 1: say you go to a website and there's a form
Speaker 1: you want to fill out in order to get access
Speaker 1: to something. So you fill out the form, you can
Speaker 1: complete it, and you submit it. That submission could be
Speaker 1: the real world event that then triggers this function, this
Speaker 1: hypothetical function I'm making up now. Unlike infrastructure as a
Speaker 1: service that's a perpetual model that means you have to
Speaker 1: keep paying a subscription for as long as you are
Speaker 1: relying upon that infrastructure. It is a day today thing.
Speaker 1: Function as a service typically only triggers a fee whenever
Speaker 1: the function itself gets triggered, so you're not paying every
Speaker 1: single day for this thing to exist. You're paying only
Speaker 1: when people are using it, which means that you can
Speaker 1: save money if it's not something that people are going
Speaker 1: crazy and using all the time. If it is, then
Speaker 1: maybe you have to look at a different model for
Speaker 1: economic purposes. But in general, it means that you can
Speaker 1: save a lot of money this way because it's very lightweight.
Speaker 1: It just exists on the top of the provider's services.
Speaker 1: Now there are other service models out there. I'm getting
Speaker 1: really tired of saying the word service, but it's that's
Speaker 1: just the term used. So there's storage as a service,
Speaker 1: that's what sounds like. That's your online document databases or
Speaker 1: photo albums or file storage or whatever. There's also video
Speaker 1: as a service. There's compute as a service. There's probably
Speaker 1: half a dozen more, but it all comes down to
Speaker 1: the concept of someone else running a process on lots
Speaker 1: of computers. You pay that someone a fee to make
Speaker 1: use of those assets, and it frees you up from
Speaker 1: having to build out your own massive computer system. Hey guys,
Speaker 1: before we go any further, I need to take a
Speaker 1: quick break to thank our sponsor. Let's get back to
Speaker 1: net Ledger and Evan Goldberg. The company ran accounting software
Speaker 1: that had a web enabled user interface, so customers could
Speaker 1: pay to use the service over the Internet rather than
Speaker 1: purchase a full software package and installed on their own machines.
Speaker 1: And one of the selling points of this approaches there's
Speaker 1: never a need to upgrade your software. And by that
Speaker 1: I mean if you buy a software package, like a
Speaker 1: conventional traditional software package, chances are sooner or later there's
Speaker 1: going to be an updated version coming along, which creates
Speaker 1: a lot of pressure on customers. So do you upgrade
Speaker 1: to the latest version? If so, you probably have to
Speaker 1: spend more money to do it. If you don't upgrade
Speaker 1: to the latest version, you might find that some of
Speaker 1: your data you work with becomes incompatible with your older
Speaker 1: legacy systems because it's meant for the newer versions of
Speaker 1: the software. So there could be features that the newer
Speaker 1: version has that your old version does not have and
Speaker 1: it doesn't support, and so you start running into compatibility issues.
Speaker 1: But with software as a service, the provider makes all
Speaker 1: the upgrades to the service on their end on the
Speaker 1: on the back end, So you're accessing the software through
Speaker 1: the Internet, whether it's through the web or through an app,
Speaker 1: whatever it may be. You don't have to worry about
Speaker 1: installing upgrades or patches necessarily. I mean sometimes you do,
Speaker 1: but that's what the app world. If you're talking about
Speaker 1: web browsers, you typically don't. So all you're doing is
Speaker 1: just accessing the service. All of the upgrades are being
Speaker 1: done behind the scenes on the provider side. So one
Speaker 1: of the benefits of this model is that you always
Speaker 1: have access to the latest version of the service as
Speaker 1: long as you remain a customer. Net Ledgers features grew
Speaker 1: the company evolved into net Suite, which better indicated that
Speaker 1: the company was offering up multiple internet based services. A
Speaker 1: short time after Goldberg found a net Suite, there was
Speaker 1: another cloud based services company that popped up called Salesforce
Speaker 1: dot Com. The founders of salesforce dot Com included one
Speaker 1: former Oracle executive named Mark bennie Off and three software
Speaker 1: developers named Frank Dominguez, Parker Harris, and Dave Mollenhoff, who
Speaker 1: had come from Left Coast Software. Their first product was
Speaker 1: a type of sales automation software, and Salesforce rapidly gained
Speaker 1: attention as it was offering up enterprise level services over
Speaker 1: a relatively simple web based interface. The company became incredibly
Speaker 1: successful and was able to weather the dot com crash
Speaker 1: and grow into a multibillion dollar global company. Net Suite
Speaker 1: also made it through the crash and became a much
Speaker 1: larger organization over time, and in two thousand and sixteen,
Speaker 1: the company was acquired by Oracle. I'll talk more about
Speaker 1: both net Suite and Oracle in upcoming episodes. But in
Speaker 1: two thousand two, Amazon made a move to maximize company efficiency.
Speaker 1: The standard practice at the time was to have enough capacity,
Speaker 1: like computer capacity, to do your work while only using
Speaker 1: ten of your capacity. But that's not very efficient. It
Speaker 1: means that nine cent of your computer capacity goes unused,
Speaker 1: and so Amazon began to explore other options that would
Speaker 1: allow them to leverage that. The answer turned out to
Speaker 1: be cloud storage and cloud computing, and in two thousand
Speaker 1: six they expanded this by introducing Amazon Web Services, which
Speaker 1: had a whole bunch of different web based services that
Speaker 1: companies can use, including mechanical turk so quick side note
Speaker 1: about Mechanical Turk because I just love this story. So
Speaker 1: Amazon markets it as a tool that allows customers to
Speaker 1: leverage human intelligence for specific tasks because humans tend to
Speaker 1: be better at certain things than computers. And by better,
Speaker 1: I mean we can do that particular type of work
Speaker 1: much more quickly and reliably. But it can be expensive
Speaker 1: to hire humans to do those tasks, especially if the
Speaker 1: tasks are very simple, and if you only need people
Speaker 1: for a short amount of time, then hiring a huge workforce,
Speaker 1: a temporary workforce, that's that's an an enormous expense, and
Speaker 1: not just money, but also in time. And so Amazon
Speaker 1: Mechanical Turk is an on demand workforce service. It's kind
Speaker 1: of like a group of people employed by Amazon to
Speaker 1: do whatever it is you need them to do with
Speaker 1: your human task issues. And then then once that's, once
Speaker 1: that projects done, they go on to do something else.
Speaker 1: And it sounds a little creepy if I'm being totally honest,
Speaker 1: But the reason why I wanted to talk about them
Speaker 1: was because of what mechanical Turk is a reference to.
Speaker 1: It's actually referencing an old piece of clockwork chicanery. The
Speaker 1: original device was called simply the turk, and it appeared
Speaker 1: to be a clockwork automaton that had the figure of
Speaker 1: a turk sitting at a chessboard. It was made by
Speaker 1: a guy named wolf Gang Fawn Kimberland. He built it
Speaker 1: in the late seventeen hundreds, like in the seventeen seventies,
Speaker 1: and upon casual glance you would think it was a
Speaker 1: robot of ingenious design, capable of defeating even skilled players
Speaker 1: in chess. But in reality, there was a human being
Speaker 1: hidden within the cabinet of the machine who was guiding
Speaker 1: the turk's movements, and a playing chess against a human opponents.
Speaker 1: So really it was just two humans playing chess, only
Speaker 1: one of the humans was hidden out of sight. Just
Speaker 1: I love that story, so I wanted to tell it quickly.
Speaker 1: Around the same time the Amazon was introducing web services,
Speaker 1: Google debuted Google Docs, which was actually based off two
Speaker 1: earlier products. One of them was Google Spreadsheets, which Google acquired.
Speaker 1: They bought it from a company called two Web Technologies,
Speaker 1: and they also acquired a company called Rightly that ended
Speaker 1: up being the basis for the Google Docs part, the
Speaker 1: actual document offering of the product. How those moves began
Speaker 1: to bring cloud computing into the world of the home
Speaker 1: user for the first time. Earlier implementations were almost completely
Speaker 1: focused on business to business operations, which means most of
Speaker 1: us never see it right when we talk about business
Speaker 1: to business stuff, it's interesting because it tells us how
Speaker 1: the stuff we interact with, how that happens, how it
Speaker 1: gets done in the background, but we don't see it directly.
Speaker 1: That's why when we talk about companies like IBM, most
Speaker 1: of us have very little experience working directly with IBM
Speaker 1: products unless it's within the realm of the office, because
Speaker 1: IBM doesn't really make products for the home consumer. The
Speaker 1: same sort of thing here was that cloud computing for
Speaker 1: the longest time was not for the home consumer. It
Speaker 1: was for businesses. But with the emergence of these kind
Speaker 1: of applications, we were starting to see people get access
Speaker 1: to that sort of stuff for the first time in
Speaker 1: a big way. And that's when it became necessary to
Speaker 1: figure out how to explain that technology to people. And
Speaker 1: that's when cloud computing really became a buzzword, and also
Speaker 1: how I came to write sixteen articles about the stuff.
Speaker 1: These days, many companies are involved with cloud services, along
Speaker 1: with net Suite and Salesforce there's Amazon, Google, Microsoft has
Speaker 1: its Azure platform, IBM is a big one, and there's
Speaker 1: tons of others. And the services being offered by these
Speaker 1: companies have grown significantly over time as well. For example,
Speaker 1: i EM offers you the chance to work on a
Speaker 1: quantum computer over the cloud, which still blows my mind.
Speaker 1: We got just a bit more to talk about with
Speaker 1: cloud computing, but before I get into it, let's take
Speaker 1: another quick break to thank our sponsor. One thing I
Speaker 1: feel we should talk about is the different types of
Speaker 1: cloud computing models. And I almost said different types of clouds,
Speaker 1: but I need to be more serious. That's a cloud joke.
Speaker 1: One type is the public cloud model. Now, these are
Speaker 1: services in which all the assets are run by a
Speaker 1: third party. So I talked about Google Docs. That's a
Speaker 1: great example of a public cloud computer model. So if
Speaker 1: your company relies on Google Suite for all of its processes, really,
Speaker 1: then you'll be relying on a public cloud. Then you've
Speaker 1: got private clouds and those models. The company itself, whatever
Speaker 1: company it is that's running the processes, it actually owns
Speaker 1: all the equipment and the services that run on that equipment.
Speaker 1: So let's say you work for a company ABC, The
Speaker 1: private cloud is also owned by ABC, and it's got
Speaker 1: all these different data centers and they're running all these processes.
Speaker 1: So you're still accessing apps and programs and storage that
Speaker 1: exists on other computers. It's just that you work for
Speaker 1: the same company that owns those computers. It's not owned
Speaker 1: by a third party. Now, you would want to use
Speaker 1: the private cloud approach if you had mission critical applications
Speaker 1: that you wanted under your full control and you didn't
Speaker 1: want to entrust that to a third party. So if
Speaker 1: you're handling really sensitive information or the processes you rely upon,
Speaker 1: or really big trade secrets, that might be the way
Speaker 1: you want to go. Or if your needs are just
Speaker 1: so particular that there's not really a provider out there
Speaker 1: that can meet your needs because they're so different from
Speaker 1: what everyone else needs, this would be the way you
Speaker 1: would go. There's a third model called the hybrid cloud
Speaker 1: that merges those two. You have both public and private
Speaker 1: cloud entities. So a company with a hybrid cloud approach
Speaker 1: would have a public cloud stuff to do to handle
Speaker 1: certain tasks, private cloud to handle other tasks, and as
Speaker 1: you might imagine, the more critical elements to the company's
Speaker 1: operations would probably run on the private cloud, while more
Speaker 1: mundane processes might be pushed to the public cloud service,
Speaker 1: and there's some sort of layer of communication and automation
Speaker 1: that allows for the exchange of data between those two
Speaker 1: clouds in a controlled manner so that the output from
Speaker 1: one can work with the information on the other. In fact,
Speaker 1: without that communication channel between the two clouds, you do
Speaker 1: not actually have a hybrid cloud. You would instead have
Speaker 1: what is called a multi cloud approach. You would have
Speaker 1: two distinct cloud services that do not communicate with each other.
Speaker 1: And there are valid reasons to go with a multi
Speaker 1: cloud approach where there is no communication between clouds. So again,
Speaker 1: let's talk about a really big company. Really big companies
Speaker 1: could have departments. They're so large and have such specific
Speaker 1: needs they do not need to communicate directly with other
Speaker 1: departments to get their their work done. Their their work
Speaker 1: may not relate directly with other departments, so in those
Speaker 1: cases it might make more sense to have a separate
Speaker 1: public or private cloud available to those departments either, so
Speaker 1: you can have multi cloud approach that uses either private
Speaker 1: or public or both. The important element here is that
Speaker 1: there isn't that communication channel between them. So as companies
Speaker 1: grow they often find that the departments inside them end
Speaker 1: up being larger than the original company was when it
Speaker 1: first got started, and at that size, finding processes that
Speaker 1: work and scale is critical. Now, at the top of
Speaker 1: the show, I mentioned that one of the big concerns
Speaker 1: about cloud computing was security, and with so many companies
Speaker 1: in trusting data and processes to third parties, security is
Speaker 1: an absolute necessity without a company would go out of business.
Speaker 1: Third parties would collapse if they were found to be insecure,
Speaker 1: and like I said, that could create a domino effect,
Speaker 1: a disastrous result among the third party's customer base. So
Speaker 1: typically cloud computing applications rely upon profiles that require authentication
Speaker 1: through some means, most commonly through a user name and password.
Speaker 1: There are cloud based services that actually managed this as well.
Speaker 1: It's called identity as a Service or i d a
Speaker 1: a s NOW. Those services and not only managed user
Speaker 1: log in information, but can designate different levels of access.
Speaker 1: So for example, the head of a department might need
Speaker 1: administrator level access to that department's data, while an employee
Speaker 1: further down the organizational chain might only require limited access.
Speaker 1: So you need to have a way of denoting that
Speaker 1: so that you don't have some low level employee suddenly
Speaker 1: have access to say, everyone's pace stub. That would be bad.
Speaker 1: Cloud providers take security seriously and it shows because they
Speaker 1: might prove to be attempting target to hackers. But the
Speaker 1: big providers of technically been more resilient to attacks than
Speaker 1: private enterprise centers have been in the past. The weak
Speaker 1: spot tends not to be the data centers or the
Speaker 1: providers or the security around them, but rather that tenuous
Speaker 1: link between provider and customer. It's that old problem where
Speaker 1: you find out that the weakest link in the security
Speaker 1: system is the people. It's always the people, because people
Speaker 1: might choose really bad passwords, which creates a security vulnerability
Speaker 1: that's difficult to protect against. You might use something like
Speaker 1: two factor authentication to help fight against that problem. That
Speaker 1: can help. That's where a person needs not only a
Speaker 1: password but some other form of authentication like a physical
Speaker 1: token in order to access the services. And at that
Speaker 1: stage and an authorized person would need to get hold
Speaker 1: not just to the password, but also of some sort
Speaker 1: of physical object a token or a cell phone or
Speaker 1: something that the target owns. So it doesn't eliminate risk,
Speaker 1: but it cuts down on it significantly. There are a
Speaker 1: lot of other buzzword like topics that tie in with
Speaker 1: cloud computing. There's big data that's all about how to
Speaker 1: leverage the huge amounts of information that's being mined on
Speaker 1: a daily basis. How do you do that in a
Speaker 1: way that's meaningful and efficient. That's frequently associated with cloud computing.
Speaker 1: Artificial intelligence and machine learning also get thrown into the mix.
Speaker 1: There are services that use machine learning to sort through
Speaker 1: big data on the cloud, for example. That way you
Speaker 1: get a big, heaping handful of buzzwords all at once,
Speaker 1: that's all in an effort to produce a particular result.
Speaker 1: Sometimes you use machine learning and big data for legitimate
Speaker 1: research purposes that can further our scientific understanding. Sometimes it's
Speaker 1: for less lofty reasons. It might be to find the
Speaker 1: best way to advertise to different groups of people in
Speaker 1: order to more effectively sell products to them. Sometimes you
Speaker 1: can get downright creepy, which as the recent Cambridge Analytical story.
Speaker 1: Have to do a full episode about what that was
Speaker 1: all about. Why raised such a fuss at some point,
Speaker 1: But that's that's for another time. It's a complicated issue
Speaker 1: that I can't just if I ran into it here,
Speaker 1: we would easily go another hour, and UH actually have
Speaker 1: to be at the conference keynote in a little bit,
Speaker 1: so I can't do that now. At the top of
Speaker 1: the show, I also mentioned that there can be an
Speaker 1: issue about ownership, like who owns the data on these services,
Speaker 1: and usually there's some pretty complicated terms of service that
Speaker 1: lays all this out, where you're essentially granting a license
Speaker 1: to the third party to have a sort of physical
Speaker 1: possession of all that information. And the reason that's necessary
Speaker 1: is because the way cloud computing works, the way you
Speaker 1: can access it through different devices and in different places,
Speaker 1: you have to give the company permission to be able
Speaker 1: to show that information. So if I store a file
Speaker 1: on my personal computer and I'm the only one who
Speaker 1: has physical access to my personal computer, I'm reasonably assured
Speaker 1: that I'm the only person who can see it. There's
Speaker 1: no real permissions that need to be made or anything
Speaker 1: like that. If I'm storing my data on someone else's
Speaker 1: machine and I want it to be clear that still
Speaker 1: my data even though it's sitting on someone else's machine,
Speaker 1: then we have to draw up these agreements. And one
Speaker 1: of those agreements might be well, if you request to
Speaker 1: see and work with this data, I need to have
Speaker 1: the permission to actually serve it to you. If you
Speaker 1: choose to share it with someone, I have to have
Speaker 1: the permission that allows me to share it so that
Speaker 1: I'm not legally responsible If you later on say, oh
Speaker 1: I didn't I don't want that person to have it. Well,
Speaker 1: if you chose to share it, by the nature of
Speaker 1: the cloud computing model, then you know that that's what happened,
Speaker 1: that it got shared. So it's largely to protect the
Speaker 1: third party providers, but it's also just this very complicated
Speaker 1: language that gives them the permission they need to do
Speaker 1: the business as they do it. So it can get
Speaker 1: pretty complicated. On casual glance, it looks like you're signing
Speaker 1: over all your data to another party, um, and you're
Speaker 1: not really doing that, at least not in most agreements.
Speaker 1: Any ethical agreement would not include that sort of information
Speaker 1: in it. But that's pretty much the overview of cloud computing.
Speaker 1: Will conclude our story on that. It's a fascinating model
Speaker 1: that has its roots all the way back in the
Speaker 1: early days of of of mainframe computers. I hope you
Speaker 1: found this episode helpful and understanding what the heck that
Speaker 1: buzzword meant in the first place. If you have suggestions
Speaker 1: for future episodes of tech Stuff, whether it is to
Speaker 1: explain something within tech, to talk about a specific technology
Speaker 1: or a company or a person in technology. Maybe you
Speaker 1: have someone in mind that I should interview or have
Speaker 1: on as a guest. Reach out and let me know.
Speaker 1: You can send me and email the addresses tech Stuff
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Speaker 1: record this show on Wednesdays and Fridays. You can actually
Speaker 1: watch me record it live in the studio. You can
Speaker 1: just go to the link Twitch dot tv slash tech Stuff.
Speaker 1: You'll see the schedule there, and there's a chat room
Speaker 1: and everything. You can join in be part of the crowd.
Speaker 1: And I look forward to seeing you and I'll talk
Speaker 1: to you again really soon. For more on this and
Speaker 1: bouthands of other topics, is it how staff works dot
Speaker 1: com