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 your host, Jonathan Strickland. I'm an executive producer with
Speaker 1: how Stuff Works in a love all things tech, and
Speaker 1: this is the second episode about natural language processing an LP,
Speaker 1: also natural language understanding and LU. The two are related.
Speaker 1: With that describes the technologies and processes we use to
Speaker 1: give machines the ability to interpret and respond to language
Speaker 1: the way we use it, so not just understanding our input,
Speaker 1: but also generating output that still follows the rules of
Speaker 1: various languages. So it's all about getting machines to conform
Speaker 1: to us rather than the other way around. If you
Speaker 1: have not listened to the episode immediately before this one,
Speaker 1: you should do that. But as I'm about to pick
Speaker 1: up where I left off, which was just after our
Speaker 1: PA pulled the plug on its Speech Understanding research project,
Speaker 1: and the research under the r PA project had shown
Speaker 1: that NLP was an even more challenging problem than had
Speaker 1: previously been anticipated. Even the simplest approaches were creating enormous
Speaker 1: demands on both the work programmers had to do to
Speaker 1: build a system out and the processing the system would
Speaker 1: have to rely upon in order to interpret language. Work
Speaker 1: in the late nineties seventies ranged into psychology. NLP researchers
Speaker 1: felt a system needed to be able to identify a
Speaker 1: user's needs and goals in order to function properly, had
Speaker 1: to understand not just the surface level meaning of a phrase,
Speaker 1: but the underlying meaning of linguistic expressions as well. Only
Speaker 1: then could you have a computer system that could collaborate
Speaker 1: with a human being in a seamless way. So, in
Speaker 1: other words, what they're saying is that you could translate
Speaker 1: stuff for interpret stuff word by word, but unless you
Speaker 1: have an understanding of what the person is trying to
Speaker 1: actually accomplish, chances are the results you're going to get
Speaker 1: back are not going to be as relevant as they
Speaker 1: could be. And so that was where the psychology was
Speaker 1: starting to take form. By the early nineteen eighties, which
Speaker 1: marks the third phase of n LP development. According to
Speaker 1: the researcher Karen spark Jones, who I talked about in
Speaker 1: the last episode, researchers were coming to terms with the
Speaker 1: idea that a scalable NLP system that relied upon the
Speaker 1: old methods of building lexicons and syntax rules just was
Speaker 1: not practical It required far too much work on the
Speaker 1: front end when designing a system to make a general
Speaker 1: purpose in LP application. The problem was just way too
Speaker 1: big to take that approach. Even with relatively narrow implementations
Speaker 1: like designing a system that would parse technical documents, you think,
Speaker 1: all right, well, the language used in technical documents is
Speaker 1: a subset of the language you would encounter in the
Speaker 1: quote unquote real world. Even with those use cases, the
Speaker 1: old methods were proving to require far too much investment
Speaker 1: in time, money, and effort on the design front. Spark
Speaker 1: Jones identifies the key focus during this phase as being
Speaker 1: on grammar and logic. During this phase, researchers developed several
Speaker 1: different grammar types. Now, grammars are sets of rules for
Speaker 1: analyzing and formalizing language. I would love to go into
Speaker 1: more detail about the different grammars that were developed during
Speaker 1: this phase or adopted for computational models, but honestly, it
Speaker 1: gets really, really heavy, really quickly. It gets extremely technical,
Speaker 1: though not on a technological side, but more on the
Speaker 1: linguistic side. And suffice it to say that a lot
Speaker 1: of research and debate centered around what is the best
Speaker 1: way to arrive at the meaning of language? How do
Speaker 1: we get to that? How how can you ascertain it
Speaker 1: is meant by what was spoken or what was written.
Speaker 1: The grammars were meant to direct NLP models to analyze
Speaker 1: language in different ways that were computationally viable and that
Speaker 1: wouldn't require the laborious process of programming everything in a
Speaker 1: word for word style. Another big area of focus at
Speaker 1: this time was on generation, meaning creating models that would
Speaker 1: allow machines to generate natural language responses to users, including
Speaker 1: responses that were extended, long examples of discourse, not just
Speaker 1: a quick message. While machines wouldn't be able to think,
Speaker 1: they would be able to put together a more sophisticated
Speaker 1: response than chatbots like Eliza that I mentioned in the
Speaker 1: last episode could manage. So the idea being, how can
Speaker 1: we make a machine that can communicate results to a
Speaker 1: person in a way that just makes sense. It's almost
Speaker 1: as if a normal human being is chatting with you.
Speaker 1: But as we understand it, it's very difficult to do
Speaker 1: this on an extended basis. You can do it for
Speaker 1: responses to individual queries, but when you start trying to
Speaker 1: create something that can carry on an actual conversation, that's
Speaker 1: where things start. To break down. In the nineties, work
Speaker 1: in n LP focused on representing words as as mathematical vectors.
Speaker 1: Many words are related to one another, so for example,
Speaker 1: hotel and motel are related. They don't mean exactly the
Speaker 1: same thing, but they mean very similar things. Then you
Speaker 1: have a term like bet and breakfast. A bet and
Speaker 1: breakfast is similar again to a hotel or a motel.
Speaker 1: It's a different thing, but it's related. So these words
Speaker 1: have similarities. They also have differences between them, but they're
Speaker 1: all more similar to each other than if I used
Speaker 1: a different word like hospital. A bet and breakfast is
Speaker 1: more like a hotel or a motel than it is
Speaker 1: a hospital. So in other words, we can group words
Speaker 1: together into vector spaces and calculate the quote unquote distances
Speaker 1: between vectors, and that determines degrees of similarity, and this
Speaker 1: is very helpful for both translation and natural language processing.
Speaker 1: There are ways to do this that even take context
Speaker 1: into account. And this relates back to what was being
Speaker 1: uh suggested by Warren Weaver when I talked about that memorandum.
Speaker 1: There's a model called skip Graham, which is essentially what
Speaker 1: he was talking about. This model takes a window of
Speaker 1: words surrounding each word in a sentence to determine context,
Speaker 1: so it's not looking at it just from a word
Speaker 1: toward basis. Let's say that I write a phrase and
Speaker 1: it says, I'm going to the bank to make a withdrawal. Now,
Speaker 1: the word bank can actually refer to a couple of
Speaker 1: different things. Right, it could be a financial institution, which
Speaker 1: is obviously what I do mean when I say that sentence.
Speaker 1: That it could also mean the area right next to
Speaker 1: a river, right the bank of a river. The Skip
Speaker 1: Graham model would take each word in that sentence and
Speaker 1: then part with a few other words that are close
Speaker 1: by to determine the meaning of the phrase. So it's
Speaker 1: looking at I'm going to the bank to make a
Speaker 1: withdrawal for bank, it might say to bank, the bank,
Speaker 1: to bank, make bank a bank withdrawal bank. By looking
Speaker 1: at these pairings, the system can figure out from context
Speaker 1: that the bank I'm talking about is probably a financial institution.
Speaker 1: I'm probably not making a withdrawal from a river bank.
Speaker 1: So it's a way of machine systems figuring out the
Speaker 1: meaning of a phrase through contextual cues by using this
Speaker 1: windowed approach. And again, Warren weaver Back had proposed such
Speaker 1: a thing. The vector approach would become more important as
Speaker 1: computer scientists made advances in neural networks. That approach also
Speaker 1: made machine translation much more effective because it no longer
Speaker 1: looked for word for word matches, but rather matches meaning
Speaker 1: based on vectors and probabilities. That's really important because once
Speaker 1: you determine the meaning of a phrase in one language,
Speaker 1: then you can look for a phrase in another language
Speaker 1: that most closely resembles the meaning of the original. Uh.
Speaker 1: This is the art of translation. A real translator, someone
Speaker 1: who's translated from one language to another, is probably not
Speaker 1: doing so word for word. Rather, they're doing meaning for
Speaker 1: meaning to make certain that the intent of what is
Speaker 1: being communicated gets through, not just the vocabulary. The ninety nineties,
Speaker 1: which sparked Jones identifies as the fourth phase of NLP
Speaker 1: development that would be the final phase in her report,
Speaker 1: saw a more concentrated focus on lexicons over syntax, and
Speaker 1: it also saw more practical applications of natural language processing,
Speaker 1: as well as leveraging the Worldwide Web to help train
Speaker 1: natural language processing models. There was an a rich source
Speaker 1: of natural language on the Worldwide Web. Pretty much every
Speaker 1: permutation you could imagine from people who are very careful
Speaker 1: and the way they construct sentences and paragraphs to people
Speaker 1: who are much more cavalier in the way they use language,
Speaker 1: whether purposefully or otherwise. And also that report from spark
Speaker 1: Jones again is dated October two thousand one, so that's
Speaker 1: where her work stops for that particular report. But nearly
Speaker 1: two decades have passed since that time, So in that time,
Speaker 1: what has changed. Well, I would argue we are now
Speaker 1: in a new phase of NLP development, one marked largely
Speaker 1: by the rise and a few key technologies. One of
Speaker 1: those is cloud computing. Cloud computing has removed the necessity
Speaker 1: to build in complex capabilities in end machines like a
Speaker 1: smartphone or a computer terminal, So an organization can create
Speaker 1: a cloud infrastructure which consists of powerful machines and data basis.
Speaker 1: Those machines could be real, they could be virtual. Virtual
Speaker 1: machines are hosted on real hardware, but they're running virtual
Speaker 1: implementations of various operating systems. So these machines provide the
Speaker 1: processing power and they house the systems that are necessary
Speaker 1: to parse language and respond appropriately, So you can think
Speaker 1: of it as the brains of natural language processing. They
Speaker 1: all exist on these very powerful computers that are in
Speaker 1: data centers. The widespread availability of the Internet and the
Speaker 1: fact that it's pretty easy to stay connected in many
Speaker 1: parts of the world make this possible. So the end
Speaker 1: user feels like the capabilities are actually housed on whatever
Speaker 1: device he or she is using, like if it's a
Speaker 1: smartphone or a computer, But in reality, all the work
Speaker 1: is actually taking place potentially thousands of miles away in
Speaker 1: a data center, and it's just being sent to you.
Speaker 1: The the queries are being sent to the center and
Speaker 1: the responses are being sent back to your device. Another
Speaker 1: big development that has helped signific piquant LEE is the
Speaker 1: pairing of artificial neural networks and as well as a
Speaker 1: deep learning the process of deep learning, so a neural
Speaker 1: network processes information in a way similar to how our
Speaker 1: brains do it. Every node in a neural network represents
Speaker 1: a neuron and it executes UH an operation upon data
Speaker 1: and then hands off this data, which has now been
Speaker 1: altered it's been transformed by this operation, to another layer
Speaker 1: of neurons with a network which do further processing, and
Speaker 1: so on and so forth. The system as a whole
Speaker 1: can evaluate calculations and assign confidence levels to them. Deep
Speaker 1: learning passes information through numerous layers to transform data and,
Speaker 1: in the context of natural language processing, extract meaning from
Speaker 1: that information. Now I've got a bit more to say
Speaker 1: about natural language processing in general, and then after that
Speaker 1: I'm going to transition to talk about recent implementations like Sirie, Alexa,
Speaker 1: Google Assistant, and Cortana. But first let's take a quick
Speaker 1: rake and thank our sponsor. In two thousand and sixteen,
Speaker 1: Google announced a system that could analyze syntax and recognize
Speaker 1: the various elements of a sentence, including verbs, nouns, adjectives,
Speaker 1: and other components. The system's name is sort of a
Speaker 1: snapshot of the zeitgeist of It was called and I'm
Speaker 1: not making this up Parsi mcpart's face. It really was.
Speaker 1: This is a parser, a a software that is meant
Speaker 1: to analyze inputs and determine what the relationships are between
Speaker 1: various components within the input. So it's parsing out the
Speaker 1: meaning of a phrase by looking at the relationship between
Speaker 1: all the different components. It was designed specifically for English
Speaker 1: language inputs. In that same announcement, Google unveiled and open
Speaker 1: source neural network framework called syntax net syntax Net tags
Speaker 1: every word in an input with a part of speech tag,
Speaker 1: and the tag describes the purpose of that word, what
Speaker 1: purpose does it serve within the sentence, within the context
Speaker 1: of that input. So, for example, it might be the
Speaker 1: subject of the sentence, or it could be an object
Speaker 1: of the sentence, or it might be the action the
Speaker 1: root the user wishes to perform upon the object. So
Speaker 1: if it identifies a verb that tends to be the
Speaker 1: root of the command. The system also determines the syntactic
Speaker 1: relationship between all the words, so not just what each
Speaker 1: word's purpose is, but how that word relates to all
Speaker 1: the other words within the input, and then it creates
Speaker 1: a dependency tree which illustrates which words depend upon others.
Speaker 1: Syntax Net also makes use of beam search. That's the
Speaker 1: strategy I talked about in the Speech Recognition podcast a
Speaker 1: couple of podcasts go so that is to help eliminate ambiguity.
Speaker 1: As sentence length increases, the number of possible interpretations of
Speaker 1: that sentence also increases dramatically. Right, the more complicated a
Speaker 1: sentence is, the easier it is to misinterpret what that
Speaker 1: sentence means, especially if you're looking at it from the
Speaker 1: perspective of a machine, So how does the computer know
Speaker 1: which interpretation is the right one? Syntax net takes a
Speaker 1: sentence and starts to parse it, beginning with a left
Speaker 1: to right approach for English, so it starts at the
Speaker 1: beginning of the sentence and works its way through. Essentially,
Speaker 1: it creates a hypothesis as to how the words relate
Speaker 1: to each other. But as it goes along, it detects
Speaker 1: possible alternate interpretations, so it starts to assign a probability
Speaker 1: score to each interpretation, Essentially how sure it is that
Speaker 1: this is on the right track. And it will keep
Speaker 1: multiple possible answers as it parses, so it doesn't toss
Speaker 1: them aside immediately. It says, all right, I'm right now,
Speaker 1: I'm pretty sure answer A is correct, but I'm going
Speaker 1: to hold on to B and C just in case. Now,
Speaker 1: if one interpretation has a particularly low score and there
Speaker 1: are several other potential interpretations that have higher scores, the
Speaker 1: system will discard the low score with the assumption that
Speaker 1: it just can't be the right answer just doesn't make
Speaker 1: sense in well formed text, that is informal text, something
Speaker 1: that has been written in a very formal approach, PARSI
Speaker 1: mcpars face does a pretty good job. In fact, a
Speaker 1: really good job has an accuracy rating that's approaching the
Speaker 1: level of a human linguist that is trained in parsing sentences.
Speaker 1: Humans who have that kind of training average at around
Speaker 1: scent accuracy, so PARSI mcpars faces right right behind them.
Speaker 1: But the key phrase there is well formed text. If
Speaker 1: you present parsi mcpar's face with more lucy goosey language,
Speaker 1: such as what you might find on your average Internet website,
Speaker 1: which I know was redundant, parsing mcpars face has a
Speaker 1: much more modest nine success rating. It's still impressive, but
Speaker 1: it's a significant drop in accuracy. Now, these sort of
Speaker 1: tools have been used in various Google products for a while,
Speaker 1: not just Google Assistant, which is the one that people
Speaker 1: tend to think about because it's the one we interact
Speaker 1: with when we are speaking to Google, but also in
Speaker 1: stuff like Gmail. If you've used Gmail and you've noticed
Speaker 1: that sometimes you get automated responses popping up that you
Speaker 1: can choose as an option, So instead of writing an email,
Speaker 1: you just select sounds good or I'll see you then,
Speaker 1: or whatever it may be. Then you have seen this
Speaker 1: technology at work, or at least you've seen the product
Speaker 1: of its work. Those automated responses are the result of
Speaker 1: a natural language understanding system that's parsing that email, identifying
Speaker 1: whatever the salient points are in the message, and then
Speaker 1: generating what are hopefully logical responses to it, so you
Speaker 1: can just choose that instead of taking the time to
Speaker 1: actually type something in. One of the key elements in
Speaker 1: natural language understanding is creating machines that can communicate with
Speaker 1: us and explain how they arrived at a certain result. Now,
Speaker 1: this falls into the concept of transparency, which is really
Speaker 1: important when we were talking about artificial intelligence. There's a
Speaker 1: real fear that AI and neural networks are creaning toward
Speaker 1: a black box scenario, and a black box describes any
Speaker 1: system where the workings of the system are hidden from
Speaker 1: our view. We cannot see how something works, and so
Speaker 1: we can only make guesses as to what's going on.
Speaker 1: I know a lot of gear heads who are exasperated
Speaker 1: with the way vehicle manufacturers are creating more of their cars, trucks,
Speaker 1: and other vehicles with systems that aren't easily accessible or modifiable.
Speaker 1: They consider those cars to be black boxes. It makes
Speaker 1: it much harder to work on a vehicle if you
Speaker 1: don't have the proprietary tools and knowledge that are specifically
Speaker 1: for that system. Now take that concept and apply it
Speaker 1: to AI, and it gets pretty scary pretty fast, particularly
Speaker 1: since we're relying on AI to do some important stuff
Speaker 1: like drive cars, make stock option deals, or help with
Speaker 1: healthcare issues, and so one area of work focuses on
Speaker 1: giving machines the capability to explain themselves, not just to
Speaker 1: provide an answer, but explain why they came up with
Speaker 1: that answer. So imagine a chess playing computer. It's playing
Speaker 1: a game of chess and it makes a move. Then
Speaker 1: imagine being able to ask the computer, why did you
Speaker 1: make that move, and then the computer could actually answer
Speaker 1: the question, explaining the logic behind the move it made.
Speaker 1: Now extend that concept to all sorts of different AI applications.
Speaker 1: If an AI stock trader suddenly buys up a ton
Speaker 1: of stocks, you might want to know exactly what prompted
Speaker 1: that decision, why did it make that purchase? And you
Speaker 1: can easily imagine situations in which you'd want to know
Speaker 1: why a machine behaved the way it did. Why did
Speaker 1: an autonomous car choose a particular route. Why did a
Speaker 1: healthcare program suggest a particular diagnosis Without getting those answers,
Speaker 1: we're just putting our faith into machines blindly, and giving
Speaker 1: a computer the ability to generate meaningful and equally important
Speaker 1: relevant explanations would be extremely helpful. So what are some
Speaker 1: of the uses of natural language processing technology. Well, one
Speaker 1: fairly simple application is in spelling and grammar checking software.
Speaker 1: If you've used a word processing program over the last
Speaker 1: few years the last couple of decades, chances are you're
Speaker 1: familiar with automatic real time spell check and grammar check features.
Speaker 1: This is possible because of the work that has been
Speaker 1: done in natural language processing. Spell check needs to take
Speaker 1: into consideration not only if a word is spelled correctly,
Speaker 1: if a word matches a word that's in the computer's lexicon,
Speaker 1: but also if it's the right word for that instance.
Speaker 1: In English, we have a lot of hominems. Those are
Speaker 1: words that sound the same aim, but I have different meanings.
Speaker 1: Now you can have hominem's that are spelled exactly the
Speaker 1: same way, and those really aren't a problem because the
Speaker 1: reader can pick up on what meaning you intended through context. Though,
Speaker 1: if you're using natural language processing to do a translation,
Speaker 1: then the NLP system needs to be able to determine
Speaker 1: which meaning the original author intended. In my earlier example
Speaker 1: about making a withdrawal at the bank, there's a hominem
Speaker 1: you know, to two versions of bank, but they mean
Speaker 1: two different things. I could also talk about bank as
Speaker 1: in the sense of a verb, as in banking off
Speaker 1: of something, but you get the point. There are also
Speaker 1: hominem's that sound the same but are spelled differently, and
Speaker 1: they have different meanings as well. So for example, they
Speaker 1: dreaded too as in t O two as in t
Speaker 1: O O, and two as in two combo. Those are
Speaker 1: three words with three different applications, three different spellings. A
Speaker 1: good spell check algorithm will be able to determine if
Speaker 1: you've used the correct one in any instance. So if
Speaker 1: you say that's two sweet, that's too sweet, but you're
Speaker 1: using the number too just in word form, the spell
Speaker 1: check will give you the old heads up and say
Speaker 1: I think you meant t O O not t w O.
Speaker 1: Fun fact, I typed that sentence into Google Docs and
Speaker 1: it said you're totes fine. BRA didn't notice it at all.
Speaker 1: Grammar checkers have to be able to analyze sentence structure
Speaker 1: and word choice and compared against the grammar program for
Speaker 1: the system. This might also help determine if the word
Speaker 1: you use was the correct one. So, for example, affect
Speaker 1: versus effect, Affect is a verb you affect something. Effect
Speaker 1: is usually a noun. It's typically the result of some action.
Speaker 1: So I could affect a drum, which is a dumb
Speaker 1: thing to say, and the effect might be that the
Speaker 1: sound I played hurt your ears. Now, if you spell
Speaker 1: the word correctly and the spell checker is only comparing
Speaker 1: the words you type against a lexicon to see if
Speaker 1: there's a match, you might not get an indication that
Speaker 1: anything is wrong because the computer system is saying, well,
Speaker 1: that word is spelled correctly. It doesn't realize it's the
Speaker 1: wrong word. But if it has a way of checking grammar,
Speaker 1: it can also make sure you're using the right word
Speaker 1: in the right context. Search engines such as Google use
Speaker 1: natural language processing to determine what it is you're looking
Speaker 1: for right, So when you're typing in a search and
Speaker 1: you hit the search button, you might get a little
Speaker 1: uh notification that says, maybe you meant this other thing,
Speaker 1: or maybe you need to search for this terminology. That's
Speaker 1: a useful feature since not everyone thinks of search the
Speaker 1: same way. I could tell a dozen people to go
Speaker 1: on Google and pull up information about Benjamin Franklin and
Speaker 1: the story about the kite, and those folks might go
Speaker 1: and perform their searches in twelve different ways. But the
Speaker 1: search engine's job is to return the best results based
Speaker 1: on the query, which means it needs to suss out
Speaker 1: what the searcher is actually looking for. So even if
Speaker 1: the twelve people all type twelve different ways of looking
Speaker 1: up this information about Benjamin Franklin and the kite story,
Speaker 1: it should respond with the most relevant results. And maybe
Speaker 1: people get slightly different search results based upon the query,
Speaker 1: but they should be more or less the same. And
Speaker 1: it can also look out for you. It could give
Speaker 1: you suggestions for search terms, should you use an incorrect
Speaker 1: spelling or you approximate a spelling, or something like that.
Speaker 1: One of the areas of opportunity for natural language processing
Speaker 1: applications in the near future is handling the massive amounts
Speaker 1: of information in big data applications. So, for example, a
Speaker 1: lawyer might want to search historical legal results using natural
Speaker 1: language to look for precedents that might help his or
Speaker 1: her case in the courtroom. A pharmaceuticals company might need
Speaker 1: to search information about clinical trials, doctors, notes, patient testimonials,
Speaker 1: and related information. And the amount of information represented by
Speaker 1: big data is truly astounding. It's enormous. It's way too
Speaker 1: much for any human to sort through. So developing a
Speaker 1: method for computers to parse a query and return relevant
Speaker 1: results is highly desirable. For a computer to understand that
Speaker 1: context understanding and air quotes and being able to give
Speaker 1: you results based upon your questions, that would be incredibly
Speaker 1: valuable for lots of different industries. And we started off
Speaker 1: talking about machine translation at the early stages of natural
Speaker 1: language processing. That's still a big area of research. Now
Speaker 1: you can get real time translation tools. You can use
Speaker 1: devices to translate from one language to another in real settings,
Speaker 1: including written languages like signs. You can just hold a
Speaker 1: camera up and get an an English translation of a
Speaker 1: sign that's written another language, and of course vice versa.
Speaker 1: That tends to be marketed as a tool for travelers,
Speaker 1: but it really shows the amazing progress we've made in
Speaker 1: natural language processing from the old days of word for
Speaker 1: word models for machine translation that we're made back during
Speaker 1: the Cold War now we've still got a far away
Speaker 1: to go with natural language processing. We've seen some incredible
Speaker 1: improvements over the past few years, but machines still don't
Speaker 1: actually understand what we're saying or what we're writing, not
Speaker 1: on a conscious level anyway. Instead, they are able to
Speaker 1: refer back to rules, either explicitly stated as in the
Speaker 1: older NLP models, or those arrived at through deep learning.
Speaker 1: Now I'm going to take a quick break, but when
Speaker 1: we come back, I'll talk a bit about the history
Speaker 1: of the voice assistance we all know and love. But first,
Speaker 1: here's another word from our sponsors. All right, So now
Speaker 1: we understand a bit about the technologies that make voice
Speaker 1: assistance possible, specifically speech recognition and natural language processing. There's
Speaker 1: obviously a lot more than that, uh the system. The
Speaker 1: system can obviously process our requests or commands and return
Speaker 1: a result using more traditional computational processes. So while the
Speaker 1: interpretation side is on speech recognition and natural language processing,
Speaker 1: there's still a lot of regular computation work that has
Speaker 1: to happen for a a personal assistant, digital assistant, a
Speaker 1: voice assistant, whatever you want to call them, to be
Speaker 1: able to respond to you. So let's take a quick
Speaker 1: stroll through the history of the major voice assistants out there,
Speaker 1: and I'm going to cover these in the order they
Speaker 1: were introduced to the public more or less, which means
Speaker 1: our very first voice assistant that will be covering in
Speaker 1: this because I'm only focusing on the really big ones. Uh.
Speaker 1: There are lots of small ones out there, but I'm
Speaker 1: looking at the ones everyone's heard about, So that means
Speaker 1: the first one we get to talk about is Apple's Sirie.
Speaker 1: Apple unveiled Sirie on April fourteen, two thousand eleven, and
Speaker 1: to be fair, Sirie existed before this. It was not
Speaker 1: an Apple creation. Syria was actually an app produced by
Speaker 1: an into and developer company called Sirie Incorporated, but Apple
Speaker 1: gobbled up that company in and brought them in house.
Speaker 1: And Apple had previously relied upon another speech recognition program
Speaker 1: called voice over, which had been used in Mac products
Speaker 1: and all iPhones since the iPhone three GS. Siri would
Speaker 1: become available starting with the iPhone for s In this announcement,
Speaker 1: Apple pointed out that earlier implementations of voice commands required
Speaker 1: users to learn the syntax of the system. You had
Speaker 1: to follow a very specific set of rules in order
Speaker 1: to get anything to work. So you give a command
Speaker 1: defined by the system. So for example, you might say
Speaker 1: call mom or play once in a lifetime. You had
Speaker 1: to do this very structured approach to whatever it was
Speaker 1: he wanted to do. But that requires the user to
Speaker 1: actually adhere to rules created by the architects of the system. Right,
Speaker 1: So Sirie was meant to be different. It was meant
Speaker 1: to be able to understand what you wanted on your terms,
Speaker 1: not based off a strict set of rules. Apple said
Speaker 1: that Siri would be able to interpret what you meant
Speaker 1: and would return relevant information to you in response. In
Speaker 1: the unveiling, they said that Siri is quote, your intelligent
Speaker 1: assistant that helps you get things done just by asking
Speaker 1: end quote. During that demonstration, they showed off how Siri
Speaker 1: could parse different phrases that had the same underlying meaning
Speaker 1: that the example they gave originally was was the weather today,
Speaker 1: and then they asked that same question five or six
Speaker 1: different times. Scott Forstall, vice president over at Apple, showed
Speaker 1: off how you could get the same weather information by
Speaker 1: asking it in these different ways. Then they showed off
Speaker 1: how Siri could interoperate with other apps, such as Apple's
Speaker 1: maps feature or through a partnership they had with Yelp.
Speaker 1: Siri could take a request, it could parse it, interpret it,
Speaker 1: send the appropriate UH request to the appropriate destination, and
Speaker 1: then serve up the response. The destination could be a
Speaker 1: web search, it could be an action within a compatible app.
Speaker 1: You get the idea, so that serie next. On July nine,
Speaker 1: two thousand twelve, Google released Android jelly Bean a k
Speaker 1: a Android four point one, and one of the features
Speaker 1: included in that operating system update, at least for certain
Speaker 1: hardware upon release, was an offshoot of Google Search called
Speaker 1: Google Now. This feature would serve up predictive cards containing
Speaker 1: information that the system had flagged as potentially being useful
Speaker 1: to you based off your activity. So let's say you
Speaker 1: spend a lot of times searching for stuff like baseball scores,
Speaker 1: Google Now would start serving you up cards that would
Speaker 1: give you scores from previous games before you could even
Speaker 1: search for them. You would just look at Google Now
Speaker 1: and you could scroll through and you see what the
Speaker 1: latest results were. Then you could actually scroll through the
Speaker 1: different cards, all of which were slowly dialing you in
Speaker 1: as a person, which was kind of creepy. And it
Speaker 1: relied a lot on natural language processing and your activities. Now,
Speaker 1: Google Now was not a voice assistant. This was sort
Speaker 1: of a one way relationship. Google was analyzing information based
Speaker 1: on your activity and then serving up information to you
Speaker 1: that might be useful. But over time the company would
Speaker 1: phase out Google Now and it gradually evolved into Google Assistant.
Speaker 1: There was also Google Voice that allows you to do
Speaker 1: things like voice search, so that also became incorporated into this.
Speaker 1: Google Assistant is a lot like Syrie. It responds to voice,
Speaker 1: It can respond to anaphores, meaning it can keep track
Speaker 1: of subject matter and respond to follow up questions that
Speaker 1: don't contain an explicit reference to the subject. So, for example,
Speaker 1: you could ask Google Assistant, what is the weather going
Speaker 1: to be like in Atlanta? And then after you get
Speaker 1: a response, you might say, what about in Seattle. Now,
Speaker 1: you have not explicitly said what is the weather in Seattle?
Speaker 1: You just said what about in Seattle. However, Google Assistant
Speaker 1: can infer that you are still talking about the weather,
Speaker 1: only now within the context of a different location. Google
Speaker 1: Assistant debuted in May, so in a way, this particular
Speaker 1: entry in our timeline spans two other debuts, because he
Speaker 1: had Google Now on one side and then Google Assistant later,
Speaker 1: but I figured it was important to acknowledge how Google
Speaker 1: Assistant grew out of the older Google Now feature. On
Speaker 1: April two, two thirteen, Microsoft introduced its own voice assistant
Speaker 1: at the Build Developer Conference. Microsoft's entry is named Cortana,
Speaker 1: after the AI character from the Halo series of video games.
Speaker 1: Microsoft integrated Cortana to work with Windows ten, Xbox One,
Speaker 1: Windows Mobile, and a few other platforms as well, including
Speaker 1: apps that were meant for other operating systems like iOS
Speaker 1: and Android. Cortana's US voices that of Jen Taylor. She
Speaker 1: actually is the voice actress who provided the voice for
Speaker 1: the character of Cortona in the Halo games. That's kind
Speaker 1: of fun, and like Siri, Cortona can interface with apps
Speaker 1: as well as performed web searches. In November, Amazon got
Speaker 1: into the game with Alexa and the Amazon Echo. Through
Speaker 1: Amazon Echo, Alexa can serve not just as a voice
Speaker 1: assistant that can retrieve information and play streaming media and
Speaker 1: that kind of thing, but also as an interface in
Speaker 1: home automation applications, and to be fair, so can Google
Speaker 1: Assistant through devices like Google Home. So you can use
Speaker 1: Alexa to interface directly with systems in your home. If
Speaker 1: they are compatible, and not surprisingly, Alexa can interface with
Speaker 1: Amazon's ordering system, allowing users to order products from Amazon
Speaker 1: directly by speaking to Alexa. No shock there. According to Amazon,
Speaker 1: developers were inspired by the Star Trek series of shows,
Speaker 1: which characters would speak out loud to computer systems and
Speaker 1: call for information or send commands to make various stuff happen.
Speaker 1: Amazon also released a developer kit to allow independent developers
Speaker 1: to create what are called Alexa skills. There's an old
Speaker 1: episode of tech Stuff where I interviewed some folks from
Speaker 1: Amazon to talk about this process. But essentially, developers will
Speaker 1: submit skills to Amazon, which can then publish those skills
Speaker 1: and allow anyone who has an Alexa enabled device to
Speaker 1: activate those skills and make use of them. Individuals can
Speaker 1: even build up their own person lies skills using a
Speaker 1: tool called Blueprints, which Amazon introduced in April. Now there
Speaker 1: are other examples I could point to. There's Samsung's Bixby
Speaker 1: which it introduced in March. There's sound Hounds virtual assistant
Speaker 1: called Hound that launched in March of But these were
Speaker 1: the ones that I really hear about the most frequently,
Speaker 1: so were the ones I wanted to kind of cover
Speaker 1: and they all work on on a similar principle. The
Speaker 1: implementations are all particular to their specific brands, but they
Speaker 1: work on under similar foundational principles of natural language processing,
Speaker 1: speech recognition, et cetera. And it's all about converging technologies
Speaker 1: that took decades of hard work to make possible. Now
Speaker 1: I want to thank listener Nate, who was the one
Speaker 1: who set me on this trail to ask about speech
Speaker 1: recognition and natural language processing and these voice assistants. Was
Speaker 1: really interesting to dive into, very very cool, fascinating stuff.
Speaker 1: Thanks a lot, Nate. If any of you out there
Speaker 1: have suggestions for future episodes of tech Stuff, maybe it's
Speaker 1: a technology or a company or a person in tech.
Speaker 1: Maybe there's someone I should interview or have on as
Speaker 1: a guest host, send me a message. The email address
Speaker 1: is tech Stuff at how stuff works dot com. Or
Speaker 1: drop me a line on Facebook or Twitter. The handle
Speaker 1: at both of those is tech Stuff H s W.
Speaker 1: Don't forget to follow us on Instagram and I'll talk
Speaker 1: to you again really soon for more on this and
Speaker 1: thousands of other topics. Is it how stuff Works dot
Speaker 1: Com