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| manual | watson | voxolab | microsoft | |
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| Artificial intelligence is said to have huge impacts our own lives and | sure intelligence is said to have huge impacts our own lives and even | intelligence is said to have huge impact on your life and | — Visual intelligence is said to have huge impacts on our lives | Special intelligence is said to have huge impacts our own lives |
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| even society. And some actually say it's the new | society %HESITATION and some actually say it's the new electricity since | even society and some actually say it's the new electricity | and even society's and some actually say it's the new electricity | and even society and some actually say it's the new electricity. |
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| electricity. Since the fifties, | the fifties %HESITATION computer sciences have been trying to program | since the 50s Computer Sciences have been trying to program | since the fifties computer scientists have been trying to | Since the 50s of computer scientists have been trying to program |
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| computer scientists have been trying to program computers to behave | computers to behave like humans so let's say for example you wanted | computers to behave like humans so let's say for example you wanted | program computers to behave like humans so let's say for example | computers to behave like humans. |
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| like humans. | ||||
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| So let's say for example you want to teach a computer what a | teacher computer what a cat is well how hard can that be right I mean | to teach your computer what a cat is how hard can that be right | you want to teach a computer what it kept its but can that be right | So let's say for example, when you want to teach |
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| cat is. Well. | a computer whether catches. | |||
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| How hard can that be, right? I mean after all, a cat is just a | Well, how hard can that be right. | |||
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| collection of shapes and colors. So, computer scientists | after all the cat is just a collection of shapes and colors and does so | I mean after all a cat is just a collection of shapes and colors | i mean after all a cat is just a collection of shapes and colors | I mean after all, a cat is just a collection |
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| computers by insists just thought okay we'll just give rules to | and I was so confused I insist just thought okay we'll just | and also computers that insists just thought okay will just give | of shapes and colors and uh so computers thought | |
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| insists just thought. | ||||
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| just thought okay we'll just give rules to the computer and it'll be | the computer and she'll be able to manage what ends recognize what | give rules to the computer and she'll be able to manage web | rules of the computer and still be able to manage what s and to | Okay, well, just give rules to the computer |
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| able to manage well, to recognize what a cat is. | and he'll be able to manage. | |||
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| So, we should just tell the computer, | a cat is so used we should just tell to %HESITATION the computer | chin to recognize what a cat is so you should we should just | recognise what a cat is so used we should just tell it through | What can't recognize what a cat is so you should we should just |
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| in mathematical language of course, that a cat has a round face, two | in my stomach mathematical language of course that the cat has a round | tell to the computer and Mathematics mathematical language of course | the computer in mathematics mathematical language of course | tell to um the computer in mathematics mathematical language. |
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| pointy ears, a chubby body, and a long tail. | face two point eight years of chubby body and a long tail but what about | that the cat has a round face 2.8 | that a cat has a round face two point eight years of chevy body | Of course, that the cat has a round face 2.8 |
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| But. what about this cat? | this cat and what about if the cat is hidden that doesn't | years of chubby body and but what about this cat and what | and a long tail about this kept and what about if the cat is | years of Chubby body and a long tail. |
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| And, what about if the cat is hidden? | But what about this cat an what about if the cat is | |||
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| That doesn't work. Giving rules to computers will only | work %HESITATION giving will sue computer will only get you so far | about is the cat is hidden that does work giving rules to computer | hidden that doesn't work giving rules to computer will only get | hidden that doesn't work, giving rules to computer |
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| get you so far. So another... | will only get you so far so another another type of another | you so far so another another type of this is another type of | will only get you so far. | |
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| another type of... another type of | so another %HESITATION another type of %HESITATION but that is another | So, in other um another typeof. | ||
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| approach was developed and this new type of approach developed | type of approach was developed and then you type of approach developed | type of approach was developed in the new type of approach | approach was developed and you type of approach development is called | But this is another type of approach was developed in the new type of |
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| was called machine learning. | where it is called machine learning machine learning gets computers | developed is called machine learning machine learning kids | machine learning machine learning gets computers the ability to | approach developed with is called machine learning machine learning |
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| Machine learning gives computers the ability to learn, without being | the ability to learn without being explicitly programs and so rather | computers the ability to learn without being explicitly programmed | learn without being explicitly programmed so rather than hand | gives computers the ability to learn without being explicitly programmed |
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| explicitly programmed. And so, rather than hand coding | than hand coding specific sets of instructions the machinist trained | at so rather than hand coding specific sets of instructions | coding specific sets of instructions the machine is trained using large | as so rather than hand coding specific sets of instructions. |
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| specific sets of instructions, the machine is trained, using large | using large amounts of data about five years ago %HESITATION a specific | the machine is trained using large amounts of data about 5 | amounts of data about five years ago a specific type of algorithm | The machine is trained using large amounts of data about 5 years ago, |
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| amounts of data. About five years ago, | years ago a specific type of algorithm west of machine learning | a specific type of algorithm was machine learning algorithm | ||
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| a specific type of algorithm was, of machine learning algorithm was | type of algorithm was of machine learning algorithm was developed | algorithm was developed called Deep learning you might have | was a machine learning algorithm was developed called deep learning | was developed called deep learning you may have heard |
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| developed, called deep learning. You might have heard of deep | called deep learning you might have heard of deep learning algorithms | heard of deep learning algorithms they were used to beat humans | you might have heard of deep learning algorithms that were | of deep learning algorithms. |
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| learning algorithms, that they were used to | that they were used to beat humans playing go or recently | used to beat humans plane go or a recently poker and deep learning | They were used to beat humans playing go or recently poker. | |
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| beat humans playing go, or recently poker. | poker %HESITATION NDP learning uses techniques third let's say loosely | playing go or a recently poker I'm in deep learning uses techniques | ||
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| Deep learning uses techniques that are, let's say | that might a Loosely inspired from how our brain works and we're | uses techniques there might say loosely inspired from how our brain | Men deep learning uses techniques start. | |
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| loosely inspired from how our brain works. | inspired from how our brain works and where had foreseen very extremely | works and we had verve seeing very extremely where the extremely | Let's say loosely inspired from our brain works and we | |
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| And, we've seen, very extremely, | had worth seeing very extremely extremely impressive results | had were seeing very extremely extremely impressive results. | ||
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| well, extremely impressive results | well yeah extremely are impressive results because they have a unique | because they have a unique ability to identify patterns from data | impressive results because they have a unique ability to identify | Because they have a unique ability is to identified patterns |
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| because they have, a unique ability | ability is to identify patterns from data and learn from it so this | |||
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| to identify patterns from data and learn from it. | and learn from it to this means that these new types of algorithms | patterns from debt and learn from it so this means that these new | from data and learn from it. | |
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| So this means that these new types of algorithms | means that these new types of algorithms can %HESITATION can do | types of algorithms can do it let's say three main can solve the main | So this means that these new types of algorithms can um can do it. | |
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| can... can do... | it let's say three main can solve three main problems so fed with | can I take three main consult three main problems to fade with | Let's say 3 main consult remain problems. | |
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| let's say three main, can solve three main problems. | problems so faced with are these algorithms can generate new stuff | |||
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| So, fed with data, these algorithms can generate new stuff. | data these algorithms can generate new stuff so for example if you train | data these organisms can generate new stuff so for example if | so for example if you train a machine a machine learning | So fed with data these algorithms can generate new stuff, |
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| So for example if you train a machine | you train a machine a machine learning algorithm with a set | so for example, if you train. | ||
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| a machine learning algorithm, with a set of artist painting, | emotional %HESITATION and machine learning algorithm with a set of | of artist painting like Picasso you feed the machine learning | algorithm with a set of artists painting like picasso you feet | A mashing machine learning algorithm with a set of artist |
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| like Picasso. You feed of machine learning algorithm | artist painting like Picasso feet of machine learning algorithm with | algorithm with Picasso paintings he will be able to reproduce | a machine learning algorithm with picasso paintings he will be able | painting like because so? |
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| with Picasso paintings, he will be able to reproduce, but style, | Picasso paintings he will be able to reproduce but style and I plan | Feed machine learning algorithm with because of paintings, | ||
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| that style in a plant on a new painting and actually we talked | to reproduce that style and a plan on a new painting and actually | he will be able to reproduce that style. | ||
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| and apply on a new painting, and actually, we talked about | on a new painting and actually we talked about Christmas very briefly | about Christmas very briefly earlier priests my uses machine | we talked about christmas very briefly earlier priest my uses | And a plant on a new painting and actually. |
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| Prisma very briefly earlier, Prisma uses | earlier priest my uses machine learning algorithms to do just that | machine learning algorithms to do just that so it can generate | We talked about Prisma very briefly earlier priest my uses machine | |
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| machine learning algorithms to do just that. | learning algorithms to do just that so I can generate you images | learning algorithms to do just that it can generate new images. | ||
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| So, you can generate new images, you can generate for example new texts, | so you can generate new images it can generate for example new texts | it can generate for example new text it's used to do translations | new images we can generate for example new text it's used to do | It can generate for example, new text. |
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| it's used to do translations. It can also do... | its use to do translations %HESITATION it can also do about that that Billy | translations it can also do not that their ability to recognize | It's used to do translations. | |
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| Um. | ||||
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| Its ability to recognize patterns is also very useful, for | to recognize pattern is also very useful for predictions to make | it can also do about that ability to recognize pattern is also | pattern is also very useful for predictions to make predictions | It can also do velvet ability to recognize pattern is also very useful |
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| predictions, to make predictions. I don't know if you've done those types of tests, | predictions %HESITATION I don't if done those type of tests you know | very useful for predictions to make predictions I don't know | i don't if dent those type of test you know cut i q tests or | for predictions to make predictions. |
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| you know, kind of IQ tests, or well, this type of tests where they | kind of IQ tests or well this have if this were there and they give | if you've done those type of test you know what kind of IQ | I don't know if you've done those type of test you know | |
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| test or this type of thing they give you a set of a set of | what kind of like you test or. | |||
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| well, they give you a set of of numbers, or shapes and then | you a set of %HESITATION of numbers or shapes and then you're supposed | numbers or shapes and then you're supposed to find the next one's | well this type of this further they gave you a set of of numbers | Well, this type of things for their they give you a set of um |
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| you're supposed to find the next one. | to find the next what's the next one so busy were you doing there | or shapes and then your supposed to find the next's the next one | of numbers or shapes and then you're supposed to find the next ones. | |
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| The next one. | ||||
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| So basically, what you're doing there is identifying patterns, and then | the next one so they see what you're doing there is identifying | so basic what you doing there is identifying patterns and then | So basically what you're doing. | |
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| predict what's coming next, and computers, and machine learning | is identifying patterns and then predict what's coming next and | patterns and then predict what's coming next and computers and | predict what's coming next and computers and machine learning | There is a defying patterns and then predict what's coming next. |
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| algorithms, are extremely good at doing that. | computers and machine learning algorithms are extremely good at | machine learning algorithms are extremely good at doing that | algorithms are extremely good at doing that so that's very useful | And computers in machine learning algorithms are |
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| doing that so %HESITATION that's very useful for example in | so that's very useful for example in the business world if you train | extremely good at doing that, so um. | ||
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| So, that's very useful, for example in | That's very useful. | |||
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| the business world. If you train an algorithm like that | the business world %HESITATION if you train an algorithm like that | for example in the business world if you train an algorithm like | For example, in the business world. | |
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| on historical sales data, it can help you forecast the demand | on history called sales that up it can help you forecast the demand | an algorithm like that on historical sales data it can | that on historical sales data it can help you forecast the demand | If you train, an algorithm like that on history called sales data. |
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| of a product. These algorithms also very good at | of a product these algorithms also very good and may seek making | help you forecast the demand of a product these are great times | of a product these algorithms also very good and music making | It can help you forecast the demand of a product. |
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| making associations. So I'm going to share with you an | associations so I'm going to share with you an example but I really | also very good at music making association's so I'm going | associations so i'm going to share with you an example that i really | Visa rhythms also very good and Messick making associations |
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| example but I really like. A machine learning algorithm | to share with you an example that I really like sewing machine | so I'm going to share with you. | ||
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| like %HESITATION so machine learning algorithm was asked to find | learning algorithm was asked to find associations between between | like so a machine learning algorithm was asked to find associations | An example that I really like, um, so uh machine learning | |
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| was asked to find associations between | associations between %HESITATION between sorry between art works | between between sorry between artworks from britain from | algorithm was asked to find associations between. | |
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| between - sorry - between art works from the... | from the dreaded from the Tate's collection of British art and Richard | sorry between artworks from the from the Tate's collection | the tate's collection of british art and images from two thousand | Um between sorry between art works from the bridge from |
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| from the Tate's collection of British art | of British art and reuter images from 2016 and here is what | the Tates Collection of British art and rotor images from 2016 | ||
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| And Reuters images from 2016. | images from two thousand sixteen and here is what the algorithm gave | sixteen and here is what algorithm gave us associations i think is | ||
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| and here, is what the algorithm gave us. Associations. | us associations I think it's quite interesting with this work upright | the acronym gave us associations I think it's quite interesting | in here is what the algorithm. | |
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| Gave us associations. | ||||
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| I think it's quite interesting. With this work, art work, | key %HESITATION type this a photograph from Reuters with this one she S. | with this work awkward qiyam type this photograph from borders | quite interesting with this what approach type this photograph | I think it's quite interesting. |
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| With this work out great Kia. | ||||
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| he typed this photograph from Reuters. | from reuters he's associated with this win so that's kind of fun | Type this photograph from Reuters. | ||
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| With this one, he associated | associated it's one so that's kind of fun but translated in | with this one chiasso Associated this one so that's kind of fun | With this one. | |
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| this one. So that's kind of fun but | He associated this one. | |||
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| translated in the business world, these | the trend in the business world and these algorithms can use me | but translated in the trend in the business World these algorithms | but translated in the trend in the business world these algorithms | So that's kind of fun, but translated in the trend |
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| algorithms can use, the association ability of these | in the business world. | |||
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| vis a vis the association ability of these algorithms can use to | can use me the association ability of these algorithms can use to | can use mean dvd association ability of these algorithms can | These algorithms can use I mean, the the Association ability | |
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| algorithms can use to segment - can be used to | of these algorithms. | |||
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| segment consumers according to similar behaviors, for example. | segment can be used to segment consumers according to similar | segment can be used to segment consumers according to similar | use to segment can be used to segment consumers according to | Can use to segment can be used to segment consumers according |
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| behaviors for example advances in the machine learning %HESITATION | behaviors for example adventures in the machine learning or not | similar behaviors for example advances in a machine learning | to similar behaviors for example? | |
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| Advances in machine learning are not only due to the | are not only do it thanks to the advances in the algorithms | only thanks to the advancements in the algorithms themselves | are not only do it thanks to the advances in the algorithms | Advances in machine learning on not only do thanks to the advancements |
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| advances in the algorithms themselves. | themselves %HESITATION these advances are due to two other | themselves these advances are due to two other factors that concurred | in the algorithms themselves. | |
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| These advances are due to two other factors that concurred | factors that come Kurt %HESITATION at the same time that's about | these advances are due to two other factors that encourage at | at the same time that developed the same time when it's picked | These advances are due to 2 other factors that can curd at the same |
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| at the same time. That developed at the same time. | the same time when it's big data %HESITATION with internet we've | the same time that developed the same time when it's picked | time that developed the same time, one, it's speak doctor. | |
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| One is "big data". With internet we've been producing | been producing lots and lots of data in this data can be used | up with internet we've been producing lots and lots of data | up so with internet we've been producing lots and lots of data | With Internet we've been producing lots and lots of data and this data |
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| lots and lots of data. And this data can be used to train | to train those algorithms to say the truth machine learning | and this data can be used to train those algorithms to say | and this that can be used to train those algorithms to say the truth | can be used to train those algorithms. |
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| those algorithms. To say the truth, machine learning | the truth machine learning algorithms what are the idea of | machine learning algorithms were already idea of machine learning | Say, the truth machine learning algorithms or or | |
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| algorithms or the idea of machine learning algorithms, | algorithms or the idea of machine learning algorithms %HESITATION | machine learning algorithms is he I mean has been developed for | algorithms is he i mean has been developed for a couple of years | the idea of machine learning. |
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| has been developed for a couple of years or | is he I mean has been developed for a couple of years in in | a couple of years if you in the decades but we were liking | and in the decades but we were lucky that that to feed them | Algorithms is he has been, he developed for a couple of |
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| even decades, but we were lacking the data to feed them | the decades but we would like to have the data to feed them | that that tattoo feed them enough so that was a big thing to do | years of even the decades, but we were liking. | |
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| enough. So, that was a big thing to help | enough so that was a big thing to to help %HESITATION these | to help these ugly things take off and the second thing I needed | enough so that was a big thing to do to help these algorithms | That data to feed them enough so that was a big thing to to help him. |
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| these algorithms takeoff and the | algorithms takeoff and the second thing I needed was computer | take off and the second thing he needed was computer power which | These algorithms take off and the second thing. | |
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| second thing needed was, computer power, which now we | a power which now we have so basically today AI takes off | was computer a power which now we have so basically today AI | now we have so basically today takes off because the stars have aligned | He did was computer power which now we have so basically today. |
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| have. So basically, | ||||
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| today AI takes off because the stars have | because the stars have aligned | takes off because the stars have aligned | AI takes off because the stars have aligned. | |
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| aligned. | ||||
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| And. |