1 00:00:04,240 --> 00:00:07,560 Speaker 1: Get in text of technology with text stuff from hastaff 2 00:00:07,600 --> 00:00:13,520 Speaker 1: what dot com. Hey there, and welcome to tech Stuff. 3 00:00:13,560 --> 00:00:17,520 Speaker 1: I am your host, Jonathan Strickland, and today I want 4 00:00:17,520 --> 00:00:22,439 Speaker 1: to talk about war. War never changes. But I'm not 5 00:00:22,440 --> 00:00:25,319 Speaker 1: talking about Fallout, even though I just recently started to 6 00:00:25,360 --> 00:00:28,400 Speaker 1: play Fallout for again because I never finished it. No, 7 00:00:28,480 --> 00:00:32,720 Speaker 1: I'm gonna talk about a war going on in artificial intelligence. 8 00:00:33,240 --> 00:00:35,800 Speaker 1: And it's not sky Net, it's not the Terminator, It's 9 00:00:35,880 --> 00:00:39,440 Speaker 1: nothing like that. No, it's really about who gets to 10 00:00:39,560 --> 00:00:42,680 Speaker 1: be your personal assistant. That's the best kind of war 11 00:00:42,800 --> 00:00:45,680 Speaker 1: because we win no matter who wins, right, we get 12 00:00:45,720 --> 00:00:50,000 Speaker 1: the best out of whichever combatants into the AI personal 13 00:00:50,000 --> 00:00:53,360 Speaker 1: assistant thunderdome. I'm throwing out a lot of references to 14 00:00:53,400 --> 00:00:56,840 Speaker 1: sci fi here. I'm gonna cut that out. So we 15 00:00:56,880 --> 00:01:01,000 Speaker 1: want to talk today. We being me and you you 16 00:01:01,040 --> 00:01:02,760 Speaker 1: can talk back. I just won't be able to hear 17 00:01:02,800 --> 00:01:07,480 Speaker 1: you about these personal digital assistants, but not the p 18 00:01:07,600 --> 00:01:09,480 Speaker 1: d as of the past. We want to talk about 19 00:01:09,560 --> 00:01:12,640 Speaker 1: the the series and the Cortanas and the Google Assistance 20 00:01:12,680 --> 00:01:16,160 Speaker 1: and things of that nature. And I want to specifically 21 00:01:16,240 --> 00:01:19,119 Speaker 1: look into how are these going to be incorporated into 22 00:01:19,160 --> 00:01:21,399 Speaker 1: our lives in the future, and what are some of 23 00:01:21,400 --> 00:01:25,400 Speaker 1: the concerns we have and what differentiates all these products 24 00:01:25,400 --> 00:01:28,479 Speaker 1: that have been sort of coming into their own over 25 00:01:28,520 --> 00:01:31,959 Speaker 1: the past few years. So to start with, you might say, well, 26 00:01:32,160 --> 00:01:37,680 Speaker 1: you know which of these assistants came first, And arguably 27 00:01:37,680 --> 00:01:41,440 Speaker 1: you could say Google actually beat everyone to the punch 28 00:01:41,520 --> 00:01:44,759 Speaker 1: by a couple of months because on June four, two eleven, 29 00:01:45,040 --> 00:01:49,040 Speaker 1: Google announced at an inside Google Search event that it 30 00:01:49,120 --> 00:01:53,400 Speaker 1: was going to roll out voice search on Google dot Com. 31 00:01:53,440 --> 00:01:58,680 Speaker 1: And the project name at Google was uh Majel or Magel, 32 00:01:59,040 --> 00:02:01,120 Speaker 1: depending upon how you want to pronounce it, but Majel 33 00:02:01,120 --> 00:02:04,480 Speaker 1: would be the way her name was actually pronounced. Named 34 00:02:04,480 --> 00:02:07,520 Speaker 1: after Majel Barrett, who was the wife of Gene Roddenberry, 35 00:02:07,560 --> 00:02:10,840 Speaker 1: the creator of Star Trek. Majel Barrett actually played the 36 00:02:10,919 --> 00:02:14,040 Speaker 1: voice of the computer system, particularly on Star Trek the 37 00:02:14,040 --> 00:02:16,280 Speaker 1: next generation. Whenever you heard the computer speak, that was 38 00:02:16,320 --> 00:02:21,080 Speaker 1: Majel Barrett's voice. She also played uh Deanna Troy's mother, 39 00:02:21,200 --> 00:02:27,480 Speaker 1: Luaxanna Troy. Anyway, they named it after her. Internally, uh 40 00:02:27,680 --> 00:02:31,079 Speaker 1: it actually doesn't have a name name, which kind of 41 00:02:31,120 --> 00:02:33,919 Speaker 1: sets it apart from some of the competitors. So the 42 00:02:33,960 --> 00:02:38,120 Speaker 1: Voice Command project was a tool from Google Labs, so 43 00:02:38,160 --> 00:02:42,640 Speaker 1: their research and development arm and on March fourteen, this 44 00:02:42,720 --> 00:02:47,520 Speaker 1: particular feature was rolled into the Google Now product and 45 00:02:47,960 --> 00:02:50,560 Speaker 1: uh it was part of the Android four point one 46 00:02:50,680 --> 00:02:54,560 Speaker 1: release that was the jelly Bean release. Now, at that point, 47 00:02:54,639 --> 00:02:57,640 Speaker 1: the speech recognition commands had evolved a little bit. It 48 00:02:57,680 --> 00:03:00,560 Speaker 1: had gone beyond some of the initial old stuff where 49 00:03:00,560 --> 00:03:02,960 Speaker 1: you could just ask Google to search something for you. 50 00:03:03,040 --> 00:03:06,560 Speaker 1: This was also a feature that was worked into Google Glass, 51 00:03:07,000 --> 00:03:09,280 Speaker 1: so if you had a pair of Google Glass, you 52 00:03:09,320 --> 00:03:12,520 Speaker 1: know that the voice command would always start with the 53 00:03:12,560 --> 00:03:16,919 Speaker 1: phrase okay, followed by Google I'm not gonna say it together, 54 00:03:17,400 --> 00:03:19,280 Speaker 1: just in case some of you are listening to your 55 00:03:19,320 --> 00:03:22,080 Speaker 1: devices or listening with a device nearby and it's on 56 00:03:22,160 --> 00:03:24,320 Speaker 1: its home screen. I don't want to activate it for 57 00:03:24,400 --> 00:03:28,040 Speaker 1: whatever reason, but you can use that phrase that would 58 00:03:28,240 --> 00:03:31,800 Speaker 1: end up alerting the virtual assistant that you wanted something, 59 00:03:31,840 --> 00:03:34,200 Speaker 1: and then you would speak whatever it was you wanted. 60 00:03:34,240 --> 00:03:37,720 Speaker 1: And over time, functionality increase, so it went beyond just 61 00:03:37,800 --> 00:03:41,280 Speaker 1: searches and into more interactive features like with an Android 62 00:03:41,280 --> 00:03:44,560 Speaker 1: phone you could set an alarm, or you could set 63 00:03:44,560 --> 00:03:48,600 Speaker 1: a reminder or review your calendar and more. As time 64 00:03:48,640 --> 00:03:53,200 Speaker 1: went on, at this point it is evolved into something 65 00:03:53,240 --> 00:03:55,840 Speaker 1: a little bit more robust than that. Even you can 66 00:03:55,880 --> 00:03:58,360 Speaker 1: start to interact with some third party stuff as well. 67 00:03:58,720 --> 00:04:02,560 Speaker 1: And at Google Io two thousand and sixteen, it became 68 00:04:02,720 --> 00:04:08,240 Speaker 1: part of Google Assistant. Now Google Assistant is really the 69 00:04:08,320 --> 00:04:13,240 Speaker 1: intelligent personal assistant product from Google. The earlier versions you 70 00:04:13,240 --> 00:04:17,039 Speaker 1: could think of a sort of a rudimentary form or 71 00:04:17,120 --> 00:04:20,200 Speaker 1: or perhaps a prototype or maybe just like these are 72 00:04:20,240 --> 00:04:24,320 Speaker 1: features that would eventually be rolled all into one finished product, 73 00:04:24,640 --> 00:04:28,160 Speaker 1: being Google Assistant. So by that argument, if you say 74 00:04:28,200 --> 00:04:31,360 Speaker 1: Google Assistant, you know, if you mark the Google I 75 00:04:32,279 --> 00:04:35,800 Speaker 1: event as its premier, then it's not the oldest. But 76 00:04:36,080 --> 00:04:40,680 Speaker 1: it dates back to June fourteen, two thousand eleven, when 77 00:04:40,680 --> 00:04:46,479 Speaker 1: Google announced this initial search voice search ability. So that 78 00:04:46,600 --> 00:04:51,159 Speaker 1: same year, in October, on October fourteenth, in fact, Apple 79 00:04:51,279 --> 00:04:55,560 Speaker 1: introduced Sirie. And I'm sure you all know what Siri is, 80 00:04:55,600 --> 00:04:57,960 Speaker 1: but just in case you don't, it's billed as an 81 00:04:57,960 --> 00:05:01,160 Speaker 1: intelligent personal assistant and it was introduced as a feature 82 00:05:01,680 --> 00:05:03,920 Speaker 1: with the iPhone for S and it's been part of 83 00:05:03,960 --> 00:05:08,119 Speaker 1: the iPhone iOS ecosystem ever since. And it uses speech 84 00:05:08,120 --> 00:05:11,120 Speaker 1: recognition to interpret user requests and responds with what is 85 00:05:11,160 --> 00:05:16,839 Speaker 1: hopefully inappropriate action. According to series creators, Apple actually scaled 86 00:05:16,960 --> 00:05:19,480 Speaker 1: back what Siri was supposed to be able to do. 87 00:05:20,200 --> 00:05:22,919 Speaker 1: They said that they had arranged for Siri to work 88 00:05:23,120 --> 00:05:27,920 Speaker 1: with about forty to forty five different apps that Apple had, 89 00:05:28,400 --> 00:05:32,520 Speaker 1: and then the company scaled that back significantly, so the 90 00:05:32,720 --> 00:05:37,120 Speaker 1: serie creators essentially sold the product to Apple. Then they 91 00:05:37,120 --> 00:05:41,000 Speaker 1: went on to create a different intelligent assistant called VIV 92 00:05:41,400 --> 00:05:45,360 Speaker 1: v I V and VIV is currently unaffiliated with any 93 00:05:45,400 --> 00:05:47,760 Speaker 1: other big names, but it has received funding from some 94 00:05:47,920 --> 00:05:50,839 Speaker 1: very wealthy folks in the text sphere, like Mark Zuckerberg, 95 00:05:50,880 --> 00:05:55,440 Speaker 1: for example. And VIV is what the creators of vivs 96 00:05:55,480 --> 00:05:57,560 Speaker 1: say that it's what Siri was supposed to be from 97 00:05:57,600 --> 00:05:59,960 Speaker 1: the get go, and and essentially they're saying that Siri, 98 00:06:00,040 --> 00:06:03,000 Speaker 1: you was kind of hampered, hamstrung, if you will, by 99 00:06:03,080 --> 00:06:06,120 Speaker 1: Apple um And we'll get into more about why that 100 00:06:06,200 --> 00:06:10,240 Speaker 1: may be in a little bit. So Sirie actually came 101 00:06:10,320 --> 00:06:14,760 Speaker 1: second after Google had announced their voice search, keeping in 102 00:06:14,800 --> 00:06:17,480 Speaker 1: mind that Siri was a different presentation, So you could 103 00:06:17,560 --> 00:06:20,400 Speaker 1: argue that Siri was really more of the first assistant, 104 00:06:21,360 --> 00:06:25,680 Speaker 1: and that the Google approach eventually evolved into an assistant. 105 00:06:26,040 --> 00:06:31,440 Speaker 1: But wasn't really at that same level back in Moving 106 00:06:31,480 --> 00:06:34,240 Speaker 1: forward in spring two thousand and fourteen, that's when Microsoft 107 00:06:34,279 --> 00:06:37,880 Speaker 1: got into the game by unveiling Cortana, which is their 108 00:06:37,880 --> 00:06:42,239 Speaker 1: intelligent assistant for the Windows Phone platform. And in twenty fifteen, 109 00:06:42,279 --> 00:06:45,560 Speaker 1: Microsoft included Cortana with Windows ten. So if you have 110 00:06:45,560 --> 00:06:48,640 Speaker 1: a Windows ten machine, Cortana is part of that, and 111 00:06:48,760 --> 00:06:51,200 Speaker 1: if you have a microphone you can actually give voice 112 00:06:51,200 --> 00:06:54,839 Speaker 1: commands to Cortana. You can also interact via text um. 113 00:06:55,080 --> 00:06:58,240 Speaker 1: Cortana is named after the AI and the Halo franchise 114 00:06:58,279 --> 00:07:01,000 Speaker 1: and is voiced by the same act Risks who provided 115 00:07:01,000 --> 00:07:02,880 Speaker 1: the voice of Cortana in the games. So you can 116 00:07:02,920 --> 00:07:05,400 Speaker 1: ask fun things about Master Chief and she always has 117 00:07:05,400 --> 00:07:10,560 Speaker 1: a interesting answer for those. All of these, by the way, 118 00:07:10,760 --> 00:07:13,640 Speaker 1: tend to have some sort of fun element to them, 119 00:07:13,680 --> 00:07:18,640 Speaker 1: where the developers clearly thought of ridiculous things you could 120 00:07:18,720 --> 00:07:23,920 Speaker 1: ask the digital assistance and built in responses that were humorous. 121 00:07:24,080 --> 00:07:26,640 Speaker 1: For example, the big one that everyone talked about with 122 00:07:26,640 --> 00:07:29,040 Speaker 1: Siri was where can I hide a body? And Syrie 123 00:07:29,080 --> 00:07:33,560 Speaker 1: would come back with nearby quarries and caves systems and 124 00:07:33,600 --> 00:07:37,480 Speaker 1: things of that nature. Now, in November two thousand fourteen, 125 00:07:38,640 --> 00:07:42,840 Speaker 1: we get our final big name in this battle, Amazon. 126 00:07:43,040 --> 00:07:45,800 Speaker 1: That's when Amazon unveiled the Echo, which is that sort 127 00:07:45,840 --> 00:07:50,600 Speaker 1: of standalone speaker system UH that has the Intelligent Assistant 128 00:07:50,640 --> 00:07:54,440 Speaker 1: Alexa incorporated into it. And like the other ones I've 129 00:07:54,440 --> 00:07:57,080 Speaker 1: mentioned so far, Alexa can follow your voice commands and 130 00:07:57,120 --> 00:07:59,960 Speaker 1: interact with the Internet as well as with other Internet 131 00:08:00,000 --> 00:08:04,040 Speaker 1: connected devices. That list of Internet connected devices Alexa can 132 00:08:04,280 --> 00:08:07,280 Speaker 1: work with is growing day by day, and Amazon is 133 00:08:07,280 --> 00:08:09,960 Speaker 1: actually trying to build out the capabilities further and as 134 00:08:09,960 --> 00:08:12,440 Speaker 1: such as hired a team to create a guide on 135 00:08:12,520 --> 00:08:16,320 Speaker 1: how to develop for Alexa. I'm going to interview one 136 00:08:16,360 --> 00:08:19,040 Speaker 1: of the developers on that team in a later episode. 137 00:08:19,480 --> 00:08:23,320 Speaker 1: We actually have that scheduled for later this summer, and 138 00:08:23,320 --> 00:08:26,200 Speaker 1: we'll talk more about what it's like to develop for 139 00:08:26,240 --> 00:08:29,760 Speaker 1: this platform and the potential of using such a platform 140 00:08:29,840 --> 00:08:33,360 Speaker 1: and in new and creative ways. So we have four 141 00:08:33,559 --> 00:08:37,000 Speaker 1: really big players in the space. We've got Apple and 142 00:08:37,160 --> 00:08:42,440 Speaker 1: Google and Microsoft and Amazon already vying to be the 143 00:08:42,480 --> 00:08:46,200 Speaker 1: big digital assistant provider. Then we have the other names, 144 00:08:46,200 --> 00:08:49,560 Speaker 1: like we've got the team behind Viv and other apps 145 00:08:49,600 --> 00:08:52,120 Speaker 1: as well that are in this space that are trying 146 00:08:52,160 --> 00:08:55,280 Speaker 1: to kind of become the voice that you interact with 147 00:08:56,000 --> 00:08:58,080 Speaker 1: um so that it can do all the things you 148 00:08:58,120 --> 00:09:01,320 Speaker 1: needed to do in a s seamless away as possible. 149 00:09:02,679 --> 00:09:05,400 Speaker 1: So one of the things we need to also look 150 00:09:05,440 --> 00:09:08,320 Speaker 1: at is how does this differentiate? How do these different players? 151 00:09:08,640 --> 00:09:12,000 Speaker 1: How are they different from one another? If they're exactly 152 00:09:12,040 --> 00:09:14,440 Speaker 1: the same as each other, then it really doesn't matter 153 00:09:14,480 --> 00:09:17,040 Speaker 1: which one you pick, right, I mean, they're kind of 154 00:09:17,080 --> 00:09:19,640 Speaker 1: depends just which platform you have available. If you have 155 00:09:19,720 --> 00:09:23,760 Speaker 1: all iOS devices, then Siri is pretty much gonna be 156 00:09:23,800 --> 00:09:26,120 Speaker 1: the one you're gonna depend upon the most most likely 157 00:09:26,120 --> 00:09:31,079 Speaker 1: at any rate. So Cortana, Siri, and Google Assistant are 158 00:09:31,120 --> 00:09:36,079 Speaker 1: all part of existing platforms like smartphones and computers, So 159 00:09:36,120 --> 00:09:43,040 Speaker 1: they are incorporated into things that we already have. You know, 160 00:09:43,440 --> 00:09:46,560 Speaker 1: you probably already have a smartphone or a computer or both, 161 00:09:47,240 --> 00:09:49,959 Speaker 1: and so it makes sense that you would incorporate your 162 00:09:49,960 --> 00:09:52,840 Speaker 1: digital assistant into that. You don't have to buy anything else. 163 00:09:53,040 --> 00:09:56,600 Speaker 1: It's right there, and you can incorporate that into other 164 00:09:56,760 --> 00:09:59,560 Speaker 1: systems that are connected to a personal network or a 165 00:09:59,600 --> 00:10:05,640 Speaker 1: home net work. Then you've got Alexa, which debuted on 166 00:10:05,800 --> 00:10:09,839 Speaker 1: a standalone device called the Echo, which again is just 167 00:10:09,880 --> 00:10:12,360 Speaker 1: this sort of intelligent speaker, a smart speaker with a 168 00:10:12,360 --> 00:10:16,480 Speaker 1: built in microphone. Google Assistant is actually following suit with 169 00:10:16,520 --> 00:10:19,480 Speaker 1: that with Google Home that was announced at Google Io two. 170 00:10:20,280 --> 00:10:22,840 Speaker 1: And Google Home is also a smart speaker with a 171 00:10:22,880 --> 00:10:26,440 Speaker 1: microphone that's gonna be available sometime later in t and 172 00:10:26,520 --> 00:10:28,760 Speaker 1: as of the recording of this podcast, I don't have 173 00:10:28,840 --> 00:10:32,200 Speaker 1: a date or a price on that, so it's hard 174 00:10:32,240 --> 00:10:34,640 Speaker 1: to say whether or not it will be competitively priced 175 00:10:34,640 --> 00:10:37,679 Speaker 1: against the Amazon Echo. It does like it's going to 176 00:10:37,720 --> 00:10:42,760 Speaker 1: be a particularly powerful version of this personal assistant, And 177 00:10:42,800 --> 00:10:45,640 Speaker 1: there are also rumors emerging that Apple is also working 178 00:10:46,400 --> 00:10:50,040 Speaker 1: on Sirie hardware, so it would be another standalone speaker 179 00:10:50,120 --> 00:10:55,320 Speaker 1: microphone system of some sort, and that Apple's Sirie platform 180 00:10:55,400 --> 00:10:59,000 Speaker 1: would exist on that. Now, as of the recording of 181 00:10:59,000 --> 00:11:01,320 Speaker 1: this podcast, we don't have confirmation on that, so there's 182 00:11:01,320 --> 00:11:05,240 Speaker 1: no timetable associated with such a thing or a price. 183 00:11:05,760 --> 00:11:08,920 Speaker 1: I would expect that any announcement of such a device 184 00:11:08,960 --> 00:11:11,800 Speaker 1: would come at one of Apple's big events, So probably, 185 00:11:12,520 --> 00:11:17,640 Speaker 1: if I had the guess, I'd say September is when 186 00:11:17,640 --> 00:11:20,360 Speaker 1: they would announce it. That's typically when they announced all 187 00:11:20,360 --> 00:11:25,320 Speaker 1: the big iPhone changes. But that's just a guess. They 188 00:11:25,400 --> 00:11:28,319 Speaker 1: might hold a single event for this particular thing, or 189 00:11:28,360 --> 00:11:29,760 Speaker 1: they might not hold an event at all, They may 190 00:11:29,800 --> 00:11:33,040 Speaker 1: just release it. That doesn't seem particularly apple like, but 191 00:11:33,080 --> 00:11:37,000 Speaker 1: it's a possibility. So once the big deal with this 192 00:11:37,080 --> 00:11:40,160 Speaker 1: technology in the first place, why should we care. Well, 193 00:11:40,200 --> 00:11:42,720 Speaker 1: for one thing, it represents huge leaps forward in the 194 00:11:42,760 --> 00:11:46,440 Speaker 1: field of artificial intelligence. So in one way, it's a 195 00:11:46,520 --> 00:11:51,320 Speaker 1: really cool glimpse at the at the the state of 196 00:11:51,360 --> 00:11:55,600 Speaker 1: the art in AI specifically, and stuff like speech recognition, 197 00:11:56,640 --> 00:11:59,040 Speaker 1: which is pretty hard stuff. I mean, we all have 198 00:11:59,160 --> 00:12:04,319 Speaker 1: different ways of pronouncing words, and depending upon your region, 199 00:12:04,960 --> 00:12:08,480 Speaker 1: you might have an accent that has a different way 200 00:12:08,520 --> 00:12:11,960 Speaker 1: of pronouncing word. For example, Uh, you know, the Brits 201 00:12:12,000 --> 00:12:15,160 Speaker 1: say aluminium and we say aluminum here in the United States. 202 00:12:15,760 --> 00:12:18,760 Speaker 1: Then even within a single country, you have different ways 203 00:12:18,760 --> 00:12:23,440 Speaker 1: of pronouncing things. And when Google first began translating speech 204 00:12:23,440 --> 00:12:27,480 Speaker 1: to text in voice messages, I noticed that it was 205 00:12:27,520 --> 00:12:32,120 Speaker 1: having a real hard time interpreting the words of some 206 00:12:32,200 --> 00:12:34,920 Speaker 1: of my friends and family. Now keep in mind I 207 00:12:34,960 --> 00:12:40,520 Speaker 1: am in the Southeast United States Georgia, and that is 208 00:12:41,760 --> 00:12:43,840 Speaker 1: we have a lot of people here with Southern accents. 209 00:12:44,600 --> 00:12:47,920 Speaker 1: I have a tiny bit of one. My parents have 210 00:12:47,960 --> 00:12:52,559 Speaker 1: a slightly stronger Southern accent. Some of my extended relatives 211 00:12:52,600 --> 00:12:56,360 Speaker 1: haven't even stronger Southern accent, and so when they would 212 00:12:56,360 --> 00:12:59,920 Speaker 1: call and leave a voicemail, Google had to guess at 213 00:13:00,040 --> 00:13:04,320 Speaker 1: what they were saying, and was not always correct. I 214 00:13:04,320 --> 00:13:06,880 Speaker 1: would have to go and listen to the voicemail because 215 00:13:06,920 --> 00:13:12,000 Speaker 1: the transcription would be completely indecipherable. Now, over time this 216 00:13:12,120 --> 00:13:16,800 Speaker 1: has improved. The speech recognition software has improved where it 217 00:13:16,880 --> 00:13:21,960 Speaker 1: can adjust for things like different accents and and the 218 00:13:22,000 --> 00:13:25,000 Speaker 1: different ways that people speak, using a lot of different 219 00:13:25,000 --> 00:13:28,800 Speaker 1: algorithms that have been based in machine learning to kind 220 00:13:28,840 --> 00:13:31,920 Speaker 1: of get a grip on what is being said and 221 00:13:31,960 --> 00:13:36,319 Speaker 1: even and anticipate what the next thing will be in 222 00:13:36,559 --> 00:13:39,559 Speaker 1: in any line of thought. Obviously, for someone like me 223 00:13:40,040 --> 00:13:44,040 Speaker 1: who stumbles over words occasionally, that's a real challenge because 224 00:13:44,080 --> 00:13:46,120 Speaker 1: sometimes I don't even know what's next to come out 225 00:13:46,160 --> 00:13:48,960 Speaker 1: of my mouth. But that's really where where that power 226 00:13:49,040 --> 00:13:53,640 Speaker 1: comes in. Now, over time, not just speech recognition has improved, 227 00:13:54,080 --> 00:13:58,360 Speaker 1: we've also had to look at the problem of natural language. Now, 228 00:13:58,440 --> 00:14:00,720 Speaker 1: natural language is how you and I could communicate with 229 00:14:00,760 --> 00:14:04,400 Speaker 1: one another. Unless it's like a really formal setting. We 230 00:14:04,520 --> 00:14:07,360 Speaker 1: usually are pretty casual with our language, and we can 231 00:14:07,360 --> 00:14:10,960 Speaker 1: make use of lots of different linguistic flourishes and tools, 232 00:14:11,040 --> 00:14:16,040 Speaker 1: things like figures of speech, metaphors, similes, puns, references, and 233 00:14:16,080 --> 00:14:19,040 Speaker 1: lots of other stuff that gives meaning to what we say. 234 00:14:19,080 --> 00:14:22,480 Speaker 1: But only if the other person also understands what's going on. 235 00:14:23,160 --> 00:14:26,960 Speaker 1: They also have to have that benefit. Otherwise it just 236 00:14:27,040 --> 00:14:30,520 Speaker 1: becomes a jumble of nonsense. I'm reminded of a Star 237 00:14:30,600 --> 00:14:34,840 Speaker 1: Trek the Next Generation episode where characters only spoke in 238 00:14:35,240 --> 00:14:40,080 Speaker 1: um allegory, and if you didn't have that cultural background, 239 00:14:40,160 --> 00:14:43,560 Speaker 1: if you didn't understand the references, you didn't understand where 240 00:14:43,600 --> 00:14:49,200 Speaker 1: the community, what, what the communication meant um. Similar problem 241 00:14:49,240 --> 00:14:52,760 Speaker 1: with machines. They don't necessarily know what we're saying all 242 00:14:52,800 --> 00:14:54,800 Speaker 1: the time. A lot of machines are not very good 243 00:14:54,800 --> 00:14:58,560 Speaker 1: at doing this. But natural language familiarity has been a 244 00:14:58,640 --> 00:15:02,640 Speaker 1: huge challenging in AI, and we're getting better at overcoming 245 00:15:02,680 --> 00:15:05,400 Speaker 1: that challenge. So at that that same Iowa event where 246 00:15:05,440 --> 00:15:09,560 Speaker 1: Google announced Google Home, they demonstrated that you could start 247 00:15:09,560 --> 00:15:13,400 Speaker 1: a conversation with your personal assistant asking something fairly specific, 248 00:15:13,480 --> 00:15:17,280 Speaker 1: such as, we're gonna go with a local reference for 249 00:15:17,280 --> 00:15:22,200 Speaker 1: for yours truly, how are the Atlanta Braves doing this season? Then? 250 00:15:22,240 --> 00:15:24,720 Speaker 1: The assistant would actually break your heart by telling you 251 00:15:24,760 --> 00:15:27,280 Speaker 1: how poorly the Braves are doing this season and it 252 00:15:27,400 --> 00:15:31,160 Speaker 1: is abysmal. And you could follow that up with when 253 00:15:31,200 --> 00:15:34,280 Speaker 1: do they play at home next? And the assistant would 254 00:15:34,400 --> 00:15:38,160 Speaker 1: understand that when you say they, you mean the Atlanta Braves, 255 00:15:38,320 --> 00:15:40,960 Speaker 1: and when you say at home, it would understand you 256 00:15:41,000 --> 00:15:45,120 Speaker 1: meant Atlanta, Georgia. So it would be able to figure 257 00:15:45,160 --> 00:15:47,720 Speaker 1: out the context of what you said without you having 258 00:15:47,760 --> 00:15:52,320 Speaker 1: to restate when do the Atlanta Braves play in Atlanta next? 259 00:15:53,080 --> 00:15:55,840 Speaker 1: You could take these little linguistic shortcuts that we would 260 00:15:55,840 --> 00:16:00,640 Speaker 1: normally do in natural conversation, but typically machines are not 261 00:16:00,760 --> 00:16:04,720 Speaker 1: great at that. They don't have the capacity to understand 262 00:16:05,320 --> 00:16:10,120 Speaker 1: how one sentence can follow another. But this is an 263 00:16:10,160 --> 00:16:13,600 Speaker 1: example of how that's changed through machine learning. So you've 264 00:16:13,600 --> 00:16:17,720 Speaker 1: gotten this new approach where you can continue a series 265 00:16:17,760 --> 00:16:22,000 Speaker 1: of questions that build on previous questions and answers, and 266 00:16:22,080 --> 00:16:26,119 Speaker 1: the Google Assistant can continue to give you relevant information, 267 00:16:26,200 --> 00:16:31,000 Speaker 1: which is a pretty powerful statement in AI. Also, you 268 00:16:31,080 --> 00:16:34,160 Speaker 1: might have heard that funny story that Google fed romance 269 00:16:34,280 --> 00:16:37,080 Speaker 1: novels to its AI to make it better at understanding 270 00:16:37,160 --> 00:16:40,880 Speaker 1: natural language, And to be fair, that's just part of 271 00:16:40,880 --> 00:16:43,800 Speaker 1: the story. Google actually fed lots of different types of 272 00:16:43,920 --> 00:16:47,720 Speaker 1: unpublished literature to its AI, all with the goal of 273 00:16:47,800 --> 00:16:50,560 Speaker 1: teaching the AI that there are many different ways to 274 00:16:50,640 --> 00:16:53,760 Speaker 1: say the same thing. So here's an example. Um, I 275 00:16:53,760 --> 00:16:57,080 Speaker 1: could say it's raining pretty hard today, or it's really 276 00:16:57,120 --> 00:17:00,280 Speaker 1: coming down out there, or it's raining cats and dogs dogs, 277 00:17:00,360 --> 00:17:03,400 Speaker 1: or it's pouring outside, and all of those mean the 278 00:17:03,440 --> 00:17:05,920 Speaker 1: same thing. But they're all different ways of saying that 279 00:17:06,000 --> 00:17:11,080 Speaker 1: it's raining really hard. And there are a lot of 280 00:17:11,080 --> 00:17:13,919 Speaker 1: other ways I could say the same you know, to 281 00:17:13,920 --> 00:17:17,360 Speaker 1: to express the same thought using different words. And that's 282 00:17:17,359 --> 00:17:22,720 Speaker 1: a challenge for machines because we as humans understand that 283 00:17:22,840 --> 00:17:24,640 Speaker 1: you can say all these different things and that all 284 00:17:24,760 --> 00:17:28,159 Speaker 1: means the same thing. But machines have to be taught that. 285 00:17:29,800 --> 00:17:32,320 Speaker 1: So romance novels, as it turns out, are a good 286 00:17:32,359 --> 00:17:35,639 Speaker 1: way to teach a an AI how to interpret different 287 00:17:35,640 --> 00:17:41,120 Speaker 1: things because romance novels are incredibly formulaic. If you were 288 00:17:41,160 --> 00:17:44,879 Speaker 1: to break down a romance novel and and you outlined 289 00:17:44,920 --> 00:17:48,199 Speaker 1: it scene by scene so that you understood where the 290 00:17:48,240 --> 00:17:51,040 Speaker 1: beats and the story were and who the characters were 291 00:17:51,040 --> 00:17:53,720 Speaker 1: in their relationships to one another, you would see that 292 00:17:53,800 --> 00:17:57,119 Speaker 1: a lot of romance novels follow the exact same structure, 293 00:17:57,320 --> 00:18:00,959 Speaker 1: the exact same plot structure, but because they're written by 294 00:18:00,960 --> 00:18:05,120 Speaker 1: different people, because the character names in places are often 295 00:18:05,200 --> 00:18:07,520 Speaker 1: changed from book to book. I mean, obviously you wouldn't 296 00:18:07,560 --> 00:18:11,200 Speaker 1: want to write the same novel forty times. It means 297 00:18:11,200 --> 00:18:13,840 Speaker 1: that you have a lot of different ways to express 298 00:18:14,040 --> 00:18:18,119 Speaker 1: the same ideas. So if you feed a whole bunch 299 00:18:18,119 --> 00:18:22,520 Speaker 1: of formulating novels into an AI to teach it humans 300 00:18:22,560 --> 00:18:25,640 Speaker 1: have lots of different ways to express the same thoughts, 301 00:18:26,359 --> 00:18:29,880 Speaker 1: that's a pretty powerful tool. Um. And again, it wasn't 302 00:18:29,920 --> 00:18:32,480 Speaker 1: the only type of story that was being fed to 303 00:18:32,960 --> 00:18:35,680 Speaker 1: Google's AI. It's just the one that caught a lot 304 00:18:35,680 --> 00:18:39,359 Speaker 1: of people's attention because it the headlines right themselves at 305 00:18:39,400 --> 00:18:42,679 Speaker 1: that point. So one thing that is really, you know, 306 00:18:42,760 --> 00:18:44,720 Speaker 1: funny about that is a lot of people made jokes 307 00:18:44,720 --> 00:18:48,439 Speaker 1: about Google AI suggesting different ways to rip a bodice 308 00:18:48,600 --> 00:18:52,040 Speaker 1: or to make a bosom heave from the whole romance 309 00:18:52,080 --> 00:18:54,080 Speaker 1: novel thing, But as it turns out, it was there 310 00:18:54,119 --> 00:18:58,800 Speaker 1: was some real thought given to using this approach. Now, 311 00:18:58,880 --> 00:19:01,959 Speaker 1: one of the ways that these assistants work so well 312 00:19:02,040 --> 00:19:05,359 Speaker 1: is to tap into information about you and to store 313 00:19:05,400 --> 00:19:08,560 Speaker 1: all of that off of the hardware so that it 314 00:19:08,640 --> 00:19:12,320 Speaker 1: can anticipate what you want and what you need and 315 00:19:12,359 --> 00:19:16,240 Speaker 1: how to fulfill that. So, for example, if I'm using 316 00:19:16,280 --> 00:19:20,720 Speaker 1: Amazon and I'm using the Echo and I'm I'm using 317 00:19:20,880 --> 00:19:25,680 Speaker 1: Alexa to purchase certain things off the Amazon Store, this 318 00:19:25,760 --> 00:19:30,840 Speaker 1: ends up tapping into that algorithm that tells Amazon what 319 00:19:31,040 --> 00:19:34,600 Speaker 1: I've bought and what I have browsed and and the 320 00:19:34,600 --> 00:19:36,680 Speaker 1: sort of stuff I'm interested in, so it can suggest 321 00:19:36,760 --> 00:19:38,920 Speaker 1: new things that I might be interested in but didn't 322 00:19:38,960 --> 00:19:41,680 Speaker 1: know about. All of that is a very powerful tool. 323 00:19:42,760 --> 00:19:48,520 Speaker 1: One of the exceptions here is Apple's SIRIE. So Apple 324 00:19:48,600 --> 00:19:52,240 Speaker 1: pretty much locks everything down into the hardware as opposed 325 00:19:52,240 --> 00:19:55,159 Speaker 1: to sharing it with third party or putting it in 326 00:19:55,200 --> 00:20:01,320 Speaker 1: the cloud. That's because Apple's revenue source selling that hardware 327 00:20:01,440 --> 00:20:04,959 Speaker 1: and related services like support plans like like a product 328 00:20:05,000 --> 00:20:09,800 Speaker 1: support or protection plans for your hardware. That's how Apple 329 00:20:09,840 --> 00:20:13,040 Speaker 1: makes its money. It's making it through selling this hardware 330 00:20:13,080 --> 00:20:16,400 Speaker 1: that it is producing, as opposed to something like Google, 331 00:20:16,480 --> 00:20:20,720 Speaker 1: which until Google Home comes out, it's selling an idea 332 00:20:21,240 --> 00:20:25,640 Speaker 1: to you and then selling you to advertisers. Uh. So 333 00:20:26,800 --> 00:20:29,320 Speaker 1: Apple That the benefits Apple in some ways because it 334 00:20:29,359 --> 00:20:32,200 Speaker 1: means that you can trust Syria a little more than 335 00:20:32,240 --> 00:20:35,480 Speaker 1: you could some of the other assistants because it's it's 336 00:20:35,520 --> 00:20:39,280 Speaker 1: mostly contained to your device. On the flip side, it 337 00:20:39,280 --> 00:20:42,359 Speaker 1: makes the actual service a little less useful because it 338 00:20:42,400 --> 00:20:45,520 Speaker 1: cannot tap into the massive resources of the Internet the 339 00:20:45,560 --> 00:20:50,400 Speaker 1: way some of these other assistants can, because again it's 340 00:20:50,440 --> 00:20:53,320 Speaker 1: all pretty much contained your device. Now I can access 341 00:20:53,320 --> 00:20:55,760 Speaker 1: I can pull stuff from the Internet for you, but 342 00:20:55,800 --> 00:20:58,960 Speaker 1: it's not as interactive as some of these other assistants are. 343 00:21:00,480 --> 00:21:05,080 Speaker 1: Uh So, there with the possibility of advertising or things 344 00:21:05,160 --> 00:21:11,040 Speaker 1: like Google or Amazon rather Amazon's integrated shopping services, you 345 00:21:11,080 --> 00:21:15,040 Speaker 1: start to see some real potential for revenue generation on 346 00:21:15,080 --> 00:21:17,879 Speaker 1: the back end. But it also brings up some questions 347 00:21:18,359 --> 00:21:22,840 Speaker 1: about privacy and security. Now. To look into that matter further, 348 00:21:23,040 --> 00:21:26,440 Speaker 1: I spoke with an expert on the subject, the founder 349 00:21:26,520 --> 00:21:30,159 Speaker 1: of a company called Big I d Dmitri Serota, and 350 00:21:30,200 --> 00:21:33,800 Speaker 1: here's what he had to say. Well, I think that 351 00:21:34,000 --> 00:21:37,679 Speaker 1: clearly there's a certain degree of inevitability around this. I 352 00:21:37,720 --> 00:21:41,280 Speaker 1: think we've moved from an age of having these technologies 353 00:21:41,320 --> 00:21:42,800 Speaker 1: and you can almost think of this as kind of 354 00:21:42,800 --> 00:21:46,679 Speaker 1: web dot dot one in terms of being responsive to 355 00:21:46,760 --> 00:21:50,879 Speaker 1: the user and personalization really being about kind of targeting you. 356 00:21:51,240 --> 00:21:54,639 Speaker 1: I think we're now shifting to an era of anticipation. 357 00:21:55,119 --> 00:21:58,320 Speaker 1: You know, the technologies are becoming smarter and they know 358 00:21:58,480 --> 00:22:01,879 Speaker 1: more about you because they uch you on so many levels, 359 00:22:01,880 --> 00:22:04,280 Speaker 1: whether you're on the web, whether you're on your mobile, 360 00:22:04,359 --> 00:22:06,640 Speaker 1: whether you're at the office, whether you're in the car, 361 00:22:07,240 --> 00:22:10,399 Speaker 1: whether you're at home, as is the case with with 362 00:22:10,480 --> 00:22:15,000 Speaker 1: Google Home and Amazon Echo and similar technologies, that they're 363 00:22:15,040 --> 00:22:19,760 Speaker 1: no longer just about kind of responding to a particular action. 364 00:22:19,920 --> 00:22:23,239 Speaker 1: They're now trying to anticipate what you'd want. And in 365 00:22:23,280 --> 00:22:25,600 Speaker 1: some degree, you know, they're becoming more like your mother 366 00:22:25,720 --> 00:22:29,239 Speaker 1: or your parents, where they know so much about you 367 00:22:29,359 --> 00:22:32,600 Speaker 1: that they anticipate your needs um and there's a good 368 00:22:32,640 --> 00:22:36,240 Speaker 1: and a bad to that, right, So just the actions 369 00:22:36,280 --> 00:22:38,840 Speaker 1: that we would take in our homes can start to 370 00:22:38,880 --> 00:22:42,720 Speaker 1: set up these these expectations, for lack of a better word, 371 00:22:42,720 --> 00:22:46,280 Speaker 1: that our technology will have about us. For example, the 372 00:22:46,800 --> 00:22:50,080 Speaker 1: easiest way I think to illustrate this to today's audience 373 00:22:50,119 --> 00:22:52,720 Speaker 1: is to talk about something like the nest thermostat, where 374 00:22:53,240 --> 00:22:55,720 Speaker 1: you have said it a certain way, and it starts 375 00:22:55,760 --> 00:22:58,520 Speaker 1: to learn what your preferences are over time and then 376 00:22:58,560 --> 00:23:01,439 Speaker 1: it begins to automatically just without you ever having to 377 00:23:01,440 --> 00:23:04,720 Speaker 1: touch it, to the point where it's even seeing quote 378 00:23:04,800 --> 00:23:07,800 Speaker 1: unquote seeing when you are home versus not home. This 379 00:23:07,880 --> 00:23:10,199 Speaker 1: is the sort of stuff that, when incorporated into a 380 00:23:10,240 --> 00:23:13,320 Speaker 1: device like Google Home, can become very powerful. But also, 381 00:23:14,000 --> 00:23:17,199 Speaker 1: like you said, has this this other side to it, 382 00:23:17,240 --> 00:23:20,680 Speaker 1: this side that that if we don't pay attention to it, 383 00:23:20,680 --> 00:23:25,840 Speaker 1: it could become potentially um harmful to us, or at 384 00:23:25,920 --> 00:23:30,040 Speaker 1: least at the at the very least anyway uh inconvenient 385 00:23:30,160 --> 00:23:34,480 Speaker 1: to us. So, for example, with our setting up Google 386 00:23:34,520 --> 00:23:37,360 Speaker 1: Home so that we would be able to control lighting 387 00:23:37,480 --> 00:23:41,640 Speaker 1: and security systems and uh thermostats, not only would we 388 00:23:42,080 --> 00:23:44,439 Speaker 1: have it set up so that it's to our preferences, 389 00:23:44,440 --> 00:23:47,399 Speaker 1: but it actually has learned when we're at home versus 390 00:23:47,400 --> 00:23:50,520 Speaker 1: when we're not at home, and what that information means 391 00:23:50,600 --> 00:23:55,280 Speaker 1: could be potentially very harmful to us. So in your mind, 392 00:23:56,280 --> 00:24:00,359 Speaker 1: where where does accountability lie? Is this something that we 393 00:24:00,480 --> 00:24:03,640 Speaker 1: ourselves are are at least partly accountable for that kind 394 00:24:03,680 --> 00:24:07,360 Speaker 1: of information? Are the companies that create this technology? Are 395 00:24:07,359 --> 00:24:11,119 Speaker 1: they accountable? It's it's such a cloudy area. Where do 396 00:24:11,200 --> 00:24:15,439 Speaker 1: you see that? So it's a mix now clearly, and 397 00:24:15,480 --> 00:24:17,520 Speaker 1: I think I want to kind of emphasize this. You 398 00:24:17,560 --> 00:24:21,880 Speaker 1: mentioned earlier how this could become inconvenient. The reality is 399 00:24:21,880 --> 00:24:24,840 Speaker 1: is that we as the consumer want this because we 400 00:24:24,920 --> 00:24:28,840 Speaker 1: want it because it is convenient. We want technologies that 401 00:24:28,880 --> 00:24:32,280 Speaker 1: are passive. We don't necessarily want to click buttons. We 402 00:24:32,320 --> 00:24:35,560 Speaker 1: want technologies that are intelligent enough to be able to 403 00:24:35,600 --> 00:24:39,240 Speaker 1: help us make decisions. Right. I mentioned earlier about anticipation, 404 00:24:39,960 --> 00:24:44,280 Speaker 1: the challenge with convenience, and convenience typically goes against the 405 00:24:44,320 --> 00:24:48,040 Speaker 1: grain of security and privacy to some degree. If we 406 00:24:48,119 --> 00:24:52,520 Speaker 1: really want a mother, uh, kind of anticipating our needs, 407 00:24:52,600 --> 00:24:55,240 Speaker 1: what we want for lunch, where we want to go 408 00:24:55,359 --> 00:24:58,359 Speaker 1: for travel, you get. You get. The negative of having 409 00:24:58,359 --> 00:25:00,800 Speaker 1: your parent with you after you've kind of left for 410 00:25:00,920 --> 00:25:03,720 Speaker 1: university or or left for the office, is that you 411 00:25:03,760 --> 00:25:06,840 Speaker 1: don't want them intruding too many places or too much 412 00:25:06,880 --> 00:25:09,320 Speaker 1: about you. You want to keep certain parts of your 413 00:25:09,359 --> 00:25:12,480 Speaker 1: life separate. And the reality is is that there's a 414 00:25:12,520 --> 00:25:15,160 Speaker 1: trade off around here. So you know, I do think 415 00:25:15,160 --> 00:25:18,720 Speaker 1: that there's a consumer drive towards this. It's just that 416 00:25:19,000 --> 00:25:21,920 Speaker 1: we are not necessarily always prepared because there's a bit 417 00:25:21,920 --> 00:25:25,240 Speaker 1: of a lag or a delay before the consequences of 418 00:25:25,280 --> 00:25:29,400 Speaker 1: having this convenience are fully made aware are aware to us. 419 00:25:29,480 --> 00:25:33,520 Speaker 1: So in terms of the responsibility of who cares about this, 420 00:25:33,640 --> 00:25:36,199 Speaker 1: we as consumers obviously care about it. You know. The 421 00:25:36,320 --> 00:25:39,920 Speaker 1: companies like Google and Amazon obviously you know, they would 422 00:25:39,960 --> 00:25:43,919 Speaker 1: argue that by personalizing service to you, they are giving 423 00:25:43,920 --> 00:25:46,800 Speaker 1: you this convenience. But the reality is it's really up 424 00:25:46,840 --> 00:25:49,119 Speaker 1: to their best efforts or what they think is the 425 00:25:49,200 --> 00:25:53,119 Speaker 1: right combination of privacy and security for now. And the 426 00:25:53,200 --> 00:25:56,719 Speaker 1: reason for that is the regulators take time to catch up. 427 00:25:57,200 --> 00:26:01,320 Speaker 1: They don't necessarily know the latest, they didn't attend Google Io, 428 00:26:01,600 --> 00:26:04,560 Speaker 1: and they don't know necessarily know how to react or respond. 429 00:26:05,040 --> 00:26:08,359 Speaker 1: So there's always gonna be this this lag between what 430 00:26:08,440 --> 00:26:12,159 Speaker 1: the consumers want, what the companies are able to deliver 431 00:26:12,240 --> 00:26:15,000 Speaker 1: in response to that need, and then what the regulators 432 00:26:15,000 --> 00:26:18,000 Speaker 1: are able to introduce in terms of a balance in 433 00:26:18,119 --> 00:26:21,359 Speaker 1: terms of rules and regulations. Uh And in this produal 434 00:26:21,440 --> 00:26:25,080 Speaker 1: case in around privacy and security, and I would argue 435 00:26:25,119 --> 00:26:27,920 Speaker 1: that the companies have it in their best interests to 436 00:26:27,960 --> 00:26:31,080 Speaker 1: handle this as carefully as possible for multiple reasons. One, 437 00:26:31,520 --> 00:26:34,359 Speaker 1: like you've just pointed out. If they do not, then 438 00:26:34,840 --> 00:26:37,120 Speaker 1: that means that you're going to get that that sort 439 00:26:37,160 --> 00:26:40,040 Speaker 1: of tick talk effect, the tick being that they take 440 00:26:40,080 --> 00:26:44,080 Speaker 1: a certain approach, the talk being that regulations are following 441 00:26:44,119 --> 00:26:48,520 Speaker 1: because if there's any mishandling, especially of a of a 442 00:26:48,600 --> 00:26:52,560 Speaker 1: chaotic scale, then there's going to be a harsh response 443 00:26:52,960 --> 00:26:55,600 Speaker 1: further down the line, and it doesn't behoove the companies 444 00:26:56,080 --> 00:27:01,239 Speaker 1: to to uh invite that in alsobviously, if they do 445 00:27:01,320 --> 00:27:04,280 Speaker 1: not prove to be responsible with that data, that reflects 446 00:27:04,320 --> 00:27:07,320 Speaker 1: poorly on them from a consumer standpoint as well, they'll 447 00:27:07,400 --> 00:27:11,200 Speaker 1: lose customers. So it's not as if there's no incentive 448 00:27:11,680 --> 00:27:14,159 Speaker 1: on the company's part to be careful, but at the 449 00:27:14,160 --> 00:27:15,960 Speaker 1: same time they want to be able to leverage that 450 00:27:16,080 --> 00:27:19,840 Speaker 1: data uh to to make as good use of it 451 00:27:19,880 --> 00:27:23,159 Speaker 1: as possible. I've often said on this show that if 452 00:27:23,200 --> 00:27:26,199 Speaker 1: you look around and you realize that the service you 453 00:27:26,240 --> 00:27:29,840 Speaker 1: are using doesn't cost you anything, that essentially that means 454 00:27:29,880 --> 00:27:32,919 Speaker 1: that you yourself are the product and that what you 455 00:27:32,960 --> 00:27:36,400 Speaker 1: are doing is generating value for another entity out there, 456 00:27:36,440 --> 00:27:38,919 Speaker 1: for example like Google, where you're using Google Search and 457 00:27:39,320 --> 00:27:42,600 Speaker 1: then turn is generating value for Google. You yourself are 458 00:27:42,640 --> 00:27:45,760 Speaker 1: are the probably being sold to other companies. So it's 459 00:27:45,920 --> 00:27:48,240 Speaker 1: it's one of those things where it's the balance between 460 00:27:48,760 --> 00:27:52,760 Speaker 1: the desire to provide this service and to make um, 461 00:27:53,119 --> 00:27:55,800 Speaker 1: you know, revenue off of of of something beyond just 462 00:27:55,920 --> 00:27:59,880 Speaker 1: selling a device like the Google Home device, and making 463 00:28:00,400 --> 00:28:03,920 Speaker 1: certain that you don't alienate your consumer base or invite 464 00:28:04,440 --> 00:28:08,720 Speaker 1: particularly restrictive regulations. UH. To that end. Of course, in 465 00:28:08,760 --> 00:28:11,359 Speaker 1: the United States, it's one story. In other parts of 466 00:28:11,359 --> 00:28:15,640 Speaker 1: the world, there are different views of privacy and security, 467 00:28:15,680 --> 00:28:18,880 Speaker 1: some of which go well beyond what is typically seen 468 00:28:18,920 --> 00:28:20,879 Speaker 1: here in the US. Do you think the device do 469 00:28:20,920 --> 00:28:23,800 Speaker 1: you think Google Home and things like Amazon's Echo, do 470 00:28:23,840 --> 00:28:26,480 Speaker 1: you think those are going to have different levels of 471 00:28:27,280 --> 00:28:29,840 Speaker 1: acceptance in different parts of the world? And where do 472 00:28:29,920 --> 00:28:33,960 Speaker 1: you think might be a case for this is probably 473 00:28:34,160 --> 00:28:35,880 Speaker 1: going to be a big success in one place. We've 474 00:28:35,920 --> 00:28:38,120 Speaker 1: heard that Amazon Niccho has been a pretty big success 475 00:28:38,160 --> 00:28:43,560 Speaker 1: so far, versus a market where it may not be. Yeah, Well, 476 00:28:43,560 --> 00:28:47,640 Speaker 1: you've seen even things like credit card adoption differ from 477 00:28:47,640 --> 00:28:51,400 Speaker 1: country to country, um, just because there are different kind 478 00:28:51,440 --> 00:28:56,840 Speaker 1: of cut cultural kind of mores around credit around potential 479 00:28:57,000 --> 00:29:00,480 Speaker 1: privacy implications, UM in terms of no kind of a 480 00:29:00,520 --> 00:29:03,800 Speaker 1: transaction and kind of the origins and so forth. UM. 481 00:29:03,920 --> 00:29:05,640 Speaker 1: And so you've seen this in Europe in particular, right, 482 00:29:05,680 --> 00:29:09,280 Speaker 1: So not all countries in Europe are equally predisposed to 483 00:29:09,360 --> 00:29:12,320 Speaker 1: using credit cards as we are in the US. So yeah, 484 00:29:12,360 --> 00:29:17,840 Speaker 1: I think there definitely will be different cultural adoption. Um. 485 00:29:18,000 --> 00:29:19,320 Speaker 1: But you know, at the end of the day, like 486 00:29:19,360 --> 00:29:21,720 Speaker 1: you you mentioned rightly, a lot of these companies, it's 487 00:29:21,720 --> 00:29:25,080 Speaker 1: in their interest to do a good job because we 488 00:29:25,080 --> 00:29:28,440 Speaker 1: we as consumers, only tend to shop from people we trust. 489 00:29:29,120 --> 00:29:32,720 Speaker 1: The challenges, of course, is that we will sometimes wonder, 490 00:29:32,840 --> 00:29:34,920 Speaker 1: just like you are right here in terms of this interview, 491 00:29:35,200 --> 00:29:36,960 Speaker 1: you know, what are the implications. You know, you could 492 00:29:37,040 --> 00:29:40,160 Speaker 1: very quickly go from a situation that appears like having 493 00:29:40,160 --> 00:29:43,200 Speaker 1: your mother around all of you around you too, having 494 00:29:43,240 --> 00:29:46,240 Speaker 1: a situation we're having big brother around you, uh in 495 00:29:47,600 --> 00:29:50,000 Speaker 1: kind of sense in the Orwellian sense where something is 496 00:29:50,040 --> 00:29:53,160 Speaker 1: so aware of every facet about your life, but maybe 497 00:29:53,160 --> 00:29:55,480 Speaker 1: they just know a little bit too much. Uh. And 498 00:29:55,560 --> 00:29:58,040 Speaker 1: so you know, we're kind of entering that phase. Right. 499 00:29:58,120 --> 00:30:02,480 Speaker 1: We've we've historically had a you places that we weren't 500 00:30:02,520 --> 00:30:06,600 Speaker 1: necessarily connected to, right and our home with the exception 501 00:30:06,600 --> 00:30:11,240 Speaker 1: obviously of our PCs and our phones have not been connected. 502 00:30:11,280 --> 00:30:13,400 Speaker 1: They've been into some degree of thanks groupe, we sit 503 00:30:13,440 --> 00:30:16,560 Speaker 1: down for dinner. Um, we're not connected to the net. 504 00:30:17,280 --> 00:30:20,920 Speaker 1: And I think what this revelation is making people aware 505 00:30:20,960 --> 00:30:23,560 Speaker 1: of is that, uh, kind of in the future, there'll 506 00:30:23,560 --> 00:30:27,560 Speaker 1: be very very few places left, um that are not networked, 507 00:30:27,560 --> 00:30:33,000 Speaker 1: where our activities are not kind of transponding or transmitting 508 00:30:33,120 --> 00:30:36,960 Speaker 1: or telegraphing kind of our activities. Uh. And you know, 509 00:30:36,960 --> 00:30:39,280 Speaker 1: it will take time for people to adjust, and as 510 00:30:39,280 --> 00:30:41,080 Speaker 1: I mentioned earlier, it will not it won't just be 511 00:30:41,120 --> 00:30:44,800 Speaker 1: about consumers and kind of buyers, but also you know, 512 00:30:44,840 --> 00:30:47,560 Speaker 1: the governments will have a say, and as you pointed out, 513 00:30:48,040 --> 00:30:50,800 Speaker 1: you know, in certain places the governments have already had 514 00:30:50,880 --> 00:30:54,520 Speaker 1: to say around privacy, like in Europe with the introduction 515 00:30:54,680 --> 00:30:59,520 Speaker 1: of the General Data Protection Regulation UM to better protect consumers. 516 00:31:00,040 --> 00:31:01,880 Speaker 1: I think we're all going to become a lot more 517 00:31:01,880 --> 00:31:07,520 Speaker 1: sensitive to the privacy implications of always being online. And 518 00:31:07,720 --> 00:31:10,840 Speaker 1: I think that we're seeing that as well, just you know, 519 00:31:11,560 --> 00:31:16,480 Speaker 1: in other areas of technology. Just recently, there was uh, 520 00:31:16,520 --> 00:31:20,880 Speaker 1: these reports coming out about the FBI's database of biometric 521 00:31:21,040 --> 00:31:24,960 Speaker 1: data and the concerns people have about that and even 522 00:31:25,000 --> 00:31:29,080 Speaker 1: interesting questions like do I do I own my own face? 523 00:31:29,600 --> 00:31:33,200 Speaker 1: Should I shouldn't I have access to data about me? 524 00:31:34,080 --> 00:31:37,800 Speaker 1: And this again, you know, we're in a world where 525 00:31:37,800 --> 00:31:42,560 Speaker 1: our technology is pervasive, and in many ways that is amazing. 526 00:31:42,560 --> 00:31:47,520 Speaker 1: It is giving us an almost seamless experience of having 527 00:31:47,600 --> 00:31:50,360 Speaker 1: our desires catered to before we can even give thought 528 00:31:50,440 --> 00:31:52,560 Speaker 1: to them. That is the big promise of the Internet 529 00:31:52,560 --> 00:31:55,160 Speaker 1: of things, and I love that idea. It is something 530 00:31:55,200 --> 00:31:57,600 Speaker 1: that really appeals to me. On the flip side, you 531 00:31:57,600 --> 00:32:02,520 Speaker 1: start to realize that you're regular actions are creating data, 532 00:32:02,600 --> 00:32:06,160 Speaker 1: and that data does in fact have value, different value 533 00:32:06,160 --> 00:32:09,520 Speaker 1: to different entities out there, and so having these sort 534 00:32:09,560 --> 00:32:13,800 Speaker 1: of technologies inviting them in. Once you have reconciled this 535 00:32:13,920 --> 00:32:17,560 Speaker 1: idea and you realize that, uh, that this is going on, 536 00:32:17,720 --> 00:32:19,720 Speaker 1: then you can start to make those strategies. How is 537 00:32:19,760 --> 00:32:22,080 Speaker 1: the was the best way of handling that both on 538 00:32:22,120 --> 00:32:25,120 Speaker 1: the the end user side and on the back end side, 539 00:32:25,520 --> 00:32:30,600 Speaker 1: so that it is a responsible approach. That's really what 540 00:32:30,600 --> 00:32:33,960 Speaker 1: what your company is looking into, right, the idea of 541 00:32:34,720 --> 00:32:42,480 Speaker 1: security and and helping companies protect uh customer data. Yeah, 542 00:32:42,640 --> 00:32:45,040 Speaker 1: so that's actually kind of very very kind of similar 543 00:32:45,080 --> 00:32:46,440 Speaker 1: to this. I think you know, one of the things, 544 00:32:46,680 --> 00:32:49,479 Speaker 1: um you were kind of touching upon is this kind 545 00:32:49,520 --> 00:32:52,959 Speaker 1: of expectation of organizations to do a better job of 546 00:32:53,000 --> 00:32:58,520 Speaker 1: safeguarding your information, essentially being responsible custodians of your data. 547 00:32:59,160 --> 00:33:02,600 Speaker 1: The challenge for most companies UM, you know, maybe with 548 00:33:02,600 --> 00:33:05,440 Speaker 1: with less sophistication than a Google, but maybe even Google, 549 00:33:06,120 --> 00:33:09,640 Speaker 1: is that they collect so much information about you, and 550 00:33:09,680 --> 00:33:13,800 Speaker 1: they collected it's so many different places and so many applications. 551 00:33:14,240 --> 00:33:17,280 Speaker 1: It doesn't necessarily mean that all that information is tied together, 552 00:33:17,760 --> 00:33:22,080 Speaker 1: but you are leaving digital footprints across organizations and so 553 00:33:22,200 --> 00:33:27,520 Speaker 1: these these companies are essentially becoming large data collection points 554 00:33:27,960 --> 00:33:29,960 Speaker 1: and it's hard for you as a consumer to know 555 00:33:30,040 --> 00:33:33,479 Speaker 1: exactly what digital footprints you've left. You want to know 556 00:33:33,600 --> 00:33:36,880 Speaker 1: what assets you've left with them and believe it or not. UM. 557 00:33:36,920 --> 00:33:40,440 Speaker 1: You know, if you think about accounting and how companies 558 00:33:40,480 --> 00:33:45,880 Speaker 1: are expensed, expected to have responsible UM tools in place 559 00:33:46,000 --> 00:33:49,240 Speaker 1: to track how much revenue comes in, how that money 560 00:33:49,320 --> 00:33:53,360 Speaker 1: is getting dispersed, who it's paying. So that's all about 561 00:33:53,360 --> 00:33:58,560 Speaker 1: accounting and financial responsibility. On the digital side, there's very 562 00:33:58,560 --> 00:34:00,760 Speaker 1: little of that today and that kind of the origin 563 00:34:00,800 --> 00:34:02,800 Speaker 1: of Big I D. You could think a big I 564 00:34:02,880 --> 00:34:06,520 Speaker 1: D as a tool set to help big companies understand 565 00:34:06,840 --> 00:34:11,120 Speaker 1: where their customer information is what's at risk or potentially 566 00:34:11,800 --> 00:34:14,280 Speaker 1: at risk, either in terms of breach or in terms 567 00:34:14,320 --> 00:34:18,319 Speaker 1: of misuse UH and then how to better understand how 568 00:34:18,360 --> 00:34:22,719 Speaker 1: that information is getting used in the organization, either to 569 00:34:22,760 --> 00:34:26,360 Speaker 1: help ensure that it's compliant with regulations or secondly that 570 00:34:26,400 --> 00:34:29,680 Speaker 1: it's complied with their own kind of privacy rules, their 571 00:34:29,719 --> 00:34:34,319 Speaker 1: own consent agreements that they've created between themselves and their consumers. 572 00:34:34,719 --> 00:34:36,960 Speaker 1: And so I think that this idea of a ledger 573 00:34:37,080 --> 00:34:41,280 Speaker 1: or accounting software for privacy information doesn't as yet exist, 574 00:34:41,360 --> 00:34:43,960 Speaker 1: and I think increasingly, just given the number of digital 575 00:34:44,040 --> 00:34:47,480 Speaker 1: touchpoints that we have with the companies we interact with, 576 00:34:48,239 --> 00:34:51,319 Speaker 1: um that it's going to be certainly a future requirement. 577 00:34:52,080 --> 00:34:55,480 Speaker 1: I think that's really interesting, and I'm thankful that there 578 00:34:55,520 --> 00:34:58,640 Speaker 1: are organizations like yours that are looking into this to 579 00:34:58,680 --> 00:35:01,520 Speaker 1: try and create those best practic This is because, as 580 00:35:01,520 --> 00:35:05,680 Speaker 1: we've seen recently, it's scholarship has shown it takes very 581 00:35:05,800 --> 00:35:09,719 Speaker 1: few data points to be able to link some information 582 00:35:09,800 --> 00:35:12,239 Speaker 1: to a specific person, and I think a lot of 583 00:35:12,280 --> 00:35:16,839 Speaker 1: companies out there may not even be aware of the 584 00:35:16,880 --> 00:35:19,200 Speaker 1: implications of some of the data they're collecting, not through 585 00:35:19,239 --> 00:35:23,319 Speaker 1: any sort of of maliciousness. It simply is, as you 586 00:35:23,400 --> 00:35:26,640 Speaker 1: point out, there's so many of these little digital touch 587 00:35:26,680 --> 00:35:31,040 Speaker 1: points that you cannot necessarily anticipate what the consequences are 588 00:35:31,120 --> 00:35:35,040 Speaker 1: from the from the very beginning. And uh, it's amazing 589 00:35:35,080 --> 00:35:38,440 Speaker 1: to me to think that this is going on everywhere, 590 00:35:38,640 --> 00:35:42,560 Speaker 1: and and it's it's a it's a snowball that's already 591 00:35:43,120 --> 00:35:45,040 Speaker 1: going down the hill. It's just going to keep on going. 592 00:35:45,560 --> 00:35:48,720 Speaker 1: It's very reassuring to hear that there are people actively 593 00:35:48,760 --> 00:35:51,600 Speaker 1: thinking about these and trying these issues and trying to 594 00:35:51,640 --> 00:35:54,360 Speaker 1: find the best ways of handling that kind of information 595 00:35:54,880 --> 00:35:59,200 Speaker 1: so that we don't have uh any or we can 596 00:35:59,239 --> 00:36:05,080 Speaker 1: avoid as many chaotic moments of absolute failure as possible. Well, again, 597 00:36:05,640 --> 00:36:10,080 Speaker 1: I think a lot of people assume that big companies 598 00:36:10,200 --> 00:36:17,120 Speaker 1: are actively pursuing the collection and selling of all of 599 00:36:17,160 --> 00:36:20,080 Speaker 1: the data, and that's not the case. Across the board, 600 00:36:20,560 --> 00:36:23,040 Speaker 1: there are companies that are collecting a great deal of 601 00:36:23,120 --> 00:36:25,719 Speaker 1: data in the pursuit of whatever business they do, but 602 00:36:26,760 --> 00:36:29,520 Speaker 1: it's not through the it's not necessarily with an intent 603 00:36:29,680 --> 00:36:34,319 Speaker 1: to do anything uh, you know, commercial with that information. 604 00:36:34,520 --> 00:36:37,560 Speaker 1: But knowing this makes it easier for those companies to 605 00:36:37,600 --> 00:36:41,640 Speaker 1: be more responsible and uh, and also to maybe even 606 00:36:41,640 --> 00:36:43,560 Speaker 1: get to a point where they change up their practices 607 00:36:43,600 --> 00:36:45,759 Speaker 1: so that they're only collecting the points of data that 608 00:36:45,800 --> 00:36:49,680 Speaker 1: are relevant to their business. Yeah, well, look, certainly that's 609 00:36:49,719 --> 00:36:53,160 Speaker 1: the intent of big ideas to help companies be more 610 00:36:53,239 --> 00:36:57,600 Speaker 1: responsible around their digital assets, the customer assets, which you 611 00:36:57,600 --> 00:37:00,520 Speaker 1: could argue are probably the most important assets. You know. 612 00:37:00,600 --> 00:37:03,520 Speaker 1: Sometimes you've heard people talk about employees, and you know 613 00:37:03,600 --> 00:37:06,279 Speaker 1: your your most important assets are your employees, and they 614 00:37:06,320 --> 00:37:09,560 Speaker 1: walk out the door every every night. Well, your customers 615 00:37:09,560 --> 00:37:12,280 Speaker 1: are pretty valuable too, because if they if they stop 616 00:37:12,600 --> 00:37:17,400 Speaker 1: patronizing you, your business suffers, and their loyalty increasingly is 617 00:37:17,520 --> 00:37:21,120 Speaker 1: very thickle. So if they don't have confidence that you 618 00:37:21,160 --> 00:37:26,680 Speaker 1: are protecting their personal information, they're kind of digital digital 619 00:37:26,719 --> 00:37:30,120 Speaker 1: footprints don't go somewhere else. They'll go to somebody that 620 00:37:30,560 --> 00:37:33,799 Speaker 1: does take better care of that, which again is why 621 00:37:33,960 --> 00:37:37,080 Speaker 1: it kind of makes sense to have uh technology that 622 00:37:37,360 --> 00:37:43,960 Speaker 1: gives organizations better, better tooling to track, manage, protect those 623 00:37:43,960 --> 00:37:47,560 Speaker 1: digital personal assets. About digital information that represents kind of 624 00:37:47,600 --> 00:37:50,120 Speaker 1: who you are, where you live, where you've been, what 625 00:37:50,280 --> 00:37:53,959 Speaker 1: you like when you're going on vacation, et cetera. Now, 626 00:37:54,000 --> 00:37:57,080 Speaker 1: I've got a question for you personally, which is that, 627 00:37:58,280 --> 00:38:01,200 Speaker 1: are you at a point where you would adopt a 628 00:38:01,280 --> 00:38:04,960 Speaker 1: technology such as Amazon Echo or Google Home or would 629 00:38:04,960 --> 00:38:09,200 Speaker 1: you personally wait a little longer or you know, where 630 00:38:09,200 --> 00:38:11,719 Speaker 1: do you stand on that? Because I can tell you 631 00:38:12,040 --> 00:38:14,520 Speaker 1: being aware of these issues, I guess it's only fair 632 00:38:14,560 --> 00:38:16,799 Speaker 1: that I answered my own question being aware of these 633 00:38:16,800 --> 00:38:20,239 Speaker 1: issues and and being cognizant of them. I I'm still 634 00:38:20,360 --> 00:38:24,560 Speaker 1: leaning towards getting one, knowing what I know, and taking 635 00:38:24,600 --> 00:38:28,000 Speaker 1: the risk in order to have the benefit. My wife 636 00:38:28,320 --> 00:38:30,759 Speaker 1: feels very differently about it, so that's why I do 637 00:38:30,840 --> 00:38:34,319 Speaker 1: not have one. But but I'm curious what, as you, 638 00:38:34,400 --> 00:38:37,319 Speaker 1: as an expert on this subject matter, how you feel 639 00:38:37,360 --> 00:38:41,160 Speaker 1: about that. Yeah, so I think there's there's two things 640 00:38:41,160 --> 00:38:44,480 Speaker 1: that come into play. Obviously, I'm a fifteen year vetter 641 00:38:44,520 --> 00:38:47,440 Speaker 1: in the security industry with a company focused on enterprise 642 00:38:47,440 --> 00:38:50,719 Speaker 1: privacy management now, so I understand some of the consequences 643 00:38:50,719 --> 00:38:53,640 Speaker 1: and repercussions. But I'm also at heart a person that 644 00:38:53,719 --> 00:38:56,279 Speaker 1: likes technology. I as a reader of Isaac Asimov as 645 00:38:56,320 --> 00:38:58,920 Speaker 1: a kid, Fine Land, all the kind of great science 646 00:38:58,920 --> 00:39:02,719 Speaker 1: fiction writers, and I realized the future is coming towards us, 647 00:39:03,160 --> 00:39:05,640 Speaker 1: and we could either try and hide or dock or 648 00:39:05,680 --> 00:39:08,600 Speaker 1: we could try and embrace it and understand the consequences. 649 00:39:08,640 --> 00:39:11,839 Speaker 1: So for me personally, UM, I look at this and 650 00:39:11,880 --> 00:39:15,880 Speaker 1: try to understand how this technology will impact our lives 651 00:39:15,880 --> 00:39:18,360 Speaker 1: going forward. So I will be an embracer of the 652 00:39:18,400 --> 00:39:21,280 Speaker 1: technology because I think, as I said at the very beginning, 653 00:39:21,320 --> 00:39:23,400 Speaker 1: there's a lot of good, there's a lot of convenience 654 00:39:23,400 --> 00:39:25,880 Speaker 1: that comes with it, but it's also important for me 655 00:39:25,920 --> 00:39:29,560 Speaker 1: to understand some of the consequences by going through it firsthand, 656 00:39:29,920 --> 00:39:32,719 Speaker 1: because at the end of the day, if I'm you know, 657 00:39:32,800 --> 00:39:36,239 Speaker 1: I'm part of a team building technology to better help 658 00:39:36,320 --> 00:39:39,840 Speaker 1: protect customer information, then I had need to understand the 659 00:39:39,840 --> 00:39:46,360 Speaker 1: implications of these new home automation, car automation technologies. Excellent, 660 00:39:46,960 --> 00:39:51,120 Speaker 1: Dmitri Saroda, thank you so much, founder and CEO of 661 00:39:51,280 --> 00:39:54,239 Speaker 1: Big I D. You really helped me and I hope 662 00:39:54,239 --> 00:39:57,600 Speaker 1: my listeners understand a bit more of the implications of this. 663 00:39:57,760 --> 00:40:00,719 Speaker 1: I realized that this sort of technology that has this 664 00:40:00,840 --> 00:40:04,920 Speaker 1: incredible connection to our personal lives, really a level of 665 00:40:05,000 --> 00:40:09,120 Speaker 1: intimacy that most technology does not have, carries with it 666 00:40:09,239 --> 00:40:11,239 Speaker 1: some things that can be a little worrisome. But I 667 00:40:11,480 --> 00:40:14,680 Speaker 1: agree with you. I think if we enter into it 668 00:40:14,760 --> 00:40:18,680 Speaker 1: with open eyes and we are aware of the challenges. 669 00:40:18,719 --> 00:40:21,520 Speaker 1: We're not denying that challenges exist, but we are aware 670 00:40:21,560 --> 00:40:24,680 Speaker 1: of them. That allows us to actually overcome those challenges 671 00:40:24,800 --> 00:40:28,479 Speaker 1: and reap the benefits of this really powerful tool. Thank 672 00:40:28,480 --> 00:40:30,440 Speaker 1: you so much for coming on the show and talking 673 00:40:30,440 --> 00:40:34,640 Speaker 1: with us. My pleasure. Care. I think it's really important 674 00:40:34,640 --> 00:40:39,400 Speaker 1: to remember that Mr Sirota actually said we should embrace technology, 675 00:40:39,440 --> 00:40:42,759 Speaker 1: but do so in a way where we're aware of 676 00:40:42,800 --> 00:40:46,280 Speaker 1: the consequences and we are doing our best to mitigate 677 00:40:46,360 --> 00:40:51,480 Speaker 1: any negative fallout from this technology moving forward. We shouldn't 678 00:40:51,520 --> 00:40:54,440 Speaker 1: deny it, we shouldn't try to stop it, but we 679 00:40:54,480 --> 00:40:57,359 Speaker 1: should definitely be responsible with the way we develop it 680 00:40:57,640 --> 00:41:00,359 Speaker 1: and the way that we use it. Potential Really, it 681 00:41:00,400 --> 00:41:04,319 Speaker 1: has the capacity to make our lives easier. I mean, 682 00:41:04,360 --> 00:41:09,840 Speaker 1: imagine being able to handle everything by by just shifting 683 00:41:09,880 --> 00:41:14,200 Speaker 1: it over to your personal assistant who lives everywhere. You 684 00:41:14,239 --> 00:41:17,239 Speaker 1: can access that personal assistant wherever you might be through 685 00:41:17,320 --> 00:41:21,520 Speaker 1: whatever computer or smartphone or standalone device you happen to 686 00:41:21,560 --> 00:41:25,279 Speaker 1: have at your disposal at that place, and access all 687 00:41:25,320 --> 00:41:30,800 Speaker 1: of that those features, everything from entertainment to handling travel 688 00:41:31,040 --> 00:41:35,080 Speaker 1: and stuff that you want taking care of that you 689 00:41:35,120 --> 00:41:37,839 Speaker 1: don't necessarily want to attend to yourself, so you can 690 00:41:37,880 --> 00:41:41,080 Speaker 1: save that time to do something else. That's a really 691 00:41:41,080 --> 00:41:45,440 Speaker 1: cool idea, And I love the promise of digital assistance, 692 00:41:45,480 --> 00:41:49,320 Speaker 1: the idea that we will slowly get towards this future 693 00:41:49,440 --> 00:41:52,720 Speaker 1: where the technology around us anticipates what we need before 694 00:41:52,719 --> 00:41:55,480 Speaker 1: we can give voice to it. I love that thought 695 00:41:56,120 --> 00:41:58,359 Speaker 1: and the idea that that my life just becomes sort 696 00:41:58,360 --> 00:42:02,520 Speaker 1: of magical as a result, because the technology is shifting 697 00:42:02,640 --> 00:42:06,000 Speaker 1: things to my whim before I can even voice what 698 00:42:06,120 --> 00:42:08,959 Speaker 1: that whim is, before I might even be aware there's 699 00:42:08,960 --> 00:42:13,640 Speaker 1: a whim. I could be whim less. I'm done saying whim. 700 00:42:13,680 --> 00:42:15,759 Speaker 1: In future episodes, I'm going to take a closer look 701 00:42:16,040 --> 00:42:19,000 Speaker 1: at what makes these technologies tick and the potential they 702 00:42:19,040 --> 00:42:20,839 Speaker 1: have to change the way we interact with the world 703 00:42:20,880 --> 00:42:24,440 Speaker 1: around us. In the meantime, if you have suggestions for 704 00:42:24,520 --> 00:42:28,279 Speaker 1: future episodes of tech Stuff, send me an email. The 705 00:42:28,400 --> 00:42:31,799 Speaker 1: address is tech Stuff at how stuff works dot com, 706 00:42:31,920 --> 00:42:34,719 Speaker 1: or drop me a line on Twitter or Facebook. The 707 00:42:34,800 --> 00:42:37,960 Speaker 1: handle for both of those is text stuff H s 708 00:42:38,280 --> 00:42:47,120 Speaker 1: W and I'll talk to you again really soon. For 709 00:42:47,239 --> 00:42:49,759 Speaker 1: more on this and bathos of other topics is how 710 00:42:49,800 --> 00:43:00,120 Speaker 1: staff works dot com. M.