1 00:00:00,520 --> 00:00:02,960 Speaker 1: Ron an Indian parts are a real problem. He said, 2 00:00:03,320 --> 00:00:06,520 Speaker 1: you've heard that guy with the podcast here, the appliance Doctor. 3 00:00:06,800 --> 00:00:09,239 Speaker 1: I'm going, there's an appliance doctor. I'm thinking, j I 4 00:00:09,240 --> 00:00:11,080 Speaker 1: don't think i've heard of this guy. I said, you 5 00:00:11,119 --> 00:00:14,360 Speaker 1: mean the car doctor and he went, yeah, the car Doctor. 6 00:00:14,560 --> 00:00:16,560 Speaker 1: And I just stuck out my hand and I said, 7 00:00:16,680 --> 00:00:18,560 Speaker 1: Van and Aian, nice to meet you. Thank god he 8 00:00:18,640 --> 00:00:20,119 Speaker 1: was up against the case because I thought he was 9 00:00:20,120 --> 00:00:20,760 Speaker 1: going to fall over. 10 00:00:20,800 --> 00:00:26,759 Speaker 2: Hesus now needs introduce myself. I'm a man, will and 11 00:00:27,000 --> 00:00:30,320 Speaker 2: say he's to meet you. 12 00:00:31,360 --> 00:00:36,159 Speaker 3: Oh, you did the car doctor and this is a sixty. 13 00:00:35,800 --> 00:00:39,160 Speaker 1: Two six y two Cadillac correct, right, All right, Well 14 00:00:39,479 --> 00:00:43,600 Speaker 1: you've eliminated components, provided you've got good used parts or 15 00:00:43,680 --> 00:00:46,760 Speaker 1: good new parts, whichever you've purchased. So we're now down 16 00:00:46,800 --> 00:00:50,600 Speaker 1: to a wiring diagram. There's nothing else there. 17 00:00:50,680 --> 00:00:54,200 Speaker 3: Brother, Welcome to the radio home of ron Ananian, the 18 00:00:54,240 --> 00:00:57,640 Speaker 3: Car Doctor. Since nineteen ninety one. This is where car 19 00:00:57,720 --> 00:01:01,440 Speaker 3: owners the world overturned to for the definitive opinion on 20 00:01:01,560 --> 00:01:05,240 Speaker 3: automotive repair. If your mechanics giving you a busy signal, 21 00:01:05,440 --> 00:01:08,959 Speaker 3: pick up the phone and call in. The garage doors. 22 00:01:08,840 --> 00:01:10,960 Speaker 1: Are open, but I am here to take your calls 23 00:01:11,280 --> 00:01:13,280 Speaker 1: at eight five five five six oh ninety nine. 24 00:01:13,240 --> 00:01:15,040 Speaker 2: Hundred and now Pee. 25 00:01:17,319 --> 00:01:20,479 Speaker 1: Ronnie, welcome a board listeners, Runnmini and the Car Doctor. 26 00:01:20,720 --> 00:01:23,080 Speaker 1: You know, we're gonna start off this hour with something 27 00:01:23,120 --> 00:01:26,039 Speaker 1: a little different. We're gonna talk with Chris Pischet. He 28 00:01:26,319 --> 00:01:30,520 Speaker 1: is from Smarter AI artificial intelligence, and we think with 29 00:01:30,640 --> 00:01:34,280 Speaker 1: the up and coming news and conversations about AI and automobiles, 30 00:01:34,280 --> 00:01:35,800 Speaker 1: it would be a great thing for you guys to 31 00:01:35,840 --> 00:01:38,080 Speaker 1: hear and listen to. Chris. Welcome to the Car Doctor. 32 00:01:38,080 --> 00:01:39,240 Speaker 1: Thanks for taking the time today. 33 00:01:40,720 --> 00:01:42,440 Speaker 2: Thanks for having me, ron It's great to be here. 34 00:01:42,720 --> 00:01:46,959 Speaker 1: You know, AI artificial intelligence, right, the average person always 35 00:01:46,959 --> 00:01:49,120 Speaker 1: thinks and we we kind of talked about this beforehand. 36 00:01:49,160 --> 00:01:51,720 Speaker 1: You know, it's it's an Arnold Schwarzenegger movie, right, artificial 37 00:01:51,720 --> 00:01:55,160 Speaker 1: intelligence from the future. But the future is now. You know, 38 00:01:55,520 --> 00:01:59,960 Speaker 1: what is artificial intelligences as you know, as you would describe, 39 00:02:00,120 --> 00:02:02,880 Speaker 1: but now here and where did it come from? What's 40 00:02:02,920 --> 00:02:05,880 Speaker 1: the history? 41 00:02:06,000 --> 00:02:10,480 Speaker 2: So, artificial intelligence is a branch of computer science that's 42 00:02:10,520 --> 00:02:16,680 Speaker 2: focused on developing systems capable of performing tasks that would 43 00:02:16,720 --> 00:02:24,040 Speaker 2: normally require human intelligence. So these include tasks like problem solving, 44 00:02:25,560 --> 00:02:35,120 Speaker 2: understanding natural language and decision making. The development of AI 45 00:02:36,000 --> 00:02:38,680 Speaker 2: traces back I guess most people would agree it traces 46 00:02:38,720 --> 00:02:42,760 Speaker 2: back to the nineteen fifties and there were a number 47 00:02:42,760 --> 00:02:50,440 Speaker 2: of pioneers of computer science that started some early research 48 00:02:50,560 --> 00:02:55,919 Speaker 2: into artificial intelligence. The most famous of these computer scientists 49 00:02:57,000 --> 00:02:59,800 Speaker 2: was a British gentleman by the name of Alan Turing, 50 00:03:00,680 --> 00:03:05,160 Speaker 2: who I think some of the people listening might recognize 51 00:03:05,200 --> 00:03:12,520 Speaker 2: the name. Alan Turing is is famous for having cracked 52 00:03:12,560 --> 00:03:17,480 Speaker 2: the uh M Germans we're using in World War Two, 53 00:03:19,080 --> 00:03:24,160 Speaker 2: So a lot of people credit him with with you know, 54 00:03:24,480 --> 00:03:28,320 Speaker 2: winning or enabling the Western forces to win World War Two. 55 00:03:29,120 --> 00:03:33,040 Speaker 2: So research has been going on in AI since about 56 00:03:33,080 --> 00:03:38,040 Speaker 2: the fifties, but I would say that for most people listening, 57 00:03:39,840 --> 00:03:46,360 Speaker 2: we've been benefiting from AI for about the last twenty years. 58 00:03:46,440 --> 00:03:52,640 Speaker 2: So for about the last twenty years, different tech companies, 59 00:03:52,680 --> 00:03:57,720 Speaker 2: different software companies have been starting to incorporate AI into 60 00:03:57,760 --> 00:04:00,880 Speaker 2: their products and services to to make them better. 61 00:04:01,440 --> 00:04:04,240 Speaker 1: Could I be driving a car with AI and I 62 00:04:04,280 --> 00:04:05,040 Speaker 1: don't even know it? 63 00:04:05,160 --> 00:04:12,640 Speaker 2: Chris, Absolutely, I think we're you know, we're all we're 64 00:04:12,640 --> 00:04:18,800 Speaker 2: all driving cars with with AI. So features like uh, 65 00:04:19,240 --> 00:04:23,120 Speaker 2: you know, collision avoidance for example, relying on on AI. 66 00:04:23,880 --> 00:04:26,839 Speaker 2: I think most you know, most cars that are manufactured 67 00:04:26,880 --> 00:04:31,080 Speaker 2: today are equipped with different forms of what we call 68 00:04:31,160 --> 00:04:36,080 Speaker 2: aid ASS driver assistance systems, and more and more cars 69 00:04:36,120 --> 00:04:41,760 Speaker 2: now are becoming equipped with d MS or driver monitoring systems, 70 00:04:42,360 --> 00:04:44,920 Speaker 2: and all of these systems are based on on AI. 71 00:04:45,560 --> 00:04:49,880 Speaker 1: Now driver monitoring systems. That that's a term new to me, 72 00:04:50,920 --> 00:04:55,680 Speaker 1: is that they're tracking the manufacturers tracking maybe what radio 73 00:04:55,760 --> 00:04:59,120 Speaker 1: stations we listen to, or whether our seatbelt is on 74 00:04:59,320 --> 00:05:02,360 Speaker 1: or not. Head position. Is that driver monitoring or is 75 00:05:02,400 --> 00:05:03,520 Speaker 1: it something more. 76 00:05:05,000 --> 00:05:09,479 Speaker 2: Probably not radio stations, but yes, as to things like 77 00:05:09,640 --> 00:05:17,080 Speaker 2: seat belts, distraction, phone use, and you know, and you 78 00:05:17,160 --> 00:05:20,880 Speaker 2: mentioned the uh, the position of the of the drivers. 79 00:05:20,880 --> 00:05:24,880 Speaker 2: That all of those things are measured and monitored by 80 00:05:25,400 --> 00:05:26,720 Speaker 2: a driver monitoring system. 81 00:05:27,000 --> 00:05:31,520 Speaker 1: What's the end goal, Chris? You know, are they trying 82 00:05:31,680 --> 00:05:35,600 Speaker 1: to create that self driving car? I mean, they are right, 83 00:05:35,680 --> 00:05:38,679 Speaker 1: but at what level? Is there a future of complete 84 00:05:38,680 --> 00:05:39,960 Speaker 1: self driving automobiles? 85 00:05:42,640 --> 00:05:45,880 Speaker 2: Absolutely, there's a future of complete self driving automobiles. I 86 00:05:45,920 --> 00:05:51,120 Speaker 2: think driver assistants and driver monitoring have I think they 87 00:05:51,120 --> 00:05:54,440 Speaker 2: have two separate goals. Okay, so the goal of driver 88 00:05:54,560 --> 00:06:00,120 Speaker 2: monitoring is simply, you know, zero fatalities or zero collisions. 89 00:06:01,320 --> 00:06:05,400 Speaker 2: A lot you know, a lot of people listening. You know, 90 00:06:05,400 --> 00:06:09,800 Speaker 2: we all we all see it every day that the 91 00:06:09,880 --> 00:06:14,440 Speaker 2: problem of complacent and distracted driving has become even a 92 00:06:14,480 --> 00:06:18,640 Speaker 2: bigger problem than the impair driving. It's now responsible for 93 00:06:18,720 --> 00:06:23,760 Speaker 2: more accidents, more more, you know, more injuries, more fatalities 94 00:06:24,360 --> 00:06:27,880 Speaker 2: than impair driving. And so, you know, the goal of 95 00:06:28,440 --> 00:06:33,400 Speaker 2: driver monitoring is to help us as drivers, to remind 96 00:06:33,480 --> 00:06:37,279 Speaker 2: us as drivers, you know, to to drive as safely 97 00:06:37,320 --> 00:06:38,360 Speaker 2: as we possibly can. 98 00:06:39,120 --> 00:06:42,520 Speaker 1: There's a there's a statistic in there right about excuse me, 99 00:06:42,560 --> 00:06:45,640 Speaker 1: that if we if we went to self driving cars, 100 00:06:45,680 --> 00:06:48,960 Speaker 1: the number of accidents would drop dramatically. What was the 101 00:06:49,040 --> 00:06:50,520 Speaker 1: number in the ninety percentile. 102 00:06:52,360 --> 00:06:58,200 Speaker 2: Yes, So, if you compare the nh TSA has been 103 00:06:58,240 --> 00:07:05,960 Speaker 2: publishing for decades the crash rate of American you know, 104 00:07:06,000 --> 00:07:11,400 Speaker 2: American vehicles, vehicles on American roads, and the number has 105 00:07:12,600 --> 00:07:17,120 Speaker 2: always been around one crash every half a million miles. 106 00:07:17,960 --> 00:07:20,080 Speaker 2: So if you do the math, I think the average 107 00:07:20,120 --> 00:07:24,560 Speaker 2: American drivers should expect to be involved in two or 108 00:07:24,600 --> 00:07:28,800 Speaker 2: three collisions, you know, during the course of their lifetime. 109 00:07:31,320 --> 00:07:37,160 Speaker 2: TESLA in the last few years has started publishing crash 110 00:07:37,240 --> 00:07:43,640 Speaker 2: rates for Tesla vehicles as a whole, and specifically for 111 00:07:43,760 --> 00:07:48,120 Speaker 2: Tesla vehicles that are equipped with their autopilot and full 112 00:07:48,160 --> 00:07:52,960 Speaker 2: self driving features. And in the most recent numbers that 113 00:07:53,040 --> 00:07:56,040 Speaker 2: Tesla published, I want to say, two or three weeks ago, 114 00:07:56,880 --> 00:07:59,760 Speaker 2: they had the crash rate down to about one every 115 00:08:00,040 --> 00:08:04,800 Speaker 2: even million miles. So, in other words, the the leading 116 00:08:06,120 --> 00:08:12,080 Speaker 2: driver assistance or self driving system today is about fourteen 117 00:08:12,200 --> 00:08:17,400 Speaker 2: times more safe than an average American driver, you know, 118 00:08:17,720 --> 00:08:19,160 Speaker 2: as measured by a crash rate. 119 00:08:19,640 --> 00:08:24,320 Speaker 1: Wow. That's so in a world of self more self 120 00:08:24,400 --> 00:08:29,200 Speaker 1: driving cars. You know, I think about the economic impact, right, 121 00:08:29,320 --> 00:08:31,840 Speaker 1: all of a sudden, what's car insurance going to cost? 122 00:08:32,040 --> 00:08:37,560 Speaker 1: Because there's less accidents and less payouts? You know, you 123 00:08:37,720 --> 00:08:41,720 Speaker 1: just you think of it so far reaching, you know, 124 00:08:41,800 --> 00:08:46,480 Speaker 1: as far as the effects of it is, it is 125 00:08:46,600 --> 00:08:49,000 Speaker 1: the is the complete self driving car. There's complete self 126 00:08:49,080 --> 00:08:53,480 Speaker 1: driving cars here now right in an ev right, Chris. 127 00:08:54,760 --> 00:09:01,960 Speaker 2: Well, So, the the technologies for self driving and you know, 128 00:09:02,040 --> 00:09:04,960 Speaker 2: for the the energy or power system for a car, 129 00:09:05,480 --> 00:09:10,280 Speaker 2: those are completely separate. So you can certainly have a 130 00:09:10,400 --> 00:09:15,600 Speaker 2: self driving gas powered car, and equally you can have 131 00:09:16,520 --> 00:09:20,360 Speaker 2: an electric powered car that's not capable of self driving. 132 00:09:20,800 --> 00:09:21,240 Speaker 1: Gotcha. 133 00:09:21,320 --> 00:09:26,240 Speaker 2: However, you know, it just so happens that that Tesla 134 00:09:26,440 --> 00:09:29,880 Speaker 2: is at the forefront of both of those technologies, and 135 00:09:30,000 --> 00:09:35,240 Speaker 2: so there, I think for most people, we associate electric 136 00:09:35,320 --> 00:09:40,240 Speaker 2: powered vehicles with self driving vehicles, even though those two 137 00:09:40,280 --> 00:09:44,479 Speaker 2: technologies don't necessarily need to be tied together, right. 138 00:09:45,000 --> 00:09:49,640 Speaker 1: Right, So you know that that self driving car that's 139 00:09:49,679 --> 00:09:51,880 Speaker 1: here now right there are a couple of manufacturers I 140 00:09:51,920 --> 00:09:53,840 Speaker 1: can think off the top of my head. General Motors 141 00:09:53,880 --> 00:09:56,360 Speaker 1: has that self driving pickup truck. They show that ad 142 00:09:56,360 --> 00:09:59,120 Speaker 1: on TV of you know, the happy family inside the 143 00:09:59,120 --> 00:10:01,120 Speaker 1: pickup truck and every but he's playing cards at the 144 00:10:01,120 --> 00:10:04,360 Speaker 1: center console and the truck's doing you know, their thing 145 00:10:04,440 --> 00:10:06,440 Speaker 1: going over the bridge, but the trailer behind them. 146 00:10:07,000 --> 00:10:07,200 Speaker 2: You know. 147 00:10:07,240 --> 00:10:11,240 Speaker 1: So self driving cars are here now, you know, do 148 00:10:11,280 --> 00:10:14,880 Speaker 1: you have any statistics or the public's reaction. What's how 149 00:10:14,880 --> 00:10:17,400 Speaker 1: does everybody see it? Are they comfortable with it? Are 150 00:10:17,440 --> 00:10:19,199 Speaker 1: they kind of shying away from it? Still? 151 00:10:25,600 --> 00:10:30,440 Speaker 2: Well? I think, uh, I think, you know, I think 152 00:10:30,480 --> 00:10:35,080 Speaker 2: the best statistic or the most reliable statistic is you know, 153 00:10:35,120 --> 00:10:39,440 Speaker 2: what what cars are people buying? And you know, Tesla 154 00:10:39,760 --> 00:10:42,319 Speaker 2: in the space of about what has it been about 155 00:10:42,360 --> 00:10:48,040 Speaker 2: five years has gone from being you know, really a tertiary. 156 00:10:48,160 --> 00:10:56,040 Speaker 2: You know, marginal market share too, you know, to number one, right, 157 00:10:56,120 --> 00:10:58,600 Speaker 2: So I think I think that tells I think that 158 00:10:58,640 --> 00:10:59,360 Speaker 2: tells the story. 159 00:10:59,440 --> 00:11:02,280 Speaker 1: Right there, there's the indicator that self driving is becoming 160 00:11:02,320 --> 00:11:04,760 Speaker 1: more accepted. Hey, Chris, we're gonna pull over and take 161 00:11:04,800 --> 00:11:06,760 Speaker 1: a pause. When we come back, I want you to 162 00:11:06,800 --> 00:11:09,800 Speaker 1: talk a little bit about you know, we had talked 163 00:11:09,840 --> 00:11:15,240 Speaker 1: off air about the scientists such as yourself that are 164 00:11:15,280 --> 00:11:19,640 Speaker 1: working on self driving mechanical issues and how they're solving 165 00:11:19,679 --> 00:11:22,600 Speaker 1: that to make that self driving car, you know, safer. 166 00:11:22,600 --> 00:11:25,160 Speaker 1: They're they're they're looking at the engine and maintenance and 167 00:11:25,320 --> 00:11:26,800 Speaker 1: things like that. Can we talk about that a little 168 00:11:26,800 --> 00:11:28,200 Speaker 1: bit w we come back, So let's pull over and 169 00:11:28,200 --> 00:11:29,920 Speaker 1: take the pause. I'm Ronning Innie in the Car Doctor. 170 00:11:29,960 --> 00:11:32,439 Speaker 1: I'm here with Chris Piche of Smarter AI, and we'll 171 00:11:32,440 --> 00:11:34,320 Speaker 1: both return right after this. Don't go away. 172 00:11:35,960 --> 00:11:43,719 Speaker 3: Now, God, that's right. 173 00:11:43,800 --> 00:11:45,959 Speaker 2: If you call and we're not live, you can leave 174 00:11:45,960 --> 00:11:47,680 Speaker 2: a message and we'll call you back to get you 175 00:11:47,720 --> 00:11:50,640 Speaker 2: on the air with Ron eight five five five six 176 00:11:50,760 --> 00:11:54,040 Speaker 2: zero nine nine zero zero. Speaking of Ron here he. 177 00:11:54,160 --> 00:11:56,960 Speaker 1: Is, and we're back, Ronning Anie in the Car Doctor. 178 00:11:56,960 --> 00:11:59,040 Speaker 1: We're here with Chris Boche if smarter AI and we're 179 00:11:59,040 --> 00:12:02,199 Speaker 1: having a conversation about artificial intelligence and automobiles, right, I'll 180 00:12:02,200 --> 00:12:05,040 Speaker 1: look to the future churs when we when we pulled away, 181 00:12:05,400 --> 00:12:10,280 Speaker 1: the conversation was about you know, we see smarter AI 182 00:12:10,360 --> 00:12:15,080 Speaker 1: or artificial intelligence. I'm sorry in vehicles today and you 183 00:12:15,120 --> 00:12:17,800 Speaker 1: know it. But we think about the mechanical side. Okay, 184 00:12:17,840 --> 00:12:21,160 Speaker 1: there's some things that that that AI can't foresee or 185 00:12:21,240 --> 00:12:24,600 Speaker 1: can it. Scientists are working now to ensure that that 186 00:12:24,840 --> 00:12:29,240 Speaker 1: mechanical vehicle is reliable and more safer. Correct. 187 00:12:30,120 --> 00:12:32,840 Speaker 2: Absolutely. One of the things that we're doing with with 188 00:12:33,000 --> 00:12:38,800 Speaker 2: AI today is we're working to improve predictive maintenance. So, 189 00:12:38,920 --> 00:12:42,440 Speaker 2: for example, rather than waiting for your engine to break 190 00:12:43,160 --> 00:12:48,559 Speaker 2: and a and an expensive repair or replacement, we're looking 191 00:12:48,600 --> 00:12:53,760 Speaker 2: to identify the early warning signs and then solve the 192 00:12:53,800 --> 00:12:59,959 Speaker 2: problem with you know, relatively inexpensive uh, predictive maintenance. 193 00:13:01,160 --> 00:13:05,200 Speaker 1: So is that just going to be on engine? Do 194 00:13:05,240 --> 00:13:07,440 Speaker 1: you do you foresee the day when you know they'll 195 00:13:07,480 --> 00:13:09,600 Speaker 1: predict Hey, we know your tires are going to wear 196 00:13:09,600 --> 00:13:11,720 Speaker 1: out at X, or your transmission is going to need 197 00:13:11,760 --> 00:13:14,400 Speaker 1: fluid service at Y. Is it going to affect the 198 00:13:14,400 --> 00:13:16,880 Speaker 1: whole car at some point? The whole vehicle at some point. 199 00:13:18,400 --> 00:13:21,880 Speaker 2: Yeah, eventually it'll it'll affect the whole, the whole vehicle. 200 00:13:23,880 --> 00:13:27,560 Speaker 2: You know, we're we're starting with with engines today because 201 00:13:28,120 --> 00:13:31,920 Speaker 2: that's typically the the most expensive part of the vehicle 202 00:13:32,080 --> 00:13:36,720 Speaker 2: to uh to repair or replace. But yeah, eventually we'll 203 00:13:37,040 --> 00:13:40,840 Speaker 2: we'll cover all of the vehicle systems and then you know, 204 00:13:40,960 --> 00:13:44,560 Speaker 2: ultimately this is just going to go to reduce the 205 00:13:44,600 --> 00:13:50,160 Speaker 2: cost of maintaining or owning a vehicle, and those costs 206 00:13:50,160 --> 00:13:52,360 Speaker 2: are going to get passed on. Cost savings are going 207 00:13:52,360 --> 00:13:53,760 Speaker 2: to get passed on to the customer. 208 00:13:53,960 --> 00:13:56,760 Speaker 1: So or is the price of a vehicle in theory 209 00:13:57,000 --> 00:14:00,760 Speaker 1: should come down? You know, they won't cost the prospect 210 00:14:01,280 --> 00:14:02,080 Speaker 1: maybe the cost. 211 00:14:01,880 --> 00:14:04,840 Speaker 2: Of owning a vehicle should come down, right, you. 212 00:14:04,840 --> 00:14:07,240 Speaker 1: Know, because I remember, jeez, I'm dating myself. But you know, 213 00:14:07,280 --> 00:14:09,560 Speaker 1: some of these vehicles today are more than my first house, 214 00:14:10,760 --> 00:14:13,720 Speaker 1: you know, for what they cost. It's it's kind of crazy. 215 00:14:14,559 --> 00:14:17,640 Speaker 1: You know, where does you know, so that car of 216 00:14:17,679 --> 00:14:20,120 Speaker 1: the future, that vehicle of the future, then that that 217 00:14:20,280 --> 00:14:24,680 Speaker 1: has more smarter, more AI in it. Right, we're going 218 00:14:24,760 --> 00:14:27,760 Speaker 1: to agree that, you know, cars today have artificial intelligence 219 00:14:27,760 --> 00:14:30,880 Speaker 1: and probably have for the past fifteen twenty years at 220 00:14:30,920 --> 00:14:35,960 Speaker 1: some level, how different how different is that car of 221 00:14:36,000 --> 00:14:38,360 Speaker 1: the of well today and the future. You know, is 222 00:14:38,400 --> 00:14:41,360 Speaker 1: it is it? Is it more software? Is it less mechanical? 223 00:14:41,440 --> 00:14:43,240 Speaker 1: Things like how are they going to make the change 224 00:14:43,240 --> 00:14:44,680 Speaker 1: and what's the transition look like. 225 00:14:46,480 --> 00:14:52,800 Speaker 2: Well, from the from the point of view of engineers 226 00:14:52,840 --> 00:14:56,960 Speaker 2: that are designing cars and companies that are manufacturing cars, 227 00:14:57,960 --> 00:15:00,000 Speaker 2: I think you hit the nail on the head. Cars 228 00:15:00,080 --> 00:15:07,160 Speaker 2: are becoming less hardware based more software based. And again 229 00:15:07,200 --> 00:15:09,160 Speaker 2: I think Tesla is a really good example of that. 230 00:15:11,680 --> 00:15:14,480 Speaker 2: My Tesla gets a software update, I think something like 231 00:15:14,920 --> 00:15:18,480 Speaker 2: you know, once every month, and so you know, every 232 00:15:18,560 --> 00:15:21,680 Speaker 2: month Tesla is improving the performance of the vehicle or 233 00:15:21,680 --> 00:15:26,800 Speaker 2: improving the features of the vehicle. Now, from the you know, 234 00:15:26,920 --> 00:15:30,760 Speaker 2: from the customer's point of view, you know, I think 235 00:15:30,760 --> 00:15:34,400 Speaker 2: customer doesn't care about hardware or software, but they do 236 00:15:34,480 --> 00:15:39,000 Speaker 2: care about performance and reliability. And so I think we 237 00:15:39,000 --> 00:15:42,080 Speaker 2: would all agree that, uh, you know, cars today have 238 00:15:42,160 --> 00:15:47,080 Speaker 2: greater performance and reliability than the previous generation of cars, 239 00:15:47,640 --> 00:15:49,960 Speaker 2: and we're just going to see that trend continue into 240 00:15:50,040 --> 00:15:50,480 Speaker 2: the future. 241 00:15:51,320 --> 00:15:57,000 Speaker 1: But will AI be able to predict you know, that 242 00:15:57,120 --> 00:16:00,440 Speaker 1: failed tie rod end or that I guess it can't right. 243 00:16:01,200 --> 00:16:04,600 Speaker 1: It can't. It can't predict a mechanical fault, but it 244 00:16:04,640 --> 00:16:08,440 Speaker 1: could predict when that system should be inspected or how 245 00:16:08,440 --> 00:16:09,480 Speaker 1: it should be inspected. 246 00:16:12,040 --> 00:16:15,600 Speaker 2: Uh No, I think you know, I think in most 247 00:16:15,600 --> 00:16:22,320 Speaker 2: cases AI can predict mechanical faults. And you know what 248 00:16:22,360 --> 00:16:28,680 Speaker 2: you'll see is at the sort of regular inspection intervals, 249 00:16:28,720 --> 00:16:31,080 Speaker 2: you know, whether it's every twelve months or every six 250 00:16:31,160 --> 00:16:35,760 Speaker 2: months that we take our car in for servicing. AI 251 00:16:36,000 --> 00:16:41,040 Speaker 2: is going to help the person doing the servicing to 252 00:16:42,680 --> 00:16:48,680 Speaker 2: you know, target potential problems, you know, before things break. 253 00:16:49,160 --> 00:16:51,440 Speaker 1: Is it going to be like the airline industry Chris 254 00:16:52,000 --> 00:16:55,680 Speaker 1: that you know, like like my understanding is they'll look 255 00:16:55,720 --> 00:16:57,880 Speaker 1: at an aircraft and say, it's got so many hours 256 00:16:57,920 --> 00:16:59,640 Speaker 1: on it. We know we have to change this part 257 00:16:59,640 --> 00:17:02,760 Speaker 1: in that part based on potential failure rates or history 258 00:17:02,760 --> 00:17:05,560 Speaker 1: of failure rates. Are automobile is going to evolve into that? 259 00:17:06,280 --> 00:17:08,440 Speaker 2: Yeah? I think I think that's right. I think you're 260 00:17:08,520 --> 00:17:12,399 Speaker 2: going to see in terms of the uh, you know, 261 00:17:12,480 --> 00:17:15,199 Speaker 2: the economic model. I think you're going to see it 262 00:17:15,280 --> 00:17:19,879 Speaker 2: evolved the way you know, the way that that airplanes 263 00:17:19,880 --> 00:17:22,760 Speaker 2: have gone, where we're going to pay you know, one 264 00:17:22,920 --> 00:17:28,720 Speaker 2: all inclusive fee and uh and the vehicle company is gonna, 265 00:17:29,359 --> 00:17:32,720 Speaker 2: uh provide the vehicle and provide sort of an all 266 00:17:32,800 --> 00:17:36,720 Speaker 2: inclusive maintenance service for you know, for that vehicle. 267 00:17:37,240 --> 00:17:38,919 Speaker 1: And and that's going to be it's gonna be like 268 00:17:38,960 --> 00:17:43,040 Speaker 1: a sort of like a subscription maybe you know, you 269 00:17:43,040 --> 00:17:45,520 Speaker 1: you you buy a monthly subscription to maintain your car, 270 00:17:45,560 --> 00:17:46,879 Speaker 1: and that's that's the way it works. 271 00:17:47,280 --> 00:17:48,240 Speaker 2: Absolutely, you know. 272 00:17:48,400 --> 00:17:52,200 Speaker 1: I read somewhere real quick before we take the pause, 273 00:17:52,520 --> 00:17:54,320 Speaker 1: I want you to think about this. I read somewhere 274 00:17:54,359 --> 00:17:56,399 Speaker 1: that they say the vehicle of the future is going 275 00:17:56,480 --> 00:17:59,440 Speaker 1: to be it's going to break. You're going to take 276 00:17:59,440 --> 00:18:01,880 Speaker 1: the license it's off. You're gonna call for an uber 277 00:18:02,320 --> 00:18:03,840 Speaker 1: and they're gonna come pick it up. They're going to 278 00:18:03,880 --> 00:18:05,800 Speaker 1: recycle it, and they're going to issue you another one 279 00:18:05,840 --> 00:18:09,000 Speaker 1: based on your level of subscription. Don't even know. Don't 280 00:18:09,040 --> 00:18:12,359 Speaker 1: even comment on that one yet. Chris Chew on that 281 00:18:12,440 --> 00:18:13,760 Speaker 1: for one. 282 00:18:13,960 --> 00:18:15,639 Speaker 2: I'm gonna take that one step further. 283 00:18:15,720 --> 00:18:17,600 Speaker 1: I think, well, I'll tell you what. Let's pull over me, 284 00:18:17,800 --> 00:18:20,000 Speaker 1: let me let me stop it here. We'll take a pause. 285 00:18:20,200 --> 00:18:21,800 Speaker 1: When we come back, we'll pick that up. I'm on 286 00:18:21,880 --> 00:18:23,919 Speaker 1: an indie in the car doctor. We'll return right after this. 287 00:18:34,840 --> 00:18:40,520 Speaker 1: M Welcome back to the future listeners on an Innie 288 00:18:40,520 --> 00:18:42,960 Speaker 1: in the Car Doctor. I'm here with Chris Pichet of 289 00:18:43,080 --> 00:18:45,919 Speaker 1: Smarter AI And when we when we pulled away and 290 00:18:45,920 --> 00:18:49,320 Speaker 1: we were getting ready for this next conversation off Eric 291 00:18:49,359 --> 00:18:51,120 Speaker 1: and here we are on are Chris and I We're 292 00:18:51,119 --> 00:18:53,639 Speaker 1: going to talk about that car of the future. Chris, 293 00:18:53,680 --> 00:18:57,000 Speaker 1: that that you break down and let's get right into it. 294 00:18:57,040 --> 00:19:01,320 Speaker 1: You break down and and you know it's it's time 295 00:19:01,400 --> 00:19:03,640 Speaker 1: is up. Your subscription base is up, So you're gonna 296 00:19:03,640 --> 00:19:05,000 Speaker 1: get a new car, right You're going to take the 297 00:19:05,040 --> 00:19:07,959 Speaker 1: license plate off that old car and call an uber? 298 00:19:08,040 --> 00:19:10,040 Speaker 1: Are you gonna have to How will that work picking 299 00:19:10,119 --> 00:19:11,200 Speaker 1: up that car in the future? 300 00:19:12,600 --> 00:19:14,680 Speaker 2: Yeah, I don't think. I don't think you're going to 301 00:19:14,760 --> 00:19:17,080 Speaker 2: have to call anybody. I think your you know, if 302 00:19:17,119 --> 00:19:21,360 Speaker 2: your car breaks down, I think your car company will 303 00:19:21,400 --> 00:19:25,760 Speaker 2: have a replacement delivered. You know, based on the level 304 00:19:25,760 --> 00:19:28,200 Speaker 2: of your subscription. It could be right away, it could 305 00:19:28,280 --> 00:19:31,040 Speaker 2: be you know, it could be the next day, depends 306 00:19:31,040 --> 00:19:33,120 Speaker 2: how much you're paying them. 307 00:19:33,200 --> 00:19:36,800 Speaker 1: So you know, is this why they invented velcrow. We're 308 00:19:36,840 --> 00:19:38,840 Speaker 1: just going to walk up, take the plates off, oh 309 00:19:38,880 --> 00:19:41,360 Speaker 1: you know, and and then that next car will will 310 00:19:41,400 --> 00:19:44,520 Speaker 1: show up. Will will AI, you know, make that phone 311 00:19:44,520 --> 00:19:46,639 Speaker 1: call for me and say hey, Ron's broken down on 312 00:19:46,680 --> 00:19:48,720 Speaker 1: the side of Route two oh two. He needs another car, 313 00:19:48,760 --> 00:19:51,520 Speaker 1: and it just it bang, it shows up and we're done. 314 00:19:51,680 --> 00:19:53,879 Speaker 1: Is that the future? And then somebody takes away the 315 00:19:53,880 --> 00:19:55,399 Speaker 1: old one and recycles. 316 00:19:54,920 --> 00:19:59,000 Speaker 2: It absolutely And I don't even think you'll need to 317 00:19:59,200 --> 00:20:01,040 Speaker 2: change the plates. So I think the plates are going 318 00:20:01,080 --> 00:20:04,240 Speaker 2: to be digital. Someone will press a button and the 319 00:20:04,240 --> 00:20:06,080 Speaker 2: plates will just light up on your new car. 320 00:20:06,440 --> 00:20:10,960 Speaker 1: So you know, now your company, smarter AI. I want 321 00:20:10,960 --> 00:20:13,520 Speaker 1: to talk about you for a minute. You know, Chris Bishet, 322 00:20:14,119 --> 00:20:16,800 Speaker 1: Where did he get this? You know, not everybody. I 323 00:20:16,800 --> 00:20:18,320 Speaker 1: don't think you wake up one day and say, hey, 324 00:20:18,359 --> 00:20:20,760 Speaker 1: I want to do computer work on artificial intelligence. Where 325 00:20:20,800 --> 00:20:22,919 Speaker 1: did you? Where did where did you come from? And 326 00:20:22,920 --> 00:20:25,120 Speaker 1: where did smarter AI come from? Your company? 327 00:20:27,119 --> 00:20:30,639 Speaker 2: So yeah, I've been in the software business for about 328 00:20:30,960 --> 00:20:35,679 Speaker 2: twenty or twenty five years. About ten years ago, I 329 00:20:35,840 --> 00:20:42,320 Speaker 2: was involved in the in the the smartphone revolutions. So 330 00:20:43,080 --> 00:20:46,679 Speaker 2: if you and people listening were anything like me, I 331 00:20:46,760 --> 00:20:50,440 Speaker 2: was walking around ten years ago with my Nokia, my Motorola, 332 00:20:50,600 --> 00:20:54,720 Speaker 2: my ericson phone and then all of a sudden, just overnight, 333 00:20:54,760 --> 00:20:58,440 Speaker 2: it seemed like everybody had an iPhone or eventually everybody 334 00:20:58,440 --> 00:21:03,360 Speaker 2: had an iPhone or an Android phone. And I had 335 00:21:04,359 --> 00:21:07,480 Speaker 2: I had played a very small role, uh in the 336 00:21:07,720 --> 00:21:13,639 Speaker 2: uh in the beginning of the smartphone business. And about 337 00:21:13,640 --> 00:21:16,520 Speaker 2: five years ago I was looking to do, uh, you know, 338 00:21:16,600 --> 00:21:21,960 Speaker 2: my next uh, my next big adventure, and uh, you know, 339 00:21:22,000 --> 00:21:26,439 Speaker 2: looking around, I saw that what had happened in the 340 00:21:26,480 --> 00:21:34,880 Speaker 2: phone business, so that transition from uh, you know, legacy 341 00:21:34,960 --> 00:21:39,800 Speaker 2: phones to smartphones, I saw that the same thing was 342 00:21:39,840 --> 00:21:44,520 Speaker 2: about to happen in cameras. So transition from cameras that 343 00:21:45,840 --> 00:21:50,240 Speaker 2: uh can store video or display video on a screen 344 00:21:51,600 --> 00:21:58,200 Speaker 2: to AI cameras that can see and listen to understand 345 00:21:58,280 --> 00:22:02,399 Speaker 2: what's happening around them. And so I founded Smarter AI 346 00:22:03,240 --> 00:22:07,400 Speaker 2: to build the software platform for AI cameras. 347 00:22:08,080 --> 00:22:10,560 Speaker 1: So are you telling me that the cameras and the 348 00:22:10,600 --> 00:22:12,639 Speaker 1: cars have artificial intelligence in them? 349 00:22:14,240 --> 00:22:18,320 Speaker 2: Oh? Absolutely, All the cameras in cars that are manufactured 350 00:22:18,320 --> 00:22:24,400 Speaker 2: today have artificial intelligence in them. Features like like how 351 00:22:24,400 --> 00:22:27,640 Speaker 2: long has lane keeping been a feature in cars? Ten 352 00:22:27,720 --> 00:22:28,560 Speaker 2: years or something. 353 00:22:28,320 --> 00:22:29,920 Speaker 1: Like ten twelve years of actual? 354 00:22:30,800 --> 00:22:33,600 Speaker 2: Yeah, yeah, yeah, I mean the these kinds of features 355 00:22:33,640 --> 00:22:39,120 Speaker 2: are all based on cameras. And I would say basic 356 00:22:39,440 --> 00:22:41,040 Speaker 2: or you know basic levels of. 357 00:22:41,000 --> 00:22:47,480 Speaker 1: AI and and so AI is there. So we're talking 358 00:22:47,480 --> 00:22:50,639 Speaker 1: about two things here today. We're talking about artificial intelligence 359 00:22:50,640 --> 00:22:53,479 Speaker 1: and the cameras to help the vehicle drive and straight 360 00:22:53,520 --> 00:22:55,879 Speaker 1: down the road and not viewer left or right. And 361 00:22:55,920 --> 00:22:58,520 Speaker 1: then we're talking about in the first part of our conversation, 362 00:22:58,560 --> 00:23:02,919 Speaker 1: we're talking about artificial intelligence running the whole car and 363 00:23:03,680 --> 00:23:05,480 Speaker 1: just being in charge. 364 00:23:05,880 --> 00:23:08,879 Speaker 2: Well, like like anything else, you have to you have 365 00:23:08,960 --> 00:23:11,800 Speaker 2: to walk before you can run. So if you know, 366 00:23:11,880 --> 00:23:16,840 Speaker 2: if if lane keeping and and collision avoidance, you know, 367 00:23:16,960 --> 00:23:22,520 Speaker 2: maybe those represented the walking uh stage or the crawling 368 00:23:22,560 --> 00:23:28,480 Speaker 2: stage of cameras and AI and vehicles. And then you 369 00:23:28,520 --> 00:23:31,720 Speaker 2: know self driving vehicles, that's the that's the running stage. 370 00:23:33,200 --> 00:23:35,959 Speaker 1: So how have the cameras evolved? 371 00:23:36,000 --> 00:23:36,119 Speaker 2: There? 372 00:23:36,160 --> 00:23:39,040 Speaker 1: Let me let me preface the question this way. A 373 00:23:39,080 --> 00:23:42,840 Speaker 1: good five six years ago, we were doing some research 374 00:23:42,880 --> 00:23:44,560 Speaker 1: here on the show, and we were talking to people 375 00:23:44,640 --> 00:23:46,280 Speaker 1: in the industry, and had I known you then I 376 00:23:46,280 --> 00:23:49,159 Speaker 1: would have gladly brought you in, Chris, that they were 377 00:23:49,240 --> 00:23:51,879 Speaker 1: talking about West Virginia. You know where I'm going to 378 00:23:51,960 --> 00:23:53,720 Speaker 1: go with this. We talked about this before we went 379 00:23:53,760 --> 00:23:56,040 Speaker 1: on air. But I want the listeners to hear it 380 00:23:56,200 --> 00:23:58,280 Speaker 1: that there was a time period where they said they 381 00:23:58,320 --> 00:24:00,960 Speaker 1: would never be self driving cars and Virginia because the 382 00:24:01,000 --> 00:24:04,480 Speaker 1: cameras weren't smart enough because West Virginia happens to be 383 00:24:04,520 --> 00:24:06,840 Speaker 1: a state with the least amount of white stripes down 384 00:24:06,880 --> 00:24:10,240 Speaker 1: the middle of the road. But that that problem has 385 00:24:10,280 --> 00:24:13,320 Speaker 1: been solved now, right, Is that because AI is advanced? 386 00:24:15,560 --> 00:24:20,000 Speaker 2: Yeah? Absolutely, The AI is getting better and better all 387 00:24:20,080 --> 00:24:26,679 Speaker 2: the time, and you know, we should expect to see 388 00:24:26,280 --> 00:24:32,280 Speaker 2: the capability of self driving cars to not not only 389 00:24:32,359 --> 00:24:37,919 Speaker 2: to continue to increase, but to increase exponentially. And the 390 00:24:37,960 --> 00:24:44,919 Speaker 2: reason for that is because the accuracy of any AI 391 00:24:45,119 --> 00:24:49,480 Speaker 2: system is based on the quantity and the quality of 392 00:24:49,680 --> 00:24:53,439 Speaker 2: data that are used to train it. So what this 393 00:24:53,600 --> 00:24:57,920 Speaker 2: means is the more cars that are on the road 394 00:24:58,600 --> 00:25:02,800 Speaker 2: with cameras and with a uh, the more data the 395 00:25:02,840 --> 00:25:06,520 Speaker 2: car companies are able to capture, and then they turn 396 00:25:06,560 --> 00:25:10,880 Speaker 2: around and use that data to train the self driving 397 00:25:10,960 --> 00:25:13,399 Speaker 2: systems to be smarter and smarter. 398 00:25:15,560 --> 00:25:19,240 Speaker 1: What's something I'm trying to think of how to ask this, Chris, 399 00:25:19,600 --> 00:25:22,760 Speaker 1: But you know, when you're training your artificial intelligence systems, 400 00:25:22,800 --> 00:25:26,159 Speaker 1: your AI at Smarter AI, as the name of your 401 00:25:26,200 --> 00:25:29,600 Speaker 1: company is, you know, what have you learned? What can 402 00:25:29,600 --> 00:25:32,320 Speaker 1: you do now that you couldn't do two years ago 403 00:25:32,560 --> 00:25:46,080 Speaker 1: with a camera? What advancements have changed? I'm sorry, right, 404 00:25:46,080 --> 00:25:47,960 Speaker 1: but things have gotten better. I mean in the or 405 00:25:48,040 --> 00:25:50,080 Speaker 1: let me, let me, let me make it a longer timeline, 406 00:25:50,320 --> 00:25:53,840 Speaker 1: in the timeframe since you started doing this, you know, 407 00:25:53,880 --> 00:25:56,480 Speaker 1: in other words, in other words, how how is it 408 00:25:56,520 --> 00:25:59,560 Speaker 1: that now no white stripes in West Virginia? Doesn't matter? 409 00:25:59,680 --> 00:26:01,720 Speaker 1: What have you taught the camera to do? Or what 410 00:26:01,720 --> 00:26:03,879 Speaker 1: what has AI been able to figure out that it 411 00:26:03,960 --> 00:26:06,720 Speaker 1: can do without having to, you know, be off of 412 00:26:06,720 --> 00:26:07,399 Speaker 1: a white strike. 413 00:26:08,600 --> 00:26:11,520 Speaker 2: I think there's I think there's two. There's two main 414 00:26:11,680 --> 00:26:16,879 Speaker 2: things that have that have you know, that that have 415 00:26:16,960 --> 00:26:20,800 Speaker 2: evolved in the last couple of years. Number one is, 416 00:26:21,000 --> 00:26:25,399 Speaker 2: and I already mentioned this, the the amount and the 417 00:26:25,560 --> 00:26:31,520 Speaker 2: quality of data that's available today to train the AI 418 00:26:31,680 --> 00:26:36,199 Speaker 2: models is an order of magnitude bigger than it was 419 00:26:36,280 --> 00:26:40,760 Speaker 2: a couple of years ago, and it'll be another order 420 00:26:40,760 --> 00:26:45,199 Speaker 2: of magnitude bigger a couple of years from now. And 421 00:26:45,240 --> 00:26:50,040 Speaker 2: the second big thing that has changed is the amount 422 00:26:50,080 --> 00:26:56,840 Speaker 2: of computing power that's built into a vehicle manufactured in 423 00:26:56,920 --> 00:27:01,320 Speaker 2: twenty twenty four is at least an order of magnitude, 424 00:27:01,320 --> 00:27:05,320 Speaker 2: if not two orders of magnitude greater than the previous 425 00:27:05,400 --> 00:27:09,359 Speaker 2: generation of vehicles. So whether we you know, whether we 426 00:27:09,520 --> 00:27:13,800 Speaker 2: realize it or not, you know, we're getting closer and 427 00:27:13,840 --> 00:27:18,880 Speaker 2: closer to being, you know, driving around with supercomputer inside 428 00:27:18,880 --> 00:27:19,520 Speaker 2: of our cars. 429 00:27:20,160 --> 00:27:20,560 Speaker 1: Wow. 430 00:27:21,000 --> 00:27:24,679 Speaker 2: And so we're able to take the combination of the 431 00:27:25,359 --> 00:27:28,199 Speaker 2: of the data and the models that we're able to 432 00:27:28,280 --> 00:27:33,760 Speaker 2: train with that data, and then you know, the the 433 00:27:33,880 --> 00:27:40,480 Speaker 2: large and growing amount of compute that's now available on 434 00:27:40,600 --> 00:27:44,440 Speaker 2: a car, and instead of just doing things like lane 435 00:27:44,520 --> 00:27:48,480 Speaker 2: keeping and collision avoidance, we can do all sorts of 436 00:27:48,520 --> 00:27:53,280 Speaker 2: other things. We can you know, we can detect pedestrians 437 00:27:53,320 --> 00:27:57,720 Speaker 2: and bicycles to keep them safe. We can you know, 438 00:27:57,760 --> 00:28:00,919 Speaker 2: we can follow the car in front of us in 439 00:28:01,520 --> 00:28:05,359 Speaker 2: situations where you know, maybe there are no lines on 440 00:28:05,400 --> 00:28:08,359 Speaker 2: the road, or maybe they're difficult to read for whatever reason. 441 00:28:10,440 --> 00:28:12,280 Speaker 2: And this is going to this is just going to 442 00:28:12,320 --> 00:28:13,840 Speaker 2: continue into the future, and. 443 00:28:13,760 --> 00:28:16,240 Speaker 1: It's just going to evolve. Hey, Chris, let's pull over 444 00:28:16,320 --> 00:28:18,440 Speaker 1: and take a pause. When we come back, maybe you 445 00:28:18,480 --> 00:28:20,399 Speaker 1: can tell us a little bit about the future. What 446 00:28:20,520 --> 00:28:22,359 Speaker 1: is the what does the future of AI look like, 447 00:28:22,400 --> 00:28:24,920 Speaker 1: What does the future of cars look like? And what's 448 00:28:24,960 --> 00:28:27,600 Speaker 1: smarter AI doing to contribute to all of this? All right, 449 00:28:27,880 --> 00:28:29,679 Speaker 1: stay put, I'm running any in the car Actor. I'm 450 00:28:29,720 --> 00:28:31,920 Speaker 1: here with Chris Pache off Smarter AI. We'll both return 451 00:28:32,040 --> 00:28:43,520 Speaker 1: right after this looking for the future. Well you found it. 452 00:28:43,600 --> 00:28:45,200 Speaker 1: Run an Ani in the car doctor here with Chris 453 00:28:45,280 --> 00:28:48,040 Speaker 1: mache Smarter AI, and we're talking about the future, the 454 00:28:48,080 --> 00:28:51,280 Speaker 1: future of automobiles. Chris. When we pulled away and took 455 00:28:51,320 --> 00:28:54,760 Speaker 1: the pause, we were talking artificial intelligence, and we had 456 00:28:54,840 --> 00:28:57,280 Speaker 1: laid out where we are currently and what your company, 457 00:28:57,320 --> 00:29:00,560 Speaker 1: smarter AI, has brought to the table. What does the 458 00:29:00,560 --> 00:29:02,800 Speaker 1: future look like for the listeners? 459 00:29:03,880 --> 00:29:07,240 Speaker 2: Well, I think if we look ahead at the next, 460 00:29:07,920 --> 00:29:11,040 Speaker 2: say two to five years, we're going to see most 461 00:29:11,160 --> 00:29:13,920 Speaker 2: cars on the road, Most new cars on the road 462 00:29:14,160 --> 00:29:17,480 Speaker 2: are going to be equipped with some form of self driving, 463 00:29:18,560 --> 00:29:23,720 Speaker 2: let's say, comparable to what Tesla offers today. But if 464 00:29:23,720 --> 00:29:27,080 Speaker 2: we look a little bit further than that, there's a 465 00:29:27,120 --> 00:29:30,959 Speaker 2: couple of technologies on the horizon, v to V and 466 00:29:31,080 --> 00:29:33,800 Speaker 2: VITA X so. V to B stands for vehicle to 467 00:29:33,880 --> 00:29:39,520 Speaker 2: vehicle and VTA X stands for vehicle to everything. And 468 00:29:39,640 --> 00:29:47,040 Speaker 2: with these two technologies, instead of one car driving itself, 469 00:29:48,920 --> 00:29:53,239 Speaker 2: think about a conductor of an orchestra, all of the 470 00:29:53,280 --> 00:29:59,360 Speaker 2: cars are going to drive themselves together, and once we 471 00:29:59,440 --> 00:30:02,800 Speaker 2: get to VITA V and VTA X, of course that's 472 00:30:02,840 --> 00:30:06,320 Speaker 2: going to continue to drive down crash rates and to 473 00:30:06,440 --> 00:30:11,240 Speaker 2: increase safety. But an extra benefit that we're going to 474 00:30:11,280 --> 00:30:14,200 Speaker 2: get from that is we're going to see a dramatic 475 00:30:14,280 --> 00:30:20,880 Speaker 2: reduction maybe even an elimination of congestion or traffic jamps. 476 00:30:21,400 --> 00:30:25,000 Speaker 2: So for people that are that are listening, that are 477 00:30:25,320 --> 00:30:29,000 Speaker 2: living in and around major cities, this is going to 478 00:30:29,000 --> 00:30:30,240 Speaker 2: be a game changer. 479 00:30:31,720 --> 00:30:34,400 Speaker 1: So, and I guess, I guess the end result here 480 00:30:34,400 --> 00:30:36,840 Speaker 1: is so then there's two benefits to AI right where 481 00:30:37,080 --> 00:30:41,720 Speaker 1: it's going to be the the idea of reduced fatalities, 482 00:30:41,760 --> 00:30:45,440 Speaker 1: reduced accidents, but lower lower pollution for the cities right, 483 00:30:45,480 --> 00:30:49,400 Speaker 1: lower smog levels and emissions coming out of vehicles, whether 484 00:30:49,440 --> 00:30:51,720 Speaker 1: they're you know, I guess on the basis of it, 485 00:30:51,880 --> 00:30:54,000 Speaker 1: if they're gas, If they're electric, that's another story. But 486 00:30:55,200 --> 00:30:57,480 Speaker 1: you know, plus the convenience of it, right, less time 487 00:30:57,520 --> 00:31:01,280 Speaker 1: sitting around in traffic, wasting time. Think of the efficiency 488 00:31:01,360 --> 00:31:05,160 Speaker 1: level if if everything orchestrated moved along. So my only 489 00:31:05,240 --> 00:31:08,280 Speaker 1: question is what if the orchestra what what if the 490 00:31:08,320 --> 00:31:10,120 Speaker 1: orchestra leader I have to say it like this, Chris, 491 00:31:10,160 --> 00:31:12,280 Speaker 1: I'm sorry, but what if the orchestra leader is a jerk? 492 00:31:14,960 --> 00:31:17,320 Speaker 1: What if he's driving bed? Then do we all drive bed? 493 00:31:17,400 --> 00:31:19,080 Speaker 1: Or is there going to be a limit on how 494 00:31:19,200 --> 00:31:21,720 Speaker 1: AI will control that or a I will be doing 495 00:31:21,800 --> 00:31:24,400 Speaker 1: the driving in other words, not the driver itself. 496 00:31:26,840 --> 00:31:32,360 Speaker 2: Well, you know, nothing is perfect, and uh and uh. 497 00:31:32,480 --> 00:31:34,800 Speaker 2: You know, all of these technologies are going to be 498 00:31:34,920 --> 00:31:41,520 Speaker 2: introduced gradually and improve over time. But you know, think 499 00:31:41,560 --> 00:31:45,920 Speaker 2: about it like this, Just to just imagine that instead 500 00:31:45,920 --> 00:31:49,120 Speaker 2: of having to you know, slow down or stop to 501 00:31:49,200 --> 00:31:52,240 Speaker 2: cut across two or three lanes of traffic to get 502 00:31:52,240 --> 00:31:54,800 Speaker 2: into the you know, the left hand lane or the 503 00:31:54,880 --> 00:31:58,640 Speaker 2: right hand lane to make that turn, just imagine that, 504 00:31:59,520 --> 00:32:04,000 Speaker 2: you know, the orchestrator is gonna completely smooth all of 505 00:32:04,040 --> 00:32:07,080 Speaker 2: that out so your car never really has to come 506 00:32:07,120 --> 00:32:11,600 Speaker 2: to a complete stop and it just it sort of 507 00:32:11,600 --> 00:32:15,640 Speaker 2: plays nicely with the other cars on the road and 508 00:32:15,720 --> 00:32:18,040 Speaker 2: everybody gets to where they need to go. 509 00:32:19,400 --> 00:32:23,840 Speaker 1: The future looks very interesting, Chris, it really does, I 510 00:32:24,200 --> 00:32:26,480 Speaker 1: tell you what. It's got to be fascinating to see 511 00:32:26,480 --> 00:32:28,480 Speaker 1: what goes on behind the scenes. Maybe we'll talk about 512 00:32:28,480 --> 00:32:30,440 Speaker 1: that next time, because you know you're you already have 513 00:32:30,480 --> 00:32:32,520 Speaker 1: an open invite to come back. My friend, how's that. 514 00:32:32,880 --> 00:32:33,720 Speaker 2: Look forward to it? 515 00:32:33,840 --> 00:32:37,600 Speaker 1: If the listeners are looking for more information about artificial 516 00:32:37,600 --> 00:32:41,280 Speaker 1: intelligence smarter AI, your company, there's got to be a website, right, 517 00:32:41,360 --> 00:32:41,800 Speaker 1: what is it? 518 00:32:42,280 --> 00:32:43,720 Speaker 2: Smarterai dot com? 519 00:32:43,760 --> 00:32:44,640 Speaker 1: How did I know that? 520 00:32:44,960 --> 00:32:45,160 Speaker 2: Right? 521 00:32:45,240 --> 00:32:50,160 Speaker 1: It's Chris. It's been a pleasure, sir, and we thank 522 00:32:50,200 --> 00:32:51,840 Speaker 1: you for your hard work and trying to make the 523 00:32:51,880 --> 00:32:53,600 Speaker 1: future safer and better for all of us. You have 524 00:32:53,640 --> 00:32:54,880 Speaker 1: yourself a great rest of the day. 525 00:32:55,760 --> 00:32:56,680 Speaker 2: Thanks for having me Ron. 526 00:32:56,760 --> 00:32:58,840 Speaker 1: You're very welcome, sir. I'm running any in the car. Doctor, 527 00:32:59,000 --> 00:33:10,800 Speaker 1: We'll return right after this. Don't go away. Well I 528 00:33:10,800 --> 00:33:12,520 Speaker 1: help you have got a good look at the future. 529 00:33:13,080 --> 00:33:15,640 Speaker 1: I appreciate it. I appreciated Chris coming by today. Chris 530 00:33:15,720 --> 00:33:19,240 Speaker 1: Biche of smarter AI. Smarterai dot com. By the way, 531 00:33:19,280 --> 00:33:21,479 Speaker 1: is the website? Uh, coming by to talk to us, 532 00:33:21,560 --> 00:33:24,520 Speaker 1: because as you can imagine, he's a busy guy. He 533 00:33:24,520 --> 00:33:27,240 Speaker 1: he's traveling all the time, and you know, he explained 534 00:33:27,240 --> 00:33:28,640 Speaker 1: to me some of the things he has to go through, 535 00:33:28,680 --> 00:33:31,040 Speaker 1: and they have to look at everything. They're they're looking 536 00:33:31,200 --> 00:33:35,160 Speaker 1: at how how we operate vehicles, and you know, I 537 00:33:35,200 --> 00:33:37,080 Speaker 1: think about all the people that we see driving and 538 00:33:37,080 --> 00:33:38,760 Speaker 1: all the things we mutter under our breath and how 539 00:33:38,800 --> 00:33:41,560 Speaker 1: people drive, and I wonder if artificial intelligence will be 540 00:33:41,600 --> 00:33:43,760 Speaker 1: able to overcome that. And if it does, you know, 541 00:33:44,080 --> 00:33:47,440 Speaker 1: she whiz, God bless it. But you know the point 542 00:33:47,440 --> 00:33:50,000 Speaker 1: of all this is, you know, what does the future 543 00:33:50,000 --> 00:33:51,959 Speaker 1: look like? Where will the car of the future take us? 544 00:33:52,000 --> 00:33:54,560 Speaker 1: And you know, how will we get there? And people 545 00:33:54,600 --> 00:33:57,120 Speaker 1: like Chris Biche are working on that to provide a 546 00:33:57,160 --> 00:33:59,640 Speaker 1: safer future, because you have to look at the overall picture, right, 547 00:34:00,600 --> 00:34:02,760 Speaker 1: you know, I've seen the studies and the reports, and 548 00:34:02,800 --> 00:34:05,120 Speaker 1: there are videos out there and conversations out there where 549 00:34:05,120 --> 00:34:10,160 Speaker 1: they talk about with artificial intelligence driving cars. They claim 550 00:34:10,560 --> 00:34:14,160 Speaker 1: that there will be no traffic lights in the world 551 00:34:14,200 --> 00:34:17,200 Speaker 1: of artificial intelligence because you won't need to slow down. 552 00:34:17,360 --> 00:34:20,680 Speaker 1: Everything will be orchestrated, you know, once we get into 553 00:34:20,719 --> 00:34:23,240 Speaker 1: that vehicle to vehicle stage or vehicle to X stage 554 00:34:23,280 --> 00:34:26,359 Speaker 1: where it talks to intersections and warns of you know, 555 00:34:26,440 --> 00:34:29,759 Speaker 1: approaching people who are approaching vehicles and so forth. So 556 00:34:30,400 --> 00:34:33,719 Speaker 1: it'll be interesting. The future looks interesting in terms of technology. 557 00:34:33,960 --> 00:34:35,840 Speaker 1: How will it affect our cars today? How will it 558 00:34:35,880 --> 00:34:40,000 Speaker 1: affect people driving older cars. A lot of questions, lots 559 00:34:40,040 --> 00:34:42,680 Speaker 1: of things to be answered, and rest assured. The Card 560 00:34:42,680 --> 00:34:44,200 Speaker 1: Doctor will be here to help you get the answers 561 00:34:44,200 --> 00:34:46,000 Speaker 1: that you need, because that's what this radio show is 562 00:34:46,040 --> 00:34:47,640 Speaker 1: all about. I want to thank you for taking the 563 00:34:47,640 --> 00:34:49,720 Speaker 1: time today. Until the next time, I want to remind 564 00:34:49,760 --> 00:34:52,000 Speaker 1: you once again whi I'm monitating in the Card Doctor. 565 00:34:52,160 --> 00:35:02,640 Speaker 1: Good mechanics aren't expensive, They're priceless, see she stopping down 566 00:35:02,680 --> 00:35:03,200 Speaker 1: at the end.