1
00:00:00,320 --> 00:00:02,720
Speaker 1: Thanks for listening to the best of Coast to Coast podcasts.

2
00:00:02,759 --> 00:00:06,199
Speaker 1: Become a Coast Insider and you can hear this complete conversation,

3
00:00:06,240 --> 00:00:10,120
Speaker 1: as well as recent shows featuring guests discussing new cases

4
00:00:10,160 --> 00:00:14,320
Speaker 1: of the troubling cattle mutilation phenomenon, worries, some instances of

5
00:00:14,440 --> 00:00:17,799
Speaker 1: clandestine c I, a torture, and the evidence that the

6
00:00:17,840 --> 00:00:21,919
Speaker 1: Lost City of Atlantis may have really once existed. Check

7
00:00:21,920 --> 00:00:25,320
Speaker 1: out these programs and many other fascinating episodes waiting for

8
00:00:25,400 --> 00:00:28,240
Speaker 1: you and the Coast to Coast archive by heading over

9
00:00:28,280 --> 00:00:30,680
Speaker 1: to Coast to Coast a m dot com and signing

10
00:00:30,760 --> 00:00:34,200
Speaker 1: up for Coast Insider. Now here's a highlight from Coast

11
00:00:34,200 --> 00:00:37,840
Speaker 1: to Coast AM on iHeart Radio. Welcome back to Coast

12
00:00:37,840 --> 00:00:39,879
Speaker 1: to Coast. We've got a great couple hours for you.

13
00:00:40,080 --> 00:00:44,160
Speaker 1: James Barrett a long fascination with artificial intelligence, which came

14
00:00:44,159 --> 00:00:46,279
Speaker 1: to a head in his life in two thousand when

15
00:00:46,280 --> 00:00:51,840
Speaker 1: he interviewed inventor Ray Kurzweil, while roboticist Rodney Brooks sci

16
00:00:51,920 --> 00:00:56,720
Speaker 1: fi legend Arthur C. Clark. Now kurs While and Brooks

17
00:00:56,760 --> 00:01:00,920
Speaker 1: were optimistic about the future they considered in fitable, Clark

18
00:01:01,080 --> 00:01:03,760
Speaker 1: was not so sure. I'm saying I think it's just

19
00:01:03,800 --> 00:01:07,080
Speaker 1: a matter of time before machines dominate mankind. He said,

20
00:01:07,360 --> 00:01:11,400
Speaker 1: intelligence will win out. James Barrett has spent the last

21
00:01:11,440 --> 00:01:15,040
Speaker 1: thirteen years researching the future of mankind and robots, and

22
00:01:15,120 --> 00:01:17,440
Speaker 1: James is at a bright future for us? Or should

23
00:01:17,560 --> 00:01:21,440
Speaker 1: we pack up right now? It's good to be here

24
00:01:21,440 --> 00:01:24,600
Speaker 1: at George. Um, you know, the future is in our hands.

25
00:01:25,360 --> 00:01:29,360
Speaker 1: But unless we make good decisions now, Um, it will

26
00:01:29,440 --> 00:01:32,000
Speaker 1: we will, it will be in the machines hands well

27
00:01:32,080 --> 00:01:39,440
Speaker 1: in our final invention. You're really concerned about artificial intelligence, Yeah, well,

28
00:01:39,600 --> 00:01:42,959
Speaker 1: I'm My book is about trying to get people to

29
00:01:43,040 --> 00:01:47,320
Speaker 1: understand that artificial intelligence is a dual use technology. It's

30
00:01:47,440 --> 00:01:49,840
Speaker 1: it's capable, and that's a technology that's capable of great

31
00:01:49,840 --> 00:01:52,680
Speaker 1: good or great harm, like like nuclear fission. You know,

32
00:01:53,840 --> 00:01:56,440
Speaker 1: fission is the power behind nuclear power plants, nuclear wet

33
00:01:56,480 --> 00:02:00,920
Speaker 1: and nuclear weapons. So we need to think of aim

34
00:02:01,000 --> 00:02:04,280
Speaker 1: more more like fission than like, you know, car manufacturing.

35
00:02:04,320 --> 00:02:12,519
Speaker 1: It's not it's not benign. What makes these machines so intelligent?

36
00:02:12,840 --> 00:02:16,400
Speaker 1: I mean, it just boggles my mind that through chips

37
00:02:16,400 --> 00:02:20,000
Speaker 1: and computers that they can make things that are so

38
00:02:20,080 --> 00:02:23,600
Speaker 1: realistic and they just you know, they just keep getting

39
00:02:23,680 --> 00:02:26,280
Speaker 1: better and better and better. How do they do that?

40
00:02:26,840 --> 00:02:29,280
Speaker 1: And you know, I'm I'm despite the title of my book,

41
00:02:29,280 --> 00:02:33,400
Speaker 1: I'm actually very very I'm fascinated by artificial intelligence. I

42
00:02:33,400 --> 00:02:37,600
Speaker 1: think it's an incredible technology UM or set of technologies. Well,

43
00:02:37,639 --> 00:02:40,880
Speaker 1: it's it's a bunch of you know, there's a bunch

44
00:02:40,919 --> 00:02:42,480
Speaker 1: of things that go in a bunch of things that

45
00:02:42,560 --> 00:02:46,000
Speaker 1: go into the secret sauce for AI. One is advances

46
00:02:46,000 --> 00:02:50,080
Speaker 1: in computer chips. You know that Moore's Law says computer

47
00:02:50,160 --> 00:02:54,240
Speaker 1: chips get double double in speed and half in price

48
00:02:54,360 --> 00:02:57,680
Speaker 1: every eighteen months, and that's been true since the nineteen sixties.

49
00:02:58,160 --> 00:03:01,640
Speaker 1: So computer chips are getting better. Um, software techniques are

50
00:03:01,639 --> 00:03:05,720
Speaker 1: getting better. There's a new uh right now, deep learning

51
00:03:05,880 --> 00:03:10,440
Speaker 1: and artificial neural nets. Those are two software techniques are

52
00:03:10,600 --> 00:03:14,920
Speaker 1: making huge headway. And there's just new sort of a

53
00:03:14,919 --> 00:03:18,480
Speaker 1: new way of thinking about artificial intelligence right now. So UM.

54
00:03:18,680 --> 00:03:22,079
Speaker 1: And finally, the third component is big data. Big data

55
00:03:22,240 --> 00:03:25,440
Speaker 1: is changing everything. So it's it's a bunch of things

56
00:03:25,440 --> 00:03:29,040
Speaker 1: that have made this an especially important time for AI.

57
00:03:29,080 --> 00:03:32,600
Speaker 1: It's a real AI revolution. What made the big leap

58
00:03:32,639 --> 00:03:36,120
Speaker 1: in computer knowledge? When you know, in the beginning you know,

59
00:03:36,160 --> 00:03:39,400
Speaker 1: they had the Univax machines and all these talent, these

60
00:03:39,440 --> 00:03:44,720
Speaker 1: types of I remember ribbons they put through paper, ribbons

61
00:03:44,720 --> 00:03:50,480
Speaker 1: with information. What happened? How did this thing explode? Well,

62
00:03:50,520 --> 00:03:53,720
Speaker 1: the personal computer gave everything a giant jump start, and

63
00:03:53,760 --> 00:03:56,760
Speaker 1: that was made possible by again the computer chip. You know,

64
00:03:56,760 --> 00:03:59,360
Speaker 1: it used to be that you needed you needed, uh

65
00:03:59,560 --> 00:04:02,160
Speaker 1: to make as many calculations as your cell phone, you

66
00:04:02,200 --> 00:04:07,400
Speaker 1: need a computer the size of a of a football field. UM.

67
00:04:07,440 --> 00:04:12,000
Speaker 1: As as the components get smaller and faster, the computers

68
00:04:12,000 --> 00:04:14,240
Speaker 1: can get smaller and faster. So now in our in

69
00:04:14,280 --> 00:04:17,520
Speaker 1: our in our smartphones, we have we have more processing

70
00:04:17,560 --> 00:04:21,279
Speaker 1: power than than than we're on any on the computers

71
00:04:21,320 --> 00:04:25,680
Speaker 1: on any of the Apollo mission. That it's it's miniaturization,

72
00:04:25,880 --> 00:04:30,600
Speaker 1: it's it's it's circuitry. But it's also UM software techniques,

73
00:04:30,600 --> 00:04:33,040
Speaker 1: you know, people who have been just writing better programs.

74
00:04:33,040 --> 00:04:36,919
Speaker 1: And then the availability of big data. UM, there's a

75
00:04:36,960 --> 00:04:40,440
Speaker 1: giant economic wind propelling the development, you know, the whole

76
00:04:40,480 --> 00:04:46,400
Speaker 1: I T landscape, the whole information technology landscape. UM. We're

77
00:04:46,440 --> 00:04:51,000
Speaker 1: automating so many things, UH, and and businesses are pursue

78
00:04:51,080 --> 00:04:54,440
Speaker 1: automation because it's cheaper than having humans do the work.

79
00:04:55,440 --> 00:04:57,400
Speaker 1: So things are getting smaller and cheaper and they're going

80
00:04:57,440 --> 00:05:01,200
Speaker 1: to continue that way. And as they get smaller, they

81
00:05:01,240 --> 00:05:06,000
Speaker 1: get faster, don't they. Yeah, yes, UM, there's been terrific

82
00:05:06,040 --> 00:05:10,680
Speaker 1: advances in UH in processing speed because of the advances

83
00:05:10,720 --> 00:05:14,760
Speaker 1: in the CPU or central processing unit. You know, right

84
00:05:14,800 --> 00:05:19,359
Speaker 1: now they're making processing processors that are designed specifically for

85
00:05:19,480 --> 00:05:22,680
Speaker 1: artificial intelligence. And what's that What that's gonna do is

86
00:05:22,800 --> 00:05:25,080
Speaker 1: when you when you right now, when you ask your

87
00:05:25,080 --> 00:05:28,320
Speaker 1: phone for directions, when you ask Siri for example, for directions,

88
00:05:29,000 --> 00:05:31,039
Speaker 1: the directions aren't found in your phone, that goes it

89
00:05:31,040 --> 00:05:33,000
Speaker 1: goes to a satellite, which goes to a data center

90
00:05:33,000 --> 00:05:37,320
Speaker 1: and they send the directions back to you instantly. Well

91
00:05:37,400 --> 00:05:40,159
Speaker 1: swim instantly and then but it's gonna be really instant

92
00:05:40,200 --> 00:05:43,880
Speaker 1: when when that AI designed chip is in your smartphone

93
00:05:44,360 --> 00:05:47,200
Speaker 1: and anything you want, like like when you're talking to Siri,

94
00:05:48,400 --> 00:05:51,640
Speaker 1: when you're talking to UM, any of the digital assistance

95
00:05:51,720 --> 00:05:53,720
Speaker 1: that the information that they will be able to access

96
00:05:53,720 --> 00:05:56,479
Speaker 1: will be will become a whole lot faster. Any of

97
00:05:56,480 --> 00:06:00,480
Speaker 1: the processes like navigation or object recognition or trend relation,

98
00:06:01,880 --> 00:06:04,479
Speaker 1: all that will be fast. You know, simultaneous translation is

99
00:06:04,480 --> 00:06:08,000
Speaker 1: coming up, so that, um, you can you can be

100
00:06:08,040 --> 00:06:11,960
Speaker 1: on Skype with someone in Russia, for example, or or China,

101
00:06:12,160 --> 00:06:18,479
Speaker 1: and you're your your computer will translate what you're saying simultaneously,

102
00:06:18,520 --> 00:06:22,720
Speaker 1: and we'll translate what they're saying simultaneously. That's that's remarkable.

103
00:06:22,839 --> 00:06:25,960
Speaker 1: So in some cases, this kind of intelligence is pretty

104
00:06:26,000 --> 00:06:28,920
Speaker 1: darn good, isn't it. You know it is, and that's

105
00:06:28,920 --> 00:06:31,960
Speaker 1: why it's it's dual use, you know, it's it's it's

106
00:06:32,000 --> 00:06:35,280
Speaker 1: in the short term, Uh, it's gonna make our lives better.

107
00:06:35,440 --> 00:06:40,200
Speaker 1: I mean, it's just really handy tools. Um. Self driving

108
00:06:40,200 --> 00:06:43,080
Speaker 1: cars are going to make far fewer accidents on the highways.

109
00:06:43,120 --> 00:06:47,080
Speaker 1: Oh gosh, that scares the heck economy, James, and I

110
00:06:47,120 --> 00:06:50,920
Speaker 1: am opposed to I am opposed to self driving trucks

111
00:06:50,960 --> 00:06:53,960
Speaker 1: because I don't want our truck drivers to lose their jobs.

112
00:06:54,720 --> 00:06:57,279
Speaker 1: That's that's right. You know, there are five million professional

113
00:06:57,360 --> 00:07:01,680
Speaker 1: drivers in the United States, truck drivers, little guys everything,

114
00:07:01,839 --> 00:07:05,960
Speaker 1: and when delivery delivery people, um, all the all that's

115
00:07:06,000 --> 00:07:08,840
Speaker 1: gonna unfortunately, they're going to be unemployed. You you see

116
00:07:08,880 --> 00:07:11,520
Speaker 1: that coming. You feel that, don't you? Oh? Yeah, absolutely,

117
00:07:11,520 --> 00:07:15,600
Speaker 1: there's you know, every state right now is they're passing

118
00:07:15,680 --> 00:07:18,400
Speaker 1: legislation to allow self driving cars and trucks on the road.

119
00:07:19,080 --> 00:07:21,720
Speaker 1: And there's a giant again, there's there's lobbyists in Washington

120
00:07:21,840 --> 00:07:25,480
Speaker 1: working for the people making self driving cars and computer

121
00:07:25,560 --> 00:07:29,280
Speaker 1: technology that are making sure these laws get passed. Um,

122
00:07:29,280 --> 00:07:32,200
Speaker 1: it's it's it's gonna happen. And then we're gonna then

123
00:07:32,760 --> 00:07:34,880
Speaker 1: people who who are who are professional drivers are going

124
00:07:34,960 --> 00:07:37,400
Speaker 1: to have to figure out what else, what else they

125
00:07:37,440 --> 00:07:40,560
Speaker 1: can do what It's not just it's not just them,

126
00:07:40,640 --> 00:07:44,560
Speaker 1: it's uh, you know, there are five million bookkeepers, back

127
00:07:44,600 --> 00:07:50,040
Speaker 1: office accounting operations people. They're all going to be unemployed. Um,

128
00:07:50,080 --> 00:07:52,840
Speaker 1: it's going to be factory workers. You know, you know

129
00:07:52,960 --> 00:07:56,840
Speaker 1: that story. I mean, oh yeah, look what happened there. Yeah.

130
00:07:57,240 --> 00:08:00,280
Speaker 1: Fox Con which is the biggest contract manufact actuely of

131
00:08:00,280 --> 00:08:04,120
Speaker 1: electronics in the world. They make the iPhone. They just

132
00:08:04,160 --> 00:08:08,280
Speaker 1: bought thirty thou robots to replace thy workers. Oh my god.

133
00:08:09,520 --> 00:08:12,040
Speaker 1: And and in the long run it's cheaper to buy

134
00:08:12,040 --> 00:08:15,560
Speaker 1: the robots, isn't it. Yeah it is. Um. In fact,

135
00:08:15,560 --> 00:08:21,120
Speaker 1: an American manufacturer robots, Rethink Robotics, has a robot named Baxter,

136
00:08:21,720 --> 00:08:26,040
Speaker 1: and Baxter costs about twenty two thousand dollars, which is

137
00:08:26,040 --> 00:08:29,240
Speaker 1: approximately what a minimum wage worker makes in a year,

138
00:08:30,480 --> 00:08:32,600
Speaker 1: except Baxtor works around the clock. You know, he works

139
00:08:32,640 --> 00:08:35,000
Speaker 1: three shifts instead of one. Show. Yeah, that's right. You

140
00:08:35,040 --> 00:08:38,040
Speaker 1: can work day in and day our. Yeah. It never

141
00:08:38,040 --> 00:08:43,920
Speaker 1: takes vacation and doesn't wine and complain. So you know, unfortunately,

142
00:08:43,920 --> 00:08:46,160
Speaker 1: that's that's that's and that's why I say it's this

143
00:08:46,320 --> 00:08:49,160
Speaker 1: is the downside. The downside in the short term is

144
00:08:49,600 --> 00:08:53,199
Speaker 1: it's gonna be huge, um, huge unemployment and just and

145
00:08:53,960 --> 00:08:58,320
Speaker 1: just economic disruption. Can it be stopped? Is this going

146
00:08:58,360 --> 00:09:01,320
Speaker 1: to stop at all the names? It's not going to stop.

147
00:09:01,480 --> 00:09:03,000
Speaker 1: You know. What we have to do is learn how

148
00:09:03,040 --> 00:09:05,320
Speaker 1: to try and try and learn how to live with

149
00:09:05,720 --> 00:09:10,760
Speaker 1: these these repercussions. You know, automation and robotics and AI

150
00:09:10,880 --> 00:09:15,320
Speaker 1: will create jobs, but the jury is out on whether

151
00:09:15,360 --> 00:09:17,880
Speaker 1: they'll create enough jobs for all the people that don't

152
00:09:18,240 --> 00:09:23,040
Speaker 1: render jobless. So it's it's kind of a wait and see.

153
00:09:23,160 --> 00:09:25,160
Speaker 1: I mean, you can't, you can't. It's it's you can't

154
00:09:25,200 --> 00:09:28,160
Speaker 1: stop it. You can't stop it. Listen to more Coast

155
00:09:28,160 --> 00:09:30,880
Speaker 1: to Coast a m. Every weeknight at one a m.

156
00:09:31,000 --> 00:09:33,880
Speaker 1: Eastern and go to Coast to coast am dot com

157
00:09:34,000 --> 00:09:34,400
Speaker 1: for more