1
00:00:00,080 --> 00:00:02,520
Speaker 1: Else by the government is off China US trade war

2
00:00:02,600 --> 00:00:03,440
Speaker 1: affecting the market.

3
00:00:06,200 --> 00:00:08,600
Speaker 2: Hi, i am Christine Home the United States Secretary of

4
00:00:08,640 --> 00:00:12,239
Speaker 2: Homeland Security. It is TSA's top priority to make sure

5
00:00:12,240 --> 00:00:15,320
Speaker 2: that you have the most pleasant and efficient airport experience

6
00:00:15,360 --> 00:00:19,119
Speaker 2: as possible while we keep you safe. However, Democrats in

7
00:00:19,160 --> 00:00:22,440
Speaker 2: Congress refuse to fund the federal government, and because of this,

8
00:00:22,920 --> 00:00:25,680
Speaker 2: many of our operations are impacted and most of our

9
00:00:25,720 --> 00:00:29,600
Speaker 2: TSA employees are working without pay. We will continue to

10
00:00:29,680 --> 00:00:31,760
Speaker 2: do all that we can to avoid delays that will

11
00:00:31,800 --> 00:00:35,120
Speaker 2: impact your travel, and our hope is that Democrats will

12
00:00:35,159 --> 00:00:37,919
Speaker 2: soon recognize the importance of opening the government.

13
00:00:38,360 --> 00:00:49,440
Speaker 1: What are you doing, Democrats? Okay, if you're traveling through

14
00:00:49,680 --> 00:00:56,520
Speaker 1: MSP anytime soon, you will not be hearing or seeing

15
00:00:56,560 --> 00:00:59,760
Speaker 1: that video from the Homeland Security Secretary.

16
00:01:00,520 --> 00:01:00,880
Speaker 3: That's right.

17
00:01:02,760 --> 00:01:08,640
Speaker 1: Metropolitan Airport Commission spokesperson Jeff Lee told Fox nine yesterday

18
00:01:08,720 --> 00:01:12,840
Speaker 1: they did receive a request to allow the TSA to

19
00:01:13,000 --> 00:01:16,960
Speaker 1: air the US Department of Homeland Security video on display

20
00:01:17,040 --> 00:01:21,240
Speaker 1: monitors within the security screening areas at the MSP, but

21
00:01:21,400 --> 00:01:25,320
Speaker 1: in typical Minnesota fashion, TSA is not to airing the

22
00:01:25,440 --> 00:01:30,959
Speaker 1: video while the MAAC, the Metropolitan Airport's Commission is evaluating

23
00:01:31,240 --> 00:01:36,160
Speaker 1: their request. Under the applicable legal requirements TSA workers do

24
00:01:36,200 --> 00:01:38,120
Speaker 1: remain on the job during the shutdown. They're doing it

25
00:01:38,120 --> 00:01:40,080
Speaker 1: without pay. I mean, you know often what ends up

26
00:01:40,720 --> 00:01:44,080
Speaker 1: happening is you get retroactive pay. Still not fun though.

27
00:01:45,000 --> 00:01:48,000
Speaker 1: MSP is one of several across the country not playing

28
00:01:48,040 --> 00:01:54,360
Speaker 1: the thirty seven second video. Other airports include Vegas, Phoenix, Seattle, Portland, Charlotte, Cleveland.

29
00:01:55,560 --> 00:01:59,560
Speaker 1: Three airports in New York are also not showing the video.

30
00:01:59,640 --> 00:02:03,200
Speaker 1: We'll talk more about the shutdown. This was a bit

31
00:02:03,240 --> 00:02:07,080
Speaker 1: of an update from what we discussed yesterday on the show.

32
00:02:08,000 --> 00:02:10,519
Speaker 1: Representative Tom Emmer will be on just after eight o'clock

33
00:02:10,560 --> 00:02:13,000
Speaker 1: this morning. Well, we'll get our shutdown update. It is

34
00:02:13,040 --> 00:02:15,160
Speaker 1: Twin City's News Talk AM eleven thirty and one oh

35
00:02:15,160 --> 00:02:17,399
Speaker 1: three five FM from the six five to one Carpet

36
00:02:17,800 --> 00:02:19,320
Speaker 1: Next Day Install Studio's.

37
00:02:19,360 --> 00:02:22,600
Speaker 3: My name is John Justice. I'm glad you're with the.

38
00:02:21,880 --> 00:02:26,919
Speaker 1: Show this morning. To vite Gartzenstein Ross talking to AI

39
00:02:27,200 --> 00:02:30,200
Speaker 1: just after six thirty this morning. Any questions that you

40
00:02:30,360 --> 00:02:34,840
Speaker 1: might have artificial intelligence related you can get those in

41
00:02:34,919 --> 00:02:39,080
Speaker 1: now via the iHeartRadio app. I actually had to consult well,

42
00:02:39,120 --> 00:02:39,760
Speaker 1: I didn't have to.

43
00:02:39,840 --> 00:02:40,320
Speaker 3: I just did.

44
00:02:41,280 --> 00:02:44,240
Speaker 1: I consulted our ai overlords a moment ago with something

45
00:02:44,240 --> 00:02:47,360
Speaker 1: that I want to share with you. Coming up in

46
00:02:47,400 --> 00:02:50,760
Speaker 1: the first couple of segments on the show, we have

47
00:02:50,840 --> 00:02:55,959
Speaker 1: audio from Tim Walls saying that President Donald Trump is

48
00:02:55,960 --> 00:02:57,120
Speaker 1: a horrible businessman.

49
00:02:58,160 --> 00:03:01,160
Speaker 3: I'm not even I'm ready to get beat. I'm not

50
00:03:01,200 --> 00:03:01,840
Speaker 3: even joking.

51
00:03:03,800 --> 00:03:07,000
Speaker 1: This is in the middle of him complaining about soybean purchases,

52
00:03:08,040 --> 00:03:11,040
Speaker 1: which I also find equally amusing. We'll talk about that

53
00:03:11,480 --> 00:03:13,640
Speaker 1: just after seven o'clock this morning, and we have a

54
00:03:13,680 --> 00:03:18,000
Speaker 1: lot of audio to share from yesterday's voter fraud hearing

55
00:03:18,400 --> 00:03:22,679
Speaker 1: at the Capitol that some lunatic dfellers decided to go

56
00:03:23,200 --> 00:03:30,080
Speaker 1: and turn into a gun reform hearing. The verbal gymnastics

57
00:03:31,200 --> 00:03:38,200
Speaker 1: by Representative Emma Greenman yesterday were truly wonderful to witness

58
00:03:38,280 --> 00:03:41,800
Speaker 1: live as I watched this voter fraud hearing taking place,

59
00:03:41,840 --> 00:03:46,600
Speaker 1: and she attempted this word play to spin this over

60
00:03:46,720 --> 00:03:50,360
Speaker 1: to how they shouldn't be talking about voter reform, they

61
00:03:50,360 --> 00:03:53,320
Speaker 1: should actually be talking about gun reform, even though this

62
00:03:53,600 --> 00:04:01,200
Speaker 1: was specifically and only a voter fraud and reform potential hearing.

63
00:04:01,360 --> 00:04:05,080
Speaker 1: So we'll get into that. Right around seven thirty on

64
00:04:05,560 --> 00:04:09,240
Speaker 1: Twin City's News Talk, an interesting thing happened with Kyle yesterday.

65
00:04:09,280 --> 00:04:11,040
Speaker 1: This is why I had to console to AI. I'll

66
00:04:11,440 --> 00:04:13,760
Speaker 1: share this with you in just a moment, really quick though.

67
00:04:13,960 --> 00:04:17,679
Speaker 1: Also an update from yesterday, along with the TSA Christy

68
00:04:17,760 --> 00:04:22,240
Speaker 1: Nomean announcement, President Donald Trump, as we reported, did posthumously

69
00:04:22,400 --> 00:04:29,000
Speaker 1: award America's highest civilian honor to Charlie Kirk yesterday. The

70
00:04:29,080 --> 00:04:33,239
Speaker 1: ceremony coincided with what would have been Kirk's thirty second birthday.

71
00:04:33,720 --> 00:04:36,680
Speaker 1: It came just a month after the Turning Point USA

72
00:04:36,720 --> 00:04:39,240
Speaker 1: founder was, of course, assassinated while speaking to the crowd

73
00:04:39,520 --> 00:04:44,800
Speaker 1: at the Utah Valley University. In a sign of Kirk's

74
00:04:44,800 --> 00:04:48,080
Speaker 1: of close ties to the administration, he was the first

75
00:04:48,120 --> 00:04:53,840
Speaker 1: recipient of the Presidential Medal of Freedom in Trump's second term.

76
00:04:54,640 --> 00:04:57,800
Speaker 1: Speaking of Trump's killing, Trump said he was assassinated in

77
00:04:57,839 --> 00:05:00,599
Speaker 1: the prime of his life for boldly speaking the truth,

78
00:05:00,960 --> 00:05:04,320
Speaker 1: for living his faith, and relentless fighting for a better

79
00:05:04,360 --> 00:05:08,960
Speaker 1: and stronger America. Yesterday, I also reported that there was

80
00:05:09,000 --> 00:05:11,800
Speaker 1: this counter I'm gonna say counter because it's not even

81
00:05:11,839 --> 00:05:14,599
Speaker 1: involved with this, but there was this protest event taking

82
00:05:14,600 --> 00:05:18,719
Speaker 1: place on the University of Minnesota, the flyers going around

83
00:05:18,720 --> 00:05:22,280
Speaker 1: attempting to paint Charlie Kirk is a white supremacist. I

84
00:05:22,360 --> 00:05:27,640
Speaker 1: watched video footage of the event yesterday. Yeah, it was

85
00:05:28,160 --> 00:05:33,840
Speaker 1: as paltry as it was pathetic. Maybe half a dozen people,

86
00:05:34,360 --> 00:05:41,120
Speaker 1: including the individuals who were conducting the event and protests,

87
00:05:42,440 --> 00:05:47,280
Speaker 1: standing there with their bull horn spouting ridiculous lies and nonsense.

88
00:05:47,320 --> 00:05:52,320
Speaker 1: So I pointed at my screen, I laughed, and I

89
00:05:52,400 --> 00:05:56,960
Speaker 1: moved on. So yesterday, I'm not gonna say where. And

90
00:05:57,000 --> 00:05:58,520
Speaker 1: I was hesitant to go and bring this up, but

91
00:05:58,520 --> 00:06:01,960
Speaker 1: I'm curious to get your thoughts on it. So my

92
00:06:02,040 --> 00:06:06,039
Speaker 1: youngest Kyle, was looking to get a job. He's going

93
00:06:06,080 --> 00:06:09,240
Speaker 1: to to technical school, but he's taking kind of a

94
00:06:09,360 --> 00:06:12,559
Speaker 1: year off of college work while he kind of figures

95
00:06:12,600 --> 00:06:14,200
Speaker 1: out what he wants to do for a career. So

96
00:06:14,240 --> 00:06:16,080
Speaker 1: he's just it's got to go get a job. It's

97
00:06:16,080 --> 00:06:18,000
Speaker 1: the rules of the house. So he went and had

98
00:06:18,040 --> 00:06:20,240
Speaker 1: an interview yesterday. I'm not going to say where. One

99
00:06:20,279 --> 00:06:25,240
Speaker 1: of the questions though, apparently what was and I and

100
00:06:25,320 --> 00:06:28,000
Speaker 1: I my understanding is that he had like a separate,

101
00:06:28,040 --> 00:06:31,400
Speaker 1: a separate application he had to fill out there. But

102
00:06:31,560 --> 00:06:35,800
Speaker 1: one of the job interview questions was our low income

103
00:06:35,880 --> 00:06:40,359
Speaker 1: people more likely to steal? And you had the option

104
00:06:40,600 --> 00:06:46,039
Speaker 1: of yes, no, or undecided. Now, at first I was

105
00:06:46,120 --> 00:06:51,360
Speaker 1: very confused by this. Why would It's a retail establishment,

106
00:06:51,480 --> 00:06:53,880
Speaker 1: so I'll add that, I'll add this to it, So

107
00:06:53,920 --> 00:06:56,640
Speaker 1: why would they be asking this question? And then I

108
00:06:56,720 --> 00:06:59,960
Speaker 1: kind of, you know, in a very relatively short period time,

109
00:07:00,160 --> 00:07:02,080
Speaker 1: I went, oh, this is probably the reason why, and

110
00:07:02,120 --> 00:07:04,279
Speaker 1: I'll give you my take coming up in just a

111
00:07:04,279 --> 00:07:06,960
Speaker 1: moment here on Twin Cities News Talk. I also went

112
00:07:07,000 --> 00:07:10,120
Speaker 1: and asked our AI overlords what they thought about this,

113
00:07:10,280 --> 00:07:14,320
Speaker 1: and they gave a rather pointed response that I will

114
00:07:14,360 --> 00:07:17,000
Speaker 1: share with you ahead of the conversation that we have

115
00:07:17,080 --> 00:07:19,680
Speaker 1: with our AI analyst of Vid Gartenstein Ross coming up.

116
00:07:19,800 --> 00:07:21,480
Speaker 1: But I'd love to get your thoughts as well. Again,

117
00:07:21,520 --> 00:07:24,960
Speaker 1: the question was it was yes, no, or or you know,

118
00:07:25,000 --> 00:07:28,280
Speaker 1: don't want to answer undecided? Our low income people more

119
00:07:28,360 --> 00:07:31,280
Speaker 1: likely to steal on a job application. I'm kind of

120
00:07:31,280 --> 00:07:33,280
Speaker 1: thinking you shouldn't even be allowed to ask that question.

121
00:07:34,360 --> 00:07:37,480
Speaker 1: AI kind of agrees with me as well. Email me

122
00:07:37,680 --> 00:07:39,960
Speaker 1: Justice at iHeartRadio dot com. As I mentioned, we will

123
00:07:39,960 --> 00:07:41,880
Speaker 1: get to your talkback. Soles are brought to you by

124
00:07:41,920 --> 00:07:45,280
Speaker 1: Lynn Dahl Realty throughout the show from the iHeart Radio Apple.

125
00:07:45,600 --> 00:07:48,320
Speaker 1: Do all of that next right here on Twin Cities

126
00:07:48,320 --> 00:07:48,680
Speaker 1: News Talk.

127
00:07:48,720 --> 00:07:50,320
Speaker 4: Good morning, and I love your show.

128
00:07:56,360 --> 00:07:59,720
Speaker 5: Good morning, John Rich here. So your question was, is

129
00:07:59,760 --> 00:08:01,880
Speaker 5: a low income people want to steal?

130
00:08:02,280 --> 00:08:05,720
Speaker 3: Well, you're applying for a low income job.

131
00:08:06,160 --> 00:08:10,600
Speaker 5: So they're seeing what your integrity is probably and the

132
00:08:10,680 --> 00:08:16,000
Speaker 5: answer should say no or undecided, because otherwise stealing is

133
00:08:16,040 --> 00:08:17,680
Speaker 5: a thought that you have in your mind and they

134
00:08:17,760 --> 00:08:19,360
Speaker 5: may not trust you.

135
00:08:20,240 --> 00:08:25,680
Speaker 3: So I had this interesting take. Thank you for that, Rich.

136
00:08:25,920 --> 00:08:27,440
Speaker 1: A few more comments we'll get to you from the

137
00:08:27,440 --> 00:08:30,520
Speaker 1: iHeartRadio app here and just a moment on Twin Cities

138
00:08:30,560 --> 00:08:33,200
Speaker 1: News Talk, John Justice broadcasting from the six to five

139
00:08:33,200 --> 00:08:36,120
Speaker 1: to one Carpet Next Day Install Studios. Devin is in

140
00:08:36,160 --> 00:08:40,120
Speaker 1: the master control booth this morning. I had another rough

141
00:08:40,200 --> 00:08:43,080
Speaker 1: night trying to fall asleep last night. I'm completely I'm

142
00:08:43,080 --> 00:08:47,240
Speaker 1: off my game, my sleep game right now. Unfortunately, need

143
00:08:47,280 --> 00:08:49,400
Speaker 1: to get my Mini leaf night gummies.

144
00:08:49,559 --> 00:08:51,760
Speaker 3: So my youngest is applying for a job.

145
00:08:51,880 --> 00:08:55,680
Speaker 1: It actually well listen, technically speaking, it's low income but

146
00:08:56,320 --> 00:08:58,720
Speaker 1: actually pays. I was kind of shocked how well it paid.

147
00:08:59,160 --> 00:09:03,280
Speaker 1: Retail I won't say where. I struggled on whether or

148
00:09:03,320 --> 00:09:04,840
Speaker 1: not I wanted to give a hint, but I'm a hint,

149
00:09:04,840 --> 00:09:06,400
Speaker 1: but I'm gonna let it go because I don't know

150
00:09:06,440 --> 00:09:11,280
Speaker 1: if this question is particular for this but this if

151
00:09:11,280 --> 00:09:15,720
Speaker 1: this is exclusive to this company, or if other places

152
00:09:15,760 --> 00:09:18,920
Speaker 1: go and put this question on job interview applications as well.

153
00:09:18,960 --> 00:09:21,960
Speaker 1: But the question was on the job interview application. Are

154
00:09:22,080 --> 00:09:25,439
Speaker 1: low income people more likely to steal? Do you believe

155
00:09:25,480 --> 00:09:29,800
Speaker 1: that low income people are more likely to steal? And

156
00:09:29,840 --> 00:09:32,840
Speaker 1: he was yes, No one decided, And you know, Kyle

157
00:09:32,960 --> 00:09:35,800
Speaker 1: saw that and he was like, uh, well, which way

158
00:09:35,840 --> 00:09:37,679
Speaker 1: I want to go on this? And he just went undecided.

159
00:09:37,960 --> 00:09:42,120
Speaker 1: I imagine most people went undecided. I'll tell you why

160
00:09:42,160 --> 00:09:46,280
Speaker 1: I think they're asking that question. It's different than Rich's

161
00:09:46,480 --> 00:09:54,600
Speaker 1: take relating to the individual who's applying and whether or

162
00:09:54,600 --> 00:09:56,240
Speaker 1: not they would steal. Let's get to a few more

163
00:09:56,280 --> 00:09:58,000
Speaker 1: of your thoughts though, before I share mine.

164
00:09:58,200 --> 00:10:03,520
Speaker 6: Good morning, John Kevin Pione. My response would be people

165
00:10:03,520 --> 00:10:07,760
Speaker 6: of low moral character are more likely to steal. People

166
00:10:07,800 --> 00:10:11,920
Speaker 6: of low moral character are also most more likely to

167
00:10:12,040 --> 00:10:17,720
Speaker 6: be lower income, So I would have to answer that honestly, yes,

168
00:10:18,400 --> 00:10:20,959
Speaker 6: but not because they are lower income.

169
00:10:21,559 --> 00:10:23,440
Speaker 1: I don't know if they gave that much space on

170
00:10:23,480 --> 00:10:24,200
Speaker 1: the application.

171
00:10:26,920 --> 00:10:32,000
Speaker 7: John, Statistically, low income socioeconomic group.

172
00:10:32,080 --> 00:10:34,520
Speaker 4: Is far more likely to commit crimes.

173
00:10:34,760 --> 00:10:38,680
Speaker 3: What does that mean it's only lowly come people commit crimes? No, right,

174
00:10:38,880 --> 00:10:39,320
Speaker 3: not at.

175
00:10:39,200 --> 00:10:44,040
Speaker 7: All, but that is statistically what the data would tell you.

176
00:10:44,320 --> 00:10:50,720
Speaker 3: Okay, let's go here, good morning. All right, low income

177
00:10:50,760 --> 00:10:54,280
Speaker 3: people more apt to steal. I don't know, let's ask

178
00:10:54,400 --> 00:10:57,760
Speaker 3: when on a writer. Wow, that was.

179
00:10:57,720 --> 00:11:03,800
Speaker 1: As it was a deep it's a deep reference for

180
00:11:04,000 --> 00:11:04,800
Speaker 1: Wenoda Ryder.

181
00:11:05,480 --> 00:11:05,800
Speaker 3: Listen.

182
00:11:06,440 --> 00:11:10,119
Speaker 1: My thought on why they asked the question was because

183
00:11:11,000 --> 00:11:14,320
Speaker 1: you're applying for a retail job in which you're working

184
00:11:14,360 --> 00:11:19,040
Speaker 1: with the public, and my guests is that they are

185
00:11:19,200 --> 00:11:22,760
Speaker 1: curious whether or not the person they are hiring would

186
00:11:22,760 --> 00:11:26,720
Speaker 1: be profiling individuals as they come into the store who

187
00:11:26,760 --> 00:11:31,680
Speaker 1: may look to be low income, or if they would

188
00:11:31,960 --> 00:11:36,240
Speaker 1: maybe by extension. If you have that view of low

189
00:11:36,320 --> 00:11:41,600
Speaker 1: income people, then you would be less inclined to offer

190
00:11:41,679 --> 00:11:44,920
Speaker 1: good customer service. I asked our AI overlords, and they

191
00:11:45,040 --> 00:11:48,720
Speaker 1: just straight up said, this is an inappropriate and bias

192
00:11:48,800 --> 00:11:51,480
Speaker 1: question and a hiring manager should not ask it during

193
00:11:51,520 --> 00:11:55,520
Speaker 1: an interview. There is no basis to assume a person's

194
00:11:55,559 --> 00:11:58,880
Speaker 1: integrity based on their income level. A potential employer who

195
00:11:59,000 --> 00:12:04,000
Speaker 1: asks this is likely trying to provoke a reaction testing

196
00:12:04,080 --> 00:12:09,160
Speaker 1: your ethical boundaries. I need to do a little bit

197
00:12:09,200 --> 00:12:11,640
Speaker 1: more digging. I did before the show started, but I

198
00:12:11,679 --> 00:12:15,000
Speaker 1: couldn't find an answer because I was wondering if this

199
00:12:15,760 --> 00:12:19,320
Speaker 1: was something that Minnesota put in place, like this was

200
00:12:19,360 --> 00:12:22,720
Speaker 1: a question that the DFL decided when they had the

201
00:12:22,800 --> 00:12:27,720
Speaker 1: trifecta that they needed to include on all job applications

202
00:12:27,760 --> 00:12:30,640
Speaker 1: because of wokeness equity.

203
00:12:31,480 --> 00:12:36,560
Speaker 8: You name it, John, I just asked our AI overlords

204
00:12:36,880 --> 00:12:41,240
Speaker 8: who was most likely to fraud in Minnesota. I responded

205
00:12:41,280 --> 00:12:45,199
Speaker 8: back to me, it is impossible to determine any specific

206
00:12:45,200 --> 00:12:48,679
Speaker 8: demographic that is most likely to commit this fraud. I

207
00:12:49,720 --> 00:12:51,800
Speaker 8: tend to disagree with that. I think we know, but

208
00:12:52,280 --> 00:12:53,920
Speaker 8: AI is being a little bit too.

209
00:12:54,360 --> 00:12:58,880
Speaker 3: Pc Well, that assessment might be true.

210
00:12:58,960 --> 00:13:01,400
Speaker 1: I also think the AI is drawing from the news stories,

211
00:13:01,400 --> 00:13:05,960
Speaker 1: and it depends on where they're compiling their reference points from,

212
00:13:06,040 --> 00:13:09,560
Speaker 1: in which case, based off of the majority of stories

213
00:13:09,600 --> 00:13:13,320
Speaker 1: relating to fraud, I'm not surprised that the AI overlords

214
00:13:13,360 --> 00:13:14,560
Speaker 1: would come up with that answer.

215
00:13:15,880 --> 00:13:16,600
Speaker 4: Good morning, John.

216
00:13:16,640 --> 00:13:19,520
Speaker 9: I would have left that question blank with an explanation

217
00:13:19,800 --> 00:13:24,240
Speaker 9: of I left this question blank because it has zero

218
00:13:24,320 --> 00:13:27,480
Speaker 9: bearings on my work ethic or my moral conduct.

219
00:13:31,559 --> 00:13:34,520
Speaker 1: I think Kyle took the right route in not answering

220
00:13:34,559 --> 00:13:36,560
Speaker 1: the question at all. I'll do a little bit more digging.

221
00:13:37,720 --> 00:13:41,520
Speaker 1: I have a suspicion that this is something that's required

222
00:13:42,000 --> 00:13:46,240
Speaker 1: on but again, I'm kind of surprised that we haven't

223
00:13:46,280 --> 00:13:49,360
Speaker 1: had individuals leaving talkbacks who have also experienced that on

224
00:13:49,640 --> 00:13:53,080
Speaker 1: job applications, unless many people just haven't gone out to

225
00:13:53,120 --> 00:13:58,640
Speaker 1: get a job as of late. Let me share this

226
00:13:58,720 --> 00:14:01,240
Speaker 1: quick one with you, just sort of off to the

227
00:14:01,360 --> 00:14:06,760
Speaker 1: side heading into this Weekend's actually know what I'm gonna

228
00:14:06,760 --> 00:14:07,400
Speaker 1: hold off on this.

229
00:14:07,480 --> 00:14:09,680
Speaker 3: I have a story out of Fox News.

230
00:14:11,040 --> 00:14:17,079
Speaker 1: Wisconsin where a Wisconsin County Democrat Party, a particular group

231
00:14:17,160 --> 00:14:21,280
Speaker 1: is handing out child bracelet to asking the question is

232
00:14:21,320 --> 00:14:24,000
Speaker 1: he dead yet? My assumption is we're getting a bit

233
00:14:24,000 --> 00:14:28,800
Speaker 1: of a preview relating to the upcoming no Kings protests

234
00:14:28,800 --> 00:14:32,200
Speaker 1: that'll be taking place this weekend. So I'm gonna hold

235
00:14:32,240 --> 00:14:35,240
Speaker 1: off on this though, because there's further commentary that I

236
00:14:35,240 --> 00:14:37,480
Speaker 1: want to be able to provide to that particular story

237
00:14:37,520 --> 00:14:39,960
Speaker 1: that we won't have a time to get into as

238
00:14:40,000 --> 00:14:43,800
Speaker 1: we prepare to talk with Devine Gartzenstein Ross, our artificial

239
00:14:43,920 --> 00:14:46,920
Speaker 1: intelligence analyst and expert.

240
00:14:47,160 --> 00:14:47,600
Speaker 3: A couple of.

241
00:14:47,560 --> 00:14:50,280
Speaker 1: Stories we'll be talking about with Devine local restaurant's using

242
00:14:50,360 --> 00:14:54,440
Speaker 1: robots now and AI to improve their bottom line straight

243
00:14:54,440 --> 00:15:00,000
Speaker 1: out of science fiction television shows and movies, robotic helpers

244
00:15:00,080 --> 00:15:03,600
Speaker 1: showing up in the dining room. Also, is AI going

245
00:15:03,640 --> 00:15:07,960
Speaker 1: to bring about a three day work week as some

246
00:15:08,080 --> 00:15:10,720
Speaker 1: individuals in the entertainment industry think people are going to

247
00:15:10,760 --> 00:15:13,640
Speaker 1: have more leisure time? My question is, well, okay, if

248
00:15:13,680 --> 00:15:15,920
Speaker 1: you go down to a three day work week, where

249
00:15:16,000 --> 00:15:18,480
Speaker 1: are people going to get the money in order to

250
00:15:19,880 --> 00:15:22,359
Speaker 1: pay for all those leisure activities.

251
00:15:24,000 --> 00:15:26,040
Speaker 3: So we'll talk about that and much more.

252
00:15:25,880 --> 00:15:27,840
Speaker 1: With Devine Gorton de sein Ross coming up.

253
00:15:27,760 --> 00:15:29,080
Speaker 3: Next year on twin City's News Talk.

254
00:15:29,120 --> 00:15:30,960
Speaker 1: Before we go to break though, let's get in a

255
00:15:31,000 --> 00:15:34,520
Speaker 1: few more of your talkback comments relating to the job

256
00:15:34,560 --> 00:15:35,480
Speaker 1: application question.

257
00:15:36,600 --> 00:15:41,120
Speaker 10: This is only about profiling John so d you got

258
00:15:41,160 --> 00:15:44,920
Speaker 10: that one right. They want to know whether their future

259
00:15:44,960 --> 00:15:51,120
Speaker 10: employee will be profiling people based on their looks. They

260
00:15:51,280 --> 00:15:54,800
Speaker 10: clearly won't know their economics situation as they walk through

261
00:15:54,840 --> 00:15:58,680
Speaker 10: the door, but they need to know how they'll profile,

262
00:15:58,840 --> 00:16:02,040
Speaker 10: if they'll profile, and if they'll profile in a negative way.

263
00:16:02,120 --> 00:16:04,720
Speaker 10: So congratulations John Justice.

264
00:16:06,000 --> 00:16:06,960
Speaker 3: Hey, good morning John.

265
00:16:07,040 --> 00:16:09,600
Speaker 11: I'm really surprised that question would be on an application

266
00:16:09,880 --> 00:16:14,600
Speaker 11: in the state of Minnesota. We have many wealthy politicians

267
00:16:14,640 --> 00:16:17,760
Speaker 11: that steal all day, every day.

268
00:16:18,480 --> 00:16:19,320
Speaker 4: Let's sad death.

269
00:16:19,400 --> 00:16:21,600
Speaker 3: We're in true story.

270
00:16:21,600 --> 00:16:24,320
Speaker 1: Thank you for the talkbacks, brought to you by Lindahl Realty,

271
00:16:24,360 --> 00:16:25,600
Speaker 1: David Gartzenstein Ross.

272
00:16:25,840 --> 00:16:33,240
Speaker 3: Next on the show, I the.

273
00:16:33,320 --> 00:16:37,320
Speaker 4: Thing is a scared question on Application World whether you're.

274
00:16:37,200 --> 00:16:40,360
Speaker 6: Legally in the country and if you can provide proof

275
00:16:41,040 --> 00:16:44,000
Speaker 6: or if you are a legal resident, because if you

276
00:16:44,080 --> 00:16:47,280
Speaker 6: want on those applications, they can haunt you back.

277
00:16:47,040 --> 00:16:50,200
Speaker 3: In your process. How much times gift change?

278
00:16:50,480 --> 00:16:54,960
Speaker 1: You have a great morning, bye, Yes, times certainly have changed.

279
00:16:55,000 --> 00:16:56,560
Speaker 3: On Walton's Mountain.

280
00:16:58,760 --> 00:17:01,480
Speaker 1: Also showing my age this morning on Twin Cities News

281
00:17:01,480 --> 00:17:03,720
Speaker 1: Talking Am eleven thirty and one oh three five FM

282
00:17:03,760 --> 00:17:07,320
Speaker 1: from the six five to one Carpet Next Day Install Studio.

283
00:17:07,680 --> 00:17:10,720
Speaker 1: The talkback was in reference to what we were talking

284
00:17:10,720 --> 00:17:13,760
Speaker 1: about a moment ago on the show. My youngest went

285
00:17:13,840 --> 00:17:16,800
Speaker 1: in for a job at interview yesterday and one of

286
00:17:16,840 --> 00:17:21,880
Speaker 1: the questions on the job application was yes, no, or undecided,

287
00:17:21,920 --> 00:17:25,560
Speaker 1: our low income people more likely to steal. So thank

288
00:17:25,560 --> 00:17:29,480
Speaker 1: you so much to everybody who left the comments regarding that.

289
00:17:29,800 --> 00:17:34,159
Speaker 1: David garten Stein Ross as CEO AT Expert to Theory

290
00:17:34,560 --> 00:17:38,720
Speaker 1: and our expert AI analyst, joins us once again this morning.

291
00:17:38,880 --> 00:17:40,399
Speaker 3: Good morning, Devid. How we doing today?

292
00:17:41,040 --> 00:17:41,480
Speaker 4: Doing great?

293
00:17:41,560 --> 00:17:43,320
Speaker 12: John, It's always a good morning when I join you

294
00:17:43,400 --> 00:17:45,080
Speaker 12: and your listeners AI.

295
00:17:45,200 --> 00:17:47,800
Speaker 1: By the way, I asked that question about the job

296
00:17:47,840 --> 00:17:52,800
Speaker 1: interview and they responded and it's of our AI overlord's

297
00:17:52,800 --> 00:17:55,679
Speaker 1: opinion that this is an inappropriate and bias question and

298
00:17:55,720 --> 00:17:59,280
Speaker 1: a hiring manager should not ask it during during an interview.

299
00:17:59,320 --> 00:18:01,280
Speaker 3: So I thought that was rather I thought that was

300
00:18:01,359 --> 00:18:02,040
Speaker 3: rather interesting.

301
00:18:04,040 --> 00:18:06,679
Speaker 4: I have so many questions about that question.

302
00:18:06,760 --> 00:18:10,120
Speaker 1: John, Well, it's a retail establishment, and the only thing

303
00:18:10,160 --> 00:18:13,640
Speaker 1: that I could what I landed on David was well,

304
00:18:14,000 --> 00:18:16,719
Speaker 1: I'm curious how that's even allowed to be asked.

305
00:18:17,000 --> 00:18:17,720
Speaker 3: But being that as it.

306
00:18:17,720 --> 00:18:21,760
Speaker 1: May, my assumption was that this was an attempt to

307
00:18:21,880 --> 00:18:25,560
Speaker 1: get an idea about whether or not the potential employee

308
00:18:25,720 --> 00:18:29,919
Speaker 1: working in retail customer service would have a level of

309
00:18:30,000 --> 00:18:34,119
Speaker 1: bias against individuals coming to a store where low income

310
00:18:34,160 --> 00:18:38,040
Speaker 1: people may come and shop. I would think most individuals

311
00:18:38,080 --> 00:18:43,360
Speaker 1: would just go and answer undecided. I'm also AI related.

312
00:18:43,400 --> 00:18:46,479
Speaker 1: Just to add one little extra nugget in here, I'm

313
00:18:46,560 --> 00:18:49,160
Speaker 1: kind of surprised that AI hasn't taken over unless I've

314
00:18:49,280 --> 00:18:52,320
Speaker 1: missed it hasn't taken over like the job at interview

315
00:18:52,920 --> 00:18:57,359
Speaker 1: process that seems to me tailor made for artificial intelligence

316
00:18:57,400 --> 00:18:58,320
Speaker 1: at this point.

317
00:18:58,320 --> 00:18:59,639
Speaker 3: But I'll hand it over to you if you have

318
00:18:59,720 --> 00:19:00,760
Speaker 3: you for their thoughts to be.

319
00:19:02,320 --> 00:19:05,600
Speaker 12: I definitely don't think AI should take over the job

320
00:19:05,600 --> 00:19:09,800
Speaker 12: interview process, right. A part of a job interview for

321
00:19:09,840 --> 00:19:12,800
Speaker 12: anybody who's going to actually have to work with another

322
00:19:12,920 --> 00:19:17,879
Speaker 12: human being is to read their body language, their eye contact,

323
00:19:18,560 --> 00:19:22,240
Speaker 12: their pauses, the connection that you feel.

324
00:19:23,359 --> 00:19:25,640
Speaker 4: I think that semi structured interviews.

325
00:19:25,200 --> 00:19:28,840
Speaker 12: Are way better than completely structured interviews, in other words,

326
00:19:28,880 --> 00:19:30,760
Speaker 12: to have a few moments where you go a little

327
00:19:30,800 --> 00:19:36,679
Speaker 12: bit off script. I definitely wouldn't outsource this to AI,

328
00:19:37,040 --> 00:19:38,240
Speaker 12: at least not quite yet.

329
00:19:38,560 --> 00:19:41,480
Speaker 1: How long, well, that was my next question, how long

330
00:19:41,600 --> 00:19:45,200
Speaker 1: until AI is going to be able to interpret all

331
00:19:45,280 --> 00:19:49,320
Speaker 1: of those things that you mentioned better than a human?

332
00:19:49,400 --> 00:19:51,399
Speaker 1: I mean, I would imagine that AI would have the

333
00:19:51,440 --> 00:19:56,080
Speaker 1: capability to analyze in great detail the very you know,

334
00:19:56,160 --> 00:19:59,720
Speaker 1: the very different metrics that you said a human currently

335
00:19:59,800 --> 00:20:00,840
Speaker 1: has better job of doing.

336
00:20:02,400 --> 00:20:04,800
Speaker 12: I agree with that, except that a lot of it's

337
00:20:04,840 --> 00:20:06,679
Speaker 12: going to be so.

338
00:20:06,960 --> 00:20:09,879
Speaker 4: One thing that AI is very good at right now

339
00:20:10,359 --> 00:20:15,600
Speaker 4: is personality tests, and there are personality.

340
00:20:14,960 --> 00:20:18,720
Speaker 12: Tests that we can use that will get at the

341
00:20:18,840 --> 00:20:23,000
Speaker 12: question of an organizational psychology terms, how well will someone

342
00:20:23,040 --> 00:20:24,040
Speaker 12: be a fit on a team?

343
00:20:24,080 --> 00:20:26,040
Speaker 4: How good a fit will someone be on a team?

344
00:20:26,040 --> 00:20:29,560
Speaker 12: Okay, that's the AI is good at, and that should

345
00:20:29,640 --> 00:20:33,119
Speaker 12: be outsourced to AI. I think that a lot of

346
00:20:33,560 --> 00:20:36,719
Speaker 12: what we're talking about, like you're fit with somebody, how

347
00:20:36,760 --> 00:20:40,400
Speaker 12: you feel about them within your particular team. If you're

348
00:20:40,440 --> 00:20:46,600
Speaker 12: good at it, you know, there's it's a individualized and

349
00:20:46,880 --> 00:20:50,760
Speaker 12: b there's a lot that comes through intuition that isn't

350
00:20:50,760 --> 00:20:54,960
Speaker 12: straight pattern recognition. Anyway, my answer is, I guess this

351
00:20:55,000 --> 00:20:58,679
Speaker 12: would be a little bit surprising. Probably do people do

352
00:20:58,760 --> 00:21:02,800
Speaker 12: a good job in general on interviewing and determining who

353
00:21:02,800 --> 00:21:03,680
Speaker 12: they want to work with?

354
00:21:04,280 --> 00:21:05,639
Speaker 4: I think the answer is probably no.

355
00:21:06,119 --> 00:21:08,639
Speaker 12: Like based on my experience, I think most people I

356
00:21:08,720 --> 00:21:11,720
Speaker 12: talk to are very used to working on teams that

357
00:21:11,760 --> 00:21:13,040
Speaker 12: they don't regard.

358
00:21:12,720 --> 00:21:15,400
Speaker 4: As good teams. So it may well be the case.

359
00:21:15,240 --> 00:21:18,639
Speaker 12: That AI is already better than the average human being

360
00:21:19,240 --> 00:21:23,119
Speaker 12: at job interview questions. All I'll say is that for me,

361
00:21:23,920 --> 00:21:26,199
Speaker 12: I would not outsource this to AI. I think I'm

362
00:21:26,240 --> 00:21:30,080
Speaker 12: good at building teams, and I don't think AI is

363
00:21:30,119 --> 00:21:34,439
Speaker 12: as effective as someone who's good at building teams. Final

364
00:21:34,480 --> 00:21:39,240
Speaker 12: thing is that, as you know, like AI isn't I'm

365
00:21:39,280 --> 00:21:41,280
Speaker 12: not going to say it's not great at interpreting video.

366
00:21:41,840 --> 00:21:44,800
Speaker 12: At the very least, it is expensive for AI to

367
00:21:44,880 --> 00:21:47,800
Speaker 12: interpret video. So you can't just like upload a video

368
00:21:47,800 --> 00:21:51,080
Speaker 12: to chat GPT and say, hey, watch this video like

369
00:21:51,240 --> 00:21:55,879
Speaker 12: it doesn't do it yet. Until AI can cost effectively

370
00:21:56,080 --> 00:21:59,080
Speaker 12: interpret video feeds, it's not going to be a great

371
00:21:59,240 --> 00:22:05,639
Speaker 12: interviewer of job candidates because to me, verbal, nonverbal's, body language,

372
00:22:06,000 --> 00:22:09,480
Speaker 12: eye contact, these are all an important part of assessica candidate.

373
00:22:09,640 --> 00:22:12,560
Speaker 1: Talking with Davite gartan Stein Rossi's the CEO at Expert

374
00:22:12,600 --> 00:22:15,320
Speaker 1: Theory and our AI analyst and expert. Before I get

375
00:22:15,359 --> 00:22:17,800
Speaker 1: into a few of the stories that I have chosen today,

376
00:22:17,840 --> 00:22:20,960
Speaker 1: let's go to the iHeartRadio app talkback and a question

377
00:22:21,040 --> 00:22:21,680
Speaker 1: for you this morning.

378
00:22:21,720 --> 00:22:25,720
Speaker 7: De Vid, Hey John, I've got a question for dgr

379
00:22:26,800 --> 00:22:31,359
Speaker 7: With the upcoming wave of AI implementation, I see it

380
00:22:31,400 --> 00:22:35,359
Speaker 7: as eliminating a mass amount of data entry and analysis jobs,

381
00:22:35,440 --> 00:22:40,800
Speaker 7: largely like counstable, pay roll banking, stuff like that. And

382
00:22:40,920 --> 00:22:44,440
Speaker 7: my gut tells me those jobs are going to reappear

383
00:22:44,680 --> 00:22:48,840
Speaker 7: in the construction industry and industry that is severely lacking

384
00:22:48,960 --> 00:22:50,080
Speaker 7: in people right now.

385
00:22:50,480 --> 00:22:53,600
Speaker 1: Interesting I wasn't aware of. That's the second part your thoughts, David.

386
00:22:54,359 --> 00:22:58,359
Speaker 4: It's really interesting observation. I think that a lot of.

387
00:23:00,800 --> 00:23:03,920
Speaker 12: I think the answer is yes would be the bottom line.

388
00:23:04,200 --> 00:23:07,280
Speaker 12: But the transferability of someone from a data entry job

389
00:23:07,359 --> 00:23:12,440
Speaker 12: to a construction job isn't automatic. You know, I I

390
00:23:12,560 --> 00:23:15,720
Speaker 12: worked construction jobs earlier in my career. It's actually interesting

391
00:23:15,760 --> 00:23:18,640
Speaker 12: how few people like by white collar, by white collar

392
00:23:18,680 --> 00:23:21,840
Speaker 12: world have worked construction jobs, which I think, like you

393
00:23:22,000 --> 00:23:25,440
Speaker 12: learn a lot, and you know, there's a lot of

394
00:23:25,480 --> 00:23:30,840
Speaker 12: people who wouldn't transfer well to a construction job. But

395
00:23:30,960 --> 00:23:32,439
Speaker 12: that being said, I think the caller is.

396
00:23:32,480 --> 00:23:35,800
Speaker 3: Right, do you have a do you have a thought?

397
00:23:36,200 --> 00:23:36,840
Speaker 8: Or well?

398
00:23:37,160 --> 00:23:41,400
Speaker 1: Always asked the question right now when it comes to robots,

399
00:23:41,680 --> 00:23:45,920
Speaker 1: just be specific. Elon Musk likes to go and tout

400
00:23:46,000 --> 00:23:50,560
Speaker 1: his his his robots, you know, on occasion, most recently,

401
00:23:50,560 --> 00:23:52,520
Speaker 1: the thing that pops into my head would be like

402
00:23:52,560 --> 00:23:55,840
Speaker 1: the premiere for tron Areas that came out and there

403
00:23:55,920 --> 00:23:57,720
Speaker 1: was a couple of the robots that were out there

404
00:23:57,760 --> 00:23:59,639
Speaker 1: and they were painted like the film and all of this.

405
00:24:00,240 --> 00:24:03,159
Speaker 3: You know, how long until you think.

406
00:24:02,920 --> 00:24:06,680
Speaker 1: That that technology has advanced to the point with AI

407
00:24:06,760 --> 00:24:12,800
Speaker 1: implementation where AI robots end up impacting something like construction jobs.

408
00:24:12,800 --> 00:24:15,199
Speaker 1: Are we a decade out less than that you have

409
00:24:15,200 --> 00:24:16,920
Speaker 1: any gauge an engage at all.

410
00:24:16,880 --> 00:24:20,320
Speaker 4: Devide, I think it's really hard to say job right.

411
00:24:22,000 --> 00:24:24,600
Speaker 12: Everything's moving so much more quickly and a lot of

412
00:24:24,640 --> 00:24:27,480
Speaker 12: the barriers that we have to implementation are going away.

413
00:24:27,640 --> 00:24:30,679
Speaker 12: So I think that anywhere where we're short staffed in

414
00:24:30,760 --> 00:24:34,520
Speaker 12: terms of labor, people will be looking for potential jobs.

415
00:24:34,720 --> 00:24:39,639
Speaker 12: There's a lot that robots could do really well within construction.

416
00:24:40,440 --> 00:24:42,720
Speaker 1: Well, and we've seen their usage on you know, when

417
00:24:42,720 --> 00:24:45,800
Speaker 1: it comes to on a smaller scale without AI relating

418
00:24:45,880 --> 00:24:48,560
Speaker 1: to you know, bomb squads and things of that nature.

419
00:24:48,640 --> 00:24:51,280
Speaker 1: So I like the idea though, as you mentioned, and

420
00:24:51,320 --> 00:24:53,800
Speaker 1: it seems to be a constant theme in our weekly

421
00:24:54,080 --> 00:24:59,520
Speaker 1: discussions of AI's implementation into places where we're currently lacking

422
00:25:00,240 --> 00:25:02,840
Speaker 1: individuals to go and do those particular positions. And I

423
00:25:02,960 --> 00:25:04,840
Speaker 1: just like it from the standpoint of it kind of

424
00:25:04,840 --> 00:25:07,120
Speaker 1: strips away the duman gloom talk that you hear from

425
00:25:07,160 --> 00:25:10,520
Speaker 1: so many other sectors about AI coming in and wiping

426
00:25:10,560 --> 00:25:12,960
Speaker 1: out jobs when the reality is they're really going to

427
00:25:12,960 --> 00:25:15,720
Speaker 1: be filling in some gaps at least for the time being.

428
00:25:16,359 --> 00:25:18,280
Speaker 12: Yeah, that's a good segue to an article that you

429
00:25:18,320 --> 00:25:22,359
Speaker 12: want to talk about, right the article about Open Hot

430
00:25:22,400 --> 00:25:23,600
Speaker 12: Pot in Saint Paul.

431
00:25:23,720 --> 00:25:24,680
Speaker 3: I'm looking at it right now.

432
00:25:25,359 --> 00:25:28,680
Speaker 12: Yeah, that's exactly what this article made me think of, John.

433
00:25:28,960 --> 00:25:34,000
Speaker 12: This is article about the Open Hot Pot restaurant in

434
00:25:34,040 --> 00:25:37,399
Speaker 12: Brooklyn Park. You know, it's been in business for thirty

435
00:25:37,520 --> 00:25:40,280
Speaker 12: years and as struggling as a lot of restaurants are

436
00:25:40,359 --> 00:25:46,840
Speaker 12: right now, and in response introduced some robots to robots

437
00:25:46,920 --> 00:25:49,080
Speaker 12: into the dining room. It's in all you can eat place,

438
00:25:49,640 --> 00:25:55,000
Speaker 12: and the robots will help diners carry plates of food

439
00:25:55,119 --> 00:25:59,280
Speaker 12: back to their tables. They you know, they're also something

440
00:25:59,320 --> 00:26:04,040
Speaker 12: that's there to prove efficiency. And you know the part

441
00:26:04,080 --> 00:26:07,280
Speaker 12: of the articles talking about this Saint Paul based company,

442
00:26:07,280 --> 00:26:10,960
Speaker 12: Eco Lab that's using artificial intelligence and data to help

443
00:26:11,040 --> 00:26:14,879
Speaker 12: restaurants reduce food waste, track sales operate more efficiently.

444
00:26:15,359 --> 00:26:16,520
Speaker 4: And this is the other side.

445
00:26:16,600 --> 00:26:20,280
Speaker 12: Right, We've talked a lot about the job side, and

446
00:26:20,520 --> 00:26:24,880
Speaker 12: one thing I've observed multiple times is that policymakers have systemically,

447
00:26:25,400 --> 00:26:30,440
Speaker 12: and you know, by deliberate choice, made human labor much

448
00:26:30,480 --> 00:26:33,880
Speaker 12: more expensive. This isn't a criticism, you know. I think

449
00:26:33,920 --> 00:26:36,400
Speaker 12: that that there are criticisms one can one can make,

450
00:26:36,400 --> 00:26:38,879
Speaker 12: but I'm not making it directly. I'm just saying that

451
00:26:38,920 --> 00:26:43,720
Speaker 12: one of the like when someone gains money, someone else

452
00:26:43,760 --> 00:26:48,600
Speaker 12: loses money. For restaurants, like increasing salaries can could be

453
00:26:48,600 --> 00:26:51,280
Speaker 12: an existential threat to a restaurant. We see lots of

454
00:26:51,320 --> 00:26:54,560
Speaker 12: restaurants going out of business. And one of the things

455
00:26:54,560 --> 00:26:58,680
Speaker 12: we'll increasingly see AI as is a way to serve

456
00:26:58,720 --> 00:27:03,240
Speaker 12: as a stop gap when either you're short handed the

457
00:27:03,320 --> 00:27:06,400
Speaker 12: construction example that you're listener through in, or else when

458
00:27:06,760 --> 00:27:10,119
Speaker 12: costs are just too high. For example, when you have

459
00:27:10,160 --> 00:27:12,960
Speaker 12: a labor intensive business, the cost of human labor might

460
00:27:13,000 --> 00:27:16,639
Speaker 12: be too high, and robots are a really good way

461
00:27:17,040 --> 00:27:20,520
Speaker 12: to fill in that gap. AI has many things. There's

462
00:27:20,880 --> 00:27:22,840
Speaker 12: costs to it, there are dangers. We talk about the

463
00:27:22,920 --> 00:27:25,439
Speaker 12: dangers a lot, but it's good to look at the

464
00:27:25,520 --> 00:27:30,000
Speaker 12: upsides as well. And I love that article John about

465
00:27:30,119 --> 00:27:31,360
Speaker 12: robots helping diners.

466
00:27:31,560 --> 00:27:33,479
Speaker 1: Well yeah, and I mean you mentioned some of what

467
00:27:33,520 --> 00:27:35,879
Speaker 1: they were doing, delivering food directly from the kitchen to

468
00:27:35,920 --> 00:27:39,520
Speaker 1: the tables and lightening the load for his servers and

469
00:27:39,520 --> 00:27:42,399
Speaker 1: freeing them to do other tasks. The owners said, they

470
00:27:42,440 --> 00:27:44,960
Speaker 1: get a free hand to do something else because we

471
00:27:45,000 --> 00:27:48,479
Speaker 1: are Because we are all you can eat. Sometimes it's

472
00:27:48,520 --> 00:27:51,359
Speaker 1: a lot of food, you know, and you cannot hold

473
00:27:51,400 --> 00:27:53,960
Speaker 1: it all in one hand. I thought it was interesting

474
00:27:54,000 --> 00:27:57,960
Speaker 1: too because the company they're working with, the Saint Paul

475
00:27:57,960 --> 00:28:02,040
Speaker 1: based Eco Lab, is combining AI and data driven innovation

476
00:28:02,200 --> 00:28:05,800
Speaker 1: to help restaurants control food waste, track sales, and operate

477
00:28:05,840 --> 00:28:09,240
Speaker 1: more efficiently and sort of keeping a more optimistic view

478
00:28:10,000 --> 00:28:12,439
Speaker 1: and observation. And I'm curious to get your thoughts that.

479
00:28:13,040 --> 00:28:16,520
Speaker 1: While we continue to see the articles written about AI

480
00:28:16,640 --> 00:28:20,480
Speaker 1: jobs in taking over human jobs, I think here presents

481
00:28:20,520 --> 00:28:25,560
Speaker 1: another example of where if this business is more successful

482
00:28:25,680 --> 00:28:28,280
Speaker 1: and they haven't let anybody go and they have the

483
00:28:28,280 --> 00:28:31,320
Speaker 1: opportunity to grow, then AI in this case would actually

484
00:28:31,359 --> 00:28:35,080
Speaker 1: be able to assist in perhaps providing even more human

485
00:28:35,160 --> 00:28:38,320
Speaker 1: jobs than they were able to have prior. So almost

486
00:28:38,360 --> 00:28:42,040
Speaker 1: the inverse would would happen using this AI technology.

487
00:28:42,040 --> 00:28:44,480
Speaker 3: Would you agree.

488
00:28:43,600 --> 00:28:44,400
Speaker 4: Yeah, I agree.

489
00:28:44,840 --> 00:28:47,840
Speaker 12: This is an area where you know when you when

490
00:28:47,840 --> 00:28:51,240
Speaker 12: you business goes out of business, you know obviously there's

491
00:28:51,360 --> 00:28:52,880
Speaker 12: a net negative.

492
00:28:54,280 --> 00:28:57,200
Speaker 4: Negative effect on jobs. Right, there's net job loss if

493
00:28:57,200 --> 00:29:01,600
Speaker 4: a business can't sus state itself. And AI is really

494
00:29:01,600 --> 00:29:02,760
Speaker 4: good for entrepreneurs.

495
00:29:02,960 --> 00:29:05,320
Speaker 12: I think we'll actually see a lot more stories like

496
00:29:05,400 --> 00:29:08,200
Speaker 12: the one you put your finger on. And part of

497
00:29:08,200 --> 00:29:12,280
Speaker 12: the reason is AI use cases are now becoming mature.

498
00:29:12,880 --> 00:29:16,240
Speaker 12: I think almost certainly, if I were to say how

499
00:29:16,240 --> 00:29:18,959
Speaker 12: this article came to be, there's one of two ways.

500
00:29:19,640 --> 00:29:22,840
Speaker 12: Either someone saw like the robot helpers this restaurant and

501
00:29:22,920 --> 00:29:26,160
Speaker 12: jumped on it, but I think much more likely a reporter.

502
00:29:25,880 --> 00:29:29,120
Speaker 4: Talked to someone at Ecolab. No criticism there.

503
00:29:29,200 --> 00:29:31,600
Speaker 12: I run a business, I understand how you need to publicize,

504
00:29:31,960 --> 00:29:34,440
Speaker 12: and this really is kind of like a PR piece

505
00:29:35,160 --> 00:29:37,800
Speaker 12: essentially for Ecolab, is what it seems like. Again, zero

506
00:29:37,840 --> 00:29:40,960
Speaker 12: criticism to our friends at Ecolab, who I've never met.

507
00:29:43,520 --> 00:29:47,480
Speaker 12: But you'll see more of this because AI really is useful, right,

508
00:29:47,560 --> 00:29:50,560
Speaker 12: and it's starting to become mature enough that we're seeing

509
00:29:50,640 --> 00:29:53,400
Speaker 12: AI put things in place that a few years ago

510
00:29:53,440 --> 00:29:56,840
Speaker 12: would have been really weird for us. You know, robots

511
00:29:56,960 --> 00:30:01,720
Speaker 12: or AI changing business processes and arrest and it's helpful

512
00:30:01,800 --> 00:30:03,880
Speaker 12: for the companies that are using it, whether it's the

513
00:30:03,920 --> 00:30:06,880
Speaker 12: restaurant or eco lab as a provider. So you'll see

514
00:30:06,920 --> 00:30:10,560
Speaker 12: a lot more of these AI has dangers. AI is

515
00:30:10,600 --> 00:30:14,000
Speaker 12: really useful and overall, like I think, what we all

516
00:30:14,080 --> 00:30:17,840
Speaker 12: understand is AI isn't going anywhere. It's here to stay,

517
00:30:18,240 --> 00:30:22,200
Speaker 12: and the transformations that it brings are just going to escalate.

518
00:30:22,320 --> 00:30:24,840
Speaker 12: So while there'll be some ludeaie pieces that are arguing,

519
00:30:24,840 --> 00:30:27,000
Speaker 12: wouldn't the world be better if we didn't have AI,

520
00:30:27,480 --> 00:30:29,160
Speaker 12: totally legit just like the world could be better if

521
00:30:29,200 --> 00:30:31,280
Speaker 12: we didn't have social media, but that's just not the

522
00:30:31,320 --> 00:30:33,480
Speaker 12: world that we live in. So you'll see a lot

523
00:30:33,520 --> 00:30:37,400
Speaker 12: more I think about these really interesting use cases that

524
00:30:37,440 --> 00:30:39,680
Speaker 12: make you say, huh, you know, I'm glad this is here.

525
00:30:40,040 --> 00:30:42,480
Speaker 1: Well, And an interesting just sidebar data point and something

526
00:30:42,520 --> 00:30:44,479
Speaker 1: I'm going to be talking about next hour on a

527
00:30:44,520 --> 00:30:48,080
Speaker 1: separate issue of some studies that have been done. You know,

528
00:30:48,120 --> 00:30:51,040
Speaker 1: some of the data is showing that actual social media

529
00:30:51,200 --> 00:30:56,120
Speaker 1: usage among young Americans is actually declining. Again, just sort

530
00:30:56,160 --> 00:31:00,320
Speaker 1: of a side note, which is surprising, lending it and

531
00:31:00,320 --> 00:31:03,320
Speaker 1: attaching to the conversation we had of the doom and

532
00:31:03,320 --> 00:31:06,040
Speaker 1: gloom aspect of AI that gets reported so often.

533
00:31:06,080 --> 00:31:07,800
Speaker 3: The same thing rings true for social.

534
00:31:07,520 --> 00:31:11,760
Speaker 1: Media, but there also is it appears to be somewhat

535
00:31:11,960 --> 00:31:13,640
Speaker 1: of a decline, and I think a lot of it

536
00:31:13,680 --> 00:31:16,360
Speaker 1: comes down to people are more educated now on just

537
00:31:16,360 --> 00:31:19,400
Speaker 1: how addictive you know, social media can be, and perhaps

538
00:31:19,400 --> 00:31:22,200
Speaker 1: are adjusting themselves accordingly. I know we are in our house,

539
00:31:22,280 --> 00:31:24,800
Speaker 1: just by an example, trying to spend more time away

540
00:31:24,840 --> 00:31:27,040
Speaker 1: from our phones and you know, less time face to

541
00:31:27,080 --> 00:31:27,800
Speaker 1: face with each other.

542
00:31:28,480 --> 00:31:28,680
Speaker 6: Yeah.

543
00:31:28,680 --> 00:31:30,440
Speaker 12: I think the fact that the people who run tech

544
00:31:30,480 --> 00:31:34,720
Speaker 12: companies and social media companies really restrict their own kids'

545
00:31:34,760 --> 00:31:37,840
Speaker 12: usage that their product tells us something.

546
00:31:37,880 --> 00:31:41,280
Speaker 1: Yeah, I have a talk back that rolls in. I

547
00:31:41,320 --> 00:31:42,480
Speaker 1: want to share it with you. Is more of a

548
00:31:42,480 --> 00:31:46,560
Speaker 1: comment relating to AI and construction Before I get to that, though,

549
00:31:46,600 --> 00:31:48,320
Speaker 1: just a quick question that popped up in the middle

550
00:31:48,360 --> 00:31:55,280
Speaker 1: of your last comments. Prior to AI, the conversation revolving

551
00:31:55,560 --> 00:32:00,480
Speaker 1: around jobs and potential job loss was focused on automation

552
00:32:01,760 --> 00:32:05,920
Speaker 1: without the AI component attached to it. Has AI had

553
00:32:06,640 --> 00:32:09,640
Speaker 1: a massive impact on automation in and of itself?

554
00:32:09,680 --> 00:32:14,040
Speaker 3: Wherein the two are combined? Has it? Has it changed

555
00:32:14,760 --> 00:32:16,040
Speaker 3: relative to that because we.

556
00:32:16,000 --> 00:32:18,800
Speaker 1: Talk more about AI now in jobs more than we

557
00:32:18,880 --> 00:32:21,760
Speaker 1: do automation in jobs. Are we just replacing one for

558
00:32:21,840 --> 00:32:25,840
Speaker 1: the other, or what changes have been made? Like if

559
00:32:25,840 --> 00:32:28,160
Speaker 1: it was there was no AI and we were still

560
00:32:28,160 --> 00:32:30,240
Speaker 1: looking at automation, I would imagine we'd still be having

561
00:32:30,280 --> 00:32:33,600
Speaker 1: a conversation about automation replacing humans in the future.

562
00:32:34,240 --> 00:32:37,160
Speaker 4: I think we've changed our vocabulary a bit, right.

563
00:32:37,200 --> 00:32:41,000
Speaker 12: Automation needs to be for blue collar whereas what we

564
00:32:41,080 --> 00:32:46,200
Speaker 12: call AI to some extent is automation for white collar jobs.

565
00:32:46,240 --> 00:32:49,400
Speaker 4: Oh okay, But like these days, for.

566
00:32:49,440 --> 00:32:52,000
Speaker 12: Any kind of automation, AI is going to be a

567
00:32:52,000 --> 00:32:54,680
Speaker 12: part of it. For any new automation built like, it's

568
00:32:54,720 --> 00:32:58,360
Speaker 12: inherently going to incorporate AI. So I'm agreeing with you.

569
00:32:58,440 --> 00:33:01,880
Speaker 12: I think that we use the two a little bit interchangeably,

570
00:33:02,360 --> 00:33:05,920
Speaker 12: as we should, because ultimately AI automates a lot of

571
00:33:05,960 --> 00:33:10,200
Speaker 12: things that humans used to do, including writing, including you know,

572
00:33:10,360 --> 00:33:12,800
Speaker 12: potentially job interviews, et cetera.

573
00:33:14,600 --> 00:33:16,880
Speaker 13: So you got to talk to the guys, yeah, AI

574
00:33:16,960 --> 00:33:20,880
Speaker 13: and construction is sort of already there, at least when

575
00:33:20,880 --> 00:33:24,120
Speaker 13: it comes to framing. There don't do a whole lot

576
00:33:24,160 --> 00:33:29,440
Speaker 13: of stick framing anymore. All the walls, floors, sometimes staircases

577
00:33:29,840 --> 00:33:32,800
Speaker 13: are all built in a factory and shift assemble to

578
00:33:32,880 --> 00:33:33,400
Speaker 13: the site.

579
00:33:33,480 --> 00:33:34,920
Speaker 4: You're just putting a puzzle together.

580
00:33:35,480 --> 00:33:37,320
Speaker 13: I'm sure they're going to have some robot that can

581
00:33:37,360 --> 00:33:39,640
Speaker 13: put the puzzle together pretty darn soon.

582
00:33:40,160 --> 00:33:40,800
Speaker 3: Yeah, I was.

583
00:33:40,920 --> 00:33:43,000
Speaker 1: I was kind of expecting somebody to chime in from

584
00:33:43,000 --> 00:33:45,440
Speaker 1: the construction industry to give us an example like that DVID.

585
00:33:46,080 --> 00:33:50,120
Speaker 12: Yeah, it's a great it's a great contribution, and I

586
00:33:50,120 --> 00:33:53,440
Speaker 12: think it's it's obviously not inconsistent with what we were saying,

587
00:33:53,520 --> 00:33:56,640
Speaker 12: like really we're looking at the question specifically of robots

588
00:33:56,720 --> 00:33:59,360
Speaker 12: being there on site to fill in for some human labor.

589
00:33:59,760 --> 00:34:03,920
Speaker 12: But a thanks to the listener for that color really

590
00:34:03,960 --> 00:34:07,800
Speaker 12: helpful and be it's exactly what I would expect.

591
00:34:08,400 --> 00:34:10,479
Speaker 1: Let's wrap on this and we'll just keep with the theme.

592
00:34:10,480 --> 00:34:14,799
Speaker 1: Another article that I sent you there was this Hollywood

593
00:34:14,880 --> 00:34:19,480
Speaker 1: power broker, essentially Army a manual talent agent, sports tycoon.

594
00:34:20,120 --> 00:34:23,279
Speaker 1: He was talking about AI He's got investors into a

595
00:34:23,320 --> 00:34:29,480
Speaker 1: new venture wherein he is in various leisure activity, sports

596
00:34:29,480 --> 00:34:32,800
Speaker 1: and whatnot, and he says the world could be down

597
00:34:32,840 --> 00:34:35,000
Speaker 1: to four day work weeks in the coming years. He

598
00:34:35,080 --> 00:34:37,959
Speaker 1: was talking with the financial times. This could go down

599
00:34:38,000 --> 00:34:40,919
Speaker 1: to three day work weeks with AI. As more people

600
00:34:41,000 --> 00:34:45,319
Speaker 1: use technology to expedite everyday tasks, there's going to be

601
00:34:45,360 --> 00:34:47,840
Speaker 1: more free time. This raises an interesting question though in

602
00:34:47,840 --> 00:34:50,680
Speaker 1: my mind, if that's true, where is the money going

603
00:34:50,760 --> 00:34:54,680
Speaker 1: to come from for individuals that are now working shorter

604
00:34:54,920 --> 00:34:59,600
Speaker 1: work weeks? And at least to another question, davide, you know,

605
00:34:59,600 --> 00:35:04,480
Speaker 1: how will AI impact the price of goods and services

606
00:35:04,520 --> 00:35:07,080
Speaker 1: and perhaps drop the price of goods and services wherein

607
00:35:07,520 --> 00:35:10,520
Speaker 1: salaries you know that would be less because you're not

608
00:35:10,520 --> 00:35:12,759
Speaker 1: working as much, may not be as crucial as they

609
00:35:13,120 --> 00:35:13,520
Speaker 1: used to be.

610
00:35:15,840 --> 00:35:17,840
Speaker 12: In my view, it will drop the price of goodsness

611
00:35:17,840 --> 00:35:20,879
Speaker 12: services full stop, just like just like the Internet did.

612
00:35:21,160 --> 00:35:25,040
Speaker 12: Right we remember after the Internet boom, the price of

613
00:35:25,080 --> 00:35:28,040
Speaker 12: a lot of goods and services dropped significantly.

614
00:35:29,200 --> 00:35:29,799
Speaker 4: I think, like the.

615
00:35:30,120 --> 00:35:33,400
Speaker 12: Four day, three day work week, it's certainly as possible.

616
00:35:33,960 --> 00:35:37,560
Speaker 12: I'm not gonna say no, but I will say we've had,

617
00:35:37,840 --> 00:35:40,960
Speaker 12: you know, for the past, you know, one hundred and

618
00:35:41,000 --> 00:35:45,440
Speaker 12: fifty years, we've seen a series of innovation after innovation

619
00:35:46,120 --> 00:35:49,000
Speaker 12: that has made parts of our lives more efficient, and

620
00:35:49,120 --> 00:35:53,200
Speaker 12: yet people work far more hours now than they did

621
00:35:53,239 --> 00:35:56,200
Speaker 12: one hundred years ago on average in the United States.

622
00:35:56,719 --> 00:36:00,439
Speaker 12: So like not saying it's wrong, I just think that

623
00:36:01,000 --> 00:36:06,279
Speaker 12: data thus far seems to contradict the view that will

624
00:36:06,360 --> 00:36:08,719
Speaker 12: end up with more leisure time. But you can you

625
00:36:08,760 --> 00:36:11,640
Speaker 12: can imagine a world where that's the case. It wouldn't

626
00:36:11,719 --> 00:36:14,040
Speaker 12: be a bad world for us to have.

627
00:36:14,120 --> 00:36:14,720
Speaker 4: On the whole.

628
00:36:15,160 --> 00:36:16,719
Speaker 12: I think there'd be you know, there'd be a lot

629
00:36:16,719 --> 00:36:20,439
Speaker 12: of folks like myself who would still put that time

630
00:36:20,520 --> 00:36:21,640
Speaker 12: in and work.

631
00:36:22,160 --> 00:36:24,560
Speaker 4: But it's possible that we'd have less.

632
00:36:25,080 --> 00:36:27,560
Speaker 12: I guess against it though, And here's the final reason

633
00:36:27,560 --> 00:36:30,560
Speaker 12: why there's a good note to end on. We talk

634
00:36:30,640 --> 00:36:33,200
Speaker 12: a lot about what is the value of a human

635
00:36:33,280 --> 00:36:37,320
Speaker 12: being in a world of super intelligence? What is the

636
00:36:37,360 --> 00:36:41,600
Speaker 12: value of our labor in a world of AI? And

637
00:36:41,880 --> 00:36:44,640
Speaker 12: to be really excellent at something and to be better

638
00:36:44,680 --> 00:36:49,279
Speaker 12: than our AI overlords, that takes time, right, Like you

639
00:36:49,280 --> 00:36:53,799
Speaker 12: actually have to put in time to outperform perform artificial intelligence.

640
00:36:54,560 --> 00:36:57,160
Speaker 12: So I think that like, if we're thinking about, hey,

641
00:36:57,200 --> 00:36:58,719
Speaker 12: we can work a four day work week, we could

642
00:36:58,719 --> 00:37:00,879
Speaker 12: work a three day work week. That works for someone

643
00:37:00,880 --> 00:37:06,040
Speaker 12: who is very highly skilled and has those skills already.

644
00:37:06,080 --> 00:37:09,759
Speaker 4: Given how disruptive AI is, we're going to have to.

645
00:37:09,920 --> 00:37:12,080
Speaker 12: You know, most people are going to have to spend

646
00:37:12,120 --> 00:37:15,960
Speaker 12: a lot of time reskilling. I don't think that translates

647
00:37:15,960 --> 00:37:18,520
Speaker 12: into a three or four day workweek. I think probably

648
00:37:18,560 --> 00:37:21,640
Speaker 12: that view is way too optimistic and looks at our

649
00:37:21,719 --> 00:37:25,279
Speaker 12: time being the value as opposed to our skills being

650
00:37:25,280 --> 00:37:29,000
Speaker 12: the value. Because if our skills are the value, skills

651
00:37:29,040 --> 00:37:33,239
Speaker 12: that can outcompete artificial intelligence take time, and so I

652
00:37:33,280 --> 00:37:35,400
Speaker 12: don't think that AI is going to on the whole

653
00:37:35,800 --> 00:37:38,839
Speaker 12: provide us with the respite that that mogul is hoping for.

654
00:37:39,520 --> 00:37:43,879
Speaker 1: David Gartenstein Ross is CEO at Expert Theory. If people

655
00:37:43,920 --> 00:37:46,359
Speaker 1: wanted to find out more about you, David, where would

656
00:37:46,360 --> 00:37:48,520
Speaker 1: you like to point them to and what you do?

657
00:37:48,600 --> 00:37:51,080
Speaker 4: They could I would love people to find out more

658
00:37:51,120 --> 00:37:51,480
Speaker 4: about me.

659
00:37:51,800 --> 00:37:54,080
Speaker 12: They can go to our website which can be found

660
00:37:54,080 --> 00:37:56,920
Speaker 12: at Expert Theory dot com, to T's in a Row

661
00:37:57,480 --> 00:38:00,000
Speaker 12: or Valancegames dot com.

662
00:38:00,239 --> 00:38:01,840
Speaker 4: We're doing some interesting things.

663
00:38:01,560 --> 00:38:05,640
Speaker 12: With AI in the world of games for education and training.

664
00:38:05,680 --> 00:38:08,440
Speaker 12: It actually relates to the reskilling conversation that we have,

665
00:38:08,560 --> 00:38:11,160
Speaker 12: and I've been privileged to have a few conversations with

666
00:38:11,239 --> 00:38:14,840
Speaker 12: some of your listeners in recent weeks helping them to

667
00:38:15,640 --> 00:38:18,600
Speaker 12: get smart with AI and think about ways that their

668
00:38:18,960 --> 00:38:21,080
Speaker 12: career can lean more into that direction. So I love

669
00:38:21,120 --> 00:38:24,480
Speaker 12: my interactions with folks in your audience, and I'm pretty

670
00:38:24,480 --> 00:38:25,479
Speaker 12: easy to reach on the whole.

671
00:38:25,800 --> 00:38:28,440
Speaker 1: Yeah, and if you don't get to a dvid'd email,

672
00:38:28,480 --> 00:38:31,040
Speaker 1: just email me Justice at iHeartRadio dot com. I'm more

673
00:38:31,040 --> 00:38:33,760
Speaker 1: than happy to go on forward those over to David

674
00:38:33,800 --> 00:38:34,680
Speaker 1: Gartenstein Ross.

675
00:38:35,160 --> 00:38:37,719
Speaker 3: Thanks again for the conversation this week. I really appreciated David.

676
00:38:38,400 --> 00:38:40,440
Speaker 4: Thank you, John. Always a pleasure. Have a great day.

677
00:38:40,800 --> 00:38:44,880
Speaker 1: So coming up, mayors of Minnesota's largest cities are asking

678
00:38:44,920 --> 00:38:49,839
Speaker 1: for more authority to implement local gun regulations, while simultaneously

679
00:38:50,480 --> 00:38:53,680
Speaker 1: also admitting that there is no legal way to go

680
00:38:53,760 --> 00:38:57,600
Speaker 1: and do that. I bring this up because we're going

681
00:38:57,640 --> 00:39:00,560
Speaker 1: to get into this Minnesota House fraud committee that took

682
00:39:00,560 --> 00:39:04,840
Speaker 1: place yesterday wherein one dfl or decided to turn what

683
00:39:05,040 --> 00:39:10,920
Speaker 1: was specific about voting and to make it about guns,

684
00:39:11,120 --> 00:39:13,440
Speaker 1: which was completely irrelevant. I got a lot of audio

685
00:39:13,480 --> 00:39:15,040
Speaker 1: to share. I want to get your thoughts from the

686
00:39:15,040 --> 00:39:17,919
Speaker 1: iHeartRadio app. It is all coming up. Kier on Twin

687
00:39:17,920 --> 00:39:18,240
Speaker 1: City's

688
00:39:18,280 --> 00:39:20,160
Speaker 3: News Talk Am eleven thirty and one oh three five

689
00:39:20,280 --> 00:39:20,560
Speaker 3: FM