1 00:00:00,000 --> 00:00:01,880 Speaker 1: There's been a lot of chat lately. We'll come back 2 00:00:01,880 --> 00:00:03,640 Speaker 1: to the wall stuff in a second, but there's been 3 00:00:03,680 --> 00:00:06,800 Speaker 1: a lot of chat lately about AI replacing jobs, and 4 00:00:06,880 --> 00:00:09,360 Speaker 1: I get it. There are signs that it's happening, little 5 00:00:09,400 --> 00:00:12,080 Speaker 1: pockets of it happening for certain roles, especially in more 6 00:00:12,160 --> 00:00:15,159 Speaker 1: junior roles. The threat is real. But there are some 7 00:00:15,280 --> 00:00:17,520 Speaker 1: things that a computer will never know and never be 8 00:00:17,600 --> 00:00:22,160 Speaker 1: able to do, like reading somebody's emotions. Burger King in 9 00:00:22,239 --> 00:00:24,360 Speaker 1: the US, this is a story out this week, is 10 00:00:24,400 --> 00:00:28,360 Speaker 1: trialing AI software to judge how courteous and friendly it's 11 00:00:28,440 --> 00:00:31,120 Speaker 1: staff are. I know, it sounds weird. That is weird. 12 00:00:31,640 --> 00:00:36,240 Speaker 1: They've got an aptly named AI assistant, Patty, apparently doing 13 00:00:36,280 --> 00:00:39,640 Speaker 1: this task for them. Now. Patty lives in their headsets, 14 00:00:40,000 --> 00:00:43,080 Speaker 1: poor things, monitoring their every word. So if you're handing 15 00:00:43,080 --> 00:00:45,840 Speaker 1: out whoppers at a drive through, Patty will apparently record 16 00:00:45,880 --> 00:00:48,400 Speaker 1: how many times you say welcome, please, and thank you. 17 00:00:49,600 --> 00:00:52,920 Speaker 1: Patty then delivers the Whopper crew a daily friendliness score. 18 00:00:53,440 --> 00:00:56,840 Speaker 1: Apart from sounding like peak micro managing and a pain 19 00:00:57,000 --> 00:01:01,280 Speaker 1: in the backside, Patty with respect actually doesn't know what 20 00:01:01,360 --> 00:01:07,040 Speaker 1: she orat is talking about. Can Patty detect sarcasm. Does 21 00:01:07,120 --> 00:01:09,560 Speaker 1: Patty know if you're dead in the eyes while you're 22 00:01:09,560 --> 00:01:13,320 Speaker 1: welcoming the next hungry customer. Customer service isn't so much 23 00:01:13,360 --> 00:01:17,000 Speaker 1: about what somebody says, but how they say it. It's 24 00:01:17,040 --> 00:01:20,080 Speaker 1: that glint in their eye, it's in an affectation of 25 00:01:20,120 --> 00:01:22,800 Speaker 1: their face in Japan, a polite bow of the head. 26 00:01:23,240 --> 00:01:26,560 Speaker 1: In New Zealand, too much talking or fake friendly could 27 00:01:26,600 --> 00:01:28,959 Speaker 1: be seen as rude. Am I right, We're a bit 28 00:01:29,040 --> 00:01:31,080 Speaker 1: like that here. We're more of a smile and polite 29 00:01:31,120 --> 00:01:35,080 Speaker 1: hand gesture type country, aren't we. Human interaction is intricate, 30 00:01:35,319 --> 00:01:39,880 Speaker 1: it's unique. It takes even trained humans time to properly 31 00:01:39,959 --> 00:01:43,479 Speaker 1: figure out how you're really feeling. We humans have more 32 00:01:43,520 --> 00:01:46,559 Speaker 1: than forty facial muscles, and using them in different ways 33 00:01:46,560 --> 00:01:51,960 Speaker 1: can apparently lead us to ten thousand subtle emotional messages 34 00:01:52,000 --> 00:01:55,240 Speaker 1: that we convey to one another. Isn't that fascinating? I 35 00:01:55,280 --> 00:01:57,440 Speaker 1: went to the bank yesterday to order an f postcard. 36 00:01:58,160 --> 00:02:00,720 Speaker 1: Bank manager came over set helow. I can't tell you 37 00:02:01,360 --> 00:02:03,040 Speaker 1: most of what she said to me, but I know 38 00:02:03,120 --> 00:02:05,600 Speaker 1: that she was lovely. Went home told my partner how 39 00:02:05,640 --> 00:02:08,920 Speaker 1: lovely this woman at the bank was. Is this a 40 00:02:09,040 --> 00:02:13,400 Speaker 1: job that AI can master. I mean, really, even if 41 00:02:13,480 --> 00:02:16,640 Speaker 1: Patty had a camera on our eyeballs, a microphone, and 42 00:02:16,680 --> 00:02:19,840 Speaker 1: a pulse checker, I don't think it could truly tell 43 00:02:20,000 --> 00:02:23,480 Speaker 1: what we're really thinking in a way that only other 44 00:02:23,639 --> 00:02:28,160 Speaker 1: humans can. For more from Early Edition with Ryan Bridge. 45 00:02:28,240 --> 00:02:31,680 Speaker 1: Listen live to news talks it'd be from five am weekdays, 46 00:02:31,919 --> 00:02:34,000 Speaker 1: or follow the podcast on iHeartRadio.