1 00:00:01,844 --> 00:00:03,166 Speaker 1: hello, habit mechanics. 2 00:00:03,166 --> 00:00:05,493 Dr john finn here, hope you're having a great week so far. 3 00:00:05,493 --> 00:00:14,035 So the the last week has been very interesting in terms of ai 4 00:00:15,397 --> 00:00:16,239 and its developments. 5 00:00:16,239 --> 00:00:23,652 We started the week, in the uk at least, with, I think, the 6 00:00:23,692 --> 00:00:30,684 government minister for technology and innovation saying 7 00:00:30,684 --> 00:00:37,959 that UK workers really needed to start building AI into their 8 00:00:37,999 --> 00:00:39,625 workflows or they'd get left behind. 9 00:00:39,625 --> 00:00:44,579 I think that was an article in the Guardian newspaper. 10 00:00:44,579 --> 00:00:49,119 I think that was an article in the Guardian newspaper. 11 00:00:49,119 --> 00:00:54,755 We saw Amazon was really the first big company. 12 00:00:55,859 --> 00:00:58,427 Amazon employ 1.5 million people in the US and significant 13 00:00:58,468 --> 00:01:03,124 numbers of other people or of people in other countries saying 14 00:01:03,124 --> 00:01:09,509 they publicly came out and said that we will be replacing some 15 00:01:09,569 --> 00:01:16,864 human roles with AI and you know no guarantees about would the 16 00:01:16,905 --> 00:01:18,751 technology create new roles? 17 00:01:18,751 --> 00:01:22,409 They didn't know, they thought it might, but they weren't sure. 18 00:01:22,409 --> 00:01:30,141 We ourselves gave our first although we've given many online 19 00:01:30,141 --> 00:01:31,004 talks about this. 20 00:01:31,004 --> 00:01:41,126 We gave our first um in-person talk to one of our partners um, 21 00:01:41,206 --> 00:01:45,531 and in fact it's one of our partner schools that we've 22 00:01:45,570 --> 00:01:51,602 worked with for, I think, 13 or 14 years now, and we were 23 00:01:51,621 --> 00:01:56,210 speaking to their parents, who are a great group of people, 24 00:01:56,331 --> 00:02:03,608 very rich, diverse professional backgrounds, many in the city of 25 00:02:03,608 --> 00:02:07,774 London, but also many in many working in the city of London, 26 00:02:07,793 --> 00:02:10,437 but also many working in the other core sectors in London. 27 00:02:10,437 --> 00:02:16,274 So it was great to talk to them about creating high-performing 28 00:02:17,539 --> 00:02:20,235 human AI teams and get their thoughts and their feedback. 29 00:02:20,235 --> 00:02:25,693 Some of the insights that were shared in the conversations 30 00:02:25,774 --> 00:02:30,799 afterwards surprised me at the speed things are going and, in 31 00:02:30,818 --> 00:02:35,132 fact, the sums of money that are being invested in by some 32 00:02:35,171 --> 00:02:35,495 businesses. 33 00:02:35,495 --> 00:02:38,094 So that was really insightful. 34 00:02:40,618 --> 00:02:45,728 The story has come into the mainstream. 35 00:02:45,728 --> 00:02:46,849 That Mark Zuckerberg. 36 00:02:46,849 --> 00:02:50,481 So this is just another interesting thing that we've 37 00:02:50,521 --> 00:02:51,925 seen this week coming to the mainstream. 38 00:02:51,925 --> 00:02:58,663 Mark Zuckerberg is paying $100 million yes, $100 million 39 00:02:59,223 --> 00:03:05,330 sign-on fees as he tries to assemble a team of, I think, 50, 40 00:03:05,330 --> 00:03:16,585 what he might call super developers working on general 41 00:03:17,967 --> 00:03:18,769 purpose AI. 42 00:03:18,769 --> 00:03:24,182 So, rather than these kind of specific agents we're seeing 43 00:03:24,223 --> 00:03:29,758 emerging rapidly creating an AI which is general, more 44 00:03:29,818 --> 00:03:35,024 human-like and possibly surpassing human intelligence or 45 00:03:35,024 --> 00:03:35,873 humans' ability. 46 00:03:35,873 --> 00:03:38,661 Well, yeah, to learn, which is what intelligence is, and I 47 00:03:38,681 --> 00:03:41,937 think probably a conversation for the podcast, because we 48 00:03:42,557 --> 00:03:45,486 sometimes misunderstand what intelligence means. 49 00:03:45,486 --> 00:03:46,721 It's just about learning. 50 00:03:46,721 --> 00:03:51,842 So that's been going on and I just think in every direction we 51 00:03:51,842 --> 00:03:57,612 look, we can see businesses are starting to make this 52 00:03:57,673 --> 00:04:04,312 transition to smaller teams of humans who are working with AI 53 00:04:04,353 --> 00:04:09,121 technologies and who can deliver significantly more value to the 54 00:04:09,121 --> 00:04:09,542 business. 55 00:04:13,568 --> 00:04:19,757 And the other thing that I've heard this week is Geoffrey 56 00:04:19,797 --> 00:04:26,591 Hinton, who is the godfather of AI, and he was quoted in a BBC 57 00:04:26,692 --> 00:04:37,137 article that talked about this Amazon statement, and Geoffrey 58 00:04:37,156 --> 00:04:38,841 Hinton spoke on a popular podcast. 59 00:04:38,841 --> 00:04:48,512 Geoffrey Hinton is basically the guy who has worked on neural 60 00:04:48,512 --> 00:04:53,180 networks all his life and there are other AI models out there, 61 00:04:54,266 --> 00:04:56,814 but he persisted with neural networks and eventually got them 62 00:04:56,814 --> 00:05:00,422 to work, and neural networks are the core technology that are 63 00:05:00,422 --> 00:05:09,593 driving the AI tools we're using today, and it was Geoffrey 64 00:05:09,593 --> 00:05:14,454 Hinton Geoffrey Hinton's story that really got me interested in 65 00:05:14,454 --> 00:05:18,488 these new AI technologies a couple of years ago when I read 66 00:05:18,548 --> 00:05:22,250 the Genius Makers book, because he's the protagonist of that 67 00:05:22,290 --> 00:05:22,492 book. 68 00:05:22,492 --> 00:05:29,798 So he's a London academic and I think he used to work at UCL, 69 00:05:31,369 --> 00:05:35,370 but there wasn't much appetite for neural networks in the UK so 70 00:05:35,370 --> 00:05:40,389 he eventually moved to canada and that's where he's done most 71 00:05:40,410 --> 00:05:45,206 of his academic work, but essentially a lot of the the 72 00:05:45,267 --> 00:05:51,463 leading thinkers in the ai space are his students, um, and 73 00:05:51,502 --> 00:05:54,771 jeffrey hinton, with some of his students, created a business 74 00:05:54,791 --> 00:05:59,391 that they sold to google and um, very interesting character. 75 00:05:59,391 --> 00:06:00,274 You should check him out. 76 00:06:00,274 --> 00:06:10,497 But he's talking about the, the potential of ai, both in what 77 00:06:10,536 --> 00:06:15,470 it can do in a beneficial way, but both some of the potential 78 00:06:15,509 --> 00:06:16,130 downsides. 79 00:06:17,735 --> 00:06:21,588 But what is very interesting is we hear a it's a bit of a 80 00:06:21,649 --> 00:06:25,456 narrative out there yeah, about ai, people saying, yeah, but it 81 00:06:25,497 --> 00:06:26,665 will create new jobs, won't it? 82 00:06:26,665 --> 00:06:30,701 Because all new technologies create new jobs and people use 83 00:06:30,742 --> 00:06:31,564 different examples. 84 00:06:31,564 --> 00:06:34,170 The one I know is the typewriter. 85 00:06:34,170 --> 00:06:39,007 People thought the typewriter would put people out of work and 86 00:06:39,007 --> 00:06:42,892 it created more jobs and lots of other technologies have done 87 00:06:42,913 --> 00:06:43,254 the same. 88 00:06:43,254 --> 00:06:47,572 And in this podcast he's saying AI is different. 89 00:06:47,572 --> 00:06:53,733 He doesn't believe we'll create lots of new jobs because it can 90 00:06:53,733 --> 00:06:56,312 do most of what most people get paid to do every day. 91 00:06:56,312 --> 00:07:00,192 So this is the reality check, because it can do most of what 92 00:07:00,233 --> 00:07:01,173 most people get to do every day. 93 00:07:01,233 --> 00:07:04,822 So this is the reality check, and sometimes I feel like it's 94 00:07:04,841 --> 00:07:06,264 just us saying this stuff, but it's not. 95 00:07:06,264 --> 00:07:06,584 It's real. 96 00:07:06,584 --> 00:07:07,767 We're not scaremongering. 97 00:07:07,767 --> 00:07:08,269 It's absolutely real. 98 00:07:08,269 --> 00:07:12,581 We want to help people to prepare and to thrive in an 99 00:07:12,661 --> 00:07:16,553 AI-powered world and I think AI gives us the opportunity to do 100 00:07:16,595 --> 00:07:21,404 that, but only if we can get brain state intelligent. 101 00:07:21,404 --> 00:07:29,331 So I just think we've had a really interesting week which 102 00:07:29,370 --> 00:07:33,887 has, I think, put a spotlight on the pace of AI development and 103 00:07:33,927 --> 00:07:41,197 it's moving faster than anybody thought in January. 104 00:07:41,197 --> 00:07:43,230 Because, you know, in January there's a lot of reports coming 105 00:07:43,271 --> 00:07:46,846 out around this stuff and it was all kind of projected that, oh, 106 00:07:46,846 --> 00:07:48,793 in five years time we might see these things. 107 00:07:48,793 --> 00:07:54,189 But you know, six months later we're starting to see big shifts 108 00:07:54,189 --> 00:07:59,043 big shifts in language, shifts in positioning, big shifts in 109 00:07:59,062 --> 00:07:59,665 case studies. 110 00:07:59,805 --> 00:08:03,675 Another thing I spoke to a friend this week who's a 111 00:08:03,815 --> 00:08:09,735 developer and he was just saying that he developed a little 112 00:08:11,478 --> 00:08:15,511 Chrome plugin Not so little, pretty clever. 113 00:08:15,511 --> 00:08:18,850 Most of us couldn't do it, but he said that he was able to 114 00:08:18,889 --> 00:08:22,358 create that in three days using ai powered tools. 115 00:08:22,358 --> 00:08:27,235 Previously he said that similar project would have taken him 116 00:08:27,276 --> 00:08:31,612 more like three weeks and, um, or maybe even three months. 117 00:08:31,612 --> 00:08:34,236 I can't remember after, but it's one of those two, both 118 00:08:34,297 --> 00:08:37,945 impressive, right, and he was saying he felt like an octopus 119 00:08:39,210 --> 00:08:39,972 that he just had. 120 00:08:39,972 --> 00:08:42,445 He could just move so quickly. 121 00:08:42,445 --> 00:08:48,283 So, yeah, just so, so, so interesting. 122 00:08:50,456 --> 00:08:53,096 We started the year with predictions, but now we're 123 00:08:53,116 --> 00:08:57,221 seeing reality, we're seeing the real-time impact of these 124 00:08:57,282 --> 00:09:02,596 technologies and a lot of the people I speak to. 125 00:09:02,596 --> 00:09:05,745 They're getting massive benefits from these technologies 126 00:09:05,745 --> 00:09:05,745 . 127 00:09:05,745 --> 00:09:09,312 But again, the key is it's brain state intelligence. 128 00:09:09,312 --> 00:09:11,977 Some people are scared of the technologies. 129 00:09:11,977 --> 00:09:14,793 I completely understand that and I have seen quite an adverse 130 00:09:14,793 --> 00:09:15,936 reaction from some people. 131 00:09:15,936 --> 00:09:21,481 I completely understand that and I have seen quite an adverse 132 00:09:21,481 --> 00:09:22,345 reaction from some people. 133 00:09:22,345 --> 00:09:24,147 Yeah, and it's understandable. 134 00:09:24,147 --> 00:09:28,951 But I think ultimately, once people start to try these 135 00:09:28,991 --> 00:09:32,297 technologies and they can see what they can do and and they 136 00:09:32,336 --> 00:09:37,028 can recognise that it's just simply a way to outsource, 137 00:09:38,716 --> 00:09:41,995 instead of having to use your own brain to do everything you 138 00:09:42,014 --> 00:09:44,716 know, we never have enough time in the day to do that, we never 139 00:09:44,735 --> 00:09:46,783 have enough brain power every day to do what we want to get 140 00:09:46,844 --> 00:09:51,722 done we can outsource some of that brain power in a very 141 00:09:51,783 --> 00:09:52,666 cost-effective way. 142 00:09:52,666 --> 00:09:59,072 But yeah, that's just a bit of a reflection really, and I think 143 00:09:59,072 --> 00:10:01,403 I'll come back and talk about neural networks in a different 144 00:10:01,423 --> 00:10:01,663 pod. 145 00:10:05,966 --> 00:10:13,755 But I think the main, the main insight I've taken away from 146 00:10:13,775 --> 00:10:19,576 this week, is just how quickly things are moving and, of course 147 00:10:19,576 --> 00:10:20,779 , they're just the things that we see in the public domain. 148 00:10:20,779 --> 00:10:29,092 Having spent many years working in professional sport behind 149 00:10:29,113 --> 00:10:31,198 the scenes, you learn that most of what you read in the sports 150 00:10:31,219 --> 00:10:32,804 page is certainly about the teams I was involved in. 151 00:10:32,804 --> 00:10:41,875 Either wasn't Well, it's often not reported the things that are 152 00:10:41,875 --> 00:10:48,288 actually going on, or if they are reported, they're very 153 00:10:48,427 --> 00:10:48,869 delayed. 154 00:10:48,869 --> 00:10:56,581 So this is only what we know about, right, but anyway, that's 155 00:10:56,581 --> 00:10:59,697 just the data that we can use right now to guide us. 156 00:10:59,697 --> 00:11:02,445 But I think the main message is this is not going away. 157 00:11:02,445 --> 00:11:08,403 It's not a fad, it's not the millennium uh bug, it's um, it's 158 00:11:08,403 --> 00:11:08,725 here. 159 00:11:08,725 --> 00:11:11,408 The sums of money that have been invested in it are enormous 160 00:11:11,408 --> 00:11:11,408 . 161 00:11:11,408 --> 00:11:15,869 The results some people are getting already and the 162 00:11:15,908 --> 00:11:21,485 technologies baby like are enormous, and I know that we can 163 00:11:21,485 --> 00:11:22,086 all use it. 164 00:11:22,086 --> 00:11:30,905 If we learn how to use it properly, we'll be safer and it 165 00:11:30,926 --> 00:11:35,773 can help us to be healthier, happier and at our best more 166 00:11:35,832 --> 00:11:36,034 often. 167 00:11:37,285 --> 00:11:38,274 So thank you for listening. 168 00:11:38,274 --> 00:11:46,788 Remember, we've got our free mini course where we talk about 169 00:11:46,828 --> 00:11:54,062 how to create high performing human AI teams, and if you 170 00:11:54,081 --> 00:11:56,320 haven't got Train your Brain for the Air Evolution book yet, 171 00:11:56,794 --> 00:11:58,783 that's getting a lot of traction as well. 172 00:11:58,783 --> 00:12:02,484 So if you haven't got it, get it. 173 00:12:02,484 --> 00:12:05,022 If you've already got it, read it. 174 00:12:05,022 --> 00:12:05,844 Read it again. 175 00:12:05,844 --> 00:12:08,041 Put the things into practice. 176 00:12:08,041 --> 00:12:12,989 Yeah, we've got some exciting news about some training we're 177 00:12:13,129 --> 00:12:16,317 working on at the moment, just to make it easier for people to 178 00:12:16,357 --> 00:12:17,399 put these things into practice. 179 00:12:17,399 --> 00:12:24,809 So wherever we are right now, it's not fixed, it's not 180 00:12:24,850 --> 00:12:25,630 deterministic. 181 00:12:25,630 --> 00:12:29,604 Wherever our brain state score is, we can do better and that's 182 00:12:29,624 --> 00:12:33,544 why we always say you're only one brain state, habit away.