1 00:00:21,774 --> 00:00:23,097 Speaker 1: hello, habit mechanics. 2 00:00:23,097 --> 00:00:25,423 Dr john finn here, hope you're having a great week so far. 3 00:00:25,423 --> 00:00:33,966 So the the last week has been very interesting in terms of ai 4 00:00:35,328 --> 00:00:36,170 and its developments. 5 00:00:36,170 --> 00:00:43,582 We started the week, in the uk at least, with, I think, the 6 00:00:43,621 --> 00:00:50,614 government minister for technology and innovation saying 7 00:00:50,614 --> 00:00:57,889 that UK workers really needed to start building AI into their 8 00:00:57,929 --> 00:00:59,555 workflows or they'd get left behind. 9 00:00:59,555 --> 00:01:04,509 I think that was an article in the Guardian newspaper. 10 00:01:04,509 --> 00:01:09,049 I think that was an article in the Guardian newspaper. 11 00:01:09,049 --> 00:01:14,685 We saw Amazon was really the first big company. 12 00:01:15,789 --> 00:01:18,358 Amazon employ 1.5 million people in the US and significant 13 00:01:18,397 --> 00:01:23,054 numbers of other people or of people in other countries saying 14 00:01:23,054 --> 00:01:29,438 they publicly came out and said that we will be replacing some 15 00:01:29,498 --> 00:01:36,794 human roles with AI and you know no guarantees about would the 16 00:01:36,835 --> 00:01:38,681 technology create new roles? 17 00:01:38,681 --> 00:01:42,340 They didn't know, they thought it might, but they weren't sure. 18 00:01:42,340 --> 00:01:50,072 We ourselves gave our first although we've given many online 19 00:01:50,072 --> 00:01:50,933 talks about this. 20 00:01:50,933 --> 00:02:01,056 We gave our first um in-person talk to one of our partners um, 21 00:02:01,135 --> 00:02:05,461 and in fact it's one of our partner schools that we've 22 00:02:05,501 --> 00:02:11,532 worked with for, I think, 13 or 14 years now, and we were 23 00:02:11,551 --> 00:02:16,140 speaking to their parents, who are a great group of people, 24 00:02:16,262 --> 00:02:23,538 very rich, diverse professional backgrounds, many in the city of 25 00:02:23,538 --> 00:02:27,704 London, but also many in many working in the city of London, 26 00:02:27,723 --> 00:02:30,367 but also many working in the other core sectors in London. 27 00:02:30,367 --> 00:02:36,204 So it was great to talk to them about creating high-performing 28 00:02:37,469 --> 00:02:40,165 human AI teams and get their thoughts and their feedback. 29 00:02:40,165 --> 00:02:45,623 Some of the insights that were shared in the conversations 30 00:02:45,704 --> 00:02:50,729 afterwards surprised me at the speed things are going and, in 31 00:02:50,748 --> 00:02:55,062 fact, the sums of money that are being invested in by some 32 00:02:55,102 --> 00:02:55,425 businesses. 33 00:02:55,425 --> 00:02:58,025 So that was really insightful. 34 00:03:00,549 --> 00:03:05,658 The story has come into the mainstream. 35 00:03:05,658 --> 00:03:06,780 That Mark Zuckerberg. 36 00:03:06,780 --> 00:03:10,412 So this is just another interesting thing that we've 37 00:03:10,451 --> 00:03:11,855 seen this week coming to the mainstream. 38 00:03:11,855 --> 00:03:18,593 Mark Zuckerberg is paying $100 million yes, $100 million 39 00:03:19,153 --> 00:03:25,260 sign-on fees as he tries to assemble a team of, I think, 50, 40 00:03:25,260 --> 00:03:36,515 what he might call super developers working on general 41 00:03:37,897 --> 00:03:38,699 purpose AI. 42 00:03:38,699 --> 00:03:44,112 So, rather than these kind of specific agents we're seeing 43 00:03:44,153 --> 00:03:49,688 emerging rapidly creating an AI which is general, more 44 00:03:49,748 --> 00:03:54,954 human-like and possibly surpassing human intelligence or 45 00:03:54,954 --> 00:03:55,804 humans' ability. 46 00:03:55,804 --> 00:03:58,591 Well, yeah, to learn, which is what intelligence is, and I 47 00:03:58,611 --> 00:04:01,867 think probably a conversation for the podcast, because we 48 00:04:02,487 --> 00:04:05,417 sometimes misunderstand what intelligence means. 49 00:04:05,417 --> 00:04:06,651 It's just about learning. 50 00:04:06,651 --> 00:04:11,772 So that's been going on and I just think in every direction we 51 00:04:11,772 --> 00:04:17,543 look, we can see businesses are starting to make this 52 00:04:17,603 --> 00:04:24,242 transition to smaller teams of humans who are working with AI 53 00:04:24,283 --> 00:04:29,050 technologies and who can deliver significantly more value to the 54 00:04:29,050 --> 00:04:29,471 business. 55 00:04:33,497 --> 00:04:39,687 And the other thing that I've heard this week is Geoffrey 56 00:04:39,727 --> 00:04:46,521 Hinton, who is the godfather of AI, and he was quoted in a BBC 57 00:04:46,622 --> 00:04:57,067 article that talked about this Amazon statement, and Geoffrey 58 00:04:57,086 --> 00:04:58,771 Hinton spoke on a popular podcast. 59 00:04:58,771 --> 00:05:08,442 Geoffrey Hinton is basically the guy who has worked on neural 60 00:05:08,442 --> 00:05:13,110 networks all his life and there are other AI models out there, 61 00:05:14,197 --> 00:05:16,745 but he persisted with neural networks and eventually got them 62 00:05:16,745 --> 00:05:20,352 to work, and neural networks are the core technology that are 63 00:05:20,352 --> 00:05:29,524 driving the AI tools we're using today, and it was Geoffrey 64 00:05:29,524 --> 00:05:34,384 Hinton Geoffrey Hinton's story that really got me interested in 65 00:05:34,384 --> 00:05:38,418 these new AI technologies a couple of years ago when I read 66 00:05:38,478 --> 00:05:42,180 the Genius Makers book, because he's the protagonist of that 67 00:05:42,221 --> 00:05:42,422 book. 68 00:05:42,422 --> 00:05:49,728 So he's a London academic and I think he used to work at UCL, 69 00:05:51,299 --> 00:05:55,300 but there wasn't much appetite for neural networks in the UK so 70 00:05:55,300 --> 00:06:00,319 he eventually moved to canada and that's where he's done most 71 00:06:00,340 --> 00:06:05,137 of his academic work, but essentially a lot of the the 72 00:06:05,197 --> 00:06:11,393 leading thinkers in the ai space are his students, um, and 73 00:06:11,432 --> 00:06:14,701 jeffrey hinton, with some of his students, created a business 74 00:06:14,721 --> 00:06:19,321 that they sold to google and um, very interesting character. 75 00:06:19,321 --> 00:06:20,204 You should check him out. 76 00:06:20,204 --> 00:06:30,427 But he's talking about the, the potential of ai, both in what 77 00:06:30,466 --> 00:06:35,400 it can do in a beneficial way, but both some of the potential 78 00:06:35,439 --> 00:06:36,060 downsides. 79 00:06:37,665 --> 00:06:41,519 But what is very interesting is we hear a it's a bit of a 80 00:06:41,579 --> 00:06:45,387 narrative out there yeah, about ai, people saying, yeah, but it 81 00:06:45,427 --> 00:06:46,596 will create new jobs, won't it? 82 00:06:46,596 --> 00:06:50,632 Because all new technologies create new jobs and people use 83 00:06:50,672 --> 00:06:51,495 different examples. 84 00:06:51,495 --> 00:06:54,100 The one I know is the typewriter. 85 00:06:54,100 --> 00:06:58,937 People thought the typewriter would put people out of work and 86 00:06:58,937 --> 00:07:02,822 it created more jobs and lots of other technologies have done 87 00:07:02,843 --> 00:07:03,185 the same. 88 00:07:03,185 --> 00:07:07,502 And in this podcast he's saying AI is different. 89 00:07:07,502 --> 00:07:13,663 He doesn't believe we'll create lots of new jobs because it can 90 00:07:13,663 --> 00:07:16,242 do most of what most people get paid to do every day. 91 00:07:16,242 --> 00:07:20,122 So this is the reality check, because it can do most of what 92 00:07:20,163 --> 00:07:21,103 most people get to do every day. 93 00:07:21,163 --> 00:07:24,752 So this is the reality check, and sometimes I feel like it's 94 00:07:24,771 --> 00:07:26,194 just us saying this stuff, but it's not. 95 00:07:26,194 --> 00:07:26,514 It's real. 96 00:07:26,514 --> 00:07:27,697 We're not scaremongering. 97 00:07:27,697 --> 00:07:28,199 It's absolutely real. 98 00:07:28,199 --> 00:07:32,512 We want to help people to prepare and to thrive in an 99 00:07:32,591 --> 00:07:36,483 AI-powered world and I think AI gives us the opportunity to do 100 00:07:36,525 --> 00:07:41,334 that, but only if we can get brain state intelligent. 101 00:07:41,334 --> 00:07:49,261 So I just think we've had a really interesting week which 102 00:07:49,300 --> 00:07:53,817 has, I think, put a spotlight on the pace of AI development and 103 00:07:53,858 --> 00:08:01,127 it's moving faster than anybody thought in January. 104 00:08:01,127 --> 00:08:03,161 Because, you know, in January there's a lot of reports coming 105 00:08:03,201 --> 00:08:06,776 out around this stuff and it was all kind of projected that, oh, 106 00:08:06,776 --> 00:08:08,723 in five years time we might see these things. 107 00:08:08,723 --> 00:08:14,120 But you know, six months later we're starting to see big shifts 108 00:08:14,120 --> 00:08:18,973 big shifts in language, shifts in positioning, big shifts in 109 00:08:18,992 --> 00:08:19,595 case studies. 110 00:08:19,735 --> 00:08:23,605 Another thing I spoke to a friend this week who's a 111 00:08:23,745 --> 00:08:29,665 developer and he was just saying that he developed a little 112 00:08:31,408 --> 00:08:35,441 Chrome plugin Not so little, pretty clever. 113 00:08:35,441 --> 00:08:38,779 Most of us couldn't do it, but he said that he was able to 114 00:08:38,819 --> 00:08:42,288 create that in three days using ai powered tools. 115 00:08:42,288 --> 00:08:47,165 Previously he said that similar project would have taken him 116 00:08:47,206 --> 00:08:51,542 more like three weeks and, um, or maybe even three months. 117 00:08:51,542 --> 00:08:54,166 I can't remember after, but it's one of those two, both 118 00:08:54,226 --> 00:08:57,875 impressive, right, and he was saying he felt like an octopus 119 00:08:59,139 --> 00:08:59,902 that he just had. 120 00:08:59,902 --> 00:09:02,375 He could just move so quickly. 121 00:09:02,375 --> 00:09:08,213 So, yeah, just so, so, so interesting. 122 00:09:10,386 --> 00:09:13,025 We started the year with predictions, but now we're 123 00:09:13,046 --> 00:09:17,151 seeing reality, we're seeing the real-time impact of these 124 00:09:17,211 --> 00:09:22,526 technologies and a lot of the people I speak to. 125 00:09:22,526 --> 00:09:25,674 They're getting massive benefits from these technologies 126 00:09:25,674 --> 00:09:25,674 . 127 00:09:25,674 --> 00:09:29,242 But again, the key is it's brain state intelligence. 128 00:09:29,242 --> 00:09:31,907 Some people are scared of the technologies. 129 00:09:31,907 --> 00:09:34,722 I completely understand that and I have seen quite an adverse 130 00:09:34,722 --> 00:09:35,865 reaction from some people. 131 00:09:35,865 --> 00:09:41,411 I completely understand that and I have seen quite an adverse 132 00:09:41,411 --> 00:09:42,274 reaction from some people. 133 00:09:42,274 --> 00:09:44,076 Yeah, and it's understandable. 134 00:09:44,076 --> 00:09:48,881 But I think ultimately, once people start to try these 135 00:09:48,921 --> 00:09:52,226 technologies and they can see what they can do and and they 136 00:09:52,266 --> 00:09:56,957 can recognise that it's just simply a way to outsource, 137 00:09:58,645 --> 00:10:01,924 instead of having to use your own brain to do everything you 138 00:10:01,944 --> 00:10:04,645 know, we never have enough time in the day to do that, we never 139 00:10:04,665 --> 00:10:06,712 have enough brain power every day to do what we want to get 140 00:10:06,774 --> 00:10:11,652 done we can outsource some of that brain power in a very 141 00:10:11,712 --> 00:10:12,596 cost-effective way. 142 00:10:12,596 --> 00:10:19,001 But yeah, that's just a bit of a reflection really, and I think 143 00:10:19,001 --> 00:10:21,332 I'll come back and talk about neural networks in a different 144 00:10:21,352 --> 00:10:21,593 pod. 145 00:10:25,895 --> 00:10:33,685 But I think the main, the main insight I've taken away from 146 00:10:33,705 --> 00:10:39,506 this week, is just how quickly things are moving and, of course 147 00:10:39,506 --> 00:10:40,709 , they're just the things that we see in the public domain. 148 00:10:40,709 --> 00:10:49,022 Having spent many years working in professional sport behind 149 00:10:49,043 --> 00:10:51,128 the scenes, you learn that most of what you read in the sports 150 00:10:51,149 --> 00:10:52,734 page is certainly about the teams I was involved in. 151 00:10:52,734 --> 00:11:01,805 Either wasn't Well, it's often not reported the things that are 152 00:11:01,805 --> 00:11:08,217 actually going on, or if they are reported, they're very 153 00:11:08,357 --> 00:11:08,798 delayed. 154 00:11:08,798 --> 00:11:16,510 So this is only what we know about, right, but anyway, that's 155 00:11:16,510 --> 00:11:19,627 just the data that we can use right now to guide us. 156 00:11:19,627 --> 00:11:22,375 But I think the main message is this is not going away. 157 00:11:22,375 --> 00:11:28,333 It's not a fad, it's not the millennium uh bug, it's um, it's 158 00:11:28,333 --> 00:11:28,654 here. 159 00:11:28,654 --> 00:11:31,338 The sums of money that have been invested in it are enormous 160 00:11:31,338 --> 00:11:31,338 . 161 00:11:31,338 --> 00:11:35,798 The results some people are getting already and the 162 00:11:35,838 --> 00:11:41,414 technologies baby like are enormous, and I know that we can 163 00:11:41,414 --> 00:11:42,016 all use it. 164 00:11:42,016 --> 00:11:50,835 If we learn how to use it properly, we'll be safer and it 165 00:11:50,855 --> 00:11:55,702 can help us to be healthier, happier and at our best more 166 00:11:55,762 --> 00:11:55,964 often. 167 00:11:57,215 --> 00:11:58,203 So thank you for listening. 168 00:11:58,203 --> 00:12:06,718 Remember, we've got our free mini course where we talk about 169 00:12:06,758 --> 00:12:13,991 how to create high performing human AI teams, and if you 170 00:12:14,011 --> 00:12:16,250 haven't got Train your Brain for the Air Evolution book yet, 171 00:12:16,724 --> 00:12:18,712 that's getting a lot of traction as well. 172 00:12:18,712 --> 00:12:22,413 So if you haven't got it, get it. 173 00:12:22,413 --> 00:12:24,951 If you've already got it, read it. 174 00:12:24,951 --> 00:12:25,774 Read it again. 175 00:12:25,774 --> 00:12:27,971 Put the things into practice. 176 00:12:27,971 --> 00:12:32,918 Yeah, we've got some exciting news about some training we're 177 00:12:33,059 --> 00:12:36,247 working on at the moment, just to make it easier for people to 178 00:12:36,287 --> 00:12:37,329 put these things into practice. 179 00:12:37,329 --> 00:12:44,739 So wherever we are right now, it's not fixed, it's not 180 00:12:44,779 --> 00:12:45,560 deterministic. 181 00:12:45,560 --> 00:12:49,533 Wherever our brain state score is, we can do better and that's 182 00:12:49,553 --> 00:12:53,474 why we always say you're only one brain state, habit away.