1 00:00:00,040 --> 00:00:02,520 Carol Cox: Whether you love AI, are curious about it, 2 00:00:02,520 --> 00:00:05,600 or are not quite sure your voice matters to 3 00:00:05,640 --> 00:00:07,880 shape the present and the future. 4 00:00:08,040 --> 00:00:10,640 I'm sharing my recent Ted style talk on why 5 00:00:10,680 --> 00:00:11,880 AI needs women. 6 00:00:11,920 --> 00:00:13,920 On this episode of the Speaking Your Brand 7 00:00:13,920 --> 00:00:21,720 podcast. More and more women are making an 8 00:00:21,720 --> 00:00:23,880 impact by starting businesses, 9 00:00:23,880 --> 00:00:26,520 running for office, and speaking up for what 10 00:00:26,520 --> 00:00:29,520 matters. With my background as a TV political 11 00:00:29,520 --> 00:00:31,120 analyst, entrepreneur, 12 00:00:31,120 --> 00:00:33,800 and speaker. I interview a coach for purpose 13 00:00:33,800 --> 00:00:36,120 driven women to shape their brands, 14 00:00:36,240 --> 00:00:38,560 grow their companies, and become recognized 15 00:00:38,560 --> 00:00:40,920 as influencers in their field. 16 00:00:41,520 --> 00:00:43,480 This is speaking your brand, 17 00:00:43,480 --> 00:00:45,840 your place to learn how to persuasively 18 00:00:45,840 --> 00:00:49,120 communicate your message to your audience. 19 00:00:50,040 --> 00:00:51,640 Hi and welcome to the Speaking Your Brand 20 00:00:51,640 --> 00:00:52,920 podcast. This is your host, 21 00:00:52,920 --> 00:00:55,560 Carol Cox. We're continuing the series that 22 00:00:55,560 --> 00:00:57,320 I've been doing all around. 23 00:00:57,360 --> 00:00:59,960 I hopefully you've enjoyed the past few 24 00:00:59,960 --> 00:01:02,320 episodes to get you thinking about ways that 25 00:01:02,320 --> 00:01:04,920 you can integrate AI into your business, 26 00:01:04,920 --> 00:01:06,680 into your content production, 27 00:01:06,680 --> 00:01:09,000 and into the work you do in in ways that 28 00:01:09,000 --> 00:01:10,760 maybe you haven't thought of before, 29 00:01:11,040 --> 00:01:12,800 and not just doing it manually by, 30 00:01:12,800 --> 00:01:14,960 say, going to ask ChatGPT to do something and 31 00:01:14,960 --> 00:01:17,160 then coming back out, but really thinking 32 00:01:17,160 --> 00:01:20,280 about how to automate what you're doing so 33 00:01:20,280 --> 00:01:22,040 that you can do more of the things that you 34 00:01:22,040 --> 00:01:23,960 love to do, like public speaking, 35 00:01:24,080 --> 00:01:26,600 thought leadership, working one on one or in 36 00:01:26,600 --> 00:01:28,160 small groups with clients, 37 00:01:28,160 --> 00:01:30,000 and so on. In this episode, 38 00:01:30,000 --> 00:01:32,280 I'm going to share with you the recent TEDx 39 00:01:32,280 --> 00:01:34,800 style talk that I delivered at an event 40 00:01:34,800 --> 00:01:36,160 called Elex. 41 00:01:36,360 --> 00:01:39,800 My topic was AI Needs Women why Your voice 42 00:01:39,800 --> 00:01:42,800 matters to shape the present and the future. 43 00:01:43,200 --> 00:01:45,720 If you would like to learn how to create AI 44 00:01:45,840 --> 00:01:48,560 automation workflows that you can use in your 45 00:01:48,560 --> 00:01:51,280 business and your content production without 46 00:01:51,280 --> 00:01:53,200 needing to know how to code, 47 00:01:53,640 --> 00:01:56,760 check out my new live online four week 48 00:01:56,760 --> 00:02:00,040 program called Automate and Amplify with AI. 49 00:02:00,560 --> 00:02:02,630 I'm going to teach you and show you and 50 00:02:02,630 --> 00:02:05,110 provide you with the AI workflows that I'm 51 00:02:05,110 --> 00:02:07,910 using so that you can integrate these into 52 00:02:07,910 --> 00:02:09,870 the work that you're doing as well. 53 00:02:09,910 --> 00:02:12,190 You can get all the details and apply at 54 00:02:12,190 --> 00:02:15,950 speaking your brand AI again that speaking 55 00:02:15,950 --> 00:02:18,590 your brand AI. 56 00:02:19,070 --> 00:02:20,750 Now let's get on to my talk. 57 00:02:21,710 --> 00:02:24,950 It was the day after Christmas 2022, 58 00:02:25,310 --> 00:02:27,750 and instead of enjoying that nice slow week 59 00:02:27,750 --> 00:02:29,750 between Christmas and New Year's, 60 00:02:29,910 --> 00:02:32,670 I was at my laptop updating all of the 61 00:02:32,670 --> 00:02:34,990 business and marketing classes that I teach 62 00:02:34,990 --> 00:02:37,870 at Full Sail University and starting to build 63 00:02:37,910 --> 00:02:41,270 AI applications for my company and podcast, 64 00:02:41,270 --> 00:02:42,470 Speaking Your Brand. 65 00:02:42,830 --> 00:02:45,830 The reason? Well, OpenAI had just released 66 00:02:45,830 --> 00:02:48,990 ChatGPT the month before and had taken the 67 00:02:48,990 --> 00:02:51,750 world by storm, surprising even the company 68 00:02:51,750 --> 00:02:53,950 itself. And as an educator, 69 00:02:53,950 --> 00:02:55,630 a marketer and a technologist, 70 00:02:55,630 --> 00:02:57,350 I'm a former software developer. 71 00:02:57,510 --> 00:03:00,270 I instantly saw that this was going to impact 72 00:03:00,270 --> 00:03:02,670 all of those fields and so much more. 73 00:03:03,350 --> 00:03:05,110 Now. I've been around long enough to see 74 00:03:05,110 --> 00:03:07,070 these hype cycles come and go. 75 00:03:07,230 --> 00:03:09,190 The next big social media platform, 76 00:03:09,190 --> 00:03:12,030 or software application or marketing strategy 77 00:03:12,030 --> 00:03:13,190 that was going to, quote, 78 00:03:13,230 --> 00:03:14,470 change everything. 79 00:03:14,710 --> 00:03:16,750 And of course, they never did. 80 00:03:17,470 --> 00:03:19,590 But AI is different. 81 00:03:19,870 --> 00:03:22,070 It is what is known as a general purpose 82 00:03:22,070 --> 00:03:25,190 technology, which means it really can change 83 00:03:25,190 --> 00:03:27,590 everything. And it most likely will. 84 00:03:28,430 --> 00:03:30,510 But I'm also a student of history, 85 00:03:30,830 --> 00:03:33,150 and I gravitated towards history because I 86 00:03:33,150 --> 00:03:35,790 was always looking for the women in the 87 00:03:35,790 --> 00:03:38,990 stories. I wanted to see women like who I 88 00:03:39,070 --> 00:03:41,750 wanted to grow up, to be sure. 89 00:03:41,750 --> 00:03:43,710 I studied the presidents and the military 90 00:03:43,710 --> 00:03:46,670 commanders, but I wanted to know what were 91 00:03:46,670 --> 00:03:47,790 the women thinking? 92 00:03:47,910 --> 00:03:49,070 What were they doing? 93 00:03:49,070 --> 00:03:50,710 What did they want to contribute? 94 00:03:50,910 --> 00:03:52,830 How did they want to shape society? 95 00:03:53,190 --> 00:03:55,750 Because it's the past that shapes who we are 96 00:03:55,750 --> 00:03:58,870 today, and it's what we do today that will 97 00:03:58,870 --> 00:04:00,190 shape the future. 98 00:04:00,630 --> 00:04:03,550 And as I look out at the tech landscape and 99 00:04:03,550 --> 00:04:06,780 all the podcast and the YouTube videos and 100 00:04:06,780 --> 00:04:08,900 the influencers on LinkedIn. 101 00:04:09,180 --> 00:04:12,020 And I look at the CEOs of AI companies and 102 00:04:12,020 --> 00:04:15,940 their boards. I see a lot of men and not as 103 00:04:15,940 --> 00:04:18,100 many women as I was like. 104 00:04:18,540 --> 00:04:20,100 And it's important for all of us, 105 00:04:20,100 --> 00:04:22,420 no matter who we are, but especially women, 106 00:04:22,420 --> 00:04:25,380 half the population to have a say, 107 00:04:25,620 --> 00:04:27,620 to be part of the conversations, 108 00:04:27,620 --> 00:04:30,580 the decision making and the leadership around 109 00:04:30,620 --> 00:04:33,900 AI because it's going to impact how we work, 110 00:04:34,100 --> 00:04:36,340 how we teach, how we learn, 111 00:04:36,620 --> 00:04:39,700 how we govern, and how we interact with each 112 00:04:39,700 --> 00:04:41,980 other. I mentioned that I'm a former software 113 00:04:41,980 --> 00:04:44,660 developer. I founded two tech companies in 114 00:04:44,660 --> 00:04:46,220 the first part of my career, 115 00:04:46,460 --> 00:04:48,980 but I don't look like the typical techie. 116 00:04:49,340 --> 00:04:51,900 You probably think of the guy in the hoodie 117 00:04:52,140 --> 00:04:53,540 hunched over his laptop, 118 00:04:53,540 --> 00:04:56,420 writing all the lines of code with millions 119 00:04:56,420 --> 00:04:59,940 of dollars of venture capital money funding 120 00:04:59,940 --> 00:05:03,180 his startup, and it is most likely a he 121 00:05:03,340 --> 00:05:08,460 because women only get between 3 and 5% of 122 00:05:08,460 --> 00:05:11,100 venture capital money for their startups, 123 00:05:11,100 --> 00:05:13,300 and that number has not changed in the past 124 00:05:13,300 --> 00:05:15,660 20 years and is actually getting worse 125 00:05:15,660 --> 00:05:17,220 recently, not better. 126 00:05:17,740 --> 00:05:20,420 But tech hasn't always been coded as male. 127 00:05:20,620 --> 00:05:22,580 Way back in the 19th century, 128 00:05:22,820 --> 00:05:25,900 Ada Lovelace pioneered how we even thought of 129 00:05:25,900 --> 00:05:27,620 what a computer could be. 130 00:05:27,780 --> 00:05:31,100 In the 1960s, NASA employed what they called 131 00:05:31,100 --> 00:05:33,300 human computers, who were women, 132 00:05:33,300 --> 00:05:34,860 primarily black women, 133 00:05:34,980 --> 00:05:37,580 as portrayed in the film Hidden Figures who 134 00:05:37,620 --> 00:05:39,620 calculated by hand. 135 00:05:39,980 --> 00:05:43,500 On paper, the trajectories of rockets talk 136 00:05:43,500 --> 00:05:44,660 about mind blowing. 137 00:05:45,180 --> 00:05:47,740 In the early part of the 1800s with the rise 138 00:05:47,740 --> 00:05:50,540 of the novel. Women like George Eliot and the 139 00:05:50,540 --> 00:05:53,820 Bronte sisters wanted to have a public voice. 140 00:05:54,180 --> 00:05:55,260 So what did they do? 141 00:05:55,460 --> 00:05:57,940 They published under male pseudonyms, 142 00:05:57,940 --> 00:05:59,260 so that, number one, they could get 143 00:05:59,260 --> 00:06:00,700 published. And number two, 144 00:06:00,740 --> 00:06:03,100 so that their work could be taken seriously. 145 00:06:03,300 --> 00:06:05,460 In the early part of the 20th century. 146 00:06:05,460 --> 00:06:07,180 I told you I was a history person. 147 00:06:07,380 --> 00:06:09,580 First Lady Eleanor Roosevelt and Dorothy 148 00:06:09,620 --> 00:06:12,500 Thompson used the new technology of radio to 149 00:06:12,540 --> 00:06:14,020 broadcast their voices, 150 00:06:14,020 --> 00:06:16,940 which was so unusual at the time for women to 151 00:06:16,980 --> 00:06:18,620 have a public voice. 152 00:06:18,980 --> 00:06:21,540 But they did so to millions of homes across 153 00:06:21,540 --> 00:06:23,500 the country. And of course, 154 00:06:23,500 --> 00:06:26,060 more recently, millions of women around the 155 00:06:26,060 --> 00:06:27,980 world have used the internet, 156 00:06:28,180 --> 00:06:30,460 social media and mobile to build their 157 00:06:30,460 --> 00:06:33,380 businesses, their brands and their careers. 158 00:06:33,500 --> 00:06:35,500 But again, as I look out at the tech 159 00:06:35,500 --> 00:06:37,940 landscape, I want to know where the women 160 00:06:37,940 --> 00:06:40,220 are, where they're making an impact and how 161 00:06:40,220 --> 00:06:42,380 we can amplify each other's voices. 162 00:06:43,020 --> 00:06:45,340 Let me give you two examples of why what 163 00:06:45,340 --> 00:06:48,260 we're doing with AI and understanding what AI 164 00:06:48,420 --> 00:06:51,020 is doing to us is so important. 165 00:06:51,700 --> 00:06:55,260 About a year ago, I asked ChatGPT to give me 166 00:06:55,260 --> 00:06:57,980 a list of five well-known women 167 00:06:58,020 --> 00:07:00,220 entrepreneurs. Sure, I could have searched 168 00:07:00,220 --> 00:07:02,500 Google, but I figured ChatGPT would give me a 169 00:07:02,500 --> 00:07:03,700 nice little summary. 170 00:07:03,820 --> 00:07:06,610 A very simple, straightforward Request that 171 00:07:06,610 --> 00:07:07,890 she starts writing. 172 00:07:07,930 --> 00:07:10,290 You know, you can see the the words coming on 173 00:07:10,290 --> 00:07:11,930 the screen and it says number one, 174 00:07:11,930 --> 00:07:13,610 Oprah Winfrey. Number two, 175 00:07:13,650 --> 00:07:16,050 Sara Blakely of Spanx and so on. 176 00:07:16,050 --> 00:07:17,770 I'm like, okay, good. And then I can see it, 177 00:07:17,770 --> 00:07:19,810 right? Number five period. 178 00:07:19,850 --> 00:07:23,410 Elon Musk and ChatGPT continues. 179 00:07:23,570 --> 00:07:26,170 Although Elon Musk is not a woman, 180 00:07:26,290 --> 00:07:28,650 he has founded these businesses, 181 00:07:28,650 --> 00:07:30,410 etc.. And I was like, okay, 182 00:07:30,450 --> 00:07:32,650 hold on a second. Chatgpt first, 183 00:07:32,690 --> 00:07:34,250 thank you for acknowledging that, 184 00:07:34,250 --> 00:07:35,690 you know, he's not a woman, 185 00:07:35,810 --> 00:07:37,770 but then why is he on my list when, 186 00:07:37,770 --> 00:07:40,090 you know, I was asking for women 187 00:07:40,130 --> 00:07:42,730 entrepreneurs. So that makes me wonder what 188 00:07:42,730 --> 00:07:45,450 is going on in the training data and the 189 00:07:45,450 --> 00:07:48,170 reinforcement that is causing it to give this 190 00:07:48,170 --> 00:07:50,690 answer. Here's a more recent example. 191 00:07:51,050 --> 00:07:52,090 A couple of months ago, 192 00:07:52,090 --> 00:07:54,730 I was co-hosting a writing and speaking 193 00:07:54,730 --> 00:07:56,970 retreat, and one of the activities that we 194 00:07:56,970 --> 00:08:00,170 had the women do was to write on post-it 195 00:08:00,170 --> 00:08:03,250 notes and ask that they have an A give that 196 00:08:03,250 --> 00:08:04,970 they would offer. So an ass could be 197 00:08:04,970 --> 00:08:07,010 something like they needed help with social 198 00:08:07,010 --> 00:08:09,610 media strategy, and to give that they would 199 00:08:09,610 --> 00:08:12,290 offer to be beta readers for someone's books. 200 00:08:12,530 --> 00:08:15,050 So they put out their post-it notes on a big 201 00:08:15,050 --> 00:08:17,290 white piece of paper, and then we needed to 202 00:08:17,330 --> 00:08:19,890 transcribe that into a spreadsheet to hand 203 00:08:19,890 --> 00:08:20,930 out to all of the women. 204 00:08:20,970 --> 00:08:22,490 Now, I could have transcribed it, 205 00:08:22,490 --> 00:08:24,410 but I figured, well, ChatGPT could do this 206 00:08:24,410 --> 00:08:25,730 and save me time. 207 00:08:25,890 --> 00:08:28,050 So I gave it the photo and I gave it very 208 00:08:28,050 --> 00:08:29,530 specific instructions. 209 00:08:29,530 --> 00:08:32,370 I told it just transcribe what you see in the 210 00:08:32,370 --> 00:08:34,450 post-it notes, do not add to it, 211 00:08:34,490 --> 00:08:36,090 do not change anything, 212 00:08:36,170 --> 00:08:37,250 just transcribe. 213 00:08:37,370 --> 00:08:38,810 So it did it. That was great. I'm looking 214 00:08:38,810 --> 00:08:40,690 over the spreadsheet and it looks good. 215 00:08:41,090 --> 00:08:46,010 And then a row sticks out to me under the ask 216 00:08:46,010 --> 00:08:48,810 column it says the per the request. 217 00:08:48,970 --> 00:08:52,450 I'm looking for women to interview who change 218 00:08:52,450 --> 00:08:54,850 their career path after marriage. 219 00:08:55,450 --> 00:08:58,810 And I'm like, huh? There is no woman here 220 00:08:58,810 --> 00:09:01,210 who's working on a book topic related to 221 00:09:01,250 --> 00:09:03,090 that, so that seems kind of odd. 222 00:09:03,370 --> 00:09:05,850 I go back to the original post-it notes and 223 00:09:05,850 --> 00:09:07,480 everything is legibly written. 224 00:09:08,160 --> 00:09:10,040 Here's what the original post-it note said. 225 00:09:10,480 --> 00:09:12,640 Looking for men to interview, 226 00:09:12,680 --> 00:09:15,120 not women looking for men to interview. 227 00:09:15,280 --> 00:09:19,680 Who changed their last name upon marriage for 228 00:09:19,680 --> 00:09:21,360 whatever reason when it was, 229 00:09:21,400 --> 00:09:22,720 quote, transcribing. 230 00:09:23,480 --> 00:09:26,200 I assume that ChatGPT is neural network, 231 00:09:26,200 --> 00:09:28,880 which is based on word associations and 232 00:09:29,080 --> 00:09:31,360 likelihood of what words go together. 233 00:09:31,920 --> 00:09:36,320 Decided that the words interviewing men who 234 00:09:36,320 --> 00:09:39,600 changed their last name upon marriage was so 235 00:09:39,600 --> 00:09:43,400 unlikely that it changed it to interview 236 00:09:43,440 --> 00:09:46,280 women who changed their career path after 237 00:09:46,280 --> 00:09:48,400 marriage, because that seemed much more 238 00:09:48,400 --> 00:09:50,920 likely to it based on all of its training 239 00:09:50,920 --> 00:09:53,400 data and its word associations. 240 00:09:53,480 --> 00:09:55,840 So you can imagine, as more and more of us 241 00:09:55,840 --> 00:09:58,560 are using AI and more students are using it. 242 00:09:58,840 --> 00:10:01,880 How are biases and stereotypes getting 243 00:10:01,880 --> 00:10:06,280 reinforced in AI versus being challenged and 244 00:10:06,280 --> 00:10:08,080 questioned and even noticed? 245 00:10:08,320 --> 00:10:10,600 So this is why it's so important for us to 246 00:10:10,640 --> 00:10:12,560 use AI. So I'm not saying don't use it. 247 00:10:12,600 --> 00:10:14,840 We absolutely need to use it so that we 248 00:10:14,840 --> 00:10:16,320 understand what it's doing, 249 00:10:16,320 --> 00:10:18,680 and we can be part of the conversations to 250 00:10:18,720 --> 00:10:21,880 question how is interacting with our kids, 251 00:10:21,880 --> 00:10:23,760 whether it's at school or with the AI 252 00:10:23,800 --> 00:10:26,680 companions, and how it's going to impact our 253 00:10:26,680 --> 00:10:28,880 work. I want us to be in the driver's seat. 254 00:10:28,960 --> 00:10:30,400 This is how we can make sure that we are 255 00:10:30,400 --> 00:10:31,840 collaborating together, 256 00:10:31,960 --> 00:10:34,680 using our voices and amplifying the voices of 257 00:10:34,680 --> 00:10:37,040 other women. I think of women I follow who 258 00:10:37,040 --> 00:10:38,600 are making a big impact, 259 00:10:38,600 --> 00:10:41,560 like Joi Palomino and her Algorithmic Justice 260 00:10:41,560 --> 00:10:43,240 League, which is making sure that the 261 00:10:43,240 --> 00:10:46,200 algorithms that are right now are influencing 262 00:10:46,200 --> 00:10:48,480 hiring decisions, criminal justice, 263 00:10:48,520 --> 00:10:51,400 law enforcement, and so on are more fair, 264 00:10:51,440 --> 00:10:54,440 transparent and unbiased in women like 265 00:10:54,440 --> 00:10:57,480 Fei-Fei, Li is Stanford University professor 266 00:10:57,480 --> 00:10:59,360 who created ImageNet. 267 00:10:59,520 --> 00:11:02,800 Without ImageNet, ChatGPT and the other AI 268 00:11:02,840 --> 00:11:05,960 tools would not be able to see and analyze 269 00:11:05,960 --> 00:11:07,960 images or create them. 270 00:11:08,080 --> 00:11:10,950 That's how innovative that technology was 271 00:11:10,950 --> 00:11:12,910 that she created. So I invite you to have 272 00:11:12,910 --> 00:11:15,750 conversations about AI in your workplaces, 273 00:11:15,910 --> 00:11:17,710 your schools, your communities, 274 00:11:17,710 --> 00:11:18,830 your governing bodies, 275 00:11:18,830 --> 00:11:21,390 and more. You could even set up an informal 276 00:11:21,430 --> 00:11:23,870 AI council where you work or at your 277 00:11:23,870 --> 00:11:25,990 children's school to bring people together to 278 00:11:25,990 --> 00:11:28,790 talk about what their ideas are around AI, 279 00:11:29,110 --> 00:11:31,070 what are their questions, their concern to 280 00:11:31,110 --> 00:11:32,310 share resources. 281 00:11:32,590 --> 00:11:34,230 Now, this Christmas break, 282 00:11:34,350 --> 00:11:37,070 hopefully I will not be at my laptop working 283 00:11:37,270 --> 00:11:39,270 on things. Instead, I'm busily having. 284 00:11:39,310 --> 00:11:41,990 I helped me to automate much of what I do on 285 00:11:41,990 --> 00:11:43,830 my computer in my business, 286 00:11:43,830 --> 00:11:46,590 so I can get back to the work that I want to 287 00:11:46,590 --> 00:11:49,790 do, which is being human and interacting with 288 00:11:49,790 --> 00:11:52,190 other humans. And I invite you all to do the 289 00:11:52,190 --> 00:11:54,750 same, because when future generations read 290 00:11:54,750 --> 00:11:57,230 the history books of this AI era, 291 00:11:57,430 --> 00:12:00,510 I want them to find plenty of women in them. 292 00:12:00,590 --> 00:12:01,270 Thank you.