1 00:00:04,975 --> 00:00:07,079 >> Ai Sarah: Al right, so we're diving deep into AI today. 2 00:00:07,127 --> 00:00:08,231 >> Ai Sarah: This sounds like a plan. 3 00:00:08,303 --> 00:00:11,207 >> Ai Sarah: You seem really interested in figuring out where it's all headed, what it all 4 00:00:11,231 --> 00:00:11,487 means. 5 00:00:11,551 --> 00:00:13,575 >> Ai Sarah: Yeah, exactly. It's moving so fast. 6 00:00:13,655 --> 00:00:15,159 >> Ai Sarah: You've got a ton of material here. 7 00:00:15,207 --> 00:00:15,439 >> Ai Sarah: Right. 8 00:00:15,487 --> 00:00:18,367 >> Ai Sarah: And it looks like our main focus is gonna be this interview 9 00:00:18,471 --> 00:00:19,671 with Said De Rie. 10 00:00:19,743 --> 00:00:22,463 >> Ai Sarah: Okay. Yeah. A data scientist and AI 11 00:00:22,519 --> 00:00:23,071 strategist. 12 00:00:23,143 --> 00:00:26,087 >> Ai Sarah: Right. From the about that Wallet podcast. And 13 00:00:26,111 --> 00:00:29,055 she's had, like, the most interesting career path. 14 00:00:29,395 --> 00:00:32,339 Finance, the Central bank of India, then a 15 00:00:32,387 --> 00:00:34,051 Master'in AI at NYU. 16 00:00:34,123 --> 00:00:36,787 >> Ai Sarah: Oh, wow. And she even worked with Dr. Jan Leiun. 17 00:00:36,931 --> 00:00:39,915 Talk about being in the right place at the right time. He's like the godfather 18 00:00:39,955 --> 00:00:41,699 of convolutional neural nets. 19 00:00:41,827 --> 00:00:44,379 >> Ai Sarah: Right. Which for anyone who doesn't know, are like 20 00:00:44,507 --> 00:00:47,251 a huge E deal in deep learning and 21 00:00:47,283 --> 00:00:48,011 computer vision. 22 00:00:48,083 --> 00:00:51,035 >> Ai Sarah: But what I think is so cool is that her career path, it really shows 23 00:00:51,075 --> 00:00:53,979 how AI is impacting, like, every industry. 24 00:00:54,027 --> 00:00:56,121 It's not just this tech thing anymore. 25 00:00:56,193 --> 00:00:58,889 >> Ai Sarah: Totally. And she's not just thinking about it. She's actually building things. 26 00:00:58,937 --> 00:01:01,601 >> Ai Sarah: Yeah, she's got her hands dirty. Uh, developing fraud 27 00:01:01,633 --> 00:01:04,529 detection models for fintech companies. And then she's also 28 00:01:04,577 --> 00:01:07,297 working with NYU's Langbone Medical School on 29 00:01:07,321 --> 00:01:10,225 this super cutting edge research using AI 30 00:01:10,305 --> 00:01:13,257 in, get this, neurosurgery, marrowurgery. 31 00:01:13,441 --> 00:01:16,113 >> Ai Sarah: That's wild. I read that they're using something called 32 00:01:16,169 --> 00:01:18,297 Large language models, or LLMs. 33 00:01:18,401 --> 00:01:21,393 >> Ai Sarah: Yeah, LLMs are like these powerful AI systems 34 00:01:21,449 --> 00:01:24,425 that can, you know, process and generate human 35 00:01:24,465 --> 00:01:27,207 language. It's the tech behind chat, 36 00:01:27,231 --> 00:01:29,871 GPT, and all those other tools that are blowing 37 00:01:29,903 --> 00:01:30,679 everyone's minds. 38 00:01:30,767 --> 00:01:33,575 >> Ai Sarah: So they can actually understand medical records and stuff? 39 00:01:33,655 --> 00:01:36,535 >> Ai Sarah: Exactly. And like, assist surgeons during super 40 00:01:36,575 --> 00:01:39,279 complex procedures or even help personalize treatment 41 00:01:39,327 --> 00:01:41,927 plans based on a patient's medical history. 42 00:01:42,071 --> 00:01:42,759 Crazy, right? 43 00:01:42,807 --> 00:01:45,791 >> Ai Sarah: It sounds kind of terrifying, honestly. Like, how do we know these AI 44 00:01:45,863 --> 00:01:48,359 systems are being trained on data that's, you 45 00:01:48,367 --> 00:01:51,343 know, accurate and unbiased? Especially 46 00:01:51,359 --> 00:01:53,463 in medicine, we're talking about life or death decisions. 47 00:01:53,559 --> 00:01:56,345 >> Ai Sarah: Right. And that's a huge point that Sumeta makes. She 48 00:01:56,385 --> 00:01:59,225 stresses that, like, even with the best AI, humans 49 00:01:59,265 --> 00:02:02,113 still need to be in the loop. It's not about replacing doctors, 50 00:02:02,169 --> 00:02:04,977 it's about giving them better tools and making sure those tools are 51 00:02:05,001 --> 00:02:06,009 used responsibly. 52 00:02:06,097 --> 00:02:09,089 >> Ai Sarah: Okay, so it's like, AI can help, but it's not taking 53 00:02:09,137 --> 00:02:09,953 over completely. 54 00:02:10,049 --> 00:02:12,777 >> Ai Sarah: Yeah, more like AI as a partner, not a 55 00:02:12,801 --> 00:02:15,657 replacement. And she talks about using, like, 56 00:02:15,721 --> 00:02:18,449 quantifiable measures of fairness to make sure 57 00:02:18,497 --> 00:02:21,393 AI systems aren't, you know, accidentally discriminating 58 00:02:21,449 --> 00:02:24,073 against Certain groups, like, say, an AI model used for loan 59 00:02:24,129 --> 00:02:26,977 approvals. If it was trained on biased 60 00:02:27,041 --> 00:02:30,017 data, it could end up denying loans to qualified 61 00:02:30,081 --> 00:02:32,913 applicants just because of their background, which would be a 62 00:02:32,969 --> 00:02:33,809 huge problem. 63 00:02:33,897 --> 00:02:34,337 >> Ai Sarah: Totally. 64 00:02:34,401 --> 00:02:34,665 >> Ai Sarah: Yeah. 65 00:02:34,705 --> 00:02:37,525 >> Ai Sarah: Okay, so what about this whole thing about AI 66 00:02:37,865 --> 00:02:39,897 taking, uh, our jobs? I mean, I know people are freaking. 67 00:02:39,921 --> 00:02:42,537 >> Ai Sarah: Out about it, right? It's a common fear, for sure. And 68 00:02:42,561 --> 00:02:45,457 somania actually addresses it head on. She thinks, like, instead of 69 00:02:45,481 --> 00:02:48,417 being scared, we need to embrace AI, learn how to use 70 00:02:48,441 --> 00:02:51,249 it, because that's going to be key to success 71 00:02:51,337 --> 00:02:52,361 in the future job market. 72 00:02:52,433 --> 00:02:55,305 >> Ai Sarah: So instead of being replaced, it's more like our jobs are going to change. 73 00:02:55,385 --> 00:02:58,057 >> Ai Sarah: Yeah, exactly. Some jobs might be automated, sure. 74 00:02:58,201 --> 00:03:01,193 But AI will also create new opportunities, and it'll 75 00:03:01,209 --> 00:03:04,065 free up people to do more creative, more fulfilling work. Like, 76 00:03:04,105 --> 00:03:06,969 imagine if AI could handle all those boring data 77 00:03:07,017 --> 00:03:09,897 entry tasks. We could focus on solving problems, 78 00:03:09,961 --> 00:03:12,497 innovating, you know, actually using our brains. 79 00:03:12,601 --> 00:03:15,489 >> Ai Sarah: That would be amazing. So she's not saying, like, run for the 80 00:03:15,497 --> 00:03:15,953 hills? 81 00:03:16,049 --> 00:03:18,945 >> Ai Sarah: Not at all. She actually gave some really practical advice for people 82 00:03:18,985 --> 00:03:20,217 who want to get into the field. 83 00:03:20,321 --> 00:03:23,153 >> Ai Sarah: Oh, like what kind of advice? I'm always curious about that. 84 00:03:23,209 --> 00:03:25,765 >> Ai Sarah: Well, she said first you need a solid foundation. 85 00:03:26,185 --> 00:03:29,049 Math, statistics, computer science, those are 86 00:03:29,057 --> 00:03:31,897 your building blocks. And then she recommended 87 00:03:31,961 --> 00:03:34,881 some great resources, like Andrew Negags 88 00:03:34,953 --> 00:03:36,105 courses on Coursera. 89 00:03:36,185 --> 00:03:38,137 >> Ai Sarah: Oh, yeah, I've heard of those. They're supposed to be good. 90 00:03:38,241 --> 00:03:41,161 >> Ai Sarah: They are. And then there's Professor Cananciani. He has all 91 00:03:41,193 --> 00:03:44,105 this amazing free stuff online. It's really great 92 00:03:44,145 --> 00:03:46,193 for anyone who wants to start learning about AI. 93 00:03:46,249 --> 00:03:48,599 >> Ai Sarah: So it's not just about being like, a tech 94 00:03:48,647 --> 00:03:51,567 genius. Anyone can learn this stuff. 95 00:03:51,631 --> 00:03:54,423 >> Ai Sarah: That's what she says. It's about being curious, being 96 00:03:54,479 --> 00:03:57,391 willing to learn, and understanding how AI can be a 97 00:03:57,423 --> 00:04:00,231 powerful tool for solving problems and creating new 98 00:04:00,263 --> 00:04:00,623 things. 99 00:04:00,719 --> 00:04:03,599 >> Ai Sarah: That's awesome. But okay, we've talked about all the potential. 100 00:04:03,647 --> 00:04:06,103 What about the downsides? Like, what about the use of 101 00:04:06,159 --> 00:04:08,935 copyrighted material to train these AI 102 00:04:08,975 --> 00:04:11,703 models? That seems like a legal nightmare waiting to 103 00:04:11,719 --> 00:04:12,159 happen. 104 00:04:12,287 --> 00:04:14,751 >> Ai Sarah: Yeah, you're hitting on a major issue there. There are, like, 105 00:04:14,823 --> 00:04:17,655 actual lawsuits happening right now over whether it's 106 00:04:17,695 --> 00:04:20,589 legal for companies to train AI models 107 00:04:20,637 --> 00:04:23,221 on copyrighted books, articles, whatever, without 108 00:04:23,253 --> 00:04:25,637 permission. Some authors are even suing 109 00:04:25,701 --> 00:04:28,157 OpenAI, the company behind Chat TPT. 110 00:04:28,301 --> 00:04:30,933 >> Ai Sarah: Wow. So it's not just a theoretical debate. It's getting 111 00:04:30,989 --> 00:04:31,445 real. 112 00:04:31,565 --> 00:04:34,165 >> Ai Sarah: Totally. And it raises a big question. 113 00:04:34,325 --> 00:04:36,665 How do we balance the need for 114 00:04:37,405 --> 00:04:39,949 massive amounts of data to train these AI 115 00:04:39,997 --> 00:04:42,709 models with the rights of content creators? It's 116 00:04:42,757 --> 00:04:43,157 messy. 117 00:04:43,221 --> 00:04:45,925 >> Ai Sarah: It sounds like it okay, and then there's the whole privacy 118 00:04:46,005 --> 00:04:48,765 issue. How do we protect people's personal information 119 00:04:48,845 --> 00:04:51,159 in a world where AI is constantly 120 00:04:51,357 --> 00:04:53,227 collecting and analyzing data? 121 00:04:53,331 --> 00:04:56,211 >> Ai Sarah: Yeah, privacy is a big one. And Simeta pointed 122 00:04:56,243 --> 00:04:58,907 to the EU's General Data Protection Regulation, the 123 00:04:58,931 --> 00:05:01,923 GDPR, as a good example of how to protect data privacy. It 124 00:05:01,939 --> 00:05:04,395 gives people more control over their personal data and 125 00:05:04,435 --> 00:05:07,115 requires companies to be transparent about how they're using it. 126 00:05:07,195 --> 00:05:10,163 >> Ai Sarah: That makes sense. But she also mentioned that the US doesn't have any federal 127 00:05:10,219 --> 00:05:11,179 data privacy laws. 128 00:05:11,227 --> 00:05:14,131 >> Ai Sarah: Right, right. And that's creating problems, especially 129 00:05:14,163 --> 00:05:16,811 for companies that operate in multiple states. 130 00:05:16,923 --> 00:05:19,265 Every state has its own rules. It's a mess. 131 00:05:19,345 --> 00:05:22,137 >> Ai Sarah: So what's being done about that? Is anyone trying to fix it? 132 00:05:22,241 --> 00:05:25,217 >> Ai Sarah: Well, there's this new executive order on AI 133 00:05:25,321 --> 00:05:28,009 from the White House. It could be a step 134 00:05:28,057 --> 00:05:30,937 towards establishing clear national guidelines, 135 00:05:31,081 --> 00:05:33,961 but it's still early days. We'll have to see how it all shakes out. 136 00:05:34,033 --> 00:05:36,897 >> Ai Sarah: So, basically, there's a lot of uncertainty about how AI will be 137 00:05:36,921 --> 00:05:37,825 regulated here. 138 00:05:37,865 --> 00:05:40,705 >> Ai Sarah: In the U.S. exactly. And that uncertainty 139 00:05:40,745 --> 00:05:43,729 just adds another layer of complexity to 140 00:05:43,817 --> 00:05:46,285 an already incredibly complex field. 141 00:05:46,635 --> 00:05:49,139 But one thing's for sure, the 142 00:05:49,187 --> 00:05:52,043 conversation around AI is just getting started, and 143 00:05:52,059 --> 00:05:53,579 we all need to be part of it. 144 00:05:53,707 --> 00:05:54,651 >> Ai Sarah: I like that. 145 00:05:54,803 --> 00:05:57,619 So, before we move on, I wanted to touch on some of the research 146 00:05:57,667 --> 00:06:00,563 Saita has done herself. Like, she worked on something called keyword 147 00:06:00,619 --> 00:06:01,099 spotting. 148 00:06:01,187 --> 00:06:03,795 >> Ai Sarah: Oh, right. Which is super important for voice assistants like 149 00:06:03,835 --> 00:06:06,707 Alexa or Siri. You know how they recognize when you say, hey, Siri, 150 00:06:06,731 --> 00:06:07,275 or whatever? 151 00:06:07,355 --> 00:06:10,355 >> Ai Sarah: Exactly. Imagine trying to talk to your phone and it can't even figure out 152 00:06:10,395 --> 00:06:11,059 what you're saying. 153 00:06:11,147 --> 00:06:13,915 >> Ai Sarah: It wouldn't be very useful. So she actually used deep 154 00:06:13,955 --> 00:06:16,617 learning to improve the accuracy of 155 00:06:16,641 --> 00:06:19,465 keyword spotting, even with all the background noise and stuff. 156 00:06:19,545 --> 00:06:22,529 >> Ai Sarah: That's really cool. It's amazing how much AI is already 157 00:06:22,617 --> 00:06:25,465 impacting our lives, even in ways we don't really 158 00:06:25,505 --> 00:06:28,377 think about. And it sounds like Sumeta's 159 00:06:28,401 --> 00:06:31,145 work is pushing the boundaries of what's possible, from 160 00:06:31,225 --> 00:06:33,833 finance to healthare to how we interact with 161 00:06:33,929 --> 00:06:35,489 technology. It's all changing. 162 00:06:35,577 --> 00:06:38,537 >> Ai Sarah: And what I find really impressive is that she's not just focused on 163 00:06:38,561 --> 00:06:41,473 the technical stuff. She's really thinking about the ethical and 164 00:06:41,529 --> 00:06:43,161 social implications of AI, too. 165 00:06:43,233 --> 00:06:44,009 >> Ai Sarah: That's crucial. 166 00:06:44,097 --> 00:06:47,055 >> Ai Sarah: Yeah. She's not just building cool tech. She's thinking about 167 00:06:47,095 --> 00:06:50,031 how it will affect people's lives and trying to make sure it's used for 168 00:06:50,063 --> 00:06:52,455 good, which is something we should all be thinking about. 169 00:06:52,535 --> 00:06:53,143 >> Ai Sarah: Absolutely. 170 00:06:53,199 --> 00:06:55,660 >> Ai Sarah: And it's really inspiring to see how Sueda, uh, 171 00:06:55,767 --> 00:06:58,615 embodies that Idea of lifelong learning. 172 00:06:58,775 --> 00:07:01,743 Even with all her knowledge and experience, she'always 173 00:07:01,799 --> 00:07:04,471 looking for new perspectives, pushing herself to learn 174 00:07:04,503 --> 00:07:04,839 more. 175 00:07:04,927 --> 00:07:07,871 >> Ai Sarah: Yeah, I noticed that she mentioned in the podcast how much she loves going 176 00:07:07,903 --> 00:07:10,863 to conferences, meetups, things like that. Not just to 177 00:07:10,879 --> 00:07:13,857 present her own work, but, uh, you know, so get 178 00:07:13,881 --> 00:07:14,489 all in. 179 00:07:14,617 --> 00:07:17,401 >> Ai Sarah: She said she really gets a lot out of those academic 180 00:07:17,473 --> 00:07:20,185 research conferences. She feels like they give her 181 00:07:20,265 --> 00:07:22,945 like a more holistic view of the 182 00:07:22,985 --> 00:07:25,881 AI landscape. You know, how all the different areas of 183 00:07:25,913 --> 00:07:28,425 AI, like natural language processing, 184 00:07:28,545 --> 00:07:31,257 knowledge, graphs, all that stuff, how it all fits together. 185 00:07:31,321 --> 00:07:34,260 >> Ai Sarah: Right. It's like seeing the big picture, how all the pieces connect. 186 00:07:34,260 --> 00:07:37,193 M Did she have any tips for making the most of 187 00:07:37,209 --> 00:07:39,961 those events? Cause they can be overwhelming sometimes. 188 00:07:40,033 --> 00:07:42,737 >> Ai Sarah: Oh, absolutely. She said the key is to 189 00:07:42,801 --> 00:07:45,587 actually engage. Like have conversations, 190 00:07:45,761 --> 00:07:48,647 ask questions. She thinks that's where the 191 00:07:48,671 --> 00:07:51,623 real learning happens, through dialogue, bouncing ideas 192 00:07:51,679 --> 00:07:54,479 off each other, challenging your own assumptions, all that good 193 00:07:54,527 --> 00:07:54,871 stuff. 194 00:07:54,943 --> 00:07:57,567 >> Ai Sarah: That makes sense. It's easy to just passively listen to 195 00:07:57,591 --> 00:08:00,471 presentations. But those side conversations, that's 196 00:08:00,503 --> 00:08:01,959 where you really get the insights. 197 00:08:02,047 --> 00:08:05,023 >> Ai Sarah: Exactly. And she had some great advice for anyone who's, you 198 00:08:05,039 --> 00:08:06,863 know, maybe a little shy about asking questions. 199 00:08:06,919 --> 00:08:09,191 >> Ai Sarah: Oh yeah, a lot of people are, including me. 200 00:08:09,223 --> 00:08:11,543 >> Ai Sarah: Sometimes she said, just do it, don't be 201 00:08:11,559 --> 00:08:14,127 afraid, because it shows you're engaged, you're 202 00:08:14,191 --> 00:08:15,095 genuinely interested. 203 00:08:15,135 --> 00:08:16,535 >> Ai Sarah: It's like you're showing initiative. 204 00:08:16,655 --> 00:08:19,239 >> Ai Sarah: Exactly. And she even said that, like, even in job 205 00:08:19,287 --> 00:08:21,743 interviews, when they ask, uh, do you have any questions? For 206 00:08:21,759 --> 00:08:24,727 us, it's not just about impressing them. 207 00:08:24,791 --> 00:08:26,103 >> Ai Sarah: Oh, I never thought of it that way. 208 00:08:26,159 --> 00:08:28,735 >> Ai Sarah: It's a chance for you to learn more about the company, the 209 00:08:28,775 --> 00:08:31,767 team, the work environment. She encourages 210 00:08:31,831 --> 00:08:34,567 people to ask specific questions. You know, about 211 00:08:34,591 --> 00:08:37,543 the projects they'd be working on, the challenges, even the day 212 00:08:37,559 --> 00:08:38,119 to day stuff. 213 00:08:38,167 --> 00:08:40,628 >> Ai Sarah: It's like you're interviewing them right back. I love that. 214 00:08:40,716 --> 00:08:43,716 >> Ai Sarah: Totally. And she also said don't be afraid to 215 00:08:43,740 --> 00:08:46,628 think outside the box when it comes to job opportunities. You 216 00:08:46,636 --> 00:08:49,244 know, don't just focus on the big tech M companies or the trendy 217 00:08:49,284 --> 00:08:49,828 startups. 218 00:08:49,916 --> 00:08:51,868 >> Ai Sarah: Right. Because AI is everywhere now. 219 00:08:51,956 --> 00:08:54,660 >> Ai Sarah: It is. So look at startups, research positions, 220 00:08:54,732 --> 00:08:57,732 nonprofits, even government jobs. You never know where 221 00:08:57,748 --> 00:08:59,148 you'll find the perfect fit. 222 00:08:59,276 --> 00:09:02,204 >> Ai Sarah: So keep an open mind. And she also had some 223 00:09:02,244 --> 00:09:04,852 specific advice for those just starting out. Right? 224 00:09:04,948 --> 00:09:07,884 >> Ai Sarah: Yeah. She said mastery, at least one programming language is 225 00:09:07,924 --> 00:09:10,873 crucial. Python or R, those are 226 00:09:10,889 --> 00:09:11,393 the big ones. 227 00:09:11,449 --> 00:09:14,209 >> Ai Sarah: I'vefordd Python is kind of the go to for AI and data 228 00:09:14,257 --> 00:09:14,761 science. 229 00:09:14,873 --> 00:09:17,681 >> Ai Sarah: It is. And that's what they use in NYU's Data Science. 230 00:09:17,753 --> 00:09:20,585 Program. But the key is to really become fluent in 231 00:09:20,625 --> 00:09:23,145 whichever language you choose. Like build a solid 232 00:09:23,185 --> 00:09:24,449 foundation so you. 233 00:09:24,457 --> 00:09:27,425 >> Ai Sarah: Can actually build things, not just like copy and paste 234 00:09:27,465 --> 00:09:28,521 code from online. 235 00:09:28,633 --> 00:09:31,577 >> Ai Sarah: Exactly. And she encouraged people to go beyond just 236 00:09:31,641 --> 00:09:34,463 using pre built libraries and tools, like 237 00:09:34,569 --> 00:09:36,955 really understand the principles of AI and machine 238 00:09:36,995 --> 00:09:39,963 learning the math and stats behind it 239 00:09:39,979 --> 00:09:40,347 all. 240 00:09:40,451 --> 00:09:43,363 >> Ai Sarah: So it's not just about being a coder, it's about understanding how 241 00:09:43,379 --> 00:09:44,803 it all works under the hood. 242 00:09:44,899 --> 00:09:47,403 >> Ai Sarah: Exactly. And that part about understanding the 243 00:09:47,419 --> 00:09:50,067 fundamentals, it really stuck with me. It's a reminder that 244 00:09:50,131 --> 00:09:52,835 AI isn't just black boxes and magic 245 00:09:52,875 --> 00:09:55,195 algorithms. It's based on scientific 246 00:09:55,235 --> 00:09:58,067 principles, math, and a deep understanding of 247 00:09:58,091 --> 00:10:01,035 how data can be used to solve problems. And Suera's 248 00:10:01,075 --> 00:10:04,043 own journey really shows that she didn't shy away from the 249 00:10:04,059 --> 00:10:07,033 hard work, mastering those foundational concepts. And you can see how 250 00:10:07,049 --> 00:10:09,057 it's paid off for her in this fast moving field. 251 00:10:09,121 --> 00:10:12,057 >> Ai Sarah: Yeah, it's like she built her career on a solid foundation and 252 00:10:12,081 --> 00:10:14,465 now she can adapt to whatever comes next. 253 00:10:14,625 --> 00:10:17,433 >> Ai Sarah: Exactly. And it's not just about her technical skills, 254 00:10:17,489 --> 00:10:20,257 it's her passion for the ethical and social side of 255 00:10:20,281 --> 00:10:21,049 AI too. 256 00:10:21,137 --> 00:10:24,129 >> Ai Sarah: Yeah, we've talked a lot about the technical stuff, but AI is going 257 00:10:24,137 --> 00:10:26,937 to have a huge impact on society. We can't forget about 258 00:10:26,961 --> 00:10:27,217 that. 259 00:10:27,281 --> 00:10:30,161 >> Ai Sarah: Absolutely. It's not just about building smarter machines. It's 260 00:10:30,193 --> 00:10:32,705 about using those machines to make our lives 261 00:10:32,785 --> 00:10:35,777 better, to solve the big problems, make the world a 262 00:10:35,801 --> 00:10:38,347 more fair and just place. And Zida 263 00:10:38,411 --> 00:10:39,363 totally gets that. 264 00:10:39,419 --> 00:10:42,331 >> Ai Sarah: She doesn't just see AI as a tool, she sees it as 265 00:10:42,363 --> 00:10:45,267 this force that can shape society for good or for 266 00:10:45,291 --> 00:10:45,571 bad. 267 00:10:45,643 --> 00:10:48,627 >> Ai Sarah: And she's committed to using her knowledge and influence to make 268 00:10:48,651 --> 00:10:51,115 sure it'used for good, which is really 269 00:10:51,235 --> 00:10:51,875 admirable. 270 00:10:51,955 --> 00:10:52,387 >> Ai Sarah: It is. 271 00:10:52,451 --> 00:10:55,363 So I guess the question is, how do we prepare for this 272 00:10:55,539 --> 00:10:58,307 future where AI is like everywhere. 273 00:10:58,411 --> 00:11:01,163 >> Ai Sarah: Well, Saita had some interesting thoughts on that. She said we need to 274 00:11:01,179 --> 00:11:04,115 embrace lifelong learning, get comfortable with the fact that technology 275 00:11:04,155 --> 00:11:06,927 is always changing. It's not about fearing the 276 00:11:06,951 --> 00:11:09,687 unknown, it's about being curious, being willing to 277 00:11:09,751 --> 00:11:12,719 explore, and, you know, step outside our comfort zones. 278 00:11:12,847 --> 00:11:15,775 >> Ai Sarah: Yeah, it's easy to feel overwhelmed by all this new stuff coming 279 00:11:15,815 --> 00:11:18,799 at us all the time. But it sounds like she's saying, like, 280 00:11:18,887 --> 00:11:20,791 lean into it, be open to it. 281 00:11:20,863 --> 00:11:23,719 >> Ai Sarah: Exactly. And sheiv even talked about how she's trying to improve her own 282 00:11:23,767 --> 00:11:26,295 life and career by focusing on those deeper 283 00:11:26,335 --> 00:11:29,135 connections, having meaningful conversations, you know, 284 00:11:29,175 --> 00:11:31,567 connecting with people from different backgrounds, different 285 00:11:31,631 --> 00:11:34,223 industries, just getting those different perspectives. 286 00:11:34,319 --> 00:11:36,831 >> Ai Sarah: It's like in this world of constant 287 00:11:36,903 --> 00:11:39,767 Information overload. She's making a conscious 288 00:11:39,831 --> 00:11:42,519 effort to focus on the things that matter, 289 00:11:42,647 --> 00:11:44,551 the things that spark new ideas. 290 00:11:44,703 --> 00:11:47,703 >> Ai Sarah: And she specifically mentioned being really interested 291 00:11:47,759 --> 00:11:50,599 in how AI is intersecting with other fields 292 00:11:50,647 --> 00:11:53,487 like journalism, economics, even the 293 00:11:53,511 --> 00:11:56,391 arts. It's about recognizing that AI isn't just 294 00:11:56,423 --> 00:11:59,403 this isolated thing, it's changing everything. And 295 00:11:59,419 --> 00:12:02,339 she's actively looking for those connections, those places where AI 296 00:12:02,387 --> 00:12:05,027 can spark new creativity, new collaborations, new 297 00:12:05,051 --> 00:12:05,515 innovations. 298 00:12:05,595 --> 00:12:08,571 >> Ai Sarah: That's really cool. It's like the possibilities are endless. AI 299 00:12:08,643 --> 00:12:11,587 helping to create new art forms, tell stories in new ways, 300 00:12:11,651 --> 00:12:13,403 connect people across cultures. 301 00:12:13,499 --> 00:12:16,395 >> Ai Sarah: It's exciting and it reminds us that the future 302 00:12:16,435 --> 00:12:19,235 of AI isn't just about, like, building 303 00:12:19,275 --> 00:12:22,083 smarter machines. It's about how we use those machines 304 00:12:22,219 --> 00:12:24,659 to enhance our lives, to solve 305 00:12:24,747 --> 00:12:27,163 problems, to make the world a better place. 306 00:12:27,299 --> 00:12:30,195 >> Ai Sarah: And speaking of solving problems, Samea's work in 307 00:12:30,235 --> 00:12:32,995 fraud detection is especially relevant in the 308 00:12:33,075 --> 00:12:35,163 financial world. You know, with fintech and all that. 309 00:12:35,219 --> 00:12:38,195 >> Ai Sarah: Oh, absolutely. Fintech has been all over AI from the very 310 00:12:38,235 --> 00:12:40,963 beginning, using it to automate stuff, uh, analyze 311 00:12:41,019 --> 00:12:43,995 data, make better decisions. And Suta is right 312 00:12:44,035 --> 00:12:46,923 there in the thick of it, leading AI initiatives at 313 00:12:46,939 --> 00:12:49,835 a micro investmentments firm and always looking for new ways 314 00:12:49,875 --> 00:12:52,163 to use AI to get better financial app. 315 00:12:52,299 --> 00:12:54,019 >> Ai Sarah: But she's also realistic about it, Right? 316 00:12:54,067 --> 00:12:54,395 >> Ai Sarah: Right. 317 00:12:54,475 --> 00:12:57,395 >> Ai Sarah: Like, not every AI solution is actually a good solution. 318 00:12:57,515 --> 00:13:00,443 >> Ai Sarah: Totally. She warned against falling into that trap. You know, the 319 00:13:00,459 --> 00:13:03,291 shiny object syndrome, um, chasing after the latest buzzwords without 320 00:13:03,323 --> 00:13:06,123 really thinking it through, like, what's the actual need, 321 00:13:06,179 --> 00:13:08,211 what's the cost? Are there any unintended 322 00:13:08,283 --> 00:13:11,195 consequences? She really encourages businesses to ask 323 00:13:11,235 --> 00:13:13,867 tough questions, understand what's going on under the 324 00:13:13,891 --> 00:13:16,627 hood, make sure the data is being handled responsibly. 325 00:13:16,731 --> 00:13:19,247 >> Ai Sarah: It's like, don't just buy the hype, do your 326 00:13:19,271 --> 00:13:19,855 homework. 327 00:13:19,975 --> 00:13:22,743 >> Ai Sarah: Exactly. And she even said that sometimes 328 00:13:22,839 --> 00:13:25,823 the simplest solution is the best. Even in the 329 00:13:25,839 --> 00:13:28,255 world of finance, which can get super 330 00:13:28,295 --> 00:13:31,231 complex, it's about using AI strategically 331 00:13:31,383 --> 00:13:34,351 to help humans do their jobs better, not to 332 00:13:34,423 --> 00:13:35,615 completely replace them. 333 00:13:35,695 --> 00:13:36,559 >> Ai Sarah: Makes sense. 334 00:13:36,727 --> 00:13:39,431 And speaking of helping humans, her work in natural 335 00:13:39,463 --> 00:13:42,391 language processing, or nlp, is really fascinating. 336 00:13:42,423 --> 00:13:45,201 We touched on it earlier, but it' such a big area with so much 337 00:13:45,233 --> 00:13:45,841 potential. 338 00:13:45,953 --> 00:13:48,585 >> Ai Sarah: She's super passionate about how NLP can be used to 339 00:13:48,665 --> 00:13:51,473 understand human language, extract meaning from text, 340 00:13:51,569 --> 00:13:53,885 even create new forms of creative expression. 341 00:13:54,185 --> 00:13:56,697 She's working on a ton of different applications, from 342 00:13:56,801 --> 00:13:59,569 analyzing financial documents and social media data to 343 00:13:59,617 --> 00:14:02,089 automating tasks and providing personalized 344 00:14:02,137 --> 00:14:04,097 recommendations. Like, it's mind blowing. 345 00:14:04,201 --> 00:14:07,177 >> Ai Sarah: It is. And I think NLP is going to be even more important as we 346 00:14:07,201 --> 00:14:10,077 generate more and more data like we, we need AI to help 347 00:14:10,101 --> 00:14:13,013 us make sense of it all, especially when it comes to language, which 348 00:14:13,029 --> 00:14:15,229 is how we communicate, how we understand the world. 349 00:14:15,317 --> 00:14:18,021 >> Ai Sarah: Totally. And Satha sees NLP as one of the 350 00:14:18,053 --> 00:14:21,045 biggest drivers of AI innovation in the years 351 00:14:21,085 --> 00:14:23,773 to come. But she's also aware of the 352 00:14:23,789 --> 00:14:26,677 potential downsides, like bias. We 353 00:14:26,701 --> 00:14:28,573 talked about that earlier with the loan applications. 354 00:14:28,669 --> 00:14:31,525 >> Ai Sarah: Right. Because if these language models are trained on biased data, 355 00:14:31,685 --> 00:14:33,293 they'll just perpetuate those biases. 356 00:14:33,349 --> 00:14:36,141 >> Ai Sarah: Exactly. And that can have real consequences in all 357 00:14:36,173 --> 00:14:38,915 sorts of areas. Credit scoring, hiring, 358 00:14:39,075 --> 00:14:41,099 even criminal justice. Its a big deal. 359 00:14:41,187 --> 00:14:44,003 >> Ai Sarah: So how do we fix that? Is it even possible to create 360 00:14:44,059 --> 00:14:46,651 AI systems that are truly fair and 361 00:14:46,683 --> 00:14:47,387 unbiased? 362 00:14:47,491 --> 00:14:50,259 >> Ai Sarah: Well, Cmenta is a big advocate for building bias 363 00:14:50,307 --> 00:14:53,283 detection and mitigation into the development process from the 364 00:14:53,299 --> 00:14:56,299 very beginning. She thinks we need to be able to measure 365 00:14:56,347 --> 00:14:59,083 fairness, have clear metrics for evaluating bias, 366 00:14:59,179 --> 00:15:02,123 and constantly monitor these models to make sure they'making fair 367 00:15:02,179 --> 00:15:04,855 decisions. Its about responsibility and 368 00:15:04,895 --> 00:15:07,463 accountability, building systems that reflect our values. 369 00:15:07,519 --> 00:15:10,455 >> Ai Sarah: So its'not enough to just build cool tech, you have to build it the right 370 00:15:10,495 --> 00:15:10,790 way. 371 00:15:10,790 --> 00:15:13,551 >> Ai Sarah: Uh, exactly. And she'putting those principles 372 00:15:13,623 --> 00:15:15,831 into practice. And her own work, especially in 373 00:15:15,863 --> 00:15:18,687 Healthare, where she'exploring how these large 374 00:15:18,751 --> 00:15:21,079 language models, the LLMs, can actually 375 00:15:21,127 --> 00:15:23,159 revolutionize how we care for patients. 376 00:15:23,287 --> 00:15:26,095 >> Ai Sarah: Yeah, she mentioned being really interested in how they can be used to 377 00:15:26,135 --> 00:15:29,097 analyze medical records, help with diagnoses, 378 00:15:29,271 --> 00:15:32,205 even personalized treatment plans. Which is 379 00:15:32,285 --> 00:15:32,829 incredible. 380 00:15:32,917 --> 00:15:35,645 >> Ai Sarah: It is. Imagine if AI could help doctors make 381 00:15:35,725 --> 00:15:38,661 faster, more accurate diagnoses, or 382 00:15:38,693 --> 00:15:41,453 even predict and prevent diseases before they develop. 383 00:15:41,509 --> 00:15:44,477 >> Ai Sarah: That would be amazing. But you know, there are obviously concerns 384 00:15:44,541 --> 00:15:47,437 about privacy and security when it comes to AI and healthare. 385 00:15:47,501 --> 00:15:50,293 >> Ai Sarah: Oh, for sure. And Sumeeda gets that. She totally 386 00:15:50,349 --> 00:15:53,109 recognizes how sensitive medical data is and how 387 00:15:53,157 --> 00:15:55,921 important it is to keep it safe. She believes AI 388 00:15:55,993 --> 00:15:58,593 can transform healthare, but it has to be done 389 00:15:58,649 --> 00:16:01,593 thoughtfully, ethically, with the patient's wellbe being 390 00:16:01,649 --> 00:16:02,425 at the forefront. 391 00:16:02,505 --> 00:16:05,193 >> Ai Sarah: Okay, that's reassuring. It sounds like she's approaching this with a lot of 392 00:16:05,209 --> 00:16:07,937 care. And her research at NYU Langone, 393 00:16:08,041 --> 00:16:10,777 especially her work on AI and neurosurgery, that's like 394 00:16:10,921 --> 00:16:11,897 the cutting edge. 395 00:16:12,001 --> 00:16:14,977 >> Ai Sarah: It is. It's a field with really high stakes. And AI has 396 00:16:15,001 --> 00:16:17,241 the potential to make a huge difference. 397 00:16:17,353 --> 00:16:20,193 >> Ai Sarah: It's amazing to think about. And it just reinforces the idea 398 00:16:20,249 --> 00:16:22,851 that AI isn't just about building smarter 399 00:16:22,883 --> 00:16:25,531 machines. It'about using technology to make 400 00:16:25,563 --> 00:16:26,723 people's lives better. 401 00:16:26,819 --> 00:16:29,803 >> Ai Sarah: Exactly. And you can see that in Saidas'work it's all about 402 00:16:29,819 --> 00:16:31,619 the human impact she. 403 00:16:31,667 --> 00:16:34,371 >> Ai Sarah: Seems driven by a real sense of purpose, like 404 00:16:34,443 --> 00:16:37,443 she wants to use her skills to actually make a difference in 405 00:16:37,459 --> 00:16:38,043 the world. 406 00:16:38,179 --> 00:16:41,115 >> Ai Sarah: Totally. And it comes through in her passion for using 407 00:16:41,155 --> 00:16:43,923 AI to address those big societal challenges, 408 00:16:43,979 --> 00:16:46,925 like improving healthare and underserved communities, 409 00:16:47,005 --> 00:16:49,805 making sure everyone has access to quality education, 410 00:16:49,965 --> 00:16:52,301 creating AI systems that are fair and 411 00:16:52,333 --> 00:16:53,165 inclusive. 412 00:16:53,325 --> 00:16:56,077 >> Ai Sarah: Yeah, we haven't talked much about that yet. How AI can be 413 00:16:56,101 --> 00:16:58,757 used to make the world a more equitable place. 414 00:16:58,861 --> 00:17:01,429 >> Ai Sarah: Well, she'a big believer in responsible 415 00:17:01,477 --> 00:17:04,325 AI development, making sure these technologies are empowering 416 00:17:04,365 --> 00:17:07,253 people, not making things worse. She sees 417 00:17:07,309 --> 00:17:10,189 AI as a way to level the playing field, to create 418 00:17:10,277 --> 00:17:12,191 opportunities for people who'been left behind. 419 00:17:12,293 --> 00:17:14,931 >> Ai Sarah: Right. Because technology can either reinforce existing 420 00:17:14,963 --> 00:17:17,947 inequalities or help to break them down. It depends on how we use 421 00:17:17,971 --> 00:17:18,395 it. 422 00:17:18,515 --> 00:17:21,363 >> Ai Sarah: Exactly. And shes working with organizations 423 00:17:21,419 --> 00:17:24,075 that are using AI for social good, 424 00:17:24,235 --> 00:17:27,147 showing that it can be a powerful force for positive change. 425 00:17:27,251 --> 00:17:29,955 >> Ai Sarah: Its inspiring to see how shes using her platform to 426 00:17:29,995 --> 00:17:32,867 advocate for a more just world, a 427 00:17:32,891 --> 00:17:35,747 world where AI is used to lift people up, not 428 00:17:35,771 --> 00:17:36,443 hold them back. 429 00:17:36,539 --> 00:17:39,123 >> Ai Sarah: Its about aligning AI with social impact 430 00:17:39,179 --> 00:17:42,051 goals, using technology to tackle the 431 00:17:42,083 --> 00:17:44,931 biggest challenges we face as a society. And 432 00:17:44,963 --> 00:17:47,923 Saeda is leading by example, showing that innovation 433 00:17:48,019 --> 00:17:50,707 and social responsibility can go hand in hand. 434 00:17:50,851 --> 00:17:53,723 >> Ai Sarah: And shes also a big advocate for getting more women into 435 00:17:53,859 --> 00:17:54,939 A.I. uh, and Tecac, right? 436 00:17:54,987 --> 00:17:57,947 >> Ai Sarah: No, absolutely. She'very vocal about that. It'a field that's 437 00:17:57,971 --> 00:18:00,547 been dominated by men for way too long, and 438 00:18:00,571 --> 00:18:03,451 she'working to change that. Mentoring other women, speaking 439 00:18:03,483 --> 00:18:06,235 at conferences, sharing her story to inspire the next 440 00:18:06,275 --> 00:18:06,739 generation. 441 00:18:06,827 --> 00:18:09,643 >> Ai Sarah: Its so important to have those different voices, those different perspectives, 442 00:18:09,739 --> 00:18:12,113 especially as AI becomes more and more powerful, total. 443 00:18:12,169 --> 00:18:15,025 >> Ai Sarah: And her success is a testament to what women can achieve in this 444 00:18:15,065 --> 00:18:17,633 field. They're pushing boundaries, innovating, 445 00:18:17,729 --> 00:18:18,553 shaping the future. 446 00:18:18,569 --> 00:18:20,969 >> Ai Sarah: Of AI So as we're wrapping up this part of the 447 00:18:21,017 --> 00:18:23,977 discussion, what would you say are the key takeaways for 448 00:18:24,001 --> 00:18:24,697 our listener? 449 00:18:24,801 --> 00:18:27,753 >> Ai Sarah: I think the biggest takeaway is that AI isn't 450 00:18:27,809 --> 00:18:30,377 something that's going to happen in the future. It's here, 451 00:18:30,441 --> 00:18:33,005 now, it's evolving super fast, 452 00:18:33,305 --> 00:18:35,953 and it's already having a huge impact on our lives. 453 00:18:36,049 --> 00:18:38,817 >> Ai Sarah: And it's not just about the tech. It's about how we use 454 00:18:38,841 --> 00:18:41,841 it, the values we build into it, the kind of future 455 00:18:41,873 --> 00:18:42,865 we want to create. 456 00:18:43,025 --> 00:18:45,969 >> Ai Sarah: SUA s journey shows us that we all have a role to play 457 00:18:46,017 --> 00:18:48,833 in shaping that future. Whether you're a tech 458 00:18:48,929 --> 00:18:51,713 expert, a business leader, or just someone who's curious 459 00:18:51,769 --> 00:18:54,769 about the world, you can be part of this AI 460 00:18:54,817 --> 00:18:55,473 revolution. 461 00:18:55,609 --> 00:18:58,525 >> Ai Sarah: So Be curious, be informed, get involved. 462 00:18:58,825 --> 00:19:01,577 Because the future of AI is being written right now 463 00:19:01,721 --> 00:19:04,529 and we all have a chance to be part of the story. You know, it's really 464 00:19:04,577 --> 00:19:07,257 inspiring to see how Saida has taken her passion 465 00:19:07,401 --> 00:19:10,177 for social good and like woven 466 00:19:10,201 --> 00:19:11,417 it into her AI work. 467 00:19:11,481 --> 00:19:14,385 >> Ai Sarah: It is. Yeah, it's like a reminder that this tech, it can be a force 468 00:19:14,425 --> 00:19:16,657 for good, you know, if it's guided by the right people. 469 00:19:16,721 --> 00:19:19,457 >> Ai Sarah: And it's not just talk. She's out there actually doing things. 470 00:19:19,561 --> 00:19:22,473 >> Ai Sarah: Oh yeah, like she mentioned working with organizations that 471 00:19:22,489 --> 00:19:25,129 are using AI to bring 472 00:19:25,177 --> 00:19:26,753 healthcare to rural areas. 473 00:19:26,809 --> 00:19:28,145 >> Ai Sarah: Oh, that's huge. 474 00:19:28,265 --> 00:19:30,765 >> Ai Sarah: Right. And places that don't have a lot of 475 00:19:30,885 --> 00:19:33,461 doctors, specialists, all that. 476 00:19:33,653 --> 00:19:35,825 Imagine an AI that could like 477 00:19:36,165 --> 00:19:38,789 diagnose basic stuff or even just give 478 00:19:38,837 --> 00:19:39,181 advice. 479 00:19:39,253 --> 00:19:42,173 >> Ai Sarah: It would change so many lives. Sometimes we get so caught up in the 480 00:19:42,189 --> 00:19:44,933 cool AI stuff like self driving cars. But it's these 481 00:19:44,989 --> 00:19:47,205 basic needs that's where the real impact is. 482 00:19:47,285 --> 00:19:49,917 >> Ai Sarah: Absolutely. And she's also working on projects using AI 483 00:19:49,981 --> 00:19:50,853 for education. 484 00:19:50,949 --> 00:19:52,117 >> Ai Sarah: Okay, yeah, that's a big one. 485 00:19:52,181 --> 00:19:54,693 >> Ai Sarah: Like imagine AI tutors that can tailor 486 00:19:54,749 --> 00:19:57,675 lessons to each student. You know, everyone learns differently. 487 00:19:57,715 --> 00:20:00,643 >> Ai Sarah: Oh, that's amazing. Especially as, like, everything becomes 488 00:20:00,699 --> 00:20:02,075 more and more tech driven. 489 00:20:02,155 --> 00:20:05,003 >> Ai Sarah: Right. We need to make sure everyone has a chance to learn no matter what 490 00:20:05,019 --> 00:20:06,027 their background is. 491 00:20:06,131 --> 00:20:09,035 >> Ai Sarah: So true. And she also talked about making AI systems 492 00:20:09,075 --> 00:20:09,739 more inclusive. 493 00:20:09,787 --> 00:20:12,427 >> Ai Sarah: Right. So they benefit everyone, not just a 494 00:20:12,451 --> 00:20:15,371 select few. And one of the keys to that is having 495 00:20:15,443 --> 00:20:17,643 diverse teams building these systems. 496 00:20:17,739 --> 00:20:20,547 >> Ai Sarah: Yeah, that makes total sense. If the people creating the 497 00:20:20,571 --> 00:20:23,043 AI don't represent the whole world, then the AI won't 498 00:20:23,059 --> 00:20:23,621 either. 499 00:20:23,763 --> 00:20:26,265 >> Ai Sarah: Exactly. And she also talked about needing more 500 00:20:26,305 --> 00:20:27,145 transparency. 501 00:20:27,265 --> 00:20:28,385 >> Ai Sarah: Okay, what does that mean? 502 00:20:28,505 --> 00:20:31,401 >> Ai Sarah: Basically, we need to be able to understand how these AI 503 00:20:31,473 --> 00:20:34,049 systems are making decisions, hold them 504 00:20:34,097 --> 00:20:34,665 accountable. 505 00:20:34,785 --> 00:20:37,657 >> Ai Sarah: So it's not enough to just build cool tech, you got to build it 506 00:20:37,721 --> 00:20:38,265 responsibly. 507 00:20:38,345 --> 00:20:41,281 >> Ai Sarah: Right. And Sada's work really embodies that. Ah, she's 508 00:20:41,313 --> 00:20:44,121 not just an AI expert, she's like a leader, an 509 00:20:44,193 --> 00:20:47,121 advocate, someone who's using her skills for 510 00:20:47,153 --> 00:20:47,537 good. 511 00:20:47,641 --> 00:20:50,489 >> Ai Sarah: It's pretty inspiring, honestly, to see someone who's so 512 00:20:50,537 --> 00:20:53,057 passionate about both the tech and the human side of it. 513 00:20:53,121 --> 00:20:56,001 >> Ai Sarah: Absolutely. And her story shows that we all have a part 514 00:20:56,033 --> 00:20:58,953 to play in shaping the future of AI. It's not just up to the 515 00:20:58,969 --> 00:20:59,833 big tech companies. 516 00:20:59,929 --> 00:21:02,769 >> Ai Sarah: So what can our listeners do? Where can they learn more, get 517 00:21:02,817 --> 00:21:03,161 involved? 518 00:21:03,233 --> 00:21:05,681 >> Ai Sarah: Well, she actually encouraged people to reach out to her. 519 00:21:05,753 --> 00:21:06,033 >> Ai Sarah: Really? 520 00:21:06,089 --> 00:21:08,929 >> Ai Sarah: How? Through her website, LinkedIn, 521 00:21:09,017 --> 00:21:11,161 even her NYU email. It's E 522 00:21:11,353 --> 00:21:14,089 Raiu Edu. 523 00:21:14,257 --> 00:21:17,161 She seems really open to connecting with people who are 524 00:21:17,273 --> 00:21:20,257 passionate about AI and, you know, using it to make a 525 00:21:20,281 --> 00:21:20,769 difference. 526 00:21:20,897 --> 00:21:23,649 >> Ai Sarah: I love that. Uh, and of course, everyone should go back and listen to that full 527 00:21:23,697 --> 00:21:25,905 interview on the about that Wallet podcast, episode 528 00:21:25,945 --> 00:21:26,921 286. 529 00:21:26,993 --> 00:21:29,793 >> Ai Sarah: Yeah, we'll definitely put a link in the show notes. And if you're 530 00:21:29,809 --> 00:21:32,777 enjoying these deep dives and want to support Anthony Weaver 531 00:21:32,801 --> 00:21:35,417 in his mission, be sure to like and subscribe to the 532 00:21:35,441 --> 00:21:38,137 channel on YouTube, your favorite podcast app, 533 00:21:38,201 --> 00:21:39,489 wherever you're listening. 534 00:21:39,577 --> 00:21:42,569 >> Ai Sarah: Yeah, we really appreciate the support and you can also sign up for 535 00:21:42,577 --> 00:21:45,001 their newsletter@aboutthatwallet.com do 536 00:21:45,113 --> 00:21:47,953 newsletter to stay up to date on all things personal 537 00:21:48,049 --> 00:21:48,685 finance. 538 00:21:49,545 --> 00:21:52,289 So as we wrap up this whole AI deep dive, 539 00:21:52,457 --> 00:21:54,521 what are like, the big takeaways here? 540 00:21:54,593 --> 00:21:57,377 >> Ai Sarah: I think the main thing is AI isn't something 541 00:21:57,401 --> 00:22:00,345 that's coming in the future. It's already here. It's changing 542 00:22:00,385 --> 00:22:02,625 things fast, and it's affecting all of us. 543 00:22:02,705 --> 00:22:05,401 >> Ai Sarah: And it's not just about the tech. It's about the choices we 544 00:22:05,433 --> 00:22:08,161 make, the values we instill in these 545 00:22:08,233 --> 00:22:10,629 systems, the kind of world we want to create. 546 00:22:10,777 --> 00:22:13,621 >> Ai Sarah: SoA'sTory shows that we all have a say in 547 00:22:13,653 --> 00:22:16,581 that. We can choose to be passive or we can step 548 00:22:16,613 --> 00:22:18,461 up and help shape the future of AI. 549 00:22:18,533 --> 00:22:20,733 >> Ai Sarah: It's like the future is being written right now. 550 00:22:20,829 --> 00:22:22,285 >> Ai Sarah: Exactly. So let's make sure it's a good one.