1 00:00:00,000 --> 00:00:18,840 Ever feel like your phone knows you a little too well? 2 00:00:18,840 --> 00:00:24,480 You browse for a pair of sneakers once and suddenly ads for them follow you across the 3 00:00:24,480 --> 00:00:28,280 internet just like a digital stalker. 4 00:00:28,280 --> 00:00:35,440 But what if I told you there's a way you can enjoy hyper-personalized AI recommendations 5 00:00:35,440 --> 00:00:39,000 without sacrificing your privacy? 6 00:00:39,000 --> 00:00:41,600 Sounds impossible, doesn't it? 7 00:00:41,600 --> 00:00:59,000 Welcome to the hidden revolution of AI personalization. 8 00:00:59,000 --> 00:01:04,040 Welcome to Data & AI with Mukundan, where we decode the future of technology in ways 9 00:01:04,040 --> 00:01:05,040 that matter to you. 10 00:01:05,040 --> 00:01:10,360 In today's episode, we're diving deep into a game-changing shift in AI, the ability to 11 00:01:10,360 --> 00:01:15,280 personalize your experience without intruding into your personal data. 12 00:01:15,280 --> 00:01:16,720 And the best part? 13 00:01:16,720 --> 00:01:20,520 This evolution is already happening. 14 00:01:20,520 --> 00:01:23,280 See here's where the problem is. 15 00:01:23,280 --> 00:01:28,000 Why AI personalization has been broken? 16 00:01:28,000 --> 00:01:30,000 Let's look at the problem. 17 00:01:30,000 --> 00:01:38,800 Have you ever noticed that streaming services like Netflix, Amazon Prime and Spotify recommend 18 00:01:38,800 --> 00:01:41,560 things that feel a little off? 19 00:01:41,560 --> 00:01:45,400 Sometimes they get it right, other times it feels like they're just throwing darts in 20 00:01:45,400 --> 00:01:47,440 the dark. 21 00:01:47,440 --> 00:01:59,560 The issue, however, is that AI models today try to learn from everyone, not just you specifically. 22 00:01:59,560 --> 00:02:02,800 Personalization has always come at a cost. 23 00:02:02,800 --> 00:02:12,000 AI models typically work by collecting vast amounts of personal data to predict your preferences. 24 00:02:12,000 --> 00:02:20,800 The problem is that data sits on centralized servers which is vulnerable to leaks, glitches 25 00:02:20,800 --> 00:02:23,880 and misuse. 26 00:02:23,880 --> 00:02:27,840 A one-size-fits-all model doesn't work. 27 00:02:27,840 --> 00:02:31,080 Imagine if everyone in the world had to wear the same shoe size. 28 00:02:31,080 --> 00:02:34,440 That's what AI personalization feels like right now. 29 00:02:34,440 --> 00:02:39,640 Streaming services which are always giving generic recommendations that don't always 30 00:02:39,640 --> 00:02:41,640 hit the mark. 31 00:02:41,640 --> 00:02:47,000 E-commerce, in e-commerce what's happening is random product suggestions instead of what 32 00:02:47,000 --> 00:02:48,000 you actually want. 33 00:02:48,000 --> 00:02:55,280 In healthcare, the diagnosis, they are ignoring your personal history or your lifestyle habits 34 00:02:55,280 --> 00:03:00,560 and then there's the biggest issue, rust. 35 00:03:00,560 --> 00:03:06,720 So you see how there's like so many issues with the one-size-fits-all model and obviously 36 00:03:06,720 --> 00:03:09,560 rust is one of the biggest reasons we have with AI. 37 00:03:09,560 --> 00:03:20,200 We're starting to feel uneasy about how much AI knows about us. 38 00:03:20,200 --> 00:03:23,040 Here are some breakthrough models I want to talk about. 39 00:03:23,040 --> 00:03:26,320 And AI that learns without spying on you. 40 00:03:26,320 --> 00:03:32,160 There's two new revolutionary techniques which are solving this problem of how much AI knows 41 00:03:32,160 --> 00:03:33,440 about us. 42 00:03:33,440 --> 00:03:36,480 Sequential layer expansion and FedSelect. 43 00:03:36,480 --> 00:03:39,320 Now these aren't just theoretical ideas. 44 00:03:39,320 --> 00:03:45,000 They're practical solutions which are reshaping AI as we speak. 45 00:03:45,000 --> 00:03:47,800 Now what is sequential layer expansion? 46 00:03:47,800 --> 00:03:52,520 I know that sounds like a mouthful but let me explain that to you. 47 00:03:52,520 --> 00:03:56,600 It's an AI that grows with you. 48 00:03:56,600 --> 00:03:58,840 So elaborate. 49 00:03:58,840 --> 00:04:06,840 Now instead of treating AI models like a fixed block, this method builds AI in layers. 50 00:04:06,840 --> 00:04:12,360 It helps to allow it to grow and adapt over time. 51 00:04:12,360 --> 00:04:15,720 Now think of it like you're training for a marathon. 52 00:04:15,720 --> 00:04:19,080 You don't start by running 26 miles, right? 53 00:04:19,080 --> 00:04:21,240 Or even 15 miles. 54 00:04:21,240 --> 00:04:24,760 You will have to build it up in layers. 55 00:04:24,760 --> 00:04:31,920 First a mile, then you do 2, 3, 4, 5, then 10, right? 56 00:04:31,920 --> 00:04:34,920 Then you get to your 26. 57 00:04:34,920 --> 00:04:38,000 Actually I'm sorry, maybe even do a half marathon first. 58 00:04:38,000 --> 00:04:46,080 Now why this is a game changer is this model starts with a basic foundation and it gradually 59 00:04:46,080 --> 00:04:50,160 adds complexity based on your data. 60 00:04:50,160 --> 00:04:56,080 This means it only learns what's relevant to you instead of overloading itself with 61 00:04:56,080 --> 00:04:59,680 irrelevant data from everyone. 62 00:04:59,680 --> 00:05:08,480 The AI keeps evolving without needing massive amounts of external data. 63 00:05:08,480 --> 00:05:11,300 So that was sequential layer expansion. 64 00:05:11,300 --> 00:05:19,280 Now the other one I wanted to talk about today was FedSelect which is precision without compromise. 65 00:05:19,280 --> 00:05:28,720 Instead of treating all AI updates as necessary, FedSelect carefully selects only the parameters 66 00:05:28,720 --> 00:05:30,720 that need adjustment. 67 00:05:30,720 --> 00:05:32,840 Now think of it like your tuning a car. 68 00:05:32,840 --> 00:05:38,040 Now instead of replacing the entire engine, you fine tune just the parts that improve 69 00:05:38,040 --> 00:05:39,040 performance. 70 00:05:39,040 --> 00:05:43,040 Why it's revolutionary? 71 00:05:43,040 --> 00:05:51,320 Because your fine tuning is an AI that adjusts only the parameters that matter for you. 72 00:05:51,320 --> 00:05:53,840 It focuses on efficiency first. 73 00:05:53,840 --> 00:06:01,240 So in that way there's no wasted computing power or unnecessary updates. 74 00:06:01,240 --> 00:06:08,160 This privacy by design as well in this, everything happens locally on your device. 75 00:06:08,160 --> 00:06:10,520 Your raw data never leaves. 76 00:06:10,520 --> 00:06:16,840 The bottom line is this, these techniques, they create an AI that's fast, efficient 77 00:06:16,840 --> 00:06:20,160 and private. 78 00:06:20,160 --> 00:06:26,800 So let's look at real world impact of how this changes our daily life. 79 00:06:26,800 --> 00:06:29,280 Streaming services that actually understand you. 80 00:06:29,280 --> 00:06:34,040 That's you open Netflix and boom, a recommendation that fits exactly what you're in the mood 81 00:06:34,040 --> 00:06:36,280 for without creepy tracking. 82 00:06:36,280 --> 00:06:42,000 As in e-commerce if you want to see smarter, more intuitive shopping. 83 00:06:42,000 --> 00:06:47,000 Now your shopping app doesn't just suggest what's trendy. 84 00:06:47,000 --> 00:06:54,040 It recommends what's right for you based on subtle local AI learning. 85 00:06:54,040 --> 00:06:59,440 And in terms of healthcare, you'll have more personalized healthcare that respects your 86 00:06:59,440 --> 00:07:01,000 privacy. 87 00:07:01,000 --> 00:07:06,840 Now imagine AI powered health plans which are tailored to your genes and your habits 88 00:07:06,840 --> 00:07:13,120 without your medical data ever leaving your device. 89 00:07:13,120 --> 00:07:15,120 Now here's the bigger picture. 90 00:07:15,120 --> 00:07:19,400 AI that respects human identity. 91 00:07:19,400 --> 00:07:21,360 The human is important. 92 00:07:21,360 --> 00:07:26,480 One major challenge in AI is scalability. 93 00:07:26,480 --> 00:07:33,920 How do we make AI work for billions of users without losing its human touch? 94 00:07:33,920 --> 00:07:38,400 The answer personalized AI at scale. 95 00:07:38,400 --> 00:07:44,680 Sequential layer expansion makes AI adaptable, learning progressively. 96 00:07:44,680 --> 00:07:51,440 And FedSelect ensures AI only adjusts what truly matters for each user. 97 00:07:51,440 --> 00:07:54,500 Here's my final takeaway. 98 00:07:54,500 --> 00:07:58,060 This isn't just about AI getting smarter. 99 00:07:58,060 --> 00:08:02,000 It's about AI becoming better for everyone. 100 00:08:02,000 --> 00:08:06,120 We need to make AI more inclusive. 101 00:08:06,120 --> 00:08:12,800 And what it means for you is more privacy, no more surveillance style tracking. 102 00:08:12,800 --> 00:08:15,840 You have better experiences. 103 00:08:15,840 --> 00:08:21,800 AI feels natural, intuitive and human. 104 00:08:21,800 --> 00:08:24,080 Get more control. 105 00:08:24,080 --> 00:08:31,120 You shape the AI, not the other way around. 106 00:08:31,120 --> 00:08:36,880 So here's what I want you to do as a next step. 107 00:08:36,880 --> 00:08:39,360 Be part of the AI revolution. 108 00:08:39,360 --> 00:08:40,360 Stay informed. 109 00:08:40,360 --> 00:08:47,080 Read up on FedSelect and sequential layer expansion and I'll link that in the show notes. 110 00:08:47,080 --> 00:08:51,080 Advocate for privacy first, AI. 111 00:08:51,080 --> 00:08:55,120 Support ethical technology, spread the word. 112 00:08:55,120 --> 00:08:57,200 Now share this episode with someone who needs to hear it. 113 00:08:57,200 --> 00:08:59,080 AI doesn't have to be creepy. 114 00:08:59,080 --> 00:09:03,440 It can be personal without being invasive. 115 00:09:03,440 --> 00:09:06,600 And that's the future we should all demand. 116 00:09:06,600 --> 00:09:13,880 Well, until next time, this is Mukundar bringing you the best of data and AI in a way that 117 00:09:13,880 --> 00:09:14,880 matters. 118 00:09:14,880 --> 00:09:16,120 Thanks for tuning in. 119 00:09:16,120 --> 00:09:21,120 Don't forget to subscribe, leave a review and follow for more AI breakthroughs that 120 00:09:21,120 --> 00:09:48,120 impact your world.