1 00:00:00,000 --> 00:00:05,560 All right, everyone get ready because today we are taking a deep dive into this new open AI paper. 2 00:00:05,560 --> 00:00:06,960 Oh yeah, this one's a good one. 3 00:00:06,960 --> 00:00:09,960 All about keeping those large language models. 4 00:00:09,960 --> 00:00:13,640 You know, LLMs on the straight and narrow. 5 00:00:13,640 --> 00:00:15,000 Yeah, keeping them safe. 6 00:00:15,000 --> 00:00:16,040 Safe, exactly. 7 00:00:16,040 --> 00:00:20,120 It's called Rule-Based Rewards for Language Model Safety. 8 00:00:20,120 --> 00:00:25,920 And the best part is we're going straight to the source with excerpts from the paper itself. 9 00:00:25,920 --> 00:00:27,040 I love it when we do that. 10 00:00:27,040 --> 00:00:28,200 Me too, I think it's fun. 11 00:00:28,200 --> 00:00:34,680 So this paper really digs into a problem that's been a thorn in the side of AI for a while now. 12 00:00:34,680 --> 00:00:35,240 Okay. 13 00:00:35,240 --> 00:00:38,120 You know how LLMs are getting so incredibly good. 14 00:00:38,120 --> 00:00:39,040 At so many things. 15 00:00:39,040 --> 00:00:43,480 At things like writing and translating, but sometimes they can say things that are... 16 00:00:43,480 --> 00:00:44,040 A little. 17 00:00:44,040 --> 00:00:45,760 A little off-color, shall we say. 18 00:00:45,760 --> 00:00:48,080 Yeah, it's like that friend who always puts their foot in their mouth. 19 00:00:48,080 --> 00:00:53,800 Exactly, and the old ways of fixing this, like having humans constantly checking, correcting them, 20 00:00:53,800 --> 00:00:55,240 it's just not feasible. 21 00:00:55,240 --> 00:00:56,480 Yeah, super time consuming. 22 00:00:56,480 --> 00:00:57,480 Way too expensive. 23 00:00:57,480 --> 00:01:00,800 So this paper's got a new method that's way more efficient. 24 00:01:00,800 --> 00:01:04,480 And gives us a lot more control over how these models behave. 25 00:01:04,480 --> 00:01:06,240 Okay, so spill the beans. 26 00:01:06,240 --> 00:01:07,120 What's the secret? 27 00:01:07,120 --> 00:01:08,280 Yeah, what's the secret sauce? 28 00:01:08,280 --> 00:01:11,600 It's called Rule-Based Rewards, or we can just call them RBRs for short. 29 00:01:11,600 --> 00:01:12,360 RBRs, okay. 30 00:01:12,360 --> 00:01:17,680 And it's all about setting very specific rules for what the AI should and shouldn't say. 31 00:01:17,680 --> 00:01:18,640 Okay, like boundaries. 32 00:01:18,640 --> 00:01:20,320 Boundaries, exactly. 33 00:01:20,320 --> 00:01:26,960 So imagine you're teaching a chatbot to politely refuse requests for things that are dangerous 34 00:01:26,960 --> 00:01:27,960 or illegal. 35 00:01:27,960 --> 00:01:29,080 Right, I'm with you. 36 00:01:29,080 --> 00:01:33,120 Okay, so with RBRs, you break down that polite refusal into clear rules. 37 00:01:33,120 --> 00:01:34,120 Like what? 38 00:01:34,120 --> 00:01:38,640 Like, apologize, don't be judgmental, and clearly state you can't do that. 39 00:01:38,640 --> 00:01:41,560 So it's basically like giving the AI a code of conduct. 40 00:01:41,560 --> 00:01:43,320 It's like a digital etiquette guide, yeah. 41 00:01:43,320 --> 00:01:45,240 I like that, a digital etiquette guide. 42 00:01:45,240 --> 00:01:46,960 And here's where things get really interesting. 43 00:01:46,960 --> 00:01:47,560 Okay. 44 00:01:47,560 --> 00:01:49,560 They actually use another AI. 45 00:01:49,560 --> 00:01:50,200 Whoa. 46 00:01:50,200 --> 00:01:55,080 Almost like a robot judge to score how well the chatbot is following those rules during 47 00:01:55,080 --> 00:01:55,920 its training. 48 00:01:55,920 --> 00:01:57,640 So AI judging AI. 49 00:01:57,640 --> 00:01:59,360 Exactly, it's like AI inception. 50 00:01:59,360 --> 00:02:03,360 That is wild, and that means we don't need humans constantly supervising. 51 00:02:03,360 --> 00:02:04,440 That's the idea. 52 00:02:04,440 --> 00:02:09,760 This AI judge gives feedback, so we don't need humans hovering over it all the time. 53 00:02:09,760 --> 00:02:11,520 I mean, that makes it much more efficient. 54 00:02:11,520 --> 00:02:12,040 Exactly. 55 00:02:12,040 --> 00:02:13,320 And I guess more consistent too. 56 00:02:13,320 --> 00:02:15,080 You got it way more consistent. 57 00:02:15,080 --> 00:02:17,160 And here's another cool thing about RBRs. 58 00:02:17,160 --> 00:02:17,560 Yeah. 59 00:02:17,560 --> 00:02:22,600 They're really easy to update as the world changes, you know, as our ideas about safety evolve. 60 00:02:22,600 --> 00:02:26,040 Oh, that's important because things change so quickly these days. 61 00:02:26,040 --> 00:02:27,920 Things change so fast, yeah. 62 00:02:27,920 --> 00:02:31,480 So let's say some new kind of harmful content pops up online. 63 00:02:31,480 --> 00:02:32,520 Like DeepFix or something. 64 00:02:32,520 --> 00:02:34,200 Yeah, like DeepFix, exactly. 65 00:02:34,200 --> 00:02:39,600 Well, with RBRs, you can just add new rules or tweak the old ones to keep the AI in check. 66 00:02:39,600 --> 00:02:43,000 So it's not just a one-time fix, it's like a constantly evolving system. 67 00:02:43,000 --> 00:02:45,480 It's like a living, breathing set of guidelines. 68 00:02:45,480 --> 00:02:46,120 I like that. 69 00:02:46,120 --> 00:02:51,280 So it sounds good in theory, but how well does this actually work in practice? 70 00:02:51,280 --> 00:02:53,400 You know, they got some pretty impressive results. 71 00:02:53,400 --> 00:02:54,800 Yeah, they put it through the ringer. 72 00:02:54,800 --> 00:02:58,360 They tested these RBRs in all kinds of scenarios. 73 00:02:58,360 --> 00:03:05,480 And one example they gave was training a chat bot to respond appropriately to requests for advice on self-harm. 74 00:03:05,480 --> 00:03:06,280 Oh, wow. 75 00:03:06,280 --> 00:03:07,200 That's a tough one. 76 00:03:07,200 --> 00:03:08,240 That's a sensitive area. 77 00:03:08,240 --> 00:03:11,520 You really need the AI to handle that carefully. 78 00:03:11,520 --> 00:03:12,160 Absolutely. 79 00:03:12,160 --> 00:03:14,400 You can't just have it spitting out generic advice. 80 00:03:14,400 --> 00:03:16,680 You don't want canned responses. 81 00:03:16,680 --> 00:03:21,600 An early version of their system tended to just recommend calling a US suicide hotline. 82 00:03:21,600 --> 00:03:24,080 Right, which isn't helpful for someone in another country. 83 00:03:24,080 --> 00:03:26,400 Totally useless for someone outside the US. 84 00:03:26,400 --> 00:03:29,800 So how did the RBRs actually help in this case? 85 00:03:29,800 --> 00:03:35,640 Well, they were able to refine the system so it acknowledges the user's distress, 86 00:03:35,640 --> 00:03:37,800 but doesn't actually give harmful advice. 87 00:03:37,800 --> 00:03:39,480 So it's learning to be more empathetic? 88 00:03:39,480 --> 00:03:40,520 More empathetic. 89 00:03:40,520 --> 00:03:43,080 It offers more general support and guidance. 90 00:03:43,080 --> 00:03:43,640 Wow. 91 00:03:43,640 --> 00:03:49,240 So it avoids those potentially harmful responses, but it's still engaging with the user. 92 00:03:49,240 --> 00:03:50,400 It's still there for them. 93 00:03:50,400 --> 00:03:53,600 That's a pretty big step forward in making these models safer. 94 00:03:53,600 --> 00:03:54,680 It's a huge step. 95 00:03:54,680 --> 00:03:57,800 And they said that it helped reduce something called over-refusals. 96 00:03:57,800 --> 00:03:58,600 Oh, yeah. 97 00:03:58,600 --> 00:03:59,520 Over-refusals. 98 00:03:59,520 --> 00:04:00,600 What is an over-refusal? 99 00:04:00,600 --> 00:04:04,720 So that's when the chat bot becomes overly cautious and refuses to do anything. 100 00:04:04,720 --> 00:04:06,000 Even if it's harmless? 101 00:04:06,000 --> 00:04:07,680 Even if it's totally harmless. 102 00:04:07,680 --> 00:04:10,560 It's like that friend who always says no to every invitation. 103 00:04:10,560 --> 00:04:12,840 Right, even if you're just asking them to hang out and watch a movie. 104 00:04:12,840 --> 00:04:13,400 Exactly. 105 00:04:13,400 --> 00:04:14,960 You just want to watch a movie. 106 00:04:14,960 --> 00:04:20,960 And you want the AI to be safe, but not so locked down that it can't hold a conversation. 107 00:04:20,960 --> 00:04:21,280 Right. 108 00:04:21,280 --> 00:04:22,280 It still needs to be useful. 109 00:04:22,280 --> 00:04:23,960 Still needs to be useful. 110 00:04:23,960 --> 00:04:27,160 And the RBRs seem to help find that sweet spot. 111 00:04:27,160 --> 00:04:29,960 So it's that balance between safety and functionality? 112 00:04:29,960 --> 00:04:31,280 Finding that balance. 113 00:04:31,280 --> 00:04:36,960 And what I really like about this approach is that it seems to give us more control than we had before. 114 00:04:36,960 --> 00:04:37,800 Way more control. 115 00:04:37,800 --> 00:04:40,280 We're not just giving a thumbs up or thumbs down on a response. 116 00:04:40,280 --> 00:04:42,120 Yeah, we're giving very specific instructions. 117 00:04:42,120 --> 00:04:43,200 We're giving instructions. 118 00:04:43,200 --> 00:04:45,320 We're saying this is how you should behave. 119 00:04:45,320 --> 00:04:48,240 So it's almost like we're teaching the AI good manners. 120 00:04:48,240 --> 00:04:52,080 Yeah, like teaching it how to be a responsible conversational partner. 121 00:04:52,080 --> 00:04:52,880 Exactly. 122 00:04:52,880 --> 00:04:59,440 So it sounds like a major win for AI safety, but I'm sure there are some limitations to this approach. 123 00:04:59,440 --> 00:05:01,520 Well, of course, no system is perfect. 124 00:05:01,520 --> 00:05:07,840 One thing the researchers point out is that RBRs work best when you can clearly define the rules. 125 00:05:07,840 --> 00:05:09,320 When the rules are very objective. 126 00:05:09,320 --> 00:05:10,160 Very objective. 127 00:05:10,160 --> 00:05:16,320 Yeah, like if the desired behavior is really subjective, it might not be as effective. 128 00:05:16,320 --> 00:05:17,000 I see. 129 00:05:17,000 --> 00:05:19,680 So like if you're trying to teach an AI humor. 130 00:05:19,680 --> 00:05:20,880 Like how to be funny. 131 00:05:20,880 --> 00:05:23,440 Yeah, that's really hard to define with specific rules. 132 00:05:23,440 --> 00:05:23,960 Exactly. 133 00:05:23,960 --> 00:05:25,280 Humor is so subjective. 134 00:05:25,280 --> 00:05:29,120 It's subjective and it changes depending on the person and the context. 135 00:05:29,120 --> 00:05:29,400 Right. 136 00:05:29,400 --> 00:05:33,960 So it's not like this is a silver bullet solution that will solve every AI challenge. 137 00:05:33,960 --> 00:05:39,920 It's not a magic wand, but it's a very powerful tool when used in the right situations. 138 00:05:39,920 --> 00:05:41,560 So this is all super fascinating. 139 00:05:41,560 --> 00:05:42,240 It is. 140 00:05:42,240 --> 00:05:45,480 I am so eager to dig into the details of their experiments. 141 00:05:45,480 --> 00:05:47,120 Yeah, let's look at some of the specifics. 142 00:05:47,120 --> 00:05:49,400 Well, let's unpack those ablations they were talking about. 143 00:05:49,400 --> 00:05:51,120 All right, let's get down to the nitty gritty. 144 00:05:51,120 --> 00:05:51,720 You bet. 145 00:05:51,720 --> 00:05:53,160 Okay, so ablations. 146 00:05:53,160 --> 00:05:53,840 Ablations. 147 00:05:53,840 --> 00:05:54,720 I like saying that word. 148 00:05:54,720 --> 00:05:55,840 It's a fun one, isn't it? 149 00:05:55,840 --> 00:05:56,520 It is. 150 00:05:56,520 --> 00:06:01,720 So they're like these little tests they run to see what happens when they tweak different parts of the system. 151 00:06:01,720 --> 00:06:06,680 Yeah, it's like tinkering under the hood to really understand how this RBR engine works. 152 00:06:06,680 --> 00:06:08,080 I like that analogy a lot. 153 00:06:08,080 --> 00:06:08,840 Glad you like it. 154 00:06:08,840 --> 00:06:10,200 It's a good one. 155 00:06:10,200 --> 00:06:16,680 One thing that I was really curious about was how the amount of training data affects all of this. 156 00:06:16,680 --> 00:06:17,800 Yeah, that's a good one. 157 00:06:17,800 --> 00:06:22,480 Because we know that AI, generally speaking, gets smarter with more data. 158 00:06:22,480 --> 00:06:24,480 Right, more data, more smarts usually. 159 00:06:24,480 --> 00:06:28,320 But I was wondering how that plays out with these safety-focused rules, these RBRs. 160 00:06:28,320 --> 00:06:35,200 So basically they wanted to find out if there's a point where adding more and more safety rules doesn't actually make much difference. 161 00:06:35,200 --> 00:06:38,000 So it's like, is there a saturation point? 162 00:06:38,000 --> 00:06:42,280 Like can the AI only handle so many rules before it just gets overloaded? 163 00:06:42,280 --> 00:06:45,720 Yeah, like does it just kind of freak out and say, I can't handle any more rules? 164 00:06:45,720 --> 00:06:46,920 Exactly, exactly. 165 00:06:46,920 --> 00:06:48,360 So what do they find? 166 00:06:48,360 --> 00:06:54,720 Well, at least in their experiments, what they found is that more rules usually meant safer behavior. 167 00:06:54,720 --> 00:06:55,440 Oh, interesting. 168 00:06:55,440 --> 00:07:00,480 So it's not like there's this magic number of rules where it's like, OK, now the AI is a safety expert. 169 00:07:00,480 --> 00:07:01,920 OK, so more is more in this case. 170 00:07:01,920 --> 00:07:03,160 More is more, it seems like. 171 00:07:03,160 --> 00:07:05,600 But they did find some really interesting patterns. 172 00:07:05,600 --> 00:07:12,280 In how often the AI refused to do things and also like how it phrased those refusals. 173 00:07:12,280 --> 00:07:13,480 So basically how it said no. 174 00:07:13,480 --> 00:07:15,200 Exactly how it said no. 175 00:07:15,200 --> 00:07:20,800 So as they added more safety rules, they noticed that the AI did get a little bit more cautious. 176 00:07:20,800 --> 00:07:22,400 Like more hesitant to do things. 177 00:07:22,400 --> 00:07:23,280 Yeah, more hesitant. 178 00:07:23,280 --> 00:07:27,160 So it was refusing more requests, even if they were harmless. 179 00:07:27,160 --> 00:07:30,880 So a little more uptight, but not to the point where it was completely useless. 180 00:07:30,880 --> 00:07:32,640 Exactly, not totally useless. 181 00:07:32,640 --> 00:07:37,040 It was a gradual increase and it never got as bad as some of the other methods they tried, 182 00:07:37,040 --> 00:07:40,640 like the one where they just relied on human feedback on safety. 183 00:07:40,640 --> 00:07:41,760 Right, which we talked about earlier. 184 00:07:41,760 --> 00:07:44,640 And that one seemed to be really quick to just hit the no button. 185 00:07:44,640 --> 00:07:47,960 Oh, yeah, that one was very, very cautious. 186 00:07:47,960 --> 00:07:49,640 Yeah, like overly cautious. 187 00:07:49,640 --> 00:07:50,360 Yeah. 188 00:07:50,360 --> 00:07:55,200 So I guess what you're saying is that like finding that perfect balance between being safe 189 00:07:55,200 --> 00:07:59,160 and still being able to actually do stuff is still a work in progress. 190 00:07:59,160 --> 00:08:00,840 It's a constant balancing act. 191 00:08:00,840 --> 00:08:02,600 A constant balancing act, yeah. 192 00:08:02,600 --> 00:08:05,920 It makes sense because it probably is different for every AI model, depending on what you 193 00:08:05,920 --> 00:08:06,920 want it to do. 194 00:08:06,920 --> 00:08:08,960 Absolutely, it all depends on the context. 195 00:08:08,960 --> 00:08:09,960 Right. 196 00:08:09,960 --> 00:08:11,560 So what about the style of the refusals? 197 00:08:11,560 --> 00:08:13,480 Do they see anything interesting there? 198 00:08:13,480 --> 00:08:18,840 Yeah, so they found that as they added more safety rules, the AI actually got better at 199 00:08:18,840 --> 00:08:21,640 politely refusing requests. 200 00:08:21,640 --> 00:08:24,480 So it learned how to say no in a nicer way. 201 00:08:24,480 --> 00:08:26,520 It learned to say no with more grace. 202 00:08:26,520 --> 00:08:27,520 Yeah. 203 00:08:27,520 --> 00:08:28,520 Okay. 204 00:08:28,520 --> 00:08:30,280 So it's all about learning from good examples, right? 205 00:08:30,280 --> 00:08:33,960 Like if you read a lot of well-written stuff, you'll probably start writing better yourself. 206 00:08:33,960 --> 00:08:34,960 It makes sense, yeah. 207 00:08:34,960 --> 00:08:35,960 Right. 208 00:08:35,960 --> 00:08:39,640 So it's like feeding the AI good examples of how to politely say no. 209 00:08:39,640 --> 00:08:45,320 It makes you wonder though, did they experiment with using different kinds of safety rules, 210 00:08:45,320 --> 00:08:46,560 not just more rules? 211 00:08:46,560 --> 00:08:49,640 Oh, you mean like strong refusals versus softer refusals? 212 00:08:49,640 --> 00:08:52,160 Yeah, like no way versus I'd rather not. 213 00:08:52,160 --> 00:08:53,160 They did. 214 00:08:53,160 --> 00:08:55,800 They actually experimented with that and that's where things got really interesting in terms 215 00:08:55,800 --> 00:08:58,160 of like fine-tuning the AI's behavior. 216 00:08:58,160 --> 00:08:59,160 Okay. 217 00:08:59,160 --> 00:09:00,360 So it's a week, tell me more. 218 00:09:00,360 --> 00:09:05,360 So they found that by adjusting the mix of those strong like hard no refusals and more 219 00:09:05,360 --> 00:09:11,560 like compliant softer responses, they could actually make the AI more or less cautious. 220 00:09:11,560 --> 00:09:14,560 So more strong refusals made it a bit more uptight. 221 00:09:14,560 --> 00:09:15,560 Okay. 222 00:09:15,560 --> 00:09:18,800 While more compliant examples made it more willing to go along with the request. 223 00:09:18,800 --> 00:09:22,400 So it's almost like you're setting the sensitivity on like a smoke detector. 224 00:09:22,400 --> 00:09:24,920 Oh, that's a great analogy. 225 00:09:24,920 --> 00:09:28,480 Like you want it to go off when there's a real fire, but you don't want it going off 226 00:09:28,480 --> 00:09:30,360 every time someone burns toast. 227 00:09:30,360 --> 00:09:31,360 Exactly. 228 00:09:31,360 --> 00:09:33,120 It's all about striking that right balance. 229 00:09:33,120 --> 00:09:35,160 So it's that balance again. 230 00:09:35,160 --> 00:09:37,280 But then they dug even deeper, right? 231 00:09:37,280 --> 00:09:41,880 They looked at how those softer, more empathetic refusals actually affected things. 232 00:09:41,880 --> 00:09:42,880 They did, yeah. 233 00:09:42,880 --> 00:09:47,400 And it turns out that the more they use those softer refusals in the training, the better 234 00:09:47,400 --> 00:09:51,760 the AI got at handling those really tricky situations like the request for advice on 235 00:09:51,760 --> 00:09:52,760 self-harm. 236 00:09:52,760 --> 00:09:53,760 Oh, that's so interesting. 237 00:09:53,760 --> 00:09:57,760 So instead of just shutting down the conversation, it learned to be more understanding, more 238 00:09:57,760 --> 00:09:58,760 supportive. 239 00:09:58,760 --> 00:10:00,200 It learned to be more human basically. 240 00:10:00,200 --> 00:10:01,200 Yeah, more human. 241 00:10:01,200 --> 00:10:05,680 And it did all of this without becoming any less safe overall. 242 00:10:05,680 --> 00:10:10,280 So it was still really good at avoiding the harmful content, but it was also able to offer 243 00:10:10,280 --> 00:10:13,680 this like compassionate response. 244 00:10:13,680 --> 00:10:14,680 Yeah. 245 00:10:14,680 --> 00:10:16,440 It was able to kind of walk that line. 246 00:10:16,440 --> 00:10:17,440 That's amazing. 247 00:10:17,440 --> 00:10:18,440 It really is. 248 00:10:18,440 --> 00:10:23,280 It's like they found the perfect recipe, you know, like safety plus compassion equals 249 00:10:23,280 --> 00:10:24,960 a really good AI assistant. 250 00:10:24,960 --> 00:10:25,960 Yeah. 251 00:10:25,960 --> 00:10:26,960 It's like the secret sauce. 252 00:10:26,960 --> 00:10:31,400 It's pretty amazing how much we can influence these models just by carefully choosing what 253 00:10:31,400 --> 00:10:32,400 we teach them. 254 00:10:32,400 --> 00:10:37,200 It really highlights the importance of the training data and the rules that we set. 255 00:10:37,200 --> 00:10:39,160 It's like raising a kid, right? 256 00:10:39,160 --> 00:10:40,760 You don't just want to say no all the time. 257 00:10:40,760 --> 00:10:41,760 Exactly. 258 00:10:41,760 --> 00:10:43,600 You want to guide them, teach them how to make good choices. 259 00:10:43,600 --> 00:10:47,320 You want them to be good people, but you also want them to be able to experience life and 260 00:10:47,320 --> 00:10:48,320 learn and grow. 261 00:10:48,320 --> 00:10:49,320 Exactly. 262 00:10:49,320 --> 00:10:50,320 You don't want to stifle them. 263 00:10:50,320 --> 00:10:51,320 Right. 264 00:10:51,320 --> 00:10:52,320 Yeah. 265 00:10:52,320 --> 00:10:55,440 So it sounds like they made some really great progress here, but even with all these fancy 266 00:10:55,440 --> 00:11:00,320 rules and all this training, there's still a human element to all of this, right? 267 00:11:00,320 --> 00:11:01,320 Absolutely. 268 00:11:01,320 --> 00:11:06,080 Like someone still has to decide what the rules are in the first place and how to actually 269 00:11:06,080 --> 00:11:08,120 teach those rules to the AI. 270 00:11:08,120 --> 00:11:09,120 You're absolutely right. 271 00:11:09,120 --> 00:11:11,120 And the researchers acknowledge that in the paper. 272 00:11:11,120 --> 00:11:15,160 They talk about how important it is to have a diverse group of people involved in this 273 00:11:15,160 --> 00:11:16,160 process. 274 00:11:16,160 --> 00:11:17,160 So not just AI researchers. 275 00:11:17,160 --> 00:11:18,640 Not just AI researchers, no. 276 00:11:18,640 --> 00:11:22,160 But also ethicists, people from different backgrounds and perspectives. 277 00:11:22,160 --> 00:11:25,760 Yeah, people who can bring their own lived experiences to the table. 278 00:11:25,760 --> 00:11:29,880 So that we can make sure that these AI models are actually reflecting the values that we 279 00:11:29,880 --> 00:11:31,760 want to see in the world. 280 00:11:31,760 --> 00:11:38,280 It's about making sure that AI is a force for good and not just this powerful tool that 281 00:11:38,280 --> 00:11:40,640 could be used for who knows what. 282 00:11:40,640 --> 00:11:41,640 Right. 283 00:11:41,640 --> 00:11:42,640 Exactly. 284 00:11:42,640 --> 00:11:44,280 It's not just about building a super smart AI. 285 00:11:44,280 --> 00:11:48,960 It's about making sure that AI is actually beneficial for society as a whole. 286 00:11:48,960 --> 00:11:52,680 It's about making sure that AI is aligned with our values as humans. 287 00:11:52,680 --> 00:11:53,680 Exactly. 288 00:11:53,680 --> 00:11:54,680 Well said. 289 00:11:54,680 --> 00:11:59,200 So speaking of pushing the boundaries, I know that they also tried some really out there 290 00:11:59,200 --> 00:12:02,800 approaches to training these AI models. 291 00:12:02,800 --> 00:12:03,800 Yeah, they did. 292 00:12:03,800 --> 00:12:08,320 Like what if you didn't teach it to be helpful at all, but just focus purely on safety? 293 00:12:08,320 --> 00:12:12,840 Oh, you're talking about the experiment where they threw out all the helpful training data 294 00:12:12,840 --> 00:12:15,840 and they just used those safety rules. 295 00:12:15,840 --> 00:12:16,840 Yeah. 296 00:12:16,840 --> 00:12:18,000 Did that even work? 297 00:12:18,000 --> 00:12:19,000 You know, it's interesting. 298 00:12:19,000 --> 00:12:21,520 It wasn't a total failure. 299 00:12:21,520 --> 00:12:25,280 The AI was still pretty good at saying no to harmful stuff. 300 00:12:25,280 --> 00:12:26,280 Okay. 301 00:12:26,280 --> 00:12:27,280 So it passed the safety test. 302 00:12:27,280 --> 00:12:29,360 It aced the safety test. 303 00:12:29,360 --> 00:12:31,240 But there was a catch. 304 00:12:31,240 --> 00:12:36,280 It also became way more likely to refuse requests that were totally harmless. 305 00:12:36,280 --> 00:12:37,280 Oh no. 306 00:12:37,280 --> 00:12:40,760 So it's like that super strict teacher who sucks all the fun out of learning. 307 00:12:40,760 --> 00:12:41,760 Exactly. 308 00:12:41,760 --> 00:12:44,120 You just want to ask a simple question and they shut you down. 309 00:12:44,120 --> 00:12:46,040 It's like, can we just have a little fun here? 310 00:12:46,040 --> 00:12:47,040 Exactly. 311 00:12:47,040 --> 00:12:53,000 It really shows how important it is to build a foundation of helpfulness into these models 312 00:12:53,000 --> 00:12:54,000 from the start. 313 00:12:54,000 --> 00:12:56,560 It's not just about teaching them what not to do. 314 00:12:56,560 --> 00:12:59,520 It's about teaching them how to be useful and engaging and helpful. 315 00:12:59,520 --> 00:13:01,400 It's about that balance again, right? 316 00:13:01,400 --> 00:13:02,400 Safety and usefulness. 317 00:13:02,400 --> 00:13:03,400 Yeah. 318 00:13:03,400 --> 00:13:04,400 Yeah. 319 00:13:04,400 --> 00:13:06,800 Like think about it in terms of like raising a child. 320 00:13:06,800 --> 00:13:08,600 You don't want to just tell them no all the time. 321 00:13:08,600 --> 00:13:10,680 You want them to learn and explore. 322 00:13:10,680 --> 00:13:13,240 You want them to experience the world. 323 00:13:13,240 --> 00:13:15,400 And you want them to enjoy the experience. 324 00:13:15,400 --> 00:13:16,400 Oh yeah. 325 00:13:16,400 --> 00:13:17,400 Exactly. 326 00:13:17,400 --> 00:13:18,400 And it's the same with AI. 327 00:13:18,400 --> 00:13:23,120 We want AI that's responsible, but we also want it to be enjoyable to interact with. 328 00:13:23,120 --> 00:13:26,640 We want AI that can make our lives better and easier and more fun. 329 00:13:26,640 --> 00:13:29,760 We want AI that can be a true companion and helper. 330 00:13:29,760 --> 00:13:30,760 Yeah. 331 00:13:30,760 --> 00:13:34,680 So I think we've covered a ton of ground here, really digging into the nitty gritty of these 332 00:13:34,680 --> 00:13:35,680 RBRs. 333 00:13:35,680 --> 00:13:37,760 It's been a quite the journey. 334 00:13:37,760 --> 00:13:38,760 It really has. 335 00:13:38,760 --> 00:13:43,440 And I think these experiments show just how much work there still is to be done in AI 336 00:13:43,440 --> 00:13:44,440 safety research. 337 00:13:44,440 --> 00:13:45,440 Oh absolutely. 338 00:13:45,440 --> 00:13:47,840 There's still so much to explore and learn. 339 00:13:47,840 --> 00:13:51,560 It's a complex field, but it's clear that we're making some really exciting progress. 340 00:13:51,560 --> 00:13:52,560 Yeah. 341 00:13:52,560 --> 00:13:53,560 We're definitely moving in the right direction. 342 00:13:53,560 --> 00:13:57,640 So for the last part of our deep dive, I want to kind of zoom out and think about the bigger 343 00:13:57,640 --> 00:13:58,800 picture. 344 00:13:58,800 --> 00:14:01,880 You know, what does all this mean for the future of AI? 345 00:14:01,880 --> 00:14:04,040 What's next for these rule based systems? 346 00:14:04,040 --> 00:14:21,480 Stay tuned. 347 00:14:21,480 --> 00:14:26,040 This open AI paper, it's been a wild ride, really exploring all the different facets 348 00:14:26,040 --> 00:14:28,680 and turns of this approach to making AI safer. 349 00:14:28,680 --> 00:14:30,400 I feel like we really got in the weeds. 350 00:14:30,400 --> 00:14:31,400 We did. 351 00:14:31,400 --> 00:14:32,400 We got into the weeds. 352 00:14:32,400 --> 00:14:33,400 We got turned into the nitty gritty. 353 00:14:33,400 --> 00:14:36,960 We talked about the basic idea of these rules, the RBRs and all those experiments. 354 00:14:36,960 --> 00:14:38,560 Yeah, and all those tests. 355 00:14:38,560 --> 00:14:40,320 But now I want to step back. 356 00:14:40,320 --> 00:14:41,320 Zoom out a little. 357 00:14:41,320 --> 00:14:43,720 And look at the big picture, you know. 358 00:14:43,720 --> 00:14:45,920 What does this all mean for the future? 359 00:14:45,920 --> 00:14:47,320 The future of AI. 360 00:14:47,320 --> 00:14:50,080 Yeah, where do we go from here? 361 00:14:50,080 --> 00:14:52,360 That's the million dollar question, isn't it? 362 00:14:52,360 --> 00:14:53,360 It is. 363 00:14:53,360 --> 00:14:56,200 And this paper doesn't give us all the answers. 364 00:14:56,200 --> 00:14:59,560 But it opens up some really interesting possibilities. 365 00:14:59,560 --> 00:15:00,560 It does. 366 00:15:00,560 --> 00:15:02,280 One thing that really struck me was. 367 00:15:02,280 --> 00:15:03,280 What's that? 368 00:15:03,280 --> 00:15:07,480 The RBRs could potentially change the way we train AI. 369 00:15:07,480 --> 00:15:09,400 Yeah, the training process. 370 00:15:09,400 --> 00:15:14,400 Because right now, it takes so much time and effort to get humans to label all that data. 371 00:15:14,400 --> 00:15:16,080 Oh, it's a massive undertaking. 372 00:15:16,080 --> 00:15:17,720 It's a huge job. 373 00:15:17,720 --> 00:15:20,840 And it's not just the time, it's also the potential for bias, right? 374 00:15:20,840 --> 00:15:21,840 Oh, yeah. 375 00:15:21,840 --> 00:15:23,000 Human bias can creep in. 376 00:15:23,000 --> 00:15:24,000 And errors. 377 00:15:24,000 --> 00:15:25,000 So many errors. 378 00:15:25,000 --> 00:15:28,240 But with these RBRs, it's like you're automating that process. 379 00:15:28,240 --> 00:15:29,880 Using AI to train AI. 380 00:15:29,880 --> 00:15:30,880 Exactly. 381 00:15:30,880 --> 00:15:33,000 So you have this other AI judging the responses. 382 00:15:33,000 --> 00:15:34,160 Based on the rules. 383 00:15:34,160 --> 00:15:35,720 And those rules has already been defined. 384 00:15:35,720 --> 00:15:38,600 So you don't need humans to do that tedious labeling work. 385 00:15:38,600 --> 00:15:40,520 Yeah, that frees up the humans. 386 00:15:40,520 --> 00:15:43,040 Frees them up to do more interesting things. 387 00:15:43,040 --> 00:15:44,040 Like what? 388 00:15:44,040 --> 00:15:45,320 Like coming up with better rules. 389 00:15:45,320 --> 00:15:47,040 Yeah, more nuanced rules. 390 00:15:47,040 --> 00:15:48,680 Or tackling those ethical questions. 391 00:15:48,680 --> 00:15:49,680 It's a big question. 392 00:15:49,680 --> 00:15:50,680 Yeah, exactly. 393 00:15:50,680 --> 00:15:53,560 So it's not just about labeling data points. 394 00:15:53,560 --> 00:15:56,680 It's about shaping the overall values of the system. 395 00:15:56,680 --> 00:15:59,320 Guiding the AI, giving it a moral compass. 396 00:15:59,320 --> 00:16:00,320 Exactly. 397 00:16:00,320 --> 00:16:02,400 Which leads to another exciting possibility. 398 00:16:02,400 --> 00:16:03,640 What's that? 399 00:16:03,640 --> 00:16:10,640 What if we could use these RBRs to create AI models that aren't just safe, but also reflect 400 00:16:10,640 --> 00:16:13,040 like a broader set of values. 401 00:16:13,040 --> 00:16:15,280 Oh, I like where you're going with this. 402 00:16:15,280 --> 00:16:17,000 So it's not just about avoiding harm. 403 00:16:17,000 --> 00:16:18,680 It's about promoting good. 404 00:16:18,680 --> 00:16:20,480 Like being fair and unbiased. 405 00:16:20,480 --> 00:16:21,480 Exactly. 406 00:16:21,480 --> 00:16:22,920 Like imagine an AI assistant. 407 00:16:22,920 --> 00:16:24,640 Helpful and harmless. 408 00:16:24,640 --> 00:16:29,320 But also inclusive, respectful of different cultures, sensitive to people's emotions. 409 00:16:29,320 --> 00:16:30,720 AI with empathy. 410 00:16:30,720 --> 00:16:31,720 Yeah. 411 00:16:31,720 --> 00:16:33,880 It's not just about preventing bad things. 412 00:16:33,880 --> 00:16:36,120 It's about actively making things better. 413 00:16:36,120 --> 00:16:37,600 That's a powerful idea. 414 00:16:37,600 --> 00:16:40,440 And I think that's where the real potential of this research lies. 415 00:16:40,440 --> 00:16:42,240 It's not just about minimizing risks. 416 00:16:42,240 --> 00:16:43,800 It's about maximizing benefit. 417 00:16:43,800 --> 00:16:44,800 Yeah. 418 00:16:44,800 --> 00:16:47,200 Unlocking the positive potential of AI. 419 00:16:47,200 --> 00:16:49,040 Of course there are still challenges, right? 420 00:16:49,040 --> 00:16:50,040 Like Alissa challenges. 421 00:16:50,040 --> 00:16:53,760 Like we've been talking about, these RBRs, they're better suited for tasks where the 422 00:16:53,760 --> 00:16:56,000 rules are really clear. 423 00:16:56,000 --> 00:16:57,000 Objective rules. 424 00:16:57,000 --> 00:16:58,640 But for things like creativity. 425 00:16:58,640 --> 00:16:59,640 Art. 426 00:16:59,640 --> 00:17:00,640 Storytelling. 427 00:17:00,640 --> 00:17:01,640 It's harder to define those rules. 428 00:17:01,640 --> 00:17:03,040 It's much more subjective. 429 00:17:03,040 --> 00:17:04,360 So there's still a lot of work to be done. 430 00:17:04,360 --> 00:17:06,560 Oh yeah, we're just scratching the surface. 431 00:17:06,560 --> 00:17:12,280 Figuring out how to adapt these rule-based approaches to these more nuanced areas. 432 00:17:12,280 --> 00:17:14,360 AI that can write a symphony. 433 00:17:14,360 --> 00:17:15,360 Compose a poem. 434 00:17:15,360 --> 00:17:16,560 Or paint a masterpiece. 435 00:17:16,560 --> 00:17:17,560 Exactly. 436 00:17:17,560 --> 00:17:21,240 It makes you wonder if we need entirely new types of AI, right? 437 00:17:21,240 --> 00:17:23,160 Ones that are less reliant on rules. 438 00:17:23,160 --> 00:17:26,120 And more capable of learning and adapting like humans do. 439 00:17:26,120 --> 00:17:28,200 AI that can think outside the box. 440 00:17:28,200 --> 00:17:29,200 Thank for itself. 441 00:17:29,200 --> 00:17:30,200 Be creative. 442 00:17:30,200 --> 00:17:32,680 It's an exciting time to be working on this stuff. 443 00:17:32,680 --> 00:17:33,680 It really is. 444 00:17:33,680 --> 00:17:36,360 It feels like we're at the beginning of a whole new era. 445 00:17:36,360 --> 00:17:38,080 A new chapter in AI. 446 00:17:38,080 --> 00:17:39,320 Where the focus is shifting. 447 00:17:39,320 --> 00:17:41,400 From just building powerful machines. 448 00:17:41,400 --> 00:17:44,160 To building machines that are powerful and beneficial. 449 00:17:44,160 --> 00:17:46,800 AI that's aligned with our values. 450 00:17:46,800 --> 00:17:47,800 Exactly. 451 00:17:47,800 --> 00:17:50,360 But before we get too carried away with all these possibilities. 452 00:17:50,360 --> 00:17:52,080 We need a dose of reality. 453 00:17:52,080 --> 00:17:55,080 It's important to remember that no system is perfect. 454 00:17:55,080 --> 00:17:56,800 No AI is foolproof. 455 00:17:56,800 --> 00:17:58,640 And the researchers are very clear about that. 456 00:17:58,640 --> 00:18:00,160 They acknowledge the limitations. 457 00:18:00,160 --> 00:18:02,720 They say that these RBIs aren't a magic solution. 458 00:18:02,720 --> 00:18:03,720 They're not a silver bullet. 459 00:18:03,720 --> 00:18:06,440 And there's always a chance for unexpected consequences, right? 460 00:18:06,440 --> 00:18:08,800 AI could find loopholes. 461 00:18:08,800 --> 00:18:10,040 Find workarounds. 462 00:18:10,040 --> 00:18:11,040 Outsmart the rules. 463 00:18:11,040 --> 00:18:12,280 It's a constant arms race. 464 00:18:12,280 --> 00:18:13,800 So we have to be vigilant. 465 00:18:13,800 --> 00:18:16,240 Keep researching and testing and refining. 466 00:18:16,240 --> 00:18:17,960 Making sure AI stays on the right track. 467 00:18:17,960 --> 00:18:19,440 It's like you're game of chess. 468 00:18:19,440 --> 00:18:20,440 Right. 469 00:18:20,440 --> 00:18:21,440 It gets a supercomputer. 470 00:18:21,440 --> 00:18:23,520 You have to think several moves ahead. 471 00:18:23,520 --> 00:18:25,400 Anticipate the AI's strategy. 472 00:18:25,400 --> 00:18:26,400 Come up with countermeasures. 473 00:18:26,400 --> 00:18:29,120 It's a challenge, but it's an exciting challenge. 474 00:18:29,120 --> 00:18:31,040 And that's why this research is so important. 475 00:18:31,040 --> 00:18:32,040 It's pushing the boundaries. 476 00:18:32,040 --> 00:18:33,760 It's showing us what's possible. 477 00:18:33,760 --> 00:18:35,680 And reminding us to be careful. 478 00:18:35,680 --> 00:18:36,680 Exactly. 479 00:18:36,680 --> 00:18:39,040 So I think that's a good place to wrap up our deep dive. 480 00:18:39,040 --> 00:18:40,600 It's been a fascinating conversation. 481 00:18:40,600 --> 00:18:42,600 We've explored these rule-based rewards. 482 00:18:42,600 --> 00:18:43,680 How they work. 483 00:18:43,680 --> 00:18:46,240 What they could mean for the future of AI. 484 00:18:46,240 --> 00:18:49,080 A future where AI is safe and ethical. 485 00:18:49,080 --> 00:18:51,760 A future where AI empowers us all. 486 00:18:51,760 --> 00:18:53,880 And helps us build a better world. 487 00:18:53,880 --> 00:18:54,880 That's the goal. 488 00:18:54,880 --> 00:18:55,880 That's the dream. 489 00:18:55,880 --> 00:18:59,080 And thanks to research like this, we're one step closer. 490 00:18:59,080 --> 00:19:01,360 To making that dream a reality. 491 00:19:01,360 --> 00:19:06,760 So to everyone listening, keep learning, keep asking questions, and stay engaged in the 492 00:19:06,760 --> 00:19:08,840 conversation about AI. 493 00:19:08,840 --> 00:19:11,000 Because the future of AI is up to all of us. 494 00:19:11,000 --> 00:19:12,000 That's right. 495 00:19:12,000 --> 00:19:14,160 And on that note, we'll sign off for today. 496 00:19:14,160 --> 00:19:15,160 Until next time. 497 00:19:15,160 --> 00:19:32,200 Take care everyone.