1 00:00:00,599 --> 00:00:07,160 AI is everywhere and everyone is talking 2 00:00:03,800 --> 00:00:08,679 about it but as it turns out the vast 3 00:00:07,160 --> 00:00:14,599 majority of what they're saying is misleading at best and at worst an 4 00:00:11,960 --> 00:00:21,640 outright deception while the feature on your Smartwatch or your new co-pilot PC 5 00:00:17,359 --> 00:00:23,640 is called AI it's not the AI that you're 6 00:00:21,640 --> 00:00:28,800 probably thinking of so we're going to take a reality Hammer to this hype 7 00:00:25,519 --> 00:00:31,119 machine and break down what AI can do 8 00:00:28,800 --> 00:00:37,480 what it can't do and why the tech industry is so eager to slap the AI 9 00:00:33,960 --> 00:00:39,760 label on absolutely everything and guys 10 00:00:37,480 --> 00:00:45,680 this Rabbit Hole goes way deeper than you think okay predictably it ends at 11 00:00:42,960 --> 00:00:52,199 money but the path to get there it turns out is really confusing on purpose which 12 00:00:49,520 --> 00:00:58,440 makes it very interesting like this interesting message from our sponsor MSI 13 00:00:55,320 --> 00:01:00,760 their AIS rs2 is a pre-built PC touting 14 00:00:58,440 --> 00:01:08,280 some impressive specs like an Intel Core I74 1400 KF 16 gigs of ddr5 RAM and a 15 00:01:05,000 --> 00:01:10,439 GeForce RTX 4070 super check it out in 16 00:01:08,280 --> 00:01:10,439 the 17 00:01:17,520 --> 00:01:24,200 description the classic definition of AI is probably best Illustrated with 18 00:01:21,920 --> 00:01:30,200 fictional examples it's what you see in sci-fi Creations like Commander Data 19 00:01:26,840 --> 00:01:32,680 Hell 9000 and GLaDOS these are are 20 00:01:30,200 --> 00:01:38,000 computers or machines that demonstrate a capacity for reason however naive 21 00:01:35,520 --> 00:01:41,759 twisted or alien it might seem to us meatbags now you'd be forgiven for 22 00:01:40,119 --> 00:01:46,640 thinking that that's still the definition of AI a lot of people seem to 23 00:01:44,280 --> 00:01:51,640 think that it is but in reality the meaning of words is ever shifting and we 24 00:01:49,399 --> 00:01:58,119 would now refer to these characters as having a gii or artificial general 25 00:01:55,640 --> 00:02:06,000 intelligence what you're referring to as AI then is in fact narrow AI or as I've 26 00:02:01,640 --> 00:02:08,360 taken to calling it AI Ani is not a 27 00:02:06,000 --> 00:02:12,680 general intelligence unto itself but rather another component of a fully 28 00:02:10,679 --> 00:02:16,879 functioning system made useful by specialized algorithms and data 29 00:02:14,720 --> 00:02:21,120 processing utilities forming a complete artificial intelligence system didn't 30 00:02:19,760 --> 00:02:27,160 think I could make that point in the style of Richard stallman's famous 31 00:02:23,760 --> 00:02:29,000 interjection well I could haha but I 32 00:02:27,160 --> 00:02:34,040 also didn't have to that previous paragraph was actually written by gp4 33 00:02:31,640 --> 00:02:39,120 Omni and this is exactly the sort of thing that modern AI does very well and 34 00:02:37,319 --> 00:02:44,720 that's because most of the time when we hear the term AI we're actually 35 00:02:41,400 --> 00:02:47,080 referring to machine learning a subset 36 00:02:44,720 --> 00:02:52,319 of AI involving algorithms that can analyze patterns in data they get 37 00:02:49,840 --> 00:02:57,599 trained on things like text multimedia or even just raw number outputs and 38 00:02:54,959 --> 00:03:01,680 using this training data they identify patterns through statistical probability 39 00:03:00,159 --> 00:03:06,599 they can be further trained through reinforcement learning then by rewarding 40 00:03:04,040 --> 00:03:11,680 correct outputs and punishing incorrect outputs kind of like training a hamster 41 00:03:09,319 --> 00:03:17,120 the results allow these algorithms to summarize predict or even generate 42 00:03:14,400 --> 00:03:21,879 something seemingly new and in many cases they are so impressive that a good 43 00:03:20,120 --> 00:03:28,480 machine learning system can be indistinguishable from classic AI or a 44 00:03:25,640 --> 00:03:34,480 gii well then minus if it looks like an AI and it quacks like in AI what's the 45 00:03:31,560 --> 00:03:40,560 difference well artificial narrow intelligence is limited to specialized 46 00:03:36,760 --> 00:03:42,280 tasks gp4 Omni specifically is a large 47 00:03:40,560 --> 00:03:46,080 language model which means that it is trained to understand and generate 48 00:03:44,319 --> 00:03:51,040 natural language like the words I'm speaking now it's basically an 49 00:03:48,239 --> 00:03:55,280 autocomplete on steroids what sets it apart from your phone's keyboard though 50 00:03:52,720 --> 00:03:59,360 is that it can also process information based on patterns that are learned 51 00:03:56,760 --> 00:04:03,480 during training including definitions mathem iCal formula and so on and so 52 00:04:01,799 --> 00:04:08,400 forth that makes it capable of generating unique output that wasn't 53 00:04:05,799 --> 00:04:13,480 part of its training data GPT has traditionally been incapable of image 54 00:04:11,079 --> 00:04:18,120 video or audio generation there are other types of generative models like 55 00:04:15,439 --> 00:04:22,560 Sora sunno or dolly that feature their own specific talents but most of them 56 00:04:20,199 --> 00:04:26,320 are incapable of operating outside of their specific Niche and all of them are 57 00:04:24,759 --> 00:04:30,479 limited by their training data in a similar Manner and because they're 58 00:04:28,400 --> 00:04:34,800 limited by their training data in many cases the answers that they give 59 00:04:32,360 --> 00:04:39,360 resemble their training data which if you're an artist or a photographer and 60 00:04:36,720 --> 00:04:45,080 your work gets added to a model is probably not your idea of fair use much 61 00:04:41,960 --> 00:04:46,639 less a good time worse when generative 62 00:04:45,080 --> 00:04:51,280 models are faced with a concept that they don't understand or they simply run 63 00:04:49,000 --> 00:04:56,160 out of tokens they can begin to hallucinate that is to say they just 64 00:04:53,919 --> 00:05:00,400 make things up as they go which is why sometimes you get eldrich Abominations 65 00:04:57,960 --> 00:05:04,479 like these with that said these limitations don't mean that machine 66 00:05:02,160 --> 00:05:09,680 learning AI is a dead end it's been deployed very effectively for diagnosing 67 00:05:07,240 --> 00:05:14,400 diseases and in other highly complex scenarios where the data is dense and 68 00:05:11,880 --> 00:05:19,720 the conclusions require interpretation these specialized models are extremely 69 00:05:16,680 --> 00:05:22,120 useful they're just also extremely not 70 00:05:19,720 --> 00:05:26,720 new simple neural networks have been in use for decades for things ranging from 71 00:05:24,680 --> 00:05:32,759 handwriting recognition to web traffic analysis and yes even video game 72 00:05:30,280 --> 00:05:36,880 these are not scripted sequences the AI is determining when your allies choose 73 00:05:35,080 --> 00:05:42,280 to advance and how to best help you out in combat and chatbots the main 74 00:05:39,520 --> 00:05:47,080 difference is that they run much faster on Modern Hardware if I had to distill 75 00:05:45,039 --> 00:05:51,520 down what artificial narrow intelligence really means then I would say it's like 76 00:05:49,319 --> 00:05:55,720 having a thousand monkeys at a thousand typewriters with a thousand pieces of 77 00:05:53,880 --> 00:05:59,639 reference material for what the outputs are supposed to look like with enough 78 00:05:58,000 --> 00:06:04,000 trial and error then they do arrive arrive at a point where they're likely 79 00:06:01,720 --> 00:06:09,560 to spit out a correct or at least correct enough solution then we take all 80 00:06:07,400 --> 00:06:14,800 those monkeys and we take a snapshot of the model State and we start feeding it 81 00:06:11,599 --> 00:06:17,599 inputs for both Fun and Profit what ani 82 00:06:14,800 --> 00:06:22,120 is to a brain then is kind of what a single app is to a computer it's a 83 00:06:20,080 --> 00:06:28,000 building block it's something your brain is capable of but it's just one of its 84 00:06:25,000 --> 00:06:30,080 many many functions shifting GE a bit 85 00:06:28,000 --> 00:06:34,360 then what would artificial general intelligence look like well it 86 00:06:33,080 --> 00:06:38,360 would need to be able to handle everything we've talked about so far 87 00:06:36,400 --> 00:06:43,240 just like your brain can take some past experiences and turn them into a new 88 00:06:40,080 --> 00:06:45,560 creation but again like your own brain 89 00:06:43,240 --> 00:06:50,400 it would need to be able to run many of these models concurrently and 90 00:06:47,599 --> 00:06:55,599 continuously train and iterate on them rather than relying on fixed snapshots 91 00:06:53,199 --> 00:07:00,000 only then would an AGI have the ability to truly learn and adapt to new things 92 00:06:58,400 --> 00:07:04,919 bringing it closer to that that classical definition of AI and really 93 00:07:02,520 --> 00:07:10,479 blur the lines between machine learning and machine Consciousness the problem is 94 00:07:08,560 --> 00:07:15,000 even if we had software that sophisticated we are nowhere close to 95 00:07:12,840 --> 00:07:22,599 being able to run an AGI even on a modern supercomput let alone on your AI 96 00:07:19,400 --> 00:07:24,199 smartphone but all right lonus you still 97 00:07:22,599 --> 00:07:29,360 haven't explained why any of this is even a problem I mean freerange meat is 98 00:07:26,840 --> 00:07:34,879 just marketing bollocks too so who cares well well truthfully in most cases I 99 00:07:31,960 --> 00:07:39,639 don't I mean cooler Master's AI thermal paste snafu I was never bothered by it 100 00:07:37,479 --> 00:07:45,280 because I never expected my paste to be sentient anyway but there are situations 101 00:07:43,199 --> 00:07:51,360 where this kind of marketing can have an impact on user safety and therefore does 102 00:07:47,800 --> 00:07:53,879 matter let's talk about Tesla Mr musk 103 00:07:51,360 --> 00:07:59,159 has said among other things that any vehicle from 2019 onward will be able to 104 00:07:56,280 --> 00:08:03,319 reach full autonomy and he's certainly put out some impressive demos both 105 00:08:01,280 --> 00:08:08,520 canned and even in the form of public beta software that you really can use 106 00:08:05,720 --> 00:08:13,360 and that's really cool but unfortunately it isn't much more than that you see to 107 00:08:11,000 --> 00:08:18,440 operate a vehicle safely it's not enough to be trained with images of painted 108 00:08:15,960 --> 00:08:23,400 lines and traffic cones stop signs pedestrians vehicle Telemetry data it's 109 00:08:21,159 --> 00:08:29,039 not even enough to be trained to predict the likely maneuvers of nearby vehicles 110 00:08:25,400 --> 00:08:32,399 and life forms on the road anything can 111 00:08:29,039 --> 00:08:35,839 happen and by definition by its very 112 00:08:32,399 --> 00:08:37,599 definition Ani is not capable of 113 00:08:35,839 --> 00:08:42,479 handling an edge case that it has never seen before even if it was by the way I 114 00:08:40,680 --> 00:08:49,040 have some really bad news for you Tesla owners out there Hardware 3.0 has about 115 00:08:46,120 --> 00:08:54,160 144 tops or trillion operations per second worth of processing power for 116 00:08:51,480 --> 00:08:58,680 context Windows 11 recall a feature that does little more than take screenshots 117 00:08:56,040 --> 00:09:04,600 and analyze your PC usage for search asks for 40 tops now to be clear tops is 118 00:09:02,880 --> 00:09:08,640 not a be all andall measure of performance and there is no way that 119 00:09:06,959 --> 00:09:13,000 Microsoft has optimized the code for recall nearly as much as Tesla has for 120 00:09:10,720 --> 00:09:17,320 full self-driving but this should still illustrate the point that Tesla either 121 00:09:14,880 --> 00:09:23,200 did or should have known that a vehicle with the AI capabilities of a family of 122 00:09:20,040 --> 00:09:25,519 iPhone 15 Pro users would never achieve 123 00:09:23,200 --> 00:09:29,680 that kind of realtime contextual awareness that's required for complex 124 00:09:27,839 --> 00:09:33,680 situations like operating a motor vehicle and they misrepresented its 125 00:09:31,800 --> 00:09:38,880 capabilities in order to sell more software that was never going to leave 126 00:09:35,760 --> 00:09:41,320 beta that is going to be a doozy of a 127 00:09:38,880 --> 00:09:46,360 class action and it's a common story that has led to this current mess where 128 00:09:43,959 --> 00:09:51,360 fuzzy definitions and impossible promises have turned AI into this 129 00:09:49,120 --> 00:09:56,800 meaningless buzzword like all the rest of them all of them refer to legitimate 130 00:09:54,680 --> 00:10:00,600 useful Technologies some of which have really come to fruition but their 131 00:09:58,800 --> 00:10:05,680 meanings have become diluted with overuse and it means that when computer 132 00:10:03,480 --> 00:10:08,959 cognition finally happens we're going to have to call it something completely 133 00:10:07,200 --> 00:10:15,279 different in order to differentiate it from all of the marketing wank on the 134 00:10:11,680 --> 00:10:17,959 subject of marketing calling ai ai 135 00:10:15,279 --> 00:10:23,640 wasn't an accident the people behind that marketing know what you think AI 136 00:10:21,079 --> 00:10:27,720 means they know that the promise and the Mystique behind the term is tantalizing 137 00:10:26,079 --> 00:10:32,640 and they know you'll click on an article that is interviewing an AI expert who 138 00:10:29,720 --> 00:10:37,079 discusses how dangerous or already alive it is they want you to buy into their 139 00:10:34,760 --> 00:10:43,000 hype they want you to buy into their stocks what we have now though really 140 00:10:39,800 --> 00:10:44,720 are decent summarization engines and 141 00:10:43,000 --> 00:10:49,360 lukewarm guessing machines that are tuned for working with different types 142 00:10:46,240 --> 00:10:52,000 of media they can't reason and anyone 143 00:10:49,360 --> 00:10:57,200 who's seen chat GPT get something ridiculously confidently wrong can 144 00:10:54,680 --> 00:11:01,240 attest to that and they also can't understand why they get these things 145 00:10:59,320 --> 00:11:06,320 wrong which mean they can't learn or improve on their own even if you 146 00:11:03,600 --> 00:11:10,600 explicitly tell them they also have a finite number of things that they can 147 00:11:08,000 --> 00:11:14,760 remember called tokens which limit their ability to maintain a train of thought 148 00:11:12,760 --> 00:11:19,160 for very long anyone who's tried to get chat GPT to write a novel or even a 149 00:11:17,160 --> 00:11:24,040 moderately complex python script can probably attest to that of course as 150 00:11:22,000 --> 00:11:28,320 time goes on some of these limitations will begin to lift and these a Ani 151 00:11:25,959 --> 00:11:32,920 models will start to look more and more like a GI to the lay person I mean just 152 00:11:31,200 --> 00:11:37,800 ask the guy who had Bing fall in love with him but at the end of the day it's 153 00:11:35,279 --> 00:11:42,120 still the same thing as it's always been to paraphrase a paper co-written by 154 00:11:39,560 --> 00:11:46,399 Emily bender and Alexander caller it's a hyper intelligent octopus that is 155 00:11:44,560 --> 00:11:50,600 observing and learning the patterns that are expected of communication and 156 00:11:48,560 --> 00:11:55,079 repeating them back more accurately over time so when faced with a novel 157 00:11:52,639 --> 00:11:59,480 situation like a bear attack that octopus has no hope of being able to 158 00:11:57,800 --> 00:12:04,800 guide you on how to defend yourself with the materials you have on hand it has no 159 00:12:01,680 --> 00:12:06,480 concept of bear or stick only what words 160 00:12:04,800 --> 00:12:10,959 most closely match the pattern that it has previously observed to further 161 00:12:08,880 --> 00:12:15,040 illustrate this if you haven't tried it before try to craft a prompt that coaxes 162 00:12:13,320 --> 00:12:20,040 stable diffusion to spit out something very specific it turns out it's pretty 163 00:12:18,079 --> 00:12:23,480 challenging and when it finally does it it's generally composed of a mosaic of 164 00:12:21,959 --> 00:12:28,839 shapes and patterns from its training model that best match the keywords that 165 00:12:26,360 --> 00:12:33,600 sounds overly simplified but that's because it is in essence what it's doing 166 00:12:31,880 --> 00:12:37,800 it takes the keywords from your prompt and starts compositing filling in the 167 00:12:35,519 --> 00:12:43,279 image until it hits for example a certain percentage of computer and desk 168 00:12:40,800 --> 00:12:47,639 and it does this without any context so if you asked it to show you a computer 169 00:12:45,160 --> 00:12:53,000 would it show you a desktop PC a laptop a server rack and for that matter from 170 00:12:49,720 --> 00:12:55,680 what era the answer is often simply yes 171 00:12:53,000 --> 00:13:00,360 because it lacks the context behind what computer means to you and it can't 172 00:12:58,440 --> 00:13:05,120 always even for a coherent depiction even in the same image so then Linus 173 00:13:03,279 --> 00:13:09,560 just don't worry about it and wait till AGI shows up and you don't have to 174 00:13:06,600 --> 00:13:14,279 bother with online dating anymore well not quite as generative AI models learn 175 00:13:12,600 --> 00:13:17,959 to create works that are closer and closer to our expectations and to 176 00:13:16,440 --> 00:13:21,880 finally understand how many fingers and teeth humans normally have we're going 177 00:13:20,240 --> 00:13:27,639 to start to find it more and more difficult to distinguish their output from real photographs or artwork or 178 00:13:25,680 --> 00:13:32,000 other people ushering in an era of unprecedented distrust and the bad news 179 00:13:30,639 --> 00:13:35,920 is that the folks in charge of helping us deal with the consequences of all of 180 00:13:33,760 --> 00:13:41,240 this have a lot less funding than the ones who are trying to sell it to us so 181 00:13:38,600 --> 00:13:45,320 be safe out there and safely check out our sponsor manscaped if you're looking 182 00:13:43,639 --> 00:13:50,720 for a way to quickly take care of your more delicate Parts manscaped is on the 183 00:13:48,279 --> 00:13:55,720 case with a performance package 5.0 Ultra it includes the lawnmower 5.0 184 00:13:53,399 --> 00:14:00,279 Ultra to trim the grass as well as the weed whacker 2.0 for your ears and nose 185 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