EP 379: AI News That Matters - October 14th, 2024

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You may think we had a quiet week in AI because we didn't get a new large language model update, but that is not the case because reportedly OpenAI may not be profitable until 2029. We got a lot of new AI powered innovation from Tesla and Microsoft is going all in on AI and health. So, yeah, even though we didn't, I think for the first time in seemingly months, get huge updates from one of the big large language model makers, AI, AI news did not stop. And neither do we. What's going on, y'? All? My name is Jordan Wilson and I'm the host of Everyday AI. Welcome. Every single Monday, we bring you the AI news that matters. So you don't have to spend hours every single day trying to keep up with what's happening in the world of AI and how it might impact your company or career. You can just do that with us every single Monday on the AI News that matters. So if you're new here, welcome to Everyday AI. This is a daily live stream, podcast and free daily newsletter helping us all do just that. Keep up. Not just keep up actually, but how we can use all of this information to get ahead. So if you are brand new here, thank you for joining us. Make sure if you haven't already, go to your everyday AI dot com. That website should be your best friend, y', all, because on our website it is literally the largest free, unbiased source of everything generative AI on the web. You can go back, we have nearly now 400 episodes. Whatever. You know, if you care about marketing, sales, non profits, whatever it is, we have categories on our website. You can go learn from the leading experts in the world. So if you haven't already, please make sure to go check that out. All right, enough chit chat, y'. All. Good morning to our live stream audience joining us. So everyone, Jay and Marie and Kurt, Mar, Michael, Kathleen, Brian, everyone else, sabbatical. Thank you for joining us. Let's just get straight into it, y'. All. A lot going on in the world of AI. Let's get caught up. So OpenAI is reportedly facing $44 billion in losses before profitability in 2029. So that is according to some recent reports from, from the information that says OpenAI is projected to incur $44 billion in losses before reaching profitability in 2029. So according to reports, OpenAI is attributing that to substantial expenditures on training, AI models, employee salaries and data acquisition. So the report estimates that OpenAI could generate around $100 billion in revenue growth, but its current spending is approximately $7 billion on model training and $1.5 billion on staffing annually. Also, Microsoft, a key investor with a $13 billion stake in OpenAI, is expected to take a 20% cut of the company's revenues, which may obviously impact OpenAI's profitability. So according to the report, OpenAI is currently generating about $2 billion from ChatGPT and $1 billion from access fees for large language models, totaling an annual revenue somewhere between three and a half to four and a four and a half billion dollars. But market analysts suggest that OpenAI will need additional funding to subs to sustain its operations following that recent 6.6 billion dollar fundraising round we talked about two weeks ago that pegged a valuation of 157 billion dollars. So there's been a lot of concerns related to the long term viability of AI startups as investor interest may be kind of peaking, potentially leading to takeovers or increased scrutiny of financial practices. Seems like, if I'm being honest, a lot, like I don't think a lot of people, aside from those that work at, you know, private equity, venture capital firms, have really worried about the long, long term sustainability of companies like OpenAI and Anthropic. Because let's be honest, when any of those companies want to raise money, they can very easily raise billions of dollars on command. So, you know, people are like, oh, you know, this means Open AI is gonna shut down, they're facing financial strain. No, OpenAI, that's not what it means. So let's just call that exactly what it is. This is, you know, typical. This is typical going from a, a startup to profitability. So, you know, don't freak out over these recent reports that says OpenAI may not be profitable until, you know, 2029. That doesn't mean you shouldn't be using their technology. You absolutely should be using their technology or Anthropic Claude or Google, Gemini or whatever is best for your business, regardless of if these large language models are making money themselves. Because right now, we, we've talked about this all along. The biggest race for these companies is for you. It is for us, right? That is what they ultimately care about. They care about attracting customers, retaining customers and, you know, profit. They'll figure that part out. But right now it's a, it's a great time to be a consumer, a business owner, you know, to Take advantage of all that these large language models have to offer. All right, let's keep it going, y'. All. Here's a big one. So the Nobel Prize in Physics has been awarded to two AI researchers for their groundbreaking work in artificial intelligence. So the recent awarding of the Nobel Prize in Physics has gone to two prominent research marking a significant milestone in the field of artificial intelligence. So, John J. Hopfield, who's 91, and Jeffrey Hinton, who's 76, received the Nobel Prize for their pioneering research that laid the groundwork for modern artificial intelligence. So the majority of their work goes back to the 80s and before, where the two have been instrumental in developing machine learning, a process where computers learn from vast amounts of data to perform tasks such as, I mean, anything AI can do. Diagnosing disease, personalizing entertainment recommendations, doing your homework, whatever AI does. So, Hopfield is a professor at Princeton University and invented the Hop field network in 1982, which was a neural network capable of mimicking certain brain functions and recalling information from partial data. Hinton is often called the godfather of AI, based in Toronto, utilized Hopfield's inventions to create networks that classify and recognize patterns in large data sets, which can be applied in various fields, including image recognition. So most of y' all have probably heard of Geoffrey Hinton. It was very famous when he departed from Google last May, in May 2023, and was driven by his desire to openly discuss the potential risks associated with AI technology, emphasizing concerns over its misuse by malicious actors. And that has definitely been going on. And we have another news story today on that exact same topic. I don't know about y' all today. Today's one of those Mondays. I need my coffee a little stronger. A little sleepy today. I don't know about you guys. Yeah, and FYI, we are an unedited, unscripted podcast. We're just bringing it to you real. All right, our next piece of AI news. Could Google be in big trouble with the Department of Justice? Maybe so. The U.S. department of justice has some recent proposals to address Google's dominance in online search, and that could significantly, significantly impact the tech giant's profitability and also its advancements in artificial intelligence. So the U.S. department of justice, the DOJ, is considering remedies that may force Google to divest key parts of its business, such as the Chrome browser in the Android operating system, which are perceived by feds to support its monopoly in online search. So other potential actions include restricting Google from collecting sensitive user data, requiring transparency in search results, and allowing websites to opt out of their content being used for AI training. That is the big one, y'. All. So following the Department of Justice announcement, Alphabet stock, Alphabet, the parent company of Google, of course dropped 1.5%, indicating at least a little investor concern over implications of these remedies and on Google's long term AI plans. So analysts are also warning about this, that these changes could obviously weaken Google's revenue and also provide more opportunities for competitors like DuckDuck, Duck Go In Microsoft, Bing and I guess Yahoo. Does anyone use Yahoo Search anymore? So especially as Google's US search ad market share is projected to dip below 50% by 2025. So yeah, that's not their search share, which is still in the 90s, but their search ad market share is expected to dip below 50%, which is kind of wild. So yeah, I think there's a lot of reasons why this is a, this is actually pretty big news. I would say the biggest is even though Google has been, I think personally unable to capitalize on the amount of data that it has. And let's be honest, just the head start, right? The, I mean, I mean the transformer infrastructure was developed or created by Google researchers, yet Google has been fairly far behind kind of in the large language model race, at least for front end products. I think Google and their Google Gemini has a great back end for developers. But if you're using the front end of, you know, Google Gemini, I think it's very far behind, even Anthropic and you know, the much smaller startups Anthropic and OpenAI. Even though Google has better, more direct, more up to date access to all training data via its googlebot. Right. So if your company has a website, obviously it wants Google and has wanted Google to scrape it and to access that information for many years. And in now doing so you also by default, you know, when you're saying hey Google, come to my website. You're also now by default saying hey Google, go ahead. You can also scrape all that content and use it for your Gemini models and all your other AI. So I think that part is particularly interesting. I'm not even so much focused on the DOJ trying to have or force Google to divest its Google Chrome browser and its Android operating system as much as I am on that AI training data piece. Right? Because that is what I think has been Google's biggest, biggest advantage that I think they haven't really taken advantage of. Because for whatever reason it seems like Google Gemini has this almost huge issue to bring up to date relevant information to its Gemini product via that kind of Google integration, which I know is kind of wild, right? Google knows everything up to the second, but Gemini, its front end chatbot kind of struggles bringing in recent and relevant information or, I don't know, I could, I could be alone in that live stream audience. What do you think? Is anyone out there using and loving Gemini on the front end? I haven't met too many people. Love, love the new Notebook lm, don't get me wrong, but not, not a huge fan of what they have going on on the front end for Gemini Chat. All right, our next piece of AI news. Speaking of things that I'm not personally the biggest fan of, Tesla's Cyber Cab unve failed to impress investors as their shares dropped immediately 9% and are now down more than 11% since the announcement. So Tesla's recent reveal of its AI powered cyber cab robotaxi concept has left investors very underwhelmed, resulting, like I said, in an almost immediate 9% drop in shareholder value and 11% since the announcement. So if you didn't miss this, this was late last week, Tesla had an event branded as we Robot. Yeah, you don't want to get Will Smith mad, you know, don't, don't, don't get too close to Will Smith's territory there. So the new Cyber Cab robo taxi concept, it's a concept. It showcased a two seater self driving vehicle that lacks conditional control, lacks traditional controls such as a steering wheel and pedals, and instead essentially the AI self driving does, you know, takes care of that for you. So CEO Elon Musk announced plans to introduce the Cyber cab by 2027 with an anticipated price tag under $30,000, but provided no specifics on manufacturing locations or really specifics on anything. So before everyone out there collectively loses their marbles on that price tag and this great innovation, it's also worth noting Tesla's recent history with new vehicle releases. So as an example, Elon famously or maybe infamously announced the Cyber truck in 2019 with a $39,000 price tag and a 2021 delivery date. We all know that didn't happen, right? So the Tesla truck actually shipped out at the very end of 2023. And now today the price tag is 95,000. So right after it came out, it actually, oh, it's actually about six figures, not $39,000. So everyone looking at this, oh, Cyber Cab 2027, you know, AI cars with no steering, wheels and pedals are going to be driving us around in three years. I would not hold my breath I would honestly say it's either going to cost about three to five times that or, and, or it's not going to be coming this decade. So yeah, I wouldn't exactly start saving your pennies for a Cyber for a thirty thousand dollar cyber cab by 2027, FYI. So a little bit more details about it. The details were impressive. If it ever, if it ever comes to fruition is a completely other story. So Musk stated that Tesla aims to launch which I mean we've been hearing about this one for years. It's unsupervised full self driving FSD capabilities in Texas and California next year for existing Model 3 and Model Y vehicles. Although the current FSD that's Full Self Driving system still requires, yeah, human oversight. So kind of many different announcements. So following the event, like I talked about, Tesla's shares marked a 12 decline year to date and 17 decline over the past year. So Tesla's not doing very good in terms of investor sentiment. Also, Elon Musk announced another new kind of AI driving vehicle with no steering wheel, no pedals called the Robovon. Yeah that's Robovan, but the Robovon, I guess it's cooler if you pronounce it Robovan. So the Robovan is designed for transporting larger, larger groups of goods or people, but that also seemingly failed to generate any real excitement, at least when it comes to analysts. And Tesla has been getting crushed recently in the past couple of days since this announcement. Also the event underscores the challenges Tesla is facing in bringing self driving cars to market, especially as competitors like Waymo already have this technology and they've already launched successful robo taxi services in certain states. You know what, I was actually in Austin, Austin, Texas last week at a keynote speech I was doing and I was super bummed that I couldn't catch a Waymo kind of taxi from the airport to my hotel. But yeah, maybe, maybe next time anyone out there let me know. Have you guys, you know, taken these Waymos or any, you know, self driving AI cars? I'm pretty interested in this. And let me know if you want to hear more about this on Everyday AI. We don't really go into self driving cars and you know, AI powered cars too much because I think it kind of starts to blur the line. Right. And is that really, you know, impactful for your business and your career? Maybe not. Aside from the fact that okay, if we do get fully automated, fully self driving cars, well you can go do business in there and you don't have to drive. So maybe you normally have a, you know, 45 minute commute, but if your car doesn't have a steering wheel or gas pedals and the law in your state allows it, okay, yeah, that turns into 45 more minutes, you know, of working, I guess. But, you know, we don't really cover this too much. But, you know, I think when Tesla comes out with a pretty big announcement like this, it's worth paying attention to. So sabbatical here says, love to see the Waymo in Atlanta. Yeah, yeah. And Cecilia saying there are already operational autonomous cabs in the Bay Area. Yeah, absolutely. All of the innovation always strikes there first. All right, our next piece of AI news, y'. All, let's keep it going. Taking a shift here and going into health. So Microsoft has unveiled a new set of AI tools at reducing burnout in healthcare workers. I think this one's pretty important, y'. All. So Microsoft has announced a suite of new health care data and artificial intelligence tools designed to alleviate and administrate. Sorry, alleviate the administrative burden on clinicians, a significant contributor to burnout in the industry. So right now, reports say that nurses spend up to 41% of their time on documentation. That's wild. So these new Microsoft AI tools could obviously be a game changer for the healthcare industry. All right, let's talk a little bit more about what these new AI tools are. Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Genai. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website will help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on Gen AI. So they include a collection of medical imaging models, a healthcare agent service, and an automated documentation solution specifically for nurses. So this comes in a set of multimodal AI models that can analyze various data types, such as medical images and clinical records, allowing healthcare organizations to develop more tailored applications for their needs. So right now, at least, Microsoft has partnered with Providence Health and Services to create a whole slide model for pathology enhancing cancer mutation prediction and subtyping. So this model is expected, like I said, to be a game changer for health systems looking to improve diagnostic capabilities. Now, let's talk the Healthcare Agent. So the Healthcare Agent service enables organizations to build AI agents that assist with tasks like answering clinical questions and identifying relevant clinical trials, potentially saving doctors a significant amount of time. Microsoft's automated documentation tool for nurses aims to streamline their workflows, which differs obviously from that of clinicians. So this tool right now is in development with input from organizations like Stanford Healthcare and my hometown place here, Northwestern Medicine in Chicago. So Microsoft's previous tool, Dax Copilot, has gained some traction among physicians. And the introduction of a similar tool for nurses highlights the company's commitment to supporting healthcare staff. So while many of these solutions are still in early development or preview stages, their potential to transform healthcare workflows is significant, particularly in light of the growing demand for efficient, technology driven solutions in the sector. Yeah, so, hey, shout out to Jay. Had a great conversation with Jay a couple of weeks ago about this very thing about AI and healthcare and burnout. So he's saying since the advent of EHRs or electronic health record, time spent documenting has increased the substantially causing burnout. And for physicians and others, a lot of time spent completing notes at home after hours. That is a huge, a huge factor here, y'. All. And I think the, I think if I'm being honest, the AI kind of the intersection of AI and medical and healthcare is ripe for disruption, right? Yeah, especially that just note taking, right? You know, so many times in the doctor, even over the last couple of years, I'm just sitting there waiting for the doctor to type notes or the nurse to type notes. Right. Luckily in their professions, right, they're okay at typing, but it's like, why are we still living, why are we still living in that, in that world also even within the same healthcare system, Right. I remember my first appointment like three years ago where the doctor's like, oh, okay. Or you know, maybe it was like two and a half years ago, the doctor said, hey, we'd like to record this and our system will automatically transcribe. And I'm like, yeah, like why aren't we doing that at all times? Right? I'd rather much. I'D much rather be talking to a physician face to face than talking to the physician's shoulder or the nurse's shoulder as they, you know, peck on the keyboard. That's not their fault. Obviously, the healthcare system, they are really overburdened. You know, they're overwhelmed. You know, I think especially since the pandemic, it's, it's just resulted in this long standing burnout for our medical system here in the us Our healthcare systems, overwhelmed. So, yeah, I'm personally looking forward to this Microsoft partnership, you know, really rolling out to these other institutions as well as the big medical players starting to integrate this technology into their existing systems. So please, let's get this fixed. You know, the fact that, let me repeat this, nurses right now are spending 41 of their time on documentation and it should be a fraction of that. That is what AI does best, right? To accurately transcribe words, to be able to put that in a computer. So, hey, healthcare system medical innovators out there, let's get on board. Whether it's with, you know, Microsoft, Google has some great options for AI in the healthcare industry, but it's about time that we unburden the healthcare industry by leveraging artificial intelligence. Yeah, we have to figure out privacy concerns. I, yes, I get that. Private health information, pii, all that. But y', all, let's tackle that because this is, is a no brainer to help not just relieve the broken health care system for doctors, for nurses, all of these people who are just overworked, but also for us, right, us humans that want to get into a doctor and we have to wait three months, six months longer because the healthcare system is so drowned out. All right, let's keep this thing going, y'. All. Couple more AI stories here. I'd say two, two big ones. We always save the, we always save the best for last here on the AI news. That matters, y'. All. All right, so OpenAI has reported a rise in AI generated fake content targeting US elections. So OpenAI has revealed that its AI models have been increasingly exploited to create fake content. Fake content aimed at influencing elections, raising concerns about the misuse of technology in the political landscape. So in 2023, OpenAI announced that they neutralized over 20 sophisticated attempts where its AI tools were used to generate misleading articles and social media comments related to elections. Notably, A set of ChatGPT accounts was identified in August that produced content on US elections, highlighting the potential for AI to spread misinformation. The company also banned several accounts from Rwanda in July that were involved in Generating election related comments for social media platforms. And y', all, this isn't like one or two, this is in the thousands. This is just bulk election misinformation. OpenAI found that many entities and state actors were using their tools for reasons that the company doesn't want to. So despite these attempts, OpenAI noted that none of the content generated achieved significant viral engagement or maintained a sustainable audience. The U.S. department of Homeland Security has expressed growing concerns about foreign interference in the upcoming November 5 elections. With countries like Russia, Iran and China potentially using AI to spread divisive information. Y', all, we are in that home stretch, right, for election season. The election coming up here in the US in about four weeks and now is the time. Especially if you are listening to this podcast, if you're watching this live stream, stay vigilant, right? You, you have to always double check anything that you read on social media. You have to double check what you read online. Because right now, and especially kind of in that last hour, right, this is when all of the kind of AI powered information is going to hit its all time high. This, you know, because here's why, right? And there's, you know, both sides are using this, right? It's, it's, it's always kind of annoying to me when I, you know, read the AI news and I'm like, oh, this political party, you know, used AI to do this. And everyone's like, oh, you're biased. And I'm like, no, this is just AI news, right? There's certain candidates and certain politicians right now that are using AI to spread a little bit more misinformation and disinformation. So you really need to stay vigilant about what you are reading. Is it true or not, what you are sharing, right? All of these AI images now that look actually fairly real and then these images are being used to create videos that are very real. So as the US election comes up in a couple of weeks, a reminder to everyone, please always stay vigilant. Get your news from trusted sources. You know, don't pay too much attention to what is spreading on Facebook and Twitter and YouTube because there is a good chance in the coming weeks when it comes to politics, there's a good chance a lot of it could be fake, right? We've kind of known that this last month, right before the election, it's going to be an onslaught because by the time the, that the general public is probably going to realize something is AI generated, it could be too late. So keep that in mind. All right, Our last piece Of AI news here, an interesting one. Apple researchers are challenging the logic of leading AI models, including OpenAI's latest 01. So a new study from Apple researchers raises the significance of questions about the logical capabilities of today's large language models, including OpenAI's latest reasoning model 01. So the research team at Apple developed a new evaluation tool called GSM Symbolic, which builds on the GSM 8K mathematical reasoning data set to test AI models more rigorously. So, yeah, if you're a dork like me and you follow all of the benchmarks for large language model, you'll probably recognize that GSM, right? So the GSM 8K is a fairly popular kind of benchmark to see how smart these models are. So that is essentially grade school math 8k with that 8k standing for 8000 math word problems. So, yeah, these are different essentially tests for large language models to see, okay, are they actually smart? Can they actually reason? So this new one from Apple researchers is similar to the very popular GSM 8K, but this one is called GSM Symbolic. So the study from Apple researchers tested a range of models, both open source and proprietary, including Google's or sorry, Meta's llama, Microsoft's Phi, Google's Gemma Mistral, and OpenAI's GPT4O and 01. So revealing that even top models struggle with real logic. So current accuracy scores on the GSM 8K data set are deemed unreliable, with performance fluctuations observed across different models. For instance, the Llama 8B model scored between 70 and 80%, while Phi 3 varied from 75 to 90%. So, yeah, huge gaps, right? 10 to 15% gaps in these tests are kind of saying they're unreliable. So researchers noted that as task difficulty increases, the variance in model performance also grows. Suggests that handling this variation may require exponentially more data. And despite achieving high scores on benchmarks, OpenAI's O1 model. So that is, you know, the one that we originally, you know, was codenamed Q Star, and then it was called Strawberry, and now it's rolled out as the O1 model. So despite achieving high scores on those benchmarks, the O1 model still exhibits performance fluctuations and makes basic errors, reinforcing the idea that they are more advanced pattern matchers than true reasoners. All right, so pretty. This didn't grab a lot of headlines because it's a little dorky, Right? Essentially, Apple researchers went, they did a bunch of very advanced benchmarking across all these models and said, hey, these models can't actually reason. Right? And then they suggested a new benchmark going forward that could better test this reasoning ability and in doing so, hopefully make that wide range, right? So essentially what they're saying is, number one, these models can actually reason, right? They can't actually use logic. They are just next token predictors. And one of the rationales behind that was, well, look at, look at this wide variance in these scores, right? Scoring anywhere from a 75 to a 90, right? If you are able to reason and repeat that reason over and over, you wouldn't have a 15 percentage kind of variation in these scores. All right? So this study highlights a growing debate between leading AI research institutions as OpenAI maintains that O1 represents a significant step toward logical agents, while Apple researchers argue that the evidence points to limitations in reasoning. Let me just cut it to you real. I think there's a lot of different ways that this study can be interpreted, right? Number one, I think we all have to understand this kind of shift that's going on in the large language model industry. Even though this is kind of OpenAI's language, but we are really taking a step, right? Kind of OpenAI, you know, released slash, leaked their five stages toward AGI a couple of months ago. You know, stage one being chatbot, stage two being reasoners, stage three being agents. So OpenAI CEO Sam Altman said a couple of weeks ago that, hey, essentially that OpenAI has achieved stage two, which is Reasoners, which are models that can reason models that can, you know, in the O1 model, essentially something that is more than a next token prediction, right? Because right now the best way to get the most out of large language models is a human has to continually guide it. A human has to write. I like having visuals, right? And you say, oh, you know, you say something to Chat GPT and then Chat GPT gives you a response and then you go back and forth, back and forth, right? And you, you might be chatting and you might have a, you know, 15, 20, 30 interactions with chat GPT in the same kind of chat window, trying to guide it to better outcomes, right? And if you're doing this correctly, right, you might say, oh, that's, you know, some, some good prompt engineering that's, you know, using, you know, chain of thought prompting. You know, there's all these different prompting techniques and essentially with these new models that are reasoning models, essentially the big companies are saying, hey, now the models themselves are going to be doing that work that the human would normally be doing, right? So normally, even though you start with giving Chat GPT or other large language models an end goal, it still takes a lot of finessing from humans to get them to that end. Goal some, you know, some subtle guidance and some, and some correction, right. So with these new reasoning models, the thought is, okay, these models are actually going to be kind of going, you know, doing the actions and all of those incremental steps, you know, kind of step by step. Right is a common phrase when prompting a large language model to go through this chain of thought, reasoning or thinking more like a human. So Apple researchers here, you have to think, right? Don't get me wrong, all these big companies have some of the most brilliant minds, but you have to look past the research, right? Why was this study even greenlit? Well, you have to remember reports last year was that Apple was spending millions, millions with an s, millions of dollars a day on developing its own internal large language model. Yet when they announced Apple Intelligence, which has not had a very successful rollout so far, right. Apple was not able to kind of release its own flagship model. So they have a small edge AI, right. So they have a kind of a small, large language model that runs on device or on computer. Right on, on your computer, on your smartphone and doesn't require the cloud. But for the most part, for most complex tasks, it's actually tapping in to OpenAI's GPT4O. So I always like to read through the, you know, the marketing speak here. So you have to think if there's some other implications here of Apple researchers. And again, Apple spending millions of dollars a day trying to develop its own large language model seemingly did not accomplish its goal in time for Apple intelligence, therefore having to rely on a third party. And there's been other reports that Apple is still looking at working in the future with other large language model developers, whether that's Google or Claude, you know, Claude from Anthropic. So you have to think if there's maybe some other, other things going on kind of behind the scenes or for the reason why this model or this study is coming out with Apple researchers saying, hey, these super smart mod, ah, they can't actually think, don't worry about them. So pretty, pretty interesting study. You know, if you're a dork like me, you might, you might read, you might enjoy reading it. The other thing, and we're going to close with this. Before I recap everything, there is a bigger argument here, right, for even how large language models work versus how the human brain works, right. I think there's been so much of a focus on saying, hey, yes, large language models, even though as they get more and More powerful. You know, keep in mind they're just next token predictors, which on the surface, yes, very true. But also, you know, it's, it's people are saying they, they make this argument as to why AI is not super innovative, as to why AI is not going to be impactful, so that the deflectors always say, oh, it's just next token prediction. It's only as smart as its data set. Right. Isn't that humans? Isn't that how our brains work? Similar to, similarly to a neural network? Right. That's what we are using. We are using the data set that we've been trained on. Right. Your own life experiences, what you learned in books, what you've read, what you've been taught. Right. Reinforcement learning, that's what humans go, you know, go through. Right. When I'm little and I break a lamp and my mom says, stop breaking the lamp, eventually my actions that I make from then on are probably going to reduce the likelihood that I'm going to break that lamp again. Right. So I mean, is there that much difference in the end with how a large language model processes data? It's trained by humans. Right? Reinforcement learning with human feedback and then the data is always updated. And yes, it's a next token prediction, but that's essentially what human brains do. Right. So you're going to continue to hear a lot of this, y', all through the rest of 2024 and into 2025, you know, as companies like Google, you know, reportedly we talked about this last week on the AI news that matters. They're reportedly working on their reasoning model. Right. So we're going to be going really shifting hard into talking about reasoning models, models that can actually quote unquote, think like humans and agentic system. So that's really what we're going to be focusing on a lot more here in the last couple of months of this year and going into 2025. So I think it's important to read studies like this, but to also know why they're coming out at the time that they're coming out. All right, yeah. Someone said here, big bogey face, just Apple intelligence face palm. Yeah, not super impressed with Apple intelligence so far. All right, so that is it for the AI news that matters this week. Let's do a very quick recap here. So first we talked about Apple is facing up to $44 billion in losses, according to a report from the information before, it may achieve profitability by 2029. The Nobel Prize in Physics was awarded to groundbreaking research in artificial intelligence from John J. Hopfield and Jeffrey Hinton. Next Google is in hot water as the U.S. department of justice has proposed some remedies for Google that could reshape the online giants kind of share in the search and AI landscape. Tesla unveiled a lot of AI powered driving capabilities in its new cybercab, reportedly to be released without steering wheels, without pedals by 2027 and for under $30,000. I don't think anyone actually believes that. And Tesla's stock went into the tank. Next Microsoft has unveiled some new AI tools aimed at reducing burnout in healthcare workers. Next OpenAI reported a rise in AI generated fake content targeting US elections. And then last but not least, Apple researchers released a new paper challenging the logic of leading AI models and their inability to reason, including OpenAI's latest O1 model. All right y', all, that was a lot. I hope this was helpful. If it was, please share this show with someone. Yeah, I know it might be your little secret at your company because now if you listen all the time, if you read our newsletter, you're probably one of the smartest people in AI at your company. So everyone's looking at you for the answers, right? Everyone's looking at Michael for the answers and looking at Marie for the answers. And Dr. Scott, right? This might be your secret, but please, we'd appreciate it if you'd share this with others. If you're listening on the podcast Spotify Apple, thank you so much for tuning in. Please make sure to follow the show, leave us a rating and a review as well. And please go to your everydayai.com Sign up for for the free daily newsletter and join us back tomorrow and every day for more Everyday AI. Thanks y'. All.

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