The AI Daily Brief: Artificial Intelligence News and Analysis - Just How Different is Apple's AI Strategy?

Episode Date: June 17, 2024

A reading and discussion inspired by https://www.oneusefulthing.org/p/what-apples-ai-tells-us-experimental ** Join Superintelligent at https://besuper.ai/ -- Practical, useful, hands on AI education through tutorials and step-by-step how-tos. Use code podcast for 50% off your first month! ** ABOUT THE AI BREAKDOWN The AI Breakdown helps you understand the most important news and discussions in AI.  Subscribe to The AI Breakdown newsletter: https://aidailybrief.beehiiv.com/ Subscribe to The AI Breakdown on YouTube: https://www.youtube.com/@AIDailyBrief Join the community: bit.ly/aibreakdown

Today on the AI Daily Brief, does Apple's AI strategy amount to a fundamentally different experiment than other companies are exploring? The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show notes. Hello, friends, happy weekend. And of course, it being the weekend, that means it's time for a long read. And this week, the big event was, of course, WWDC Apple, finally announcing its AI

strategy, and so it's only appropriate that we cover some analysis of that strategy here on the long read. For that, we turn to Ethan Mollock's recent blog post, What Apple's AI tells us, experimental models. Ethan is a professor at Wharton and an author of a new book about AI and a frequent appearer on the long reads here on the AI Daily Brief. I'm going to turn it over to the 11 Labs version of myself to quote unquote read this, and then we will come back for a little discussion. What Apple's AI tells us, experimental models. I wanted to give some quick thoughts on the Apple AI, sorry, Apple intelligence, release. I haven't used it myself, and we don't know everything about their approach, but I think the release highlights something important happening in AI right now.

Experimentation with four kinds of models. AI models, models of use, business models, and mental models of the future. What is worth paying attention to is how all the AI giants are trying many different approaches to see what works. I am going to broadly stereotype some of the future. I am going to broadly stereotype some of these views. No company is a monolith and all the AI organizations are doing many different things, but in broad strokes, an interesting picture is emerging. Section, AI models. As I wrote in the last post, the power of the foundation model you use is a big deal, because the largest frontier models are out of the box, better at most things than smaller models, even smaller specialized models. Remember Bloomberg GPT, which was a specially trained finance LLM drawing on all of Bloomberg's data? It made a bunch

of firms decide to train their own models to reap the benefits of their special information and data. You may not have seen that GPT4, the old pre-turbo version with a small context window, without specialized finance training or special tools, beat Bloomberg GPT on almost all finance tasks. This demonstrates a pattern. The most advanced generalist AI models often outperforms specialized models, even in the specific domains those specialized models were designed for. That means that if you want a model that can do a lot, reason over massive amounts of text help you generate ideas right in a non-robotic way, you want to use one of the three frontier models, GPT4-O, Gemini 1.5, or Claude3 Opus. But these models are expensive to train and slow and

expensive to run, which leaves room for much smaller models that aren't as good as the frontier models but can run cheaply and easily, even on a PC or phone. This isn't new. Back in December, I was able to run Mistral 7B, a model slightly less advanced than the original chat GPT, directly on my phone without an internet connection. I also ran Mixtrol, a model for the same company that slightly outperforms the original chat GPT on my gaming computer. All of the tech companies have been releasing these sorts of small, fairly powerful models, with the idea that they can handle simple questions on the hardware of your devices, and then call a larger model in the cloud when they need help. You aren't getting anywhere

near the smarts of a frontier model, but if you want to do straightforward things, make a Siri that works or make my photos more vivid, these models are often more than enough. Many of the companies betting on frontier models like Google have also released faster and cheaper models to fill this niche and are deploying them to phones as well. Apple does not have a frontier model, Google and Microsoft OpenAI have a large lead in that space, but they have created a bunch of small models that run on the AI-focused chips in Apple products, and they have built a medium-sized model that the iPhone can call in the cloud when it needs help. The model that runs on your phone is pretty close in abilities to the version of Mistral that I was using above, but much faster

and optimized to reduce errors, and the version that runs in the cloud is better than the original chat GPT, but not that much better. These smaller, weaker models give Apple a lot of control over AI use on their systems and offloads a lot of work to the phone or computer, but they still don't have a frontier model, so they're working with OpenAI to send GPT4 the questions that are too hard for Apple's models to answer. Companies are clearly still experimenting with which models or sets of models to offer. Section. Models of Use. Large language models are Swiss army knives of the mind. They can help with a wide range of intellectual tasks, though they do some badly, the toothpick in the Swiss Army knife, and some not at all.

Knowing what they are good or bad at is a process of learning by doing and acquiring expertise. That requires both expertise with the models themselves. The rule of thumb in my book is 10 hours of use to learn what the models do, and also expertise with the work you are trying to get the AI to do. Within your area of expertise, experimentation with AI is easy, since you know when it messes up. But outside of that, it can be challenging because AI is weird. The makers of frontier models do not have strong views about how their systems can be used,

and so they are not optimized for any one task. Working with advanced models is more like working with a human being, a smart one that makes mistakes and has weird moods sometimes. Frontier models are more likely to do extraordinary things, but are also more frustrating and often unnerving to use. Contrast this with Apple's narrow focus on making AI get stuff done for you. For example, I can ask Gemini 1.5 to look a bunch of PDFs of comics, read my emails to learn about my sense of humor,

and suggest the comics that might appeal to me. Pretty amazing stuff. But I can ask Siri with AI to send this photo to my friend, Sarah after making the colors pop. For many people, the second use case is actually the more natural, intuitive, and useful one. A machine that can do anything much of the time, but also sometimes does something entirely different, is harder to understand than a narrow AI that just does what you want. With the caveat, we don't know how well the Apple system works. Google is also going to be releasing smaller AI models that are local to phones. And Microsoft is taking a similar approach to Apple with a

business twist. They have implemented co-pilots in their key office apps. They do a really good job of providing easily understood it just works, mostly integration of AI into work in easy ways. But both the Apple and app-specific copilot models are constrained, which limits their upside, as well as their downside. The potential gains to AI, the productivity boosts and innovation, along with the weird risks, come from the larger, less-constrained models. And the benefits come from figuring out how to apply AI to your own use cases, even though that takes work. Frontier models thus have a very different approach to use cases than more constrained models. Take a look at this demo from OpenAI, where GPT40, rather flirtatiously, helps someone work through an interview and compare it to this demo of Apple's AI-powered Siri, helping with appointments, radically different philosophies at work.

Section business models. The best access to an advanced model cost you $20 a month. At least that is what OpenAI and Google and Anthropic and Microsoft decided. And of course, all of these companies sell API access charged by usage to businesses and individuals directly. yet increasingly some advanced AI access is free, including to co-pilot and chat GPT-4-O. Apple sounds like they will start with free service as well, but may decide to charge in the future. The truth is that everyone is exploring this space and how they make money and cover costs is still unclear. Though there is a lot of money out there, OpenAI is one of the fastest growing tech companies in history,

with revenues reaching $2 billion. To a large extent, the future of AI will be shaped by the degree to which AI companies figure out sustainable business models, so expect to see more experimentation. What every one of these companies needs to succeed, however, is trust. There are a lot of reasons why people don't trust AI companies, from their unclear use of training data to their plans for an AI-dominated future to their often opaque management. But what most people mean by trust is the question of privacy. Will AI use what I give it as training data? And that has long been answered. All of the AI companies offer options where they agree to not use your data for training, and the legal implications for breaching these agreements would be dire. But Apple goes to

many steps further, putting extra work into making sure it could never learn about your data, even if it wanted to. Only the local AI on your phone accesses personal data, and anything handed to the cloud AI is encrypted, processed anonymously, and instantly erased in ways that would be very hard for anyone to intercept. To the extent that data is given to open AI, it is also anonymous and requires explicit permission. Between the limited use cases and the privacy focus, this is a very ethical use of AI, though we still know little about Apple's training data. We will see if that is enough to get the public to trust AI more. Section Models of the Future.

There is a specter haunting all AI development, the specter of AGI, artificial general intelligence, the hypothetical machine better than humans at every intellectual tasks. This is the explicit goal of open AI and anthropic, and it is something they hope to achieve in the near term. For people who genuinely believe they are building AGI

soon, almost nothing else is important. The AI models along the way to AGI are mere stepping stones, not anything you want to build a business around because they will be replaced by better models soon. OpenAI systems may feel unpolished because the company believes that future models will significantly advance AI capabilities. As a result, they may not be investing heavily in refining systems that will likely be outdated as new models are released. I do not know if AGI is achievable, but I know that the mere idea of AGI being possible soon bends everything around it, resulting in wide differences in approach and philosophy and AI implementations.

While Apple is building narrow AI systems that can actually actually, accurately answer questions about your personal data. Tell me when my mother is landing. OpenAI wants to build autonomous agents that would complete complex tasks for you. You know those emails about the new business I want to start. Could you figure out what I should do to register it so that it is best for my taxes and do that? The first is, as Apple demonstrated, science fact, while the second is science fiction, at least for now. Every major AI company argues the technology will evolve further and has teased mysterious future additions to their systems. In contrast, what we are seeing from Apple is a clear and practical vision of how AI can help most users without a

lot of effort today. In doing so, they are hiding much of the power and quirks of LLMs from their users. Having companies take many approaches to AI is likely to lead to faster adoption in the long term. And as companies experiment, we will learn more about which sets of models are correct. All right, so back to real non-11 labs NLW now for just a quick set of wrap-up thoughts. Two things that I want to explore. The first is, is Apple building a fundamentally different type of AI system? And the second is, how divergent from, for example, Open AI really is it? As to the first question, I think it is very clear that Apple is taking a different strategy. Apple is making it clear that they believe that the vast

majority of people will have a first interaction with artificial intelligence that is not about creating some cool image with mid-jurney or discovering radical productivity gains at work, but is instead just a simpler, faster, better way to do things they're already doing in a day-to-day sort of way. It's why they used what seemed like boring, unimpressive even examples, like figuring out when your mom's flight is going to touch down. Indeed, I think in some ways people were struck at that presentation by how much the new generative AI-powered Siri is what they just might have expected from Siri in the first place. Just this simple assistant that has all the relevant information about you that you've put on your phone and can answer questions and do things. for you on that basis. So yes, in that sense, Apple is taking the most aggressively banal, day-to-day, focused, prosaic, however you want to describe it, approach to their AI strategy

than we've seen from any big tech company. As to the second question, however, whether their strategy is fundamentally and over-the-long-term divergent from, for example, open AIs, which, as Ethan puts it, wants to build autonomous agents that would complete complex tasks for you, I'm a lot less sure. In fact, I kind of think that both of these strategies are driving towards the same place, that in the future, the way that we interact with computers and with software, will not be pointing and clicking a mouse, will not be search engines,

will not be many of the modalities we're most comfortable with today. Instead, it will be us talking to some personalized assistant agent application that mediates our interactions with every other software application, etc. Sure, maybe OpenAI is starting with a more ambitious, set of agentic use cases, but I don't think the ambition for the new Siri is to stop at telling you when your mom's flight gets in. I think that Apple just believes that that's a better starting point for the vast majority of normal people. So I actually think the race is on for the very same thing, agents that can complete complex tasks. They're just coming at it a different way.

I think it is very likely that both strategies can be successful for different types of demographics, and I am excited to see this battle play out in the real world as people get to vote with their For now, though, that is going to do it for today's AI Daily Brief. Until next time, peace.