Perplexity Pages

My experiments with Perplexity continue. This alternate search app takes a different approach to getting answers from the Internet. Rather than giving you a list of links to read, it reads the Internet and tries to give you an answer with footnotes going back to the links it reads. I think it’s a good idea, and Perplexity was early to this game. Google is now following suit to less effect, but I’m sure they’ll continue to work on it.

I recently got an email from Perplexity about a new feature called Perplexity Pages, where you can give it a prompt, and it will build a web page about a subject of interest to you. Just as an experiment, I had it create a page on woodworking hand planes. I fed it a few headings, and then it generated this page. The page uses the Perplexity method of giving you information with footnotes to the websites it’s reading. I fed it a few additional topics, and it generated more content. Then, I pressed “publish” with no further edits. The whole experiment took me five minutes to create.

The speed at which these web pages can be created is both impressive and, in a way, unsettling. If we can generate web pages this quickly, it’s only a matter of time before we face significant challenges in distinguishing reliable information from the vast sea of content on the Internet. In any case, I invite you to explore my five-minute hand plane website.

On Apple Getting AI Help

Bloomberg reports that Apple is in talks with Google (and possibly OpenAI) about a deal to run iPhone AI features through these third-party providers. It’s all sketchy at this point but it doesn’t seem out of the realm of possibility.

Capacity is one of Apple’s big problems in terms of AI. There are just so many iPhones out in the wild that if you add an AI feature requiring any cloud processing, they would have to have massive server capacity to keep up with it.

That’s just one more reason why the on-device model makes sense. However, if there was something they did want to do on the server or make available to their users to be done on a server, they’re going to need help. So the real question would be whether Apple makes that deal or just does AI processing on device.

Transcripts in Apple Podcasts

With the iOS 17.4 update, the Podcasts app from Apple now has the ability to create transcripts of podcasts. This is great news. For years, people have asked me to add transcripts to the Mac Power Users and my other shows, but the problem has always been that it is cost prohibitive. With the explosion of artificial intelligence over the last year or two, that is no longer the case. And not only that, it’s built-in to the app, so we don’t even need to produce it ourselves.

iPad in landscape mode showing the Podcasts app from Apple. The episode shown is from the Mac Power Users podcast, entitled “I Got to Be the Hero.” You can see the artwork and the play controls on the left, and the new live transcription feature on the right, with some text highlighted at the top.

A couple nice features is that the transcript is searchable and tapping on an area of the transcript jumps the audio to that point.

This is a really nice update to Podcasts. Is it going to be enough to pull me away from Overcast? Probably not. But I’m at least going to take a serious look.

Apple Licensing Data for its AI Training

The New York Times reports Apple is in negotiations to license published materials for training their generative AI model. This shouldn’t be a surprise. A few years ago, when image processing was the big thing, everyone thought Apple would fall behind because they weren’t collecting all our images for data processing. Then I saw Craig Federighi explain how Apple could get pictures of mountains and that they didn’t need mine.

This is similar to how Machine Learning requires a data set to train. Again, Apple is looking to buy data as opposed to setting its AI loose on the Internet. I really wish I had a better idea about what Apple is thinking to do with AI.

A Different Take on Apple and AI

William Gallagher is a pretty clever guy, and I enjoyed his take on Apple and AI over at AppleInsider. Based on Apple’s latest paper, they seem (unsurprisingly) interested in looking for ways to run Large Language Models (LLMs) on memory-constrained local devices. In other words, AI without the cloud. We saw this a few years ago with image processing. Apple wants to have the tools while preserving user privacy. Just from speaking to Labs members in privacy-conscious businesses, I expect this will be very popular if it works.

Is AI Apple’s Siri Moonshot?

The Information has an article by Wayne Ma reporting Apple is spending “millions of dollars a day” on Artificial Intelligence initiatives. The article is pay-walled, but The Verge summarizes it nicely.

Apple has multiple teams working on different AI initiatives throughout the company, including Large Language Models (LLMs), image generation, and multi-modal AI, which can recognize and produce “images or video as well as text”.

The Information article reports Apple’s Ajax GPT was trained on more than 200 billion parameters and is more potent than GPT 3.5.

I have a few points on this.

First, this should be no surprise.

I’m sure folks will start writing about how Apple is now desperately playing catch-up. However, I’ve seen no evidence that Apple got caught with its pants down on AI. They’ve been working on Artificial Intelligence for years. Apple’s head of AI, John Giannandrea, came from Google, and he’s been with Apple for years. You’d think that people would know by now that just because Apple doesn’t talk about things doesn’t mean they are not working on things.

Second, this should dovetail into Siri and Apple Automation.

If I were driving at Apple, I’d make the Siri, Shortcuts and AI teams all share the same workspace in Apple Park. Thus far, AI has been smoke and mirrors for most people. If Apple could implement it in a way that directly impacts our lives, people will notice.

Shortcuts with its Actions give them an easy way to pull this off. Example: You leave 20 minutes late for work. When you connect to CarPlay, Siri asks, “I see you are running late for work. Do you want me to text Tom?” That seems doable with an AI and Shortcuts. The trick would be for it to self-generate. It shouldn’t require me to already have a “I’m running late” shortcut. It should make it dynamically as needed. As reported by 9to5Mac, Apple wants to incorporate language models to generate automated tasks.

Similarly, this technology could result in a massive improvement to Siri if done right. Back in reality, however, Siri still fumbles simple requests routinely. There hasn’t been the kind of improvement that users (myself included) want. Could it be that all this behind-the-scenes AI research is Apple’s ultimate answer on improving Siri? I sure hope so.

Specific vs. General Artificial Intelligence

The most recent episode of the Ezra Klein podcast includes an interview with Google’s head of DeepMind, Demis Hassabis, whose AlphaFold project was able to use artificial intelligence to predict the shape of proteins essential for addressing numerous genetic diseases, drug development, and vaccines.

Before the AlphaFold project, human scientists, after decades of work, had solved around 150,000 proteins. Once AlphaFold got rolling, it solved 200 million protein shapes, nearly all proteins known, in about a year.

I enjoyed the interview because it focused on Artificial Intelligence to solve specific problems (like protein folds) instead of one all-knowing AI that can do anything. At some point in the future, a more generic AI will be useful, but for now, these smaller specific AI projects seem the best path. They can help us solve complex problems while at the same time being constrained to just those problems while we humans figure out the big-picture implications of artificial intelligence.