Give Siri Some Space, Please (AVALON Podcast Episode)
In the 24th episode of the AVALON podcast, Neil discussed the following topics/subjects:
What's on the Agenda (the bulk of the discussion modeled after Apple’s Monday morning meetings)
Revisiting more personal Siri (what happened, where things stand, what's next).
Bloomberg's report regarding Apple white labeling Gemini for Siri
Private Cloud Compute.
How Apple is playing the long (AI) game with its device/OS/app ecosystem.
Why I'm skeptical of ChatGPT providing utility for the mass market.
Lighting Round (two topics two minutes each)
Apple unveils iPhone Pocket.
Apple leans into pro-human Apple TV intro theme.
Give Me a Break (something ridiculous that jumped out at me)
My TI-83 Plus fiasco (Amazon headaches).
Tell Me More (something intriguing that has my attention)
Derek Thompson’s “The Monks in the Casino” article, and his theory that young people are more risk averse in the physical world yet more risk-seeking in the digital world.
Listen to the first four minutes of the episode below:
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How Apple Intelligence Was Developed, Ranking Apple Intelligence Features, AAPL’s Wacky Price Action
There is value found in spending time on the details regarding how Apple Intelligence was developed and functions. We will then go over my thoughts on the various Apple Intelligence features, ranking the features from most impressive to most concerning. We will then address AAPL price action.
Happy Thursday. It’s been a very busy week. As a reminder, we still have some WWDC news to go over early next week.
There’s a lot to cover today, so let’s jump right in.
How Apple Intelligence Was Developed
During Apple’s Platforms State of the Union presentation (available to view here), which is aimed at developers, the company went into detail regarding how Apple Intelligence was developed.
Apple Intelligence began with an on-device large language model that Apple built to achieve a certain power/size balance that allowed it run on device yet power the experiences Apple wanted to offer. Apple relied on fine-tuning to “teach” the model certain tasks such as text summarization, proofreading etc. Instead of producing different models with different expertise objectives, Apple relied on adapters which basically gave the foundation model specialization capabilities.
Apple then compressed the model (16-bit per parameter down to 4-bit) with on-device being the goal.
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