106 items tagged “apple”
2024
Apple’s Knowledge Navigator concept video (1987) (via) I learned about this video today while engaged in my irresistible bad habit of arguing about whether or not "agents" means anything useful.
It turns out CEO John Sculley's Apple in 1987 promoted a concept called Knowledge Navigator (incorporating input from Alan Kay) which imagined a future where computers hosted intelligent "agents" that could speak directly to their operators and perform tasks such as research and calendar management.
This video was produced for John Sculley's keynote at the 1987 Educom higher education conference imagining a tablet-style computer with an agent called "Phil".
It's fascinating how close we are getting to this nearly 40 year old concept with the most recent demos from AI labs like OpenAI. Their Introducing GPT-4o video feels very similar in all sorts of ways.
mlx-vlm (via) The MLX ecosystem of libraries for running machine learning models on Apple Silicon continues to expand. Prince Canuma is actively developing this library for running vision models such as Qwen-2 VL and Pixtral and LLaVA using Python running on a Mac.
I used uv to run it against this image with this shell one-liner:
uv run --with mlx-vlm \
python -m mlx_vlm.generate \
--model Qwen/Qwen2-VL-2B-Instruct \
--max-tokens 1000 \
--temp 0.0 \
--image https://static.simonwillison.net/static/2024/django-roadmap.png \
--prompt "Describe image in detail, include all text"
The --image option works equally well with a URL or a path to a local file on disk.
This first downloaded 4.1GB to my ~/.cache/huggingface/hub/models--Qwen--Qwen2-VL-2B-Instruct folder and then output this result, which starts:
The image is a horizontal timeline chart that represents the release dates of various software versions. The timeline is divided into years from 2023 to 2029, with each year represented by a vertical line. The chart includes a legend at the bottom, which distinguishes between different types of software versions. [...]
mlx-whisper
(via)
Apple's MLX framework for running GPU-accelerated machine learning models on Apple Silicon keeps growing new examples. mlx-whisper is a Python package for running OpenAI's Whisper speech-to-text model. It's really easy to use:
pip install mlx-whisper
Then in a Python console:
>>> import mlx_whisper
>>> result = mlx_whisper.transcribe(
... "/tmp/recording.mp3",
... path_or_hf_repo="mlx-community/distil-whisper-large-v3")
.gitattributes: 100%|███████████| 1.52k/1.52k [00:00<00:00, 4.46MB/s]
config.json: 100%|██████████████| 268/268 [00:00<00:00, 843kB/s]
README.md: 100%|████████████████| 332/332 [00:00<00:00, 1.95MB/s]
Fetching 4 files: 50%|████▌ | 2/4 [00:01<00:01, 1.26it/s]
weights.npz: 63%|██████████ ▎ | 944M/1.51G [02:41<02:15, 4.17MB/s]
>>> result.keys()
dict_keys(['text', 'segments', 'language'])
>>> result['language']
'en'
>>> len(result['text'])
100105
>>> print(result['text'][:3000])
This is so exciting. I have to tell you, first of all ...Here's Activity Monitor confirming that the Python process is using the GPU for the transcription:
This example downloaded a 1.5GB model from Hugging Face and stashed it in my ~/.cache/huggingface/hub/models--mlx-community--distil-whisper-large-v3 folder.
Calling .transcribe(filepath) without the path_or_hf_repo argument uses the much smaller (74.4 MB) whisper-tiny-mlx model.
A few people asked how this compares to whisper.cpp. Bill Mill compared the two and found mlx-whisper to be about 3x faster on an M1 Max.
Update: this note from Josh Marshall:
That '3x' comparison isn't fair; completely different models. I ran a test (14" M1 Pro) with the full (non-distilled) large-v2 model quantised to 8 bit (which is my pick), and whisper.cpp was 1m vs 1m36 for mlx-whisper.
I've now done a better test, using the MLK audio, multiple runs and 2 models (distil-large-v3, large-v2-8bit)... and mlx-whisper is indeed 30-40% faster
Here Are All of the Apple Intelligence Features in the iOS 18.1 Developer Beta (via) Useful rundown from Juli Clover at MacRumors of the Apple Intelligence features that are available in the brand new iOS 18.1 beta, available to developer account holders with an iPhone 15 or iPhone 15 Pro Max or Apple Silicon iPad.
I've been trying this out today. It's still clearly very early, and the on-device model that powers Siri is significantly weaker than more powerful models that I've become used to over the past two years. Similar to old Siri I find myself trying to figure out the sparse, undocumented incantations that reliably work for the things I might want my voice assistant to do for me.
My early Siri AI experience has just underlined the fact that, while there is a lot of practical, useful things that can be done with small models, they really lack the horsepower to do anything super interesting.
The [Apple Foundation Model] pre-training dataset consists of a diverse and high quality data mixture. This includes data we have licensed from publishers, curated publicly-available or open-sourced datasets, and publicly available information crawled by our web-crawler, Applebot. We respect the right of webpages to opt out of being crawled by Applebot, using standard robots.txt directives.
Given our focus on protecting user privacy, we note that no private Apple user data is included in the data mixture. Additionally, extensive efforts have been made to exclude profanity, unsafe material, and personally identifiable information from publicly available data (see Section 7 for more details). Rigorous decontamination is also performed against many common evaluation benchmarks.
We find that data quality, much more so than quantity, is the key determining factor of downstream model performance.
Python 3.12 change results in Apple App Store rejection
(via)
Such a frustrating demonstration of the very worst of Apple's opaque App Store review process. The Python 3.12 standard library urllib package includes the string itms-services, and after much investigation Eric Froemling managed to determine that Apple use a scanner and reject any app that has that string mentioned anywhere within their bundle.
Russell Keith-Magee has a thread on the Python forum discussing solutions. He doesn't think attempts to collaborate with Apple are likely to help:
That definitely sounds appealing as an approach - but in this case, it’s going to be screaming into the void. There’s barely even an appeals process for app rejection on Apple’s App Store. We definitely don’t have any sort of channel to raise a complaint that we could reasonably believe would result in a change of policy.
What Apple unveiled last week with Apple Intelligence wasn't so much new products, but new features—a slew of them—for existing products, powered by generative AI.
[...] These aren't new apps or new products. They're the most used, most important apps Apple makes, the core apps that define the Apple platforms ecosystem, and Apple is using generative AI to make them better and more useful—without, in any way, rendering them unfamiliar.
Transcripts on Apple Podcasts (via) I missed this when it launched back in March: the Apple Podcasts app now features searchable transcripts, including the ability to tap on text and jump to that point in the audio.
Confusingly, you can only tap to navigate using the view of the transcript that comes up when you hit the quote mark icon during playback - if you click the Transcript link from the episode listing page you get a static transcript without the navigation option.
Transcripts are created automatically server-side by Apple, or podcast authors can upload their own edited transcript using Apple Podcasts Connect.
Contrast [Apple Intelligence] to what OpenAI is trying to accomplish with its GPT models, or Google with Gemini, or Anthropic with Claude: those large language models are trying to incorporate all of the available public knowledge to know everything; it’s a dramatically larger and more difficult problem space, which is why they get stuff wrong. There is also a lot of stuff that they don’t know because that information is locked away — like all of the information on an iPhone.
Apple’s terminology distinguishes between “personal intelligence,” on-device and under their control, and “world knowledge,” which is prone to hallucinations – but is also what consumers expect when they use AI, and it’s what may replace Google search as the “point of first intent” one day soon.
It’s wise for them to keep world knowledge separate, behind a very clear gate, but still engage with it. Protects the brand and hedges their bets.
Introducing Apple’s On-Device and Server Foundation Models. Apple Intelligence uses both on-device and in-the-cloud models that were trained from scratch by Apple.
Their on-device model is a 3B model that "outperforms larger models including Phi-3-mini, Mistral-7B, and Gemma-7B", while the larger cloud model is comparable to GPT-3.5.
The language models were trained on unlicensed scraped data - I was hoping they might have managed to avoid that, but sadly not:
We train our foundation models on licensed data, including data selected to enhance specific features, as well as publicly available data collected by our web-crawler, AppleBot.
The most interesting thing here is the way they apply fine-tuning to the local model to specialize it for different tasks. Apple call these "adapters", and they use LoRA for this - a technique first published in 2021. This lets them run multiple on-device models based on a shared foundation, specializing in tasks such as summarization and proof-reading.
Here's the section of the Platforms State of the Union talk that talks about the foundation models and their fine-tuned variants.
As Hamel Husain says:
This talk from Apple is the best ad for fine tuning that probably exists.
The video also describes their approach to quantization:
The next step we took is compressing the model. We leveraged state-of-the-art quantization techniques to take a 16-bit per parameter model down to an average of less than 4 bits per parameter to fit on Apple Intelligence-supported devices, all while maintaining model quality.
Still no news on how their on-device image model was trained. I'd love to find out it was trained exclusively using licensed imagery - Apple struck a deal with Shutterstock a few months ago.
Private Cloud Compute: A new frontier for AI privacy in the cloud. Here are the details about Apple's Private Cloud Compute infrastructure, and they are pretty extraordinary.
The goal with PCC is to allow Apple to run larger AI models that won't fit on a device, but in a way that guarantees that private data passed from the device to the cloud cannot leak in any way - not even to Apple engineers with SSH access who are debugging an outage.
This is an extremely challenging problem, and their proposed solution includes a wide range of new innovations in private computing.
The most impressive part is their approach to technically enforceable guarantees and verifiable transparency. How do you ensure that privacy isn't broken by a future code change? And how can you allow external experts to verify that the software running in your data center is the same software that they have independently audited?
When we launch Private Cloud Compute, we’ll take the extraordinary step of making software images of every production build of PCC publicly available for security research. This promise, too, is an enforceable guarantee: user devices will be willing to send data only to PCC nodes that can cryptographically attest to running publicly listed software.
These code releases will be included in an "append-only and cryptographically tamper-proof transparency log" - similar to certificate transparency logs.
Thoughts on the WWDC 2024 keynote on Apple Intelligence
Today’s WWDC keynote finally revealed Apple’s new set of AI features. The AI section (Apple are calling it Apple Intelligence) started over an hour into the keynote—this link jumps straight to that point in the archived YouTube livestream, or you can watch it embedded here:
[... 855 words]A look at Apple’s new Transformer-powered predictive text model. Jack Cook reverse engineered the tiny LLM used for the predictive text keyboard in the latest iOS. It appears to be a GPT-2 style custom model with 34M parameters and a 15,000 token vocabulary.
The MacBook Airs are Apple’s best-selling laptops; the iPad Pros are Apple’s least-selling iPads. I think it’s as simple as this: the current MacBook Airs have the M3, not the M4, because there isn’t yet sufficient supply of M4 chips to satisfy demand for MacBook Airs.
openelm/README-pretraining.md. Apple released something big three hours ago, and I’m still trying to get my head around exactly what it is.
The parent project is called CoreNet, described as “A library for training deep neural networks”. Part of the release is a new LLM called OpenELM, which includes completely open source training code and a large number of published training checkpoint.
I’m linking here to the best documentation I’ve found of that training data: it looks like the bulk of it comes from RefinedWeb, RedPajama, The Pile and Dolma.
iOS 17.4 Introduces Alternative App Marketplaces With No Commission in EU. The most exciting detail tucked away in this story about new EU policies from iOS 17.4 onwards: “Apple is giving app developers in the EU access to NFC and allowing for alternative browser engines, so WebKit will not be required for third-party browser apps.”
Finally, browser engine competition on iOS! I really hope this results in a future worldwide policy allowing such engines.
2023
Through the Ages: Apple CPU Architecture (via) I enjoyed this review of Apple’s various CPU migrations—Motorola 68k to PowerPC to Intel x86 to Apple Silicon—by Jacob Bartlett.
First Impressions of Vision Pro and VisionOS. John Gruber’s description of his thirty minute Vision Pro demo includes a bunch of details I haven’t seen described anywhere else, including how calibration and corrective lenses work and how precise and stable the overlays of additional information are.
apple-notes-to-sqlite (via) With the help of ChatGPT I finally figured out just enough AppleScript to automate the export of my notes to a SQLite database. AppleScript is a notoriously read-only language, which is turns out makes it a killer app for LLM-assisted coding.
2021
Notes on Notes.app. Apple’s Notes app keeps its data in a SQLite database at ~/Library/Group\ Containers/group.com.apple.notes/NoteStore.sqlite—but it’s pretty difficult to extract data from. It turns out the note text is stored as a gzipped protocol buffers object in the ZICNOTEDATA.ZDATA column. Steve Dunham did the hard work of figuring out how it all works—the complexity stems from Apple’s use of CRDT’s to support seamless multiple edits from different devices.
Technology does not need vast troves of personal data stitched together across dozens of websites and apps in order to succeed. Advertising existed and thrived for decades without it, and we're here today because the path of least resistance is rarely the path of wisdom.
— Tim Cook
2020
Why is Apple’s M1 Chip So Fast? (via) This explanation by Erik Engheim is exactly the right level of nerdery for me.
The open secret Jennings filled me in on is that OpenStreetMap (OSM) is now at the center of an unholy alliance of the world’s largest and wealthiest technology companies. The most valuable companies in the world are treating OSM as critical infrastructure for some of the most-used software ever written. The four companies in the inner circle— Facebook, Apple, Amazon, and Microsoft— have a combined market capitalization of over six trillion dollars.
Apple now receives an estimated $8 billion to $12 billion in annual payments — up from $1 billion a year in 2014 — in exchange for building Google’s search engine into its products. It is probably the single biggest payment that Google makes to anyone and accounts for 14 to 21 percent of Apple’s annual profits.
Without touching upon the question of who’s right and who’s wrong in the specific case of Basecamp’s Hey app, or the broader questions of what, if anything, ought to change in Apple’s App Store policies, an undeniable and important undercurrent to this story is that the business model policies of the App Store have resulted in a tremendous amount of resentment. This spans the entire gamut from one-person indies all the way up to the handful of large corporations that can be considered Apple’s peers or near-peers.
Apple password-manager-resources (via) Apple maintain on open source repository full of heuristics for implementing smart password managers. It lists password rules for different sites (e.g. min/max length, special characters required), change password URLs for different services and sites that share credential backends—like icloud.com and apple.com. They accept pull requests!
Using SQL to Look Through All of Your iMessage Text Messages (via) Dan Kelch shows how to access the iMessage SQLite database at ~/Library/Messages/chat.db—it’s protected under macOS Catalina so you have to enable Full Disk Access in the privacy settings first. I usually use the macOS terminal app but I installed iTerm for this because I’d rather enable full disk access to a separate terminal program than let anything I’m running in my regular terminal take advantage of it. It worked! Now I can run “datasette ~/Library/Messages/chat.db” to browse my messages.
2018
Somebody should write up how the early-2000s push for open standards and the Web Standards Project’s advocacy are a major factor in why Apple was able to create its enormously valuable comeback. Put another way, one of the killer moments of the first iPhone demo was Jobs saying it had the “real” web, not the “baby” web, by demonstrating the NYT homepage. That would’ve been IE-only & Windows-only if not for effective advocacy from the web standards community.
2017
Apple reserves new emojis for point releases, instead of major upgrades, to incentivize people to keep updating. Very smart strategy.