Apple Research

A new research paper from Apple details a technique that speeds up large language model responses, while preserving output quality. Here are the details.

Today, Apple published on its Machine Learning Research blog, select recordings from its 2024 Workshop on Human-Centered Machine Learning (HCML), highlighting its work on responsible AI development.

A new Apple-backed study, in collaboration with Aalto University in Finland, introduces ILuvUI: a vision-language model trained to understand mobile app interfaces from screenshots and from natural language conversations. Here’s what that means, and how they did it.

A new Apple-supported study argues that your behavior data (movement, sleep, exercise, etc.) can often be a stronger health signal than traditional biometric measurements like heart rate or blood oxygen. To prove it, the researchers developed a foundation model trained on behavioral data collected from wearables, and it performed surprisingly well. Here are the details.

There’s no shortage of rumors about Apple’s plans to release camera-equipped wearables. And while it’s easy to get fatigued by yet another wave of upcoming AI-powered hardware, one powerful use case often gets lost in the shuffle: accessibility.

Apple quietly dropped a new AI model on Hugging Face with an interesting twist. Instead of writing code like traditional LLMs generate text (left to right, top to bottom), it can also write out of order, and improve multiple chunks at once.

The result is faster code generation, at a performance that rivals top open-source coding models. Here’s how it works.

As AI agents come closer to taking real actions on our behalf (messaging someone, buying something, toggling account settings, etc.), a new study co-authored by Apple looks into how well do these systems really understand the consequences of their actions. Here’s what they found out.

Today, most generative image models basically fall into two main categories: diffusion models, like Stable Diffusion, or autoregressive models, like OpenAI’s GPT-4o. But Apple just released two papers that show how there might be room for a third, forgotten technique: Normalizing Flows. And with a dash of Transformers on top, they might be more capable than previously thought.

As more users start relying on AI for writing tasks like email drafts and document summaries, one common frustration remains: the output often sounds way too generic. Even when models like ChatGPT or Gemini are given detailed prompts, they rarely nail a user’s individual tone or voice without plenty of manual tweaking. Apple is now proposing a solution.

There’s a new Apple research paper making the rounds, and if you’ve seen the reactions, you’d think it just toppled the entire LLM industry. That is far from true, although it might be the best attempt to bring to the mainstream a discussion that the ML community has been having for ages. Here is why this paper matters.

As part of its fantastic body of work on speech and voice models, Apple has just published a new study that takes a very human-centric approach to a tricky machine learning problem: not just recognizing what was said, but how it was said. And the accessibility implications are monumental.

Apple is headed to Nashville next week, where it will showcase new computer vision research and run live technical demos at this year’s IEEE/CVF Conference on Computer Vision and Pattern Recognition.

The Apple Research team has published a pretty interesting study that investigated whether AI models can estimate heart rate from stethoscope recordings, even though they weren’t specifically trained with that purpose in mind. The short answer is: yes. And this is great news. Here’s why.

Ask any non-native English speaker, and they’ll probably tell you that LLMs tend to perform much better in Shakespeare’s language than in their own

Sometimes, the difference is subtle. Sometimes, not so much. Sometimes, it’s downright dangerous, as shown in this 2023 Carnegie Mellon study, which found that non-English inputs could more easily bypass safety filters.

Now, Apple has co-authored a study proposing a new method that could close part of this gap.

Apple on Wednesday released a new version of its Apple Research app, which is aimed at letting users contribute to scientific health research using their Apple devices. With version 5.0, users can now share family health history with studies. Read on as we detail everything that’s new with the update.