Thermal earring from University of Washington researchers can continually measure earlobe temperature for 28 days on a single battery charge and be personalized by the wearer, with potential for monitoring fever, stress, ovulation and more.
University of Washington
UW-developed smart earrings can monitor a person’s temperature
University of Washington researchers introduced the Thermal Earring, a wireless wearable that continuously monitors a user’s earlobe temperature. Potential applications include tracking signs of...
The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models is now live.
arXiv.org
The Sound of Healthcare: Improving Medical Transcription ASR...
In the rapidly evolving landscape of medical documentation, transcribing clinical dialogues accurately is increasingly paramount. This study explores the potential of Large Language Models (LLMs)...
OS-Copilot: Towards Generalist Computer Agents with Self-Improvement
Outperforms previous methods by 35%, showcasing strong generalization to unseen applications.
Learns to control and self-improve on Excel and Powerpoint with minimal supervision.
Paper here.
Outperforms previous methods by 35%, showcasing strong generalization to unseen applications.
Learns to control and self-improve on Excel and Powerpoint with minimal supervision.
Paper here.
os-copilot.github.io
TWITTER BANNER TITLE META TAG
TWITTER BANNER DESCRIPTION META TAG
👍4
⚡️The future of ML is to use 4 bit precision for the parameters and 128 bit precision for the learning rate.
Fractal patterns were discovered when plotting a grid search over neural network hyperparameters.
It suggests a fundamental link between mathematical fractals and machine learning dynamics.
The discovery could lead to new approaches in tuning and understanding the quite complex landscapes of neural network hyperparameters.
Brilliant work by Jascha Sohl-Dickstein.
Fractal patterns were discovered when plotting a grid search over neural network hyperparameters.
It suggests a fundamental link between mathematical fractals and machine learning dynamics.
The discovery could lead to new approaches in tuning and understanding the quite complex landscapes of neural network hyperparameters.
Brilliant work by Jascha Sohl-Dickstein.
arXiv.org
The boundary of neural network trainability is fractal
Some fractals -- for instance those associated with the Mandelbrot and quadratic Julia sets -- are computed by iterating a function, and identifying the boundary between hyperparameters for which...
❤4⚡1🔥1🤡1
Regenerate_Banking_with_AI_1707744410.pdf
3.8 MB
Regenerate banking with AI. IBM’s vision.
1. Almost 8 in 10 institutions (78%)
are tactically implementing generative AI for at least one use case.
Their tactical approaches vary but trend higher in the risk and compliance space, as well as in client engagement. Additionally, 8% of institutions take a broader, more systematic approach by implementing generative AI across a wider set of business domains scaling throughout the bank.
2. AI priorities reflect omnipresent concerns about risk—and client relationships.
Almost 60% of generative AI decision makers see higher value in risk control, compliance reporting, and client engagement. Keeping data private and earning client trust is essential to winning engagements.
3. AI governance is a must-have.
Every banker should be an AI risk manager. More than 60% of banking CEOs indicate new vulnerabilities for cybersecurity (76%), legal uncertainty related to operations (72%), difficulties in controlling outcome accuracy (67%), and prejudice from model bias (65%).
1. Almost 8 in 10 institutions (78%)
are tactically implementing generative AI for at least one use case.
Their tactical approaches vary but trend higher in the risk and compliance space, as well as in client engagement. Additionally, 8% of institutions take a broader, more systematic approach by implementing generative AI across a wider set of business domains scaling throughout the bank.
2. AI priorities reflect omnipresent concerns about risk—and client relationships.
Almost 60% of generative AI decision makers see higher value in risk control, compliance reporting, and client engagement. Keeping data private and earning client trust is essential to winning engagements.
3. AI governance is a must-have.
Every banker should be an AI risk manager. More than 60% of banking CEOs indicate new vulnerabilities for cybersecurity (76%), legal uncertainty related to operations (72%), difficulties in controlling outcome accuracy (67%), and prejudice from model bias (65%).
OpenAI CEO Altman says UAE could serve as an AI ‘regulatory sandbox’ for the world
Altman says world will need a unified policy to rein in future advances in AI. UAE has invested heavily in AI and made it a key policy consideration.
The world will need a unified policy to rein in future advances in AI, said the OpenAI leader, whose ChatGPT thrust the technology into the mainstream. “I think, for a bunch of reasons, the UAE would be set up to be a leader in the discussions about that,” he said on Tuesday
Altman says world will need a unified policy to rein in future advances in AI. UAE has invested heavily in AI and made it a key policy consideration.
The world will need a unified policy to rein in future advances in AI, said the OpenAI leader, whose ChatGPT thrust the technology into the mainstream. “I think, for a bunch of reasons, the UAE would be set up to be a leader in the discussions about that,” he said on Tuesday
South China Morning Post
OpenAI’s Altman says UAE could be a global sandbox to test AI technologies
Altman says UAE could be a leader in discussions about global regulation of AI technologies.
🦄2
Open AI announced Memory and new controls for ChatGPT
testing the ability for ChatGPT to remember things you discuss to make future chats more helpful
testing the ability for ChatGPT to remember things you discuss to make future chats more helpful
OpenAI
Memory and new controls for ChatGPT
We’re testing the ability for ChatGPT to remember things you discuss to make future chats more helpful. You’re in control of ChatGPT’s memory.
Nous Research introduced Hermes 2 Llama-2 70B.
It's the org's largest model and is trained on over 1M synthetic data entries, also featuring advanced prompt formats for enhanced dialogue interaction.
It's the org's largest model and is trained on over 1M synthetic data entries, also featuring advanced prompt formats for enhanced dialogue interaction.
Zuckerberg on IG today: "I finally tried Apple’s Vision Pro.
And you know I have to say that, before this, I expected that Quest would be the better value for most people since it’s like really good and it’s like seven times less expensive.
But after using it, I just don’t think that Quest is the better value, I think Quest is the better product, period."
And you know I have to say that, before this, I expected that Quest would be the better value for most people since it’s like really good and it’s like seven times less expensive.
But after using it, I just don’t think that Quest is the better value, I think Quest is the better product, period."
🤡1
A multimodal 7B model with 1M context length
World Model on Million-Length Video And Language With RingAttention.
Open-sources 7B models capable of processing long text documents and videos of over 1M tokens
Paper here
World Model on Million-Length Video And Language With RingAttention.
Open-sources 7B models capable of processing long text documents and videos of over 1M tokens
Paper here
arXiv.org
World Model on Million-Length Video And Language With Blockwise...
Enabling long-context understanding remains a key challenge in scaling existing sequence models -- a crucial component in developing generally intelligent models that can process and operate over...
A team of Stanford graduates in their 20s has overtaken tech giants like Huawei, Nvidia, and Google DeepMind in the competitive field of using AI to predict the weather.
Semafor
Little-known startup takes the AI weather prediction crown
WindBorne is using low-cost weather balloons to gather detailed data, giving it an edge in predictive capabilities.
OpenAI has been developing a web search product that would bring the Microsoft-backed startup into more direct competition with Google
It isn’t clear whether the search product would be separate from ChatGPT, the chatbot OpenAI runs and which also uses Bing’s index of the web to retrieve information to answer some questions.
But ChatGPT, which runs in Microsoft’s data centers, isn’t as fast as Google in answering questions.
OpenAI could be looking to speed up the service, which can be slow because it also does tasks like proofreading email drafts, generating poetry or computer code.
OpenAI wouldn’t be the first AI startup to try to disrupt the search market. Perplexity, a search engine startup led by former OpenAI researcher Aravind Srinivas, aims to answer queries with succinct, AI-generated responses that cite high quality sources across the web. Perplexity’s search engine relies on OpenAI’s large language models as well as its own models and open source AI, Srinivas has said.
It isn’t clear whether the search product would be separate from ChatGPT, the chatbot OpenAI runs and which also uses Bing’s index of the web to retrieve information to answer some questions.
But ChatGPT, which runs in Microsoft’s data centers, isn’t as fast as Google in answering questions.
OpenAI could be looking to speed up the service, which can be slow because it also does tasks like proofreading email drafts, generating poetry or computer code.
OpenAI wouldn’t be the first AI startup to try to disrupt the search market. Perplexity, a search engine startup led by former OpenAI researcher Aravind Srinivas, aims to answer queries with succinct, AI-generated responses that cite high quality sources across the web. Perplexity’s search engine relies on OpenAI’s large language models as well as its own models and open source AI, Srinivas has said.
The Information
OpenAI Develops Web Search Product in Challenge to Google
OpenAI has been developing a web search product that would bring the Microsoft-backed startup into more direct competition with Google, according to someone with knowledge of OpenAI’s plans. The search service would be partly powered by Bing, this person…
WhisperKit v0.2 is out
On-device Inference for Apple Watch with WhisperKit shows just how little resources you need on iPhone and Mac.
On-device Inference for Apple Watch with WhisperKit shows just how little resources you need on iPhone and Mac.
GitHub
GitHub - argmaxinc/argmax-oss-swift: On-device Speech AI for Apple Silicon
On-device Speech AI for Apple Silicon. Contribute to argmaxinc/argmax-oss-swift development by creating an account on GitHub.
Uniswap V4 launch is just around the corner.
It's not just an upgrade; it's a transformation from protocol to platform.
Imagine the iPhone finally getting the Apple Store.
Uniswap v4 will launch shortly after the Ethereum Dencun upgrade. The v4 includes EIP-1153: Transient storage that is needed for Uniswap v4 to reduce network costs.
The V4 introduces a "singleton" contract, consolidating all pools within a single framework, which reduces pool creation gas cost by 99% and enables cheaper multi-pool swaps.
Yet, the "Hooks" system is the game changer. Hooks convert Uniswap v4 into a platform.
Think of them as "plugins" or "extensions" that allow for the execution of customized code during key events within a pool.
Hooks enable:
• onchain limit orders
• time-weighted average market making
• depositing out-of-range liquidity into lending protocols
• autocompound LP fees
• KYC
The implications of hooks are big.
With the launch of the App Store, Apple no longer needed to develop iPhone apps themselves.
Instead, it opened the door for anyone to build and distribute their own apps, reaching millions of Apple users and monetizing their work.
But Apple isn't a benevolent company. They take a 30% fee cut for every transaction.
You see, protocols face liquidity issues when launching a protocol, but with hooks devs can experiment and launch their own protocols while using Uniswap as liquidity.
This in turn will bring even more liquidity to Uniswap, potentially attracting it from other DEXes.
The liquidity sourcing from V4 has the potential to make Uniswap the dominant liquidity layer for all DeFi, from trading to lending.
It's not just an upgrade; it's a transformation from protocol to platform.
Imagine the iPhone finally getting the Apple Store.
Uniswap v4 will launch shortly after the Ethereum Dencun upgrade. The v4 includes EIP-1153: Transient storage that is needed for Uniswap v4 to reduce network costs.
The V4 introduces a "singleton" contract, consolidating all pools within a single framework, which reduces pool creation gas cost by 99% and enables cheaper multi-pool swaps.
Yet, the "Hooks" system is the game changer. Hooks convert Uniswap v4 into a platform.
Think of them as "plugins" or "extensions" that allow for the execution of customized code during key events within a pool.
Hooks enable:
• onchain limit orders
• time-weighted average market making
• depositing out-of-range liquidity into lending protocols
• autocompound LP fees
• KYC
The implications of hooks are big.
With the launch of the App Store, Apple no longer needed to develop iPhone apps themselves.
Instead, it opened the door for anyone to build and distribute their own apps, reaching millions of Apple users and monetizing their work.
But Apple isn't a benevolent company. They take a 30% fee cut for every transaction.
You see, protocols face liquidity issues when launching a protocol, but with hooks devs can experiment and launch their own protocols while using Uniswap as liquidity.
This in turn will bring even more liquidity to Uniswap, potentially attracting it from other DEXes.
The liquidity sourcing from V4 has the potential to make Uniswap the dominant liquidity layer for all DeFi, from trading to lending.
👍4
Meta released V-JEPA, a method for teaching machines to understand and model the physical world by watching videos.
V-JEPA vision models trained with a feature prediction objective using self-supervised learning. The models are able to understand and predict what is going on in a video, even with limited information.
It learns by predicting missing or obscured parts of a video in its internal feature space. Unlike generative approaches that fill in missing pixels, this flexible approach enables up to 6x improvements in training and sample efficiency.
The models were pre-trained on entirely unlabeled data, and a small amount of labeled data can be used to train a task-specific prediction head on top after pre-training.
V-JEPA vision models trained with a feature prediction objective using self-supervised learning. The models are able to understand and predict what is going on in a video, even with limited information.
It learns by predicting missing or obscured parts of a video in its internal feature space. Unlike generative approaches that fill in missing pixels, this flexible approach enables up to 6x improvements in training and sample efficiency.
The models were pre-trained on entirely unlabeled data, and a small amount of labeled data can be used to train a task-specific prediction head on top after pre-training.
Meta AI
V-JEPA: The next step toward advanced machine intelligence
We’re releasing the Video Joint Embedding Predictive Architecture (V-JEPA) model, a crucial step in advancing machine intelligence with a more grounded understanding of the world.
👍3😁1
OpenAI released Sora — model which creates minute-long videos from a text prompt.
OpenAI Help Center
What to know about the Sora discontinuation | OpenAI Help Center
Google presents a Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
huggingface.co
Paper page - A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
Join the discussion on this paper page
Magic raised $117m, which is building a superhuman software engineer.
If a copilot generates $10b of revenue, how much is a colleague worth?
Magic has trained a groundbreaking model with many millions of tokens of context that performed far better in our evals than anything we've tried before.
They're using it to build an advanced AI programmer that can reason over your entire codebase and the transitive closure of your dependency tree.
If a copilot generates $10b of revenue, how much is a colleague worth?
Magic has trained a groundbreaking model with many millions of tokens of context that performed far better in our evals than anything we've tried before.
They're using it to build an advanced AI programmer that can reason over your entire codebase and the transitive closure of your dependency tree.
Magic
Magic is an AI company that is working toward building safe AGI to accelerate humanity’s progress on the world’s most important problems.
Reddit has reportedly signed a $60M annual deal granting an AI firm access to its user-generated content for model training.
Notably, the deal is coming prior to the company’s upcoming IPO.
Notably, the deal is coming prior to the company’s upcoming IPO.
Bloomberg.com
Reddit Signs AI Content Licensing Deal Ahead of IPO
Reddit Inc. has signed a contract allowing a company to train its artificial intelligence models on the social media platform’s content, according to people familiar with the matter, as it nears the potential launch of its long-awaited initial public offering.
BioMistral is a new 7B foundation model for medical domains, based on Mistral and further trained PubMed Central.
- top open-source medical Large Language Model (LLM) in its weight class
- Apache License
- includes base models, fine tunes, and quantized versions.
- top open-source medical Large Language Model (LLM) in its weight class
- Apache License
- includes base models, fine tunes, and quantized versions.
huggingface.co
BioMistral/BioMistral-7B · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Andrej Karpathy was leaving OpenAI 4 days ago, and now he released an implementation of the Byte Pair Encoding algorithm behind GPT and most LLMs.
Byte Pair Encoding: "Minimal, clean, educational code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization."
Byte Pair Encoding: "Minimal, clean, educational code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization."
GitHub
GitHub - karpathy/minbpe: Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.
Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization. - karpathy/minbpe