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."
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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.
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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.
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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
Cool new idea from DeepMind.
They evaluate LMs by giving them a piece of code, having them describe it, and then asking the LM to rewrite that code given only the description.
The metric is the similarity between the original code and the rewritten code.
They evaluate LMs by giving them a piece of code, having them describe it, and then asking the LM to rewrite that code given only the description.
The metric is the similarity between the original code and the rewritten code.
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Game changer. You can now visualize your RAG Data.
UMAP is dimensionality reduction techniques that transforms complex, high-dimensional data into a clear and interactive 2D map.
It can also be used for debugging and improving the performance of your RAG models.
UMAP is dimensionality reduction techniques that transforms complex, high-dimensional data into a clear and interactive 2D map.
It can also be used for debugging and improving the performance of your RAG models.
Medium
Visualize your RAG Data — EDA for Retrieval-Augmented Generation
How to use UMAP dimensionality reduction for Embeddings to show Questions, Answers and their relationships to source documents with…
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🚀 Groq is claiming that its technology could replace GPUs in AI tasks with its powerful chip and software
Groq's LPU is faster than Nvidia GPUs, handling requests and responding more quickly.
Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks.
The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down.
If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do.
Groq's LPU is faster than Nvidia GPUs, handling requests and responding more quickly.
Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks.
The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down.
If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do.
Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding
Improves the decoding speed of Vicuna-33B by up to 2.37x and Llama2-70B offloading speed by up to 10.33x
Improves the decoding speed of Vicuna-33B by up to 2.37x and Llama2-70B offloading speed by up to 10.33x
The Gemini 1.5 Pro model guide is live
Gemini 1.5 Pro is among the most powerful long context LLMs available today.
Gemini 1.5 Pro shows impressive capabilities around multimodal reasoning, video understanding, long document question answering, code reasoning on entire codebases, and in-context learning.
One insight from testing this model is that we will have different kinds of LLMs that support different types of use cases. Gemini 1.5 Pro is not meant to be a model to reign among all. The long context LLMs are not meant to cover every use case imaginable, they are meant to unlock complex use cases that were unimaginable before with LLMs.
Gemini 1.5 Pro is among the most powerful long context LLMs available today.
Gemini 1.5 Pro shows impressive capabilities around multimodal reasoning, video understanding, long document question answering, code reasoning on entire codebases, and in-context learning.
One insight from testing this model is that we will have different kinds of LLMs that support different types of use cases. Gemini 1.5 Pro is not meant to be a model to reign among all. The long context LLMs are not meant to cover every use case imaginable, they are meant to unlock complex use cases that were unimaginable before with LLMs.
www.promptingguide.ai
Gemini 1.5 Pro | Prompt Engineering Guide
A Comprehensive Overview of Prompt Engineering
Mixed_Reality_for_Work_Collaboration_Education_more_1708447794.pdf
14.3 MB
Meta released a guide as were approached by individuals & enterprises that want to take full advantage of the metaverse at hand.
Here's what you'll discover inside:
1. Introduction to Mixed Reality: Understand the basics and the immense potential MR holds for businesses.
2. Impactful use cases: Explore how MR is reshaping industries in areas like product design, construction, specialised training, and more.
3. Meta Quest Headsets Overview: A detailed look at Meta’s MR headsets, helping you choose the right one for your needs.
4. Practical Launch Tips: From choosing apps to setting up hardware, get actionable advice for a successful MR implementation.
5. Meta Quest for Business: Learn about the enterprise-grade solutions for managing MR devices and applications effectively.
Here's what you'll discover inside:
1. Introduction to Mixed Reality: Understand the basics and the immense potential MR holds for businesses.
2. Impactful use cases: Explore how MR is reshaping industries in areas like product design, construction, specialised training, and more.
3. Meta Quest Headsets Overview: A detailed look at Meta’s MR headsets, helping you choose the right one for your needs.
4. Practical Launch Tips: From choosing apps to setting up hardware, get actionable advice for a successful MR implementation.
5. Meta Quest for Business: Learn about the enterprise-grade solutions for managing MR devices and applications effectively.
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Hot off the press from Andrej karpathy 🔥
Everything you need to know about LLM tokenization. If you’re a developer using AI, you are going to want to watch this.
Everything you need to know about LLM tokenization. If you’re a developer using AI, you are going to want to watch this.
YouTube
Let's build the GPT Tokenizer
The Tokenizer is a necessary and pervasive component of Large Language Models (LLMs), where it translates between strings and tokens (text chunks). Tokenizers are a completely separate stage of the LLM pipeline: they have their own training sets, training…
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Exclusive deepdive on how ChatGPT scaled to 100M weekly users: and five engineering challenges the team faced and how they are tackling it.
Pragmaticengineer
Scaling ChatGPT: Five Real-World Engineering Challenges
Just one year after its launch, ChatGPT had more than 100M weekly users. In order to meet this explosive demand, the team at OpenAI had to overcome several scaling challenges. An exclusive deepdive.
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