#mobileLLM

[phonemantra-blog]

Google's recent #TheAndroidShow episode unveiled exciting details about upcoming advancements in the Android universe. Discussions about the Mobile World Congress (MWC), the anticipated Android 15 update, and the intriguing Gemini Nano AI model generated a buzz. However, a bombshell announcement left Pixel 8 users scratching their heads – the absence of Gemini Nano, Google's most lightweight AI model, in the Pixel 8. This unexpected revelation sparked concerns about the future of on-device AI capabilities for Pixel 8 owners. Hardware Hurdles Hinder Gemini Nano Integration Terence Zhang, a Google developer relations engineer, shed light on the situation. He explained that "some hardware limitations" within the Pixel 8 prevent it from supporting Gemini Nano. This news may come as a disappointment to Pixel 8 enthusiasts eager to experience the potential benefits of on-device AI advancements. However, Zhang offered a ray of hope by hinting at the future integration of Gemini Nano into other high-end devices. This suggests Google's unwavering commitment to expanding the reach of its mobile-friendly large language model (LLM), Gemini Nano, with a focus on premium smartphones. A Tale of Two Pixels: The AI Divide Interestingly, while the Pixel 8 faces limitations with Gemini Nano, its pricier sibling, the Pixel 8 Pro, is confirmed to have the AI model on board. This creates a disparity in on-device AI capabilities between Google's latest flagship phones. Unfortunately, Google hasn't yet provided a detailed explanation for the hardware limitations hindering Gemini Nano's inclusion in the Pixel 8. The lack of transparency leaves Pixel 8 users with unanswered questions and a sense of potential missed opportunities. Potential Implications of Missing Out on Gemini Nano The absence of Gemini Nano in the Pixel 8 might have several implications: Limited On-Device AI Features: Without Gemini Nano, the Pixel 8 might rely more heavily on cloud-based AI for functionalities like enhanced voice recognition, image processing, and smart assistant capabilities. This could potentially lead to slower response times or a requirement for an internet connection for some features that might otherwise operate seamlessly on-device. Privacy Concerns: Increased reliance on cloud-based AI could raise privacy concerns for some users who might prefer on-device processing for more sensitive tasks. Missed Performance Optimization: Gemini Nano is designed for efficient on-device processing that can potentially improve smartphone performance by offloading tasks from the main processor. Without it, the Pixel 8 might experience slightly less efficient resource allocation for certain AI-powered features. However, it's important to note that Google hasn't yet revealed the specific functionalities Gemini Nano might have enabled on the Pixel 8. Therefore, the overall impact of its absence remains somewhat unclear. Looking Ahead: What Does This Mean for Pixel 8 Users? While the lack of Gemini Nano support in the Pixel 8 is a surprise, it doesn't necessarily signify a significant loss in overall functionality. Here's a breakdown to help Pixel 8 users navigate this situation: Pixel 8 Still Boasts Powerful AI: The Pixel 8 likely utilizes other AI models for core functionalities. It's important to wait for official information from Google regarding the on-device AI capabilities present in the Pixel 8. Potential Software Updates: Google might introduce software updates in the future that improve or expand AI functionalities on the Pixel 8, even without Gemini Nano. Focus on Other Strengths: The Pixel 8 likely boasts other strengths beyond AI, such as camera quality, display performance, or user interface design. Consider these aspects when evaluating the overall value proposition of the Pixel 8. FAQs Q: Why is the Pixel 8 not getting the Gemini Nano AI model? A: Google cites "hardware limitations" as the reason for excluding Gemini Nano from the Pixel 8. Specific details haven't been disclosed yet. Q: What are the potential benefits of Gemini Nano? A: Specific functionalities haven't been officially revealed by Google. However, Gemini Nano, being a mobile-friendly LLM, is likely designed for efficient on-device processing of tasks like voice recognition, and image processing, and potentially enhancing smart assistant capabilities. Q: Should I be worried about missing out on Gemini Nano with the Pixel 8? A: It's difficult to say definitively without knowing the specific features Gemini Nano would have enabled on the Pixel 8. Pixel 8 users will still have access to other AI models for core functionalities. It's advisable to wait for official information from Google regarding the on-device AI capabilities of the Pixel 8.

[ai-news]

The widespread adoption of large language models (LLMs) has ushered in significant advancements across fields such as conversational AI, content generation, and on-device applications. However, the heavy reliance on extensive cloud resources to depl #AI #ML #Automation

[jcmarchi]

The Most Important Algorithm for Transformers

New Post has been published on https://thedigitalinsider.com/the-most-important-algorithm-for-transformers/

The Most Important Algorithm for Transformers

FlashAttention has a new version. Plus some important research milestones and major funding activity in AI.

Created Using Ideogram

Next Week in The Sequence:

Edge 413: Our series about autonomous agents continues with an exploration of semantic memory. We review Meta AI’s MM-LLM research to augment video models with memory and we dive into the Qdrant vector DB stack.

Edge 414: We dive into HUSKY, a new agent optimized for multi-step reasoning.

You can subscribe to The Sequence below:

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📝 Editorial: The Most Important Algorithm for Transformers

There are few algorithms that have had as much impact on the recent generation of transformer architectures as FlashAttention. Originally developed by researchers from Princeton University, including the renowned Tri Dao, FlashAttention and its successor FlashAttention-2 were able to improve the performance of attention mechanisms in GPUs by minimizing read-writes. Almost immediately after the original publication, FlashAttention was rapidly adopted within the new generation of transformers. There were not many complaints about FlashAttention, but one of the few was that it was unable to take full advantage of new hardware architectures. For instance, FlashAttention-2 is only able to achieve 35% utilization of max FLOPs in H100 GPUs.

But now we have a new version.

Last week, a group of AI researchers from Meta, Princeton University, NVIDIA, and other AI labs published the paper and open-source code for FlashAttention-3. The new version of the method uses several techniques to speed up attention in H100 GPUs, exploiting the asynchrony of the tensor cores. The result is simple: FlashAttention-3 is blazing fast. The new model achieves 75% theoretical max FLOP utilization in H100, which results in practical 1.5-2x performance improvements. The new algorithm is also able to use lower precision numbers, which reduces the memory footprint.

FlashAttention-3 is an exciting development in generative AI algorithms. This method will almost certainly lead to improvements in large context windows in LLMs and better inference performance on modern GPU architectures. Impressive progress!

🔎 ML Research

FlastAttention-3

A group of AI researchers from Meta, Princeton University, Together AI, NVIDIA and others published a paper unveiling the new version of the famous FlastAttention algorithm. FlashAttention-3 takes advantages of the latest GPU advancements achieving 2x the performance of its predecessor and also exceling in long context LLM tasks —> Read more.

Sub-Billion Parameter Models for Mobile

Meta AI published a paper introducing MobileLLM, a sub-billion parameter model optimized for on-device scenarios. MobileLLM uses a specific structure of embedding and attention layers that optimizes its efficiency relative to its size —> Read more.

Generative Teaching for Agents

Microsoft Research published a paper unveiling AgentInstruct, an agentic framework for creating syntethic data. Specifically, AgentInstruct focuses on datasets used for instruction tuning of base models —> Read more.

Evaluating Multimodal Foundation Models

Researchers from Carnegie Mellon University published a paper introducing the holitic evaluation of multimodal models(HEMM) framework . HEMM sets the primitives to systematically evaluate multimodal models across different tasks such as basic skills, information flow, and real-world use cases —> Read more.

A Unified AI Database

Microsoft Research published a paper proposing VBase, the foundation for a unified database for vector, relational and scalar data types. The core of VBase is based on a property called relaxed monotonicity that enables the unification of the different data types models —> Read more.

Contamination in Code Generation Benchmarks

Researchers from Cohere published a paper providing evidence of the levels of contamination of code generation benchmarks in major LLMs. The paper also proposes a Less Basic Python Problems, a new benchmark more resilient to contamination —> Read more.

Autoregressive Models for Text-Image Generation

The team bedhind the Generative AI Research Lab(GAIR) published a paper unveileing ANOLE, an autoregressive multimodal model for image and text generation. ANOLE is based on Meta AI’s Chameleon which guarantees a data and parameter efficient fine-tuning strategy —> Read more.

🤖 Cool AI Tech Releases

Claude High Quality Prompts

Anthropic released some features to evaluate and generate high quality prompts for Claude —> Read more.

MInference

Microsoft released some demos of its MInference method for optimizing LLM inference performance —> Read more.

AutoGen Models

Microsoft AutoGen added support for non OpenAI models —> Read more.

🛠 Real World AI

Ad Inference at Meta

Meta shares some details about the AI inference architecture powering its ad serving system —> Read more.

📡AI Radar

Hebbia, a platform that uses AI to analyze large documents, raised $130 million in new funding.

OpenAI and Los Alamos National Laboraty announced a strategic alliance for bioscience research.

Defense AI startup Helsing raised $487 million to expand to countries neighboring Russia.

AI video startup Captions raised a $60 million Series C.

Enso raised $6 million to bring AI agents to SMBs.

Hayden AI, an AI vision platform for smart cities, raised $90 million in a new round.

NeuralFabric, a platform focused on micro-foundation models, unveiled a new small LLM for sustainability.

Fireworks AI raised $52 million to lead the shift to compound AI systems.

OpenAI and Arianna Huffington launched Thrive AI, a new AI health coach.

Groq unveiled new performance improvements to its fast inference LLM-engine

Amazon released a standalone Guardrails API in its Bedrock platform.

Enterprise AI startup Writer unveiled an impressive set of capabilities .

Microsoft and Apple dropped their plans to join the OpenAI board.

Amazon announced a new challenge to advance coding LLMs.

Exein announced a $15 mission Series B for robotic security.

Medal raised $13 million at a $333 million valuation to build a contextual AI assistant.

AI construction startup Buildots raised $15 million from Intel.

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

[hackernewsrobot]

MobileLLM: Optimizing Sub-Billion Parameter Language Models for On-Device Use

https://github.com/facebookresearch/MobileLLM

[ai-news]

The evolution of large language models (LLMs) marks a revolutionary stride towards simulating human-like understanding and generating natural language. These models, through their capacity to process and analyze vast datasets, have significantly inf #AI #ML #Automation