🔥 AI's Hottest Research Updates: S-LoRA + MedCPT + CORNN + Relax.....

This newsletter brings AI research news that is much more technical than most resources but still digestible and applicable

Hey Folks!

This newsletter will discuss some cool AI research papers, AI tools, and AI Startups. Happy learning!

👉 What is Trending in AI/ML Research?

How can the "pretrain-then-finetune" paradigm in large language model deployment be optimized for efficient serving? This paper introduces S-LoRA, a system designed for a scalable serving of numerous Low-Rank Adaptation (LoRA) adapters. These adapters, derived from a base model, are stored in the main memory, with the relevant ones fetched to the GPU memory for current queries. S-LoRA's unique feature, Unified Paging, manages dynamic adapter weights and varying KV cache tensors within a unified memory pool, minimizing GPU memory fragmentation. It incorporates a novel tensor parallelism strategy and custom CUDA kernels for heterogeneous batching of LoRA computation. S-LoRA significantly outperforms existing libraries, boosting throughput by up to 4 times and increasing the number of served adapters substantially. This system facilitates scalable serving of multiple fine-tuned models, paving the way for extensive, customized fine-tuning services.

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How can we effectively classify the capabilities and behaviors of Artificial General Intelligence (AGI) models? This paper proposes a framework, analogous to the levels of autonomous driving, to categorize AGI models based on performance, generality, and autonomy. By analyzing existing AGI definitions, the authors distill six key principles for a practical AGI ontology, emphasizing capabilities over mechanisms, and the distinction between generality and performance. The proposed 'Levels of AGI' are based on the depth (performance) and breadth (generality) of capabilities. The paper reflects on how current systems align with this framework and underscores the need for robust benchmarks to evaluate AGI models. Furthermore, it explores the interplay between AGI levels and deployment considerations, highlighting the importance of responsible Human-AI Interaction paradigms for the safe implementation of advanced AI systems.

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How can we improve information retrieval (IR) in biomedicine without extensive query-article annotations? This paper introduces "MedCPT", a first-of-its-kind Contrastively Pre-trained Transformer model designed for zero-shot semantic IR in the biomedical field. To develop MedCPT, a vast dataset of 255 million user click logs from PubMed was utilized. This data facilitated contrastive learning, enabling the training of an integrated retriever and re-ranker system. MedCPT demonstrates superior performance, surpassing various models including those as large as GPT-3-sized cpt-text-XL, across six biomedical IR tasks. Additionally, it excels in generating nuanced biomedical article and sentence representations. This model offers immediate applicability in diverse real-world biomedical IR applications.

How can we efficiently train data-constrained recurrent neural networks (dRNNs) to interpret and control large neural populations based on extensive neural recording data? This paper introduces "Convex Optimization of Recurrent Neural Networks (CORNN)", a method designed to address the inefficiencies and scalability issues of existing dRNN training algorithms. CORNN dramatically accelerates the training process—achieving speeds about 100 times faster than traditional methods—while maintaining or improving modeling accuracy. Tested on simulations involving thousands of neurons, CORNN effectively handled various computational tasks and proved robust against model mismatches and subsampling challenges. By enabling the rapid training of dRNNs with millions of parameters, CORNN represents a significant advancement in real-time neural network modeling and offers a potent tool for understanding and manipulating neural computation.

How can dynamic shape computations in large language models be optimized for diverse backend environments? This paper introduces "Relax", a compiler abstraction designed to enhance the deployment of dynamic machine learning workloads. Relax implements symbolic shape annotations, enabling global tracking of dynamic shape computations throughout a program. It also features a cross-level abstraction that combines computational graphs, tensor programs, and library calls into a unified representation. This facilitates cross-level optimizations. The framework, tailored for dynamic shape models, shows competitive performance with existing hand-optimized systems on various platforms. Notably, it extends the deployment capabilities of dynamic models to mobile phones, embedded devices, and web browsers.

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Featured AI Tools For You

  • SaneBox*: SaneBox: AI-powered email management that saves you time and brings sanity back to your inbox. Voted Best Productivity Apps for 2023 on PCMag. Sign up today and save $25 on any subscription. [Email and Productivity]

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  • Otter AI*: Get a meeting assistant that records audio, writes notes, automatically captures slides, and generates summaries. [Meeting]

  • Browse AI*: Browse AI empowers businesses to extract data from diverse sources with no-code scraping robots. [Automation and Business]

  • Notion*: Notion is an all-in-one workspace for teams and individuals, offering note-taking, task management, project management, and more. [Productivity]

  • VirtuLook AI by Wondershare*: VirtuLook is an AI-powered image generator that helps users create product photos with ease and save costs. [Image Generator]

  • Retouch4me*: Retouch4me's plugins make photo retouching such a breeze, ensuring professional results every time. [Photo Editing]

  • Motion*: Motion is an AI-powered daily schedule planner that helps you be more productive. [Productivity and Automation]

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🦙 Featured AI Startups

  • Meet Sully.ai: An AI-Powered Startup Building AI Agents to Automate Healthcare Tasks with their AI Scribe, AI Nurse, and more

  • Meet Vellum AI: The Dev Platform for Production LLM Apps

  • Meet PhaseV: An AI-Powered Startup Utilizing Advanced Machine Learning to Combine Clinical Knowledge with Statistical Innovation

  • Meet Govly: An Artificial Intelligence Powered Market Network for Government Contractors

  • Meet Luminar AI: An AI-Powered Photo Editing Software from Skylum

  • Meet CentML: A Machine Learning Startup that Offers Optimization Solutions for ML Inference and Training

  • Meet Confident AI: The Startup Bringing Trust to LLM Apps

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