Aastha Jain

Check out this highly recommended course for those interested in optimizing their models: - Introduction to Quantization - Symmetric and Asymmetric Quantization Modes - Hands-on Implementation from Scratch using PyTorch - covering Per Tensor, Per Channel (Axis), and Per Group techniques - Weights Packing: Exploring 2 or 4 Bits Quantization Methods https://lnkd.in/gMAuWXHm

43

A recent breakthrough titled "Matrix Multiplication-Free LLMs" demonstrates a huge advancement in the area of Large Language Models (LLMs) by reducing computational costs. The authors have eliminated MatMul operations from LLMs, claiming to 10 times reduction in memory usage and a 25.6% increase in training speed, all while maintaining strong performance at billion-parameter scales. Paper link: https://lnkd.in/ggph8qXc #AI #machinelearning #deeplearning #LLMs

16

1. IMO, agent patterns are one of the most interesting ways to extend LLMs. memary is based on leveraging a graph database (neo4j) for long-term memory (to get around the limited memory inherent in even the largest context windows in LLMs) and augmenting it with other tools like web search and Computer Vision. https://lnkd.in/gfFcBqJx Looks interesting. 2. More about how consolidation effects the entire healthcare industry: https://lnkd.in/gXJKqNJB The Change hack/ransomware attack not only affected claims, service, refills, etc. but now CMS & NCQA are delaying the entire HEDIS reporting process (used for Medicare/Medicaid quality ratings) due to the impact. 3. NARA bans ChatGPT use agency-wide. https://lnkd.in/gW8T94JU Federal Government agencies see this as a risk, including their information leaking and being used to train models (which may then output the wrong information or show PII/PHI other proprietary information). 4. Oh great, now I can't even be angry anymore :) https://lnkd.in/gcrN9mjx

1

1 Comment

A couple of recent college graduates from India against AI finetuning giants like Anyscale and Run:ai? I met Arko C at a NASSCOM event last October. He was building Xylem AI (YC S24) , a platform that allows you to train and deploy LLMs in production. So naturally most Indian VCs rejected him. Because how could a team of three recent college grads compete with the likes SV startups like Anyscale that have raised hundreds of millions of dollars, have teams of experienced AI researchers and a stash of H100 GPUs? Arko hustled and closed two Fortune 500 companies for paid contracts. I personally know unicorns that haven't managed to do this. He then got into YC in the last round in May. There's a lot to still be proved out, but Arko exemplifies how valuable grit is in building startups. I’m doing a webinar with him tomorrow on challenges of using LLMs in production. Link to register in the comments. Disclaimer: I'm a small investor in Xylem.

153

5 Comments

A new research paper by our incredible teams at Google Research and Google DeepMind shows the strong performance of Med-Gemini across a variety of multimodal tasks, including  2D and 3D imaging in radiology, histopathology, ophthalmology, and dermatology, and genomics. Med-Gemini ranked best in class on chest X-ray and CT scan report generation, and is able to perform genomics-based disease prediction and mortality risk more accurately than a standard linear polygenic risk score (PRS) based approach. This work couldn't have been possible without close collaboration with our partners, including a panel of specialists who assessed and rated our models as part of this research. These promising results showcase the potential of LLMs to transform medicine as we know it. https://lnkd.in/dHx8_DYh

282

5 Comments

Will LLMs lead us to AGI? LLMs, in their current form, are showing early signs of human-like capabilities. They are superior to humans at various tasks while also performing sub-par at various tasks. The mechanics of how LLMs have been made to learn is very different from how humans learn. While we humans start learning by trial and error, it's very inefficient way for machines to learn. At some point in time, we humans transition to learning new concepts through language ( reading, hearing and watching videos / images) Videos and images can be translated to language representation, so in essence, if one is capable to understanding language, that should be sufficient to learn new concepts. So, I believe, learning through language is quite an efficient mechanism to train machines to think like humans. The same cannot be said about learning Mathematical concepts which might require a different model of training. But, in summary, LLMs are definitely a step in the right direction and could lead to AGI over the next decade with the pace of research and efforts going in this direction. The way machines learn will be different from how humans learn and that should be scary for humans. --- I have been reading this book on a theory of human intelligence and trying to correlate and translate on how best this could lead to superior Machine Learning.

20

2 Comments

Excited to be open-Sourcing long context (128K) Llama-3 70B - Llama-3-Giraffe-70B and Giraffe-Instruct From Abacus.AI The biggest issue with the Llama-3 family is that they are fairly small context models. So, even though they are as performant as GPT-4 on critical benchmarks, they can only be used in the real world with a bigger context. We just open-sourced two 128K long-context fine-tunes for Llama-3-70B, which effectively puts it on par with GPT-4 regarding context length support and real-world usage. At Abacus AI, we already use Llama-3 on a few production workloads as a great base model for our customer fine-tunes. Links to both models - https://lnkd.in/gPX_XpD6 https://lnkd.in/gaeEq94t

3

The key hurdle in deploying AI at scale is bridging the gap in trust, security, and compliance of LLM outputs. I'm excited to be presenting at the Databricks Data + AI Summit on how we at Dynamo AI accelerate this last mile for enterprise customers!

16

Track the Training !!! . No its not a tongue twister its an essential need while doing your ML experiments. With Wandb.ai build models faster, fine-tune LLMs, develop GenAI applications with confidence by complete end to end tracking of resource utilization and runtime logs. In this video we will explore how to integrate Hugging Face Autotrain finetuning with wandb.ai to track resource utilization in real-time. #autotrain #nocode #wandbai #huggingface #genai #finetuning #ml #llm #ai CHeckout wandb.ai - https://wandb.ai/site https://lnkd.in/eA9q9RQy

33

2 Comments

What if your queries required access to multiple sub-related data from different documents, for an LLM to parse and make sense of before returning a coherent and accurate output? We often ask complicated questions, right? 🔹Well to solve this problem, these researchers released Multi-Head RAG (MRAG) which leverages activations of Transformer’s multi-head attention layer, instead of the decoder layer, as keys for fetching multi-aspect documents. They are taking advantage of the fact that multi-head attention can learn to capture different data aspects. 🔹The interesting part is that MRAG is both cost-effective and energy-efficient. In other words, it does not require additional LLM queries, multiple model instances, increased storage, or multiple inference passes over the embedding model. 🔹Moreover, MRAG can be seamlessly integrated with existing RAG frameworks and benchmarking tools like RAGAS as well as different classes of data stores. 🔹The researchers provide a comprehensive evaluation methodology, including specific metrics, synthetic datasets, and real world use cases to demonstrate MRAG’s effectiveness. 🔸Do check it out! #genai #technology #artificialintelligence Click #linkedangle and follow for more content Set the 🔔 notification on my page, don’t miss a post!

181

10 Comments

Your LLMs deserve better input. Reader is a trending Github repo that converts any URL to an LLM-friendly input with a simple prefix https://r.jina.ai/. Get improved output for your agent and RAG systems.

20

1 Comment

Looking forward to this panel where Divakar Tantravahi, Meduri Ravi Kumar, and Cosimo Spera will talk about the latest AI technologies that are changing product and customer support, along with their real world examples and practical strategies. If you have not registered, use this link - https://lnkd.in/gdTMgryA #ai #businessoutcomes #product #customer

22

Routing the control flow within an RAG application based on the user’s query intent can help us create more useful and powerful Retrieval Augmented Generation (RAG) based applications. #llms #rag #chatgpt4 #gemini #aiml #llmops https://lnkd.in/d54eVrHZ

8

Let's unlock the Future of Mobile AI! 🚀 Say hello to 📱 MobileAIBench, our new open source framework for assessing mobile-readiness of your LLMs and LMMs. You can quickly and easily test your models on a variety of benchmarks spanning NLP, multimodality, and trust & safety. Using our iOS app, test your model’s on-device performance such as RAM consumption, Battery Drain rate, latency etc.. Paper: https://lnkd.in/gteK8aww Code: https://lnkd.in/gY8ta8Fz #AI #MobileAI #Benchmarking #NLP #LLMs

81

3 Comments

Just Arrived in Vienna for #ICML2024, I will be presenting our work on improving GNNs' generalization ability with ensemble learning (https://lnkd.in/gimXe5fS) at Oral 5A (Thu 25 Jul 10:45 a.m. — 11 a.m.) and Poster 2407 (Thu 25 Jul 11:30 a.m. — 1 p.m.). Paper title: From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble. A heartfelt thank you to all my collaborators for their incredible support; this work would not have been possible without them. I am also looking forward to reconnecting with old friends and making new ones. If you are attending ICML or are interested in my research, please join me during my presentation session. Additionally, please feel free to reach out to exchange ideas if you are interested in topics such as (1) improving graph model generalization across different datasets, and (2) foundational models for time series forecasting. See you in Vienna! 🇦🇹 #ICML2024 #MachineLearning #ArtificialIntelligence

14

I love the insights. We're starting to see a new landscape of enterprise search in data. And to your point, I think today's search is even more crazily limited in data / lakehouses (like, come on, regex matches are still the only supported SQL search query in year 2024)

13

#EDSS scores can be estimated with very good performance using a #machine learning model applied to clinical notes, thus increasing the utility of real-world data sources for research purposes. ⭐ Poster (OM1 and Janssen): Marci C, Joshi K, Severtson S, Jha M. The Association Between Adherence to Esketamine Nasal Spray Therapy Dosing Regimen and Changes in Depressive Symptoms Among Patients With Treatment-Resistant Depression in the U.S. ASCP May 2024. https://lnkd.in/efzuKJB4 OM1, Inc. #realworlddata #rwd #om1 #healthcareit #bigdata #phenom #realworldevidence #rwe #heor #marketaccess #commercial #pharma #healtheconomics #medicalaffairs #launch #access #patient #multiplesclerosis #patientjourney #patientoutcomes #outcomesresearch #ASCP

7

Nice to have 'Top New Release' status on Amazon for the latest book (my 25th) that explores an alternative paradigm for quantum computation. Illustrations (111) by Dr. Narendra Madhav Joshi. My take is that we owe it to ourselves at this early stage of the industry's lifecycle to explore alternative approaches. Here is a brief overview: "This book attempts to reveal something of the vastly different and fundamentally creative quality of computing that must accompany any computation involving the quantum-levels. To linearly project digital computing laurels manifest as increasing speeds and the ability to process vaster amounts of information, as the inevitable trajectory of quantum computing is perhaps, in the aphorism attributed to the Buddha, to look only at the finger and to miss the moon and the sky that it is pointing to. The cover figure highlights all that is being missed, summarized as an egg-like structure synonymous with the term ‘Hiranyagarbha’ in Sanskrit. This depicts the womb of creation abundant with many layers and patterns reflecting luminescence, due to the constant superposition and entanglement inherent to the quantum computation that derives from a light-centered interpretation of quantum dynamics." The book is available here: https://lnkd.in/gXu4H9uW #quantumcomputation #quantumtech #technologyleadership #cosmology #systemsthinking

16

5 Comments