Naveen Rao

Linear attention is an hot research topic with important industrial applications. The problem is inherently quadratic, but there are certain linear solutions under given hyphotesis. This is a a good repo for SOTA, including evaluation benchmarks. If you are a Researcher don't miss the opportunity.

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This is a great first step in understanding how scaling dynamics extend beyond language to other types of sequential data. Time is a universal axis, understanding how models learn optimally over data collected over time is a crucial step in the broader AI research agenda.

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There is so much research going into bringing the power of LLMs into the Enterprise context. The main techniques are k-shot learning or prompt engineering, RAG (Retrieval Augmented Generation) & Model Fine tuning (not to mention many variations of these e.g. Agentic RAG, GraphRAG etc.) However, I came across an interesting take on this from IBM and they are calling it "InstructLab" - where the claim is that they are able to teach the LLM "new skills" grounded in an Enterprise context. On the surface it looks like a k-shot learning + fine turning on steroids. Approach seems to be to utilize a small "skills training dataset" in order to generate a large synthetic dataset (perhaps utilizing a specialized LLM) that is then incorporated into the LLM weights natively. This could be a very interesting concept as long as the cost of adding new skills is not very high (when compared to fine tuning). Exciting times! #genAI #enterpriseAI

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2 Comments

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

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Automatically evaluating your question answering systems (RAG) in NLP https://lnkd.in/gdVBNTQ8 measure all components such as - the tendency for hallucinations in the generation step or the recall for the retrieval step... better than using conventional metrics and benchmarks for evaluating QA systems like ROUGE and BLUE and avoids the creation of a large golden dataset #LLM #RAG #NLP

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Could RAFT (Retrieval-Augmented Fine-Tuning) be the next evolution of RAG architecture? I am following this with great interest. https://lnkd.in/eHDx3rHz

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🚀 We are thrilled to announce the release of our new paper: "The Evolution of Multimodal Model Architectures." In this paper, we provide a bird's-eye view of all the state-of-the-art multimodal (large) models that have emerged since the inception of Transformers. It highlights the pros and cons of each model architecture, grouping them into four broad categories. These categories can serve as a blueprint to help you determine which architecture is best suited for your specific scenario. A huge thank you to all the co-authors and especially to Shakti Wadekar for putting this paper together during his PhD at Purdue University. Paper: 🔗 [https://lnkd.in/gZHQUS6c] Authors: Shakti Wadekar, Abhishek Chaurasia, Aman Chadha, and Eugenio Culurciello #AI #MachineLearning #MultimodalModels #Transformers #Research #PurdueUniversity

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🚨An interesting paper was recently published in 𝐍𝐚𝐭𝐮𝐫𝐞 𝐌𝐞𝐝𝐢𝐜𝐢𝐧𝐞 about #Causal #ML for predicting treatment #outcomes. ML is revolutionizing the assessment and safety of drugs. By offering flexible, data-driven methods for predicting treatment outcomes, including efficacy and toxicity, it allows for estimating individualized treatment effects. This means that clinical decision-making can now be personalized to individual patient profiles, leading to better treatment outcomes. 👉 Example: Will my survival be better if I take treatment 🅰️ or 🅱️? Will my survival improve upon treatment? 👉 This allows for individualized "what if" reasoning in #medicine about effectiveness & safety of drugs http://rdcu.be/dFb9K ---- 💡 Phenom: AI Digital Phenotyping E-book: https://lnkd.in/exW-wPkm 💡 Machine Learning Generated Risk Model to Predict Unplanned Hospital Admission in Heart Failure https://lnkd.in/e3qif-8D 💡 Using Real World Data & AI to Phenotype HFpEF Patients & Advance Heart Failure Research | OM1, Inc. https://lnkd.in/e7hUs9EY #outcomesresearch #ai #bigdata #preventativemedicine #personalizedmedicine #medicalaffairs #rwe #rwd #patientjourney #patientoutcomes #patientcentricity #heor #healtheconomics #marketaccess #realworldoutcomes #realworldevidence #healthcareit #pharma #cardiology #heartfailure #predictiveanalytics #valuebasedcare #value #dataanalytics

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Understanding how to perform operations on tensors is crucial due to their fundamental nature

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I'll do even a braver poll than last, but only because of this Eric Topol, MD article in Science Magazine :) Multimodal large AI models will be used in medical forecasting (disease risk, onset, trajectory) A) now B) soon but at population level only C) sooner than in diagnostics D) later than in diagnostics E) never https://lnkd.in/gGBHSptv

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Spatial Reasoning did not ‘emerge’ spontaneously in Large Language Models (LLMs) the way so many reasoning capabilities did. Humans have specialized, highly capable spatial reasoning capabilities that LLMs have not replicated. But every subsequent release of the major models — GPT, Claude, Gemini- promises better multimedia support, and all will accept and try to use uploaded graphics along with texts. #llms #chatgpt4 #gemini #llmops #aiml #ml https://lnkd.in/dFSWKqKs

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Thank you Cassie Kozyrkov for this great analysis and insight, I now dont need to spend any more time on these bizarre and frankly vague levels on how to achieve AGI, I just dont get it. Sharing another AGI levels definition and framework that did make more sense to me and can actually be baked into existing AI roadmaps if AGI is your ambition. Levels of AGI for Operationalizing Progress on the Path to AGI by Meredith Ringel Morris, Jascha Sohl-dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clement Farabet, Shane Legg [Submitted on 4 Nov 2023 (v1), last revised 5 Jun 2024 (this version, v4)] https://lnkd.in/gXum9sQq And I tend to agree with Cassie’s “AI is a tool” mindset. The only way to have a role in shaping the future is to shape it today by using, testing, applying, and developing AI for the problems staring right at us - climate, health, education, digital equity, and many many more day to day frictions in almost any industry costing us significant time and resources✌🏽

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Hallucinations in LLMs are inevitable, no seriously! Josh Fritzsche and I have worked hard at Voze to find a way to tune, prompt and even plead 🙏 with LLMs to prevent them from the occasional 🍄 hallucination. It seemed like the better we have done the harder the edge cases have been to solve and eventually we realized that they tend to frequently happen when LLMs predict or make anticipations on "nothing". I remember at one point nearly going insane with frustration because when LLMs had a hallucination, they were EPIC! 😱 Then we pushed through it and benefited for the effort! How did we solve this common problem? We didn't (Not completely), we got better at identifying the issues as they happened. Once we identified in the data we were able to start looking for patterns. The same catalysts that cause it to happen once if left unchanged caused it over and over again. Our solution is astoundingly simple. Once you know that it's likely to happen..... let it! Accept it will 🍄, but also be preemptive in not focusing on it and use alternative data sources, generators or providers. Change the conditions that cause it but continue to monitor it as it happens. As Dan Caffee is fond of saying to me on a regular bases. "Dusty we need the system to be adaptive and provide mechanisms to learn from it's past." Well he is right! All we needed was data and the people that contribute to it's quality. Huge props 👏 to strong support from Kathy and Janelles audit teams. We were armed with a knowledge and a desire to learn from past mistakes. Armed with confidence that we could correct these problems we could focus on the data to prevent their impact going forward. When doing millions of LLM (Generative AI) calls regularly Josh and I have learned that hallucinations will happen. We also know exactly how we will handle and prevent it going forward! Failure is not doing anything and accepting defeat.

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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.

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Its going to be all about SYnthetic Data in 5 years!!! Even NVIDIA is building open models to serve this gap fuelled by the insatiable appetite of LLMs. NVIDIA has introduced Nemotron-4 340B, a suite of open models designed to generate synthetic data for training large language models (LLMs) across various industries. These models, optimized for NVIDIA NeMo and TensorRT-LLM, include instruct and reward models that enhance data quality and LLM performance. By providing a scalable and cost-effective way to create high-quality training data, Nemotron-4 340B supports the development of custom LLMs, offering extensive customization options and enterprise-grade support through the NVIDIA AI Enterprise platform. For more details, visit NVIDIA Blog https://lnkd.in/gb4pQCcQ

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Large language models (LLMs) are encountering a significant hurdle in their training journey: the looming scarcity of high-quality internet data. By 2026, projections indicate a depletion of this essential resource, compelling researchers to shift towards model-generated or synthetic data for training purposes. This transition unveils a dual landscape of possibilities and pitfalls. While some research indicates that scaling synthetic data can enhance performance in intricate reasoning tasks, other studies unveil a troubling pattern. Training on synthetic data has the potential to deteriorate model performance, exacerbate biases, spread misinformation, and fortify unwanted stylistic traits. Read more about this critical challenge and its implications: https://lnkd.in/e5V6id7f #ai #llm #data #syntheticdata #learnitall #msadvocate

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A good summary of the scale challenges facing LLMs -- nothing new here for AI researchers, but good for everyone else. Anyone who thinks transformers and LLMs are the end game for AI, and that scale is all that's needed (or attention), have some very expensive lessons in their near future. This article just touches on it, but for this topic the human brain is directly relevant as it's proof of what's possible for efficiency. Although it's been true for many years that ML and DL can surpass humans in increasing numbers of specialty functions, the amount of resources required for LLMs to surpass humans in generalizable functions is stunning (financial, computing, electricity, and water). We're talking on the order of billions x more efficient in the human brain for some functions (as measured by energy). Personally, I could care less whether Satya and Sam want to partner and build a $100 billion super duper data center (other than environmental impact), but given the known inherent security problems and risks in even much smaller scale models, the corporate execs and U.S. Government who allow it would be criminally negligent. And I mean that literally -- they should be held personally accountable for realized risks, including criminal negligence if an applicable event manifests as many of us expect is only a matter of time. They either know the risks (Sam has admitted many, Satya some) or should have known. Period. If they want to spend that much for R&D, more power to them -- work to cure diseases in a controlled lab, but DO NOT MAKE IT AVAILABLE TO EVERY EVIL DOER ON THE PLANET. The catastrophic events, should they manifest as expected, will likely occur in much the same way as sleeper cells in terrorist groups, though it could be a religious cult, a state-sponsored group, organized crime -- impossible to forecast, which makes it that much riskier. I fear our POTUS is clueless to the greatest national security risk in our lifetime.

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Check out our latest AI+EDU paper at OOPSLA 2024! Syntactic bugs and semantic bugs are intrinsically different. A tool that fixes syntactic bugs does not check test case results. A tool that fixes semantic bugs can be started only if the target program has no syntactic bug. But what if a program contains both types of errors, which is happening way too often in intro-level students' programs?  😔 Powered by LLM, PyDex is the first tool to repair both syntactic and semantic bugs in real-world Python programs, with an accuracy of 96.5%. Paper link: https://lnkd.in/euW2SWAF

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