Alexis Ahiekpor-Dravi

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

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Fine-tuning can be used both for customizing an LLM to your business domain and have the result increase the inference speed.

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It was a great serendipity to meet Eivind Antonsen Segtnan and to support Segtnan.ai effort to practically implement their recent research in hospitals to make disease testing up to 80% more efficient. Btw, their paper just got published in Nature Scientific Reports: https://lnkd.in/dgR56Jj2. Congrats! TL;DR This solution has the potential to save millions when testing for diseases such as Covid-19, HIV, and chlamydia, by grouping and testing samples from multiple individuals together. Segtnan.ai's mobile app integrates AI to tailor risk assessments and employ data-driven methods to optimize group size. Their groundbreaking solution can significantly increase testing capacity and save both time and money in the healthcare system.

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𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰: 𝐏𝐢𝐨𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 Recently I happened to see this nice video ( 𝘩𝘵𝘵𝘱𝘴://𝘸𝘸𝘸.𝘺𝘰𝘶𝘵𝘶𝘣𝘦.𝘤𝘰𝘮/𝘸𝘢𝘵𝘤𝘩?𝘷=𝘴𝘢𝘭78𝘈𝘊𝘵𝘎𝘛𝘤 ) by Andrew Ng in which he talks about Agentic workflows. In Language Model (LLM) development, we provide a zero-shot prompt and receive an answer. We may review it and add more prompts to get it to a desired shape. This is exactly what the agentic workflow does. Basically, 𝘢 𝒃𝒖𝒏𝒄𝒉 𝒐𝒇 𝑨𝑰 𝒂𝒈𝒆𝒏𝒕𝒔 𝒘𝒐𝒓𝒌 𝒕𝒐𝒈𝒆𝒕𝒉𝒆𝒓 𝒊𝒏 𝒂𝒏 𝒊𝒕𝒆𝒓𝒂𝒕𝒊𝒗𝒆 𝒘𝒂𝒚 𝒕𝒐 𝒑𝒓𝒐𝒅𝒖𝒄𝒆 𝒂 𝒅𝒆𝒔𝒊𝒓𝒆𝒅 𝒐𝒖𝒕𝒑𝒖𝒕. Moreover, he even claims the output produced by agentic workflow is much better than the output produced single LLM which might sound obvious but what surprised me was, as per the “𝑯𝒖𝒎𝒂𝒏𝑬𝒗𝒂𝒍” coding benchmark, agents-based approach beating plain LLM by quite a margin (see the picture below). The plain 𝐆𝐏𝐓 3.5 was at 48% but 𝑳𝑫𝑩+𝑹𝒆𝒇𝒍𝒆𝒄𝒕𝒊𝒐𝒏 (𝘈 𝘓𝘢𝘳𝘨𝘦 𝘓𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘔𝘰𝘥𝘦𝘭 𝘋𝘦𝘣𝘶𝘨𝘨𝘦𝘳 𝘷𝘪𝘢 𝘝𝘦𝘳𝘪𝘧𝘺𝘪𝘯𝘨 𝘙𝘶𝘯𝘵𝘪𝘮𝘦 𝘌𝘹𝘦𝘤𝘶𝘵𝘪𝘰𝘯 𝘚𝘵𝘦𝘱-𝘣𝘺-𝘴𝘵𝘦𝘱) fared at impressive 95.1% with the same GPT 3.5. The plain 𝐆𝐏𝐓 4 was at 67% but 𝑨𝒈𝒆𝒏𝒕𝑪𝒐𝒅𝒆𝒓 (𝘔𝘶𝘭𝘵𝘪-𝘈𝘨𝘦𝘯𝘵-𝘣𝘢𝘴𝘦𝘥 𝘊𝘰𝘥𝘦 𝘎𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩 𝘐𝘵𝘦𝘳𝘢𝘵𝘪𝘷𝘦 𝘛𝘦𝘴𝘵𝘪𝘯𝘨 𝘢𝘯𝘥 𝘖𝘱𝘵𝘪𝘮𝘪𝘴𝘢𝘵𝘪𝘰𝘯) fared at whooping 96.3% with the same GPT 4.  You can get more information about the evaluation and the models at 𝘩𝘵𝘵𝘱𝘴://𝘱𝘢𝘱𝘦𝘳𝘴𝘸𝘪𝘵𝘩𝘤𝘰𝘥𝘦.𝘤𝘰𝘮/𝘴𝘰𝘵𝘢/𝘤𝘰𝘥𝘦-𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯-𝘰𝘯-𝘩𝘶𝘮𝘢𝘯𝘦𝘷𝘢𝘭. He also mentioned the following are the 4 design patterns that are emerging in the agentic workflow. 1.    Reflection 2.    Tool use 3.    Planning 4.    Multi-agent collaboration If you are interested in learning more, instead of me explaining each of these patterns, I would encourage watching the video :) #ai #agent #agenticworkflow #gpt

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👉🏼 GPT-4 Turbo with Vision fails to outperform text-only GPT-4 Turbo in the Japan Diagnostic Radiology Board Examination 🤓 Yuichiro Hirano 👇🏻 https://lnkd.in/eguBsnAm 🔍 Focus on data insights: - GPT-4TV correctly answered 45% of questions - GPT-4 T correctly answered 41% of questions - No significant performance difference between the two models (P = 0.44) 💡 Main outcomes and implications: - No significant enhancement in accuracy observed with GPT-4TV using image input - GPT-4TV responses received lower legitimacy scores compared to GPT-4 T responses 📚 Field significance: - The study highlights the importance of evaluating the performance of multimodal language models in specific contexts like diagnostic radiology examinations 🗄️: #datainsights #radiology #GPT-4Turbo #performanceevaluation

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This is a wonderful paper. It shows how clever design (with simplicity and elegance) in architecture can improve the performance of transformers in arithmetic, rather than by simply throwing in more data and compute. https://lnkd.in/gZNnifc3

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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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Check out our latest post on Clappy.ai, "Leverage Layout-Aware RAG for More Accurate Retrieval of Knowledge from Documents." Discover how to harness the power of Retrieval-Augmented Generation (RAG) for documents in PDF, Doc, and Markdown formats. Dive into the specifics of implementation and explore our reference repository to get started with advanced RAG for your knowledge base. Don't miss out on this essential read! Continue reading here for all the details: The Power of Hierarchical Indexing with RAG. (https://lnkd.in/gbiVHfcH)

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First test email from Zeeq.ai came through overnight. Zeeq (rhymes with "seek") is a personalized AI agent that watches ClinicalTrials.gov for updates of #clinicaltrials for trials of interest and generates answers to your questions delivered straight to your inbox whenever a trial gets added or updated. Had a few hiccups with reading the change feed from the FDA, but looks like it's almost there and getting closer to full rollout (a few more days of observing, I think!). Whether you are a #patient, #pharma exec, #biotech industry analyst, or academic researcher, Zeeq can help you stay on top of the most relevant trials of interest with personalized questions and answers. Free to sign up at https://zeeq.ai - no usernames or passwords, no accounts.

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This is a very interesting article on sparse vectors, which can help overcome limitations of vector search related to domain specific keywords. And of course, the code in the example is clear and clean! Sparse vectors have a few values and many zeros. Dense vectors have limited or no zeroes. But really, what we're talking about semantic search on terms where the context is not present or harder to infer. The author presents this as specialized keyword search such as medical terms or other domain specific terminology. "These scores reflect the significance of each term in the context of the document or query, guiding the model to allocate more weight to terms that are likely to be more meaningful for retrieval purposes." Highly recommend reading. Nice work Paul Iusztin. And a nod to Qdrant and their vector search technology that supports sparse vectors. https://lnkd.in/gcBWfTg8

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Abstractions leak in the worst case. So it's always good the low level details behind the abstraction. Despite this, abstractions are super important: What if there was no abstraction in computer networks. Imagine every backend developer implementing TCP in their web server. It would be total chaos!

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I spoke today with computational bio expert Jefferson Parker, PhD (at NullSet Informatics Solutions) for our State of AI in Biotech report. He shared some interesting career advice for those working in AI: 9 out of 10 leaders in biotech want AI in their scientific development, but do not know WHY. 1 out of 10 DO know why. They probably have the best data, and that is a blessed place to be and develop in your career. Always ask why leaders want AI and much more will be revealed. And for leaders, understanding why you want AI (beyond the hype and promises) will lead to a team culture that prioritizes data and let's the best talent flourish. It's win-win.

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At Valence Vibrations, we’re constantly asked what methods we use to detect emotion. Some companies offer facial recognition or NLP sentiment analysis, while we’re focused on tonal audio analysis. In our experience, audio data offers the most emotional nuance and the most applicability to different situations. Check out my blog for a run down of each method of emotion classification, and why we feel our audio methods and models are the best bet for highly accurate and accessible emotion AI. 

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This week, I was discussing Bayes’ theorem with one of the scientists on my team. It is one of my favorite statistical theory, and is often counterintuitive. Here is an example: Imagine a rare disease X, affecting 1 in 1000 people. Assume, there's a diagnostic test with 99% accuracy in detecting the disease when present, with a 2% false positive rate. Let’s say someone (Person A) takes the test, and it comes back positive. What's the likelihood that they actually have the disease? At first glance, one might think that the probability of having the disease, given a positive test result, would be high – especially since the test is 99% accurate with only a 2% false positive rate. But is that the case? Bayes’ theorem can give the exact answer, here is the number crunching: Prob (Having the disease X) = P(A) = 0.1% Prob (Not having the disease X) = P(A’) = 1- P(A) = 99.9% Prob (Getting a positive test result, when the disease is present) = P(B∣A) = 99% Prob (Getting a positive test result, when the disease is not present) = P(B∣A’) =2% From law of total probability, Prob (Getting a positive test result) = P(B)=P(B∣A) ×P(A)+P(B∣A′) ×P(A′) = 2.1% From Bayes’ theorem, Prob (Having the disease, when the test is positive) = P(A∣B) = P(B∣A) ×P(A)​ / P(B) = 0.04726 = 4.71% So, despite the positive test result, the likelihood that person A actually has the disease X is only 4.7% (in other words, the likelihood that person A does not have the disease X even with a positive test result is quite high, at 95.3%). This is what makes Bayes' Theorem so fascinating! #DataScience #Statistics #Bayes’Theorem

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Common Culprits Behind CrashLoopBackOff There are a few usual suspects that can trigger endless crashes and restarts: 👻Resource constraints: If a pod doesn't have enough memory allocated, it can run out and crash. Carefully configure pod resource requests and limits. 👻Image issues: Using an image without proper permissions or pulling the wrong image can lead to crashes. 👻Configuration errors: Simple mistakes like typos in the pod spec can prevent containers from starting correctly. 👻External service problems: If your pod relies on an external service or database that's unavailable, it's going to have issues. 👻Uncaught exceptions: Bugs or errors in application code that aren't handled properly will cause crashes. 👻Liveness probe misconfiguration: If liveness probes are configured poorly, they can cause unnecessary restarts. https://hubs.la/Q02vTSxF0

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That's a good summary Manny Bernabe - Reflecting on an insightful article from O'Reilly about LLMs, I was struck by this point: "LLMs are also broadly accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into their products. While the barrier to entry for building AI products has been lowered, creating those effective beyond a demo remains a deceptively difficult endeavor." This democratization reminds me of the printing press's impact on biblical texts, which expanded access but also introduced new interpretive challenges. As we navigate these advancements, it's crucial to balance accessibility with responsible use. At Ushur, we've been at the forefront of leveraging LLMs in our backend/knowledge work automation as well as carefully introducing them on the conversational side via our Chatbots and other modes. More will be coming out on that. Read more here: O'Reilly Article #AI #LLM #Technology #Innovation #History #Democratization #ResponsibleAI #Ushur

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Today I'm launching the latest weekend hackathon: https://trialsight.ai. It's an #AI agent that watches for the latest updates from the #FDA on #ClinicalTrials.gov. When it finds a trial that matches your personalized criteria, it answers your questions about the trial and delivers each update as a personalized newsletter to your inbox. It's completely free and requires no account registration. Unsubscribe at any time. It's the perfect way to stay on top of the latest clinical trials registered with the FDA whether you're a patient, #lifescience industry analyst, #healthcare worker, or #pharma executive. Check it out, try the preview, and join the waitlist!

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New Mindset Needed for Large Language Models - https://buff.ly/3XbN4vp #AI #ML #LLMs #genAI