Timothy Goebel

Responsible and accountable AI models are essential as generative AI continues to revolutionize industries. The rise of this technology, however, presents challenges in understanding how these models make decisions. Mechanistic interpretability addresses this by going beyond traditional methods that track statistical relationships. A recent research work by a team from #Anthropic has involved visualizing which neurons activate in response to specific prompts, advancing monosemanticity—ensuring each neuron’s activation correlates to a single, clear function. This step is crucial for fostering more transparent and safer AI systems in various applications. This is a great progress toward interpreting the thinking mechanisms of large language models (LLMs). #GenerativeAI #AI #LLMs #GenerativeAI #Grainger #MachineLearning #DeepLearning #ExplainableAI #DataScience

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Using generative AI, NVIDIA operations built an AI Planner agent, developed on NVIDIA Inference Microservices (NIM). The agent leverages LLM, NeMo Retriever and CuOpt NIM to reduce re-planning time from hours to just seconds. https://nvda.ws/4bIfS2T

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Using generative AI, NVIDIA operations built an AI Planner agent, developed on NVIDIA Inference Microservices (NIM). The agent leverages LLM, NeMo Retriever and CuOpt NIM to reduce re-planning time from hours to just seconds. https://nvda.ws/4bIfS2T

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Building a Retrieval Augmented Generation (RAG) app? Take a look at Alexander Gilmore's new article, in which he explores a few alternative graph data models that can be used to enhance these applications and the unique benefits each model provides. Discover how to build a more efficient & powerful app! https://bit.ly/3VTPfCY #RAG #LLms #Neo4j #graphdatabase

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Your software should make it easier to conduct population modeling, model development and diagnosis, and use that data to make the right decisions for your program. See how Monolix compares. https://lnkd.in/gVVr-s8E

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Are you ready for the ways generative AI models are creating large-scale disruption across near-term, mid-term, and long-term time horizons? Learn to make sense of this rapidly changing landscape with IFTF Three Horizons of AI training. Register for next session: June 11, 18, and 25. https://buff.ly/3vfWGJY

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If you're curious about how Machine Learning and LLMs work--it's not magic or the robot uprising!--we've compiled a comprehensive reading list that we share with new Elicit employees. We find the Applications in Science section particularly interesting: https://buff.ly/4bl7B4o

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Hybrid search and custom reranking with LanceDB Applying Reranking along with Semantic search can significantly improve your retrieval quality with relatively small additions. For RAG applications, better retrieval can significantly improve downstream generation. Trying out this feature on your existing LanceDB workflows, checkout for detailed information - https://lnkd.in/gFCpz4fE Checkout other interesting blogs - https://blog.lancedb.com/ #RAG #vectordb #reranking #hybridsearch #ai

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Interested in finding out how to enable faster product design decisions with centralized data management and visualization of simulation results, while encouraging collaboration across teams and maintaining versioning controls with an authoritative source of truth? Check out this article by Tom Marnik to learn more! #SPDM #Minerva

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Yann LeCun to Elon Musk: "Curious to know how you could possibly do real-time camera image understanding in FSD without ConvNets, TBH." An interesting question, is it not? I am old enough to remember when edge detection and feature extraction and segmentation was done via traditional engineering - by carefully examining the domain and developing domain-specific and computationally efficient models and then testing and trying and tweaking and tinkering until it all worked. And once the relevant features are extracted, further "image understanding" does not actually need ConvNets, no? Other ideas?

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