Bryan Bischof

This is more of a "talking to myself in public" thing than any kind of fully fleshed out hypothesis, so take this with the appropriate grain(s) of salt. But if I were interested in AI and were looking for an angle to explore that might yield something interesting, I'd start working on learning "Multiscale Mathematics" (also known as "Multiscale Modeling"). This is rooted in my personal belief that there has to be a "multi-scale" aspect to intelligence because there is such an aspect inherent in our underlying physical reality.

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Interesting article. With the ever increasing size of context windows, massive prompts could potentially outperform fine-tuning for LLMs, based on a study from CMU. That means depending on situations, one can choose to front load their cost to fine-tune LLMs, or to have larger prompts and pay more per transaction to get to similar performance. https://lnkd.in/euFTP8QS

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Foundational models, or large language models (LLMs), train at the scale of the entire internet to acquire the vocabulary, syntax, and narrative structure of human language. Fine-tuning involves using a smaller, domain-specific dataset to grant additional specificity to a pre-existing foundational model that has already acquired patterns and features from massive underlying training datasets. By utilizing the existing knowledge embedded in a pre-trained language model, we unlock high-performance language understanding, but fine-tuning helps to get the right answer - not just an answer that sounds right - with a minimum of hallucinations or incorrect info. In healthcare, out-of-the-box, foundational models are often too generic to sufficiently answer the precision of a healthcare query. Foundational language models are trained on a massive scale of data, which includes jokes, incorrect information, misunderstandings, and misinformation, along with the sum total of human knowledge. The problem is, if you ask a large language model to give you factually correct information when it has been trained on BOTH accurate and inaccurate information - you’re playing a bit of a dangerous game. Enter fine-tuning language models. In the old days, we used to build a single model for a single task - for example, classifying procedures into evidence-based or not as laid out by clinical practice guidelines, for patients with particular disease markers. Each disease would have its own model, which took a long time to build and didn’t scale particularly well, and we struggled with the sparsity and inherent high-dimensionality of healthcare data. By fine-tuning LLMs, however, we're able to specialize those foundational models to achieve greater domain specificity and specialization, leverage the unique predictive and planning capabilities of LLMs, and cut down on the incorrect information that such models inevitably produce, while still preserving the underlying language prowess of the LLM. If improved accuracy and specificity wasn't enough of a selling point, there's another notable upside to fine-tuning foundational models for use in a clinical context. In terms of computational efficiency, using LLMs out of the box on specific tasks can require massive compute (and spend) that amounts to computational overkill for the problem. Some LLMs charge per query, some by compute load or time - either way, as a business running LLMs in a production environment, minimizing the number of LLM calls means optimizing your compute expenditures, too. When LLMs fail at scale in healthcare, it's usually along the fault line of failure to specify adequately. Fine-tuning large language models along domain-specific lines is one way to effectively productize genAI for healthcare use cases. #genAI #LLM #ai #healthcareAI #healthtech #largelanguagemodels #generativeAI #chatGPT

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Why is AI making “information overload” so much worse? We’re caught between technologists' rhetoric touting AI's benefits and our lived experience of low-quality AI content flooding our feeds. It all comes down to context. AI models are inherently surface-level, lacking the deeper understanding we gain over time. This context allows us to revisit a problem or hear a common piece of advice and think, “Wow, it’s so obvious now.” Check out our latest blog post, in which we attempt to formalize the 4 Levels of Context and map it to the current state of AI in software engineering. Let me know your thoughts! https://lnkd.in/ep5Uy3zN

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Our new technical blog post details the inner workings of Nuro's LAMBDA model, a real-time reasoning engine for autonomous driving! https://lnkd.in/eQxQfAUN We're proud to demonstrate the advanced capabilities of LAMBDA and to showcase its capacity to reason intelligently about the wide variety of challenging scenarios that our Nuro vehicles encounter and need to handle autonomously. LAMBDA has shown that it can explain complex interactions and describe safe and responsible behavior in response. What we've shared in this post is one of the many ways that we're building and utilizing foundational models for autonomous driving. The generality and sophistication of the LAMBDA model has led us to explore applying this technology to benefit Nuro's autonomy on-board in addition to improving developer efficiency and Nuro's safety case off-board. We look forward to sharing more of these advancements in the near future! Many thanks to all the contributors to this project. If you want to join us on our mission to advance the state of the art in autonomous robotics please apply here! https://lnkd.in/e9km-dV2 #nuro #ai #deeplearning #autonomousvehicles #robotics

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