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Large Language Models (LLMs) like ChatGPT have gained popularity for their human-imitating capabilities in tasks like question answering, text summarization, and language translation. However, the extent to which these models truly understand the underlying data-generating process…

Reinforcement Learning (RL) Overview Reinforcement Learning is widely used in science and technology to improve processes and systems. However, it struggles with a key issue: Sample Inefficiency. This means RL often requires thousands of attempts to…

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Understanding the Challenges in Therapeutic Development Creating new drugs is expensive and takes a long time, often requiring 10-15 years and up to $2 billion. Many drug candidates fail during clinical trials. Successful drugs must interact…

Impact of Large Language Models on Academic Writing Large language models (LLMs), such as ChatGPT, are increasingly used in scholarly literature, raising concerns about authenticity and originality. Detecting changes in writing style and vocabulary in biomedical…

OpenELM, a state-of-the-art open language model, prioritizes reproducibility and transparency in large language models. It employs a layer-wise scaling strategy to efficiently allocate parameters within each layer, resulting in enhanced accuracy. For instance, with a parameter…

Practical AI Solutions for Enhancing Small Language Models’ Reasoning Capabilities Introduction Large language models (LLMs) face challenges in complex reasoning tasks, but practical solutions are being developed to enhance the reasoning capabilities of smaller language models…

The article on Towards Data Science explains how leveling up your game agent can help you win more challenging games.

Streamlining Drug Discovery with AI Solutions Challenges in Drug Discovery Drug discovery is expensive and time-consuming, with only one successful drug emerging from every million compounds tested. While advanced screening technologies like high-throughput screening (HTS) help…

The Value of Abstra: AI-Powered Business Process Scaling The challenges of hiring new employees, scaling operations, and complying with new laws are common as companies grow. Improving internal processes for onboarding, customer service, and finance systems…

NYU and Google AI researchers demonstrate LLMs’ deductive reasoning using in-context learning and chain-of-thought prompting. They explore LLMs’ ability to generalize to more intricate proofs and identify that in-context examples with unfamiliar deduction principles promote better…

The tutorial discusses efficient dataset sampling techniques in Python. It compares three methods: uniform, random, and Latin Hypercube Sampling (LHS). Uniform sampling is simple but scales poorly with dimensions. Random sampling is straightforward, better for large…

UC San Diego researchers have developed a new framework called DYffusion for spatiotemporal forecasting using a diffusion model. The framework incorporates a temporal inductive bias to reduce learning times and memory requirements. It produces accurate probabilistic…

A new diffusion-based continuous GNN model has been developed that improves generalization capabilities.

Introducing INDUS: Domain-Specific Large Language Models (LLMs) for Advanced Scientific Research Practical Solutions and Value Large Language Models (LLMs) like INDUS, trained on specialized corpora, excel in natural language understanding and generation for scientific domains such…

Researchers from MIT have developed a guardian system that improves the safety and performance of autonomous aircraft. The system uses visual attention to monitor both the pilot and itself during flight, and intervenes if attention discrepancies…

The article discusses different ways that data science teams can create value for organizations. It highlights four categories: metrics and measurement, AI/ML product or product features, strategic insights, and operational decision products. Understanding which category your…

The Practical Value of Quantum Machine Learning for Accelerating EEG Signal Analysis Overview The field of quantum computing, initially inspired by Richard Feynman and developed by David Deutsch, has led to rapid advancements in quantum algorithms…