Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning

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A/B testing has become a gold standard for modern technological companies to conduct policy evaluation. Yet, its application to time series experiments, where policies are sequentially assigned over time, remains challenging. Existing designs suffer from two limitations: (i) they do not fully leverage the entire history for treatment allocation; (ii) they rely on strong assumptions to approximate the objective function (e.g., the mean squared error of the estimated treatment effect) for optimizing the design. We first establish an […]

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Orion helium leak no threat to Artemis II reentry but will require redesign

Orion helium leak no threat to Artemis II reentry but will require redesign

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Apart from pesky issues with the spacecraft’s toilet and waste disposal system, most of the Artemis II mission has proceeded like clockwork. NASA has made few changes to the flight plan since the launch of the lunar flyby mission on April 1. But ground controllers revamped the timeline Wednesday as the Artemis II astronauts zoomed toward Earth after a close encounter with the Moon earlier this week. The four astronauts were supposed to take manual control of their […]

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A Peek at Trends in Machine Learning

A Peek at Trends in Machine Learning

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Have you looked at Google Trends? It’s pretty cool — you enter some keywords and see how Google Searches of that term vary through time. I thought — hey, I happen to have this arxiv-sanity database of 28,303 (arxiv) Machine Learning papers over the last 5 years, so why not do something similar and take a look at how Machine Learning research has evolved over the last 5 years? The results are fairly fun, so I thought I’d post. (Edit: machine learning is […]

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The Great Compression: How AI Model Distillation Is Rewriting the Rules of the Industry

The Great Compression: How AI Model Distillation Is Rewriting the Rules of the Industry

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How knowledge transfers from large teacher models to smaller student models through distillation techniques. Image: GaussianWaves The Free Lunch That Came With a Bill In October 2019, a team at Hugging Face, a French-American AI startup, published a model called DistilBERT. It retained 97% of its parent model’s performance on language-understanding benchmarks while running 60% faster and carrying 40% fewer parameters. “The numbers looked like a free lunch. Six years later, the bill arrived — as a geopolitical crisis.” When Distillation […]

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Sentiment Analysis with Text and Audio Using AWS Generative AI Services: Approaches, Challenges, and Solutions

Sentiment Analysis with Text and Audio Using AWS Generative AI Services: Approaches, Challenges, and Solutions

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This post is co-written by Instituto de Ciência e Tecnologia Itaú (ICTi) and AWS. Sentiment analysis has grown increasingly important in modern enterprises, providing insights into customer opinions, satisfaction levels, and potential frustrations. As interactions occur largely through text (such as social media, chat applications, and ecommerce reviews) or voice (such as call centers and telephony), organizations need robust methods to interpret these signals at scale. By accurately identifying and classifying a customer’s emotional state, companies can deliver […]

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Rethinking Multimodal Fusion for Time Series: Auxiliary Modalities Need Constrained Fusion

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arXiv:2603.22372v1 Announce Type: new Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting. However, most existing methods provide limited gains, often improving performance only in specific datasets or relying on architecture-specific designs that limit generalization. In this paper, we show that multimodal models with naive fusion strategies (e.g., simple addition or concatenation) often underperform unimodal TS models, which we attribute to the uncontrolled integration […]

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MIT Schwarzman College of Computing and MBZUAI launch international collaboration to shape the future of AI

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The MIT Schwarzman College of Computing and the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) recently celebrated the launch of the MIT–MBZUAI Collaborative Research Program, a new effort to strengthen the building blocks of artificial intelligence and accelerate its use in pressing scientific and societal challenges. Under the five-year agreement, faculty, students, and research staff from both institutions will collaborate on fundamental research projects to advance the technological foundations of AI and its applications in three core […]

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Discoverability matters: Open access models and the translation of science into patents

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arXiv:2604.06229v1 Announce Type: new Abstract: Scientific research is a key input into technological innovation, yet not all scientific knowledge is equally mobilized in patents. This paper examines how different scientific publishing models shape both the selection of scientific publications cited in patents and their cognitive alignment with patented technologies. Using large-scale data on non-patent references linking patents to scientific publications, combined with metadata from OpenAlex, we compare the Open Access (OA) structure of patent-cited science to that of […]

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The life of a prescription at Amazon Pharmacy

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The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

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Shattering the Shortcut: A Topology-Regularized Benchmark for Multi-hop Medical Reasoning in LLMs

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arXiv:2603.12458v1 Announce Type: new Abstract: While Large Language Models (LLMs) achieve expert-level performance on standard medical benchmarks through single-hop factual recall, they severely struggle with the complex, multi-hop diagnostic reasoning required in real-world clinical settings. A primary obstacle is “shortcut learning”, where models exploit highly connected, generic hub nodes (e.g., “inflammation”) in knowledge graphs to bypass authentic micro-pathological cascades. To address this, we introduce ShatterMed-QA, a bilingual benchmark of 10,558 multi-hop clinical questions designed to rigorously evaluate deep […]

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