Machine Learning

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Google removes some AI health summaries after investigation finds “dangerous” flaws

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On Sunday, Google removed some of its AI Overviews health summaries after a Guardian investigation found people were being put at risk by false and misleading information. The removals came after the newspaper found that Google’s generative AI feature delivered inaccurate health information at the top of search results, potentially leading seriously ill patients to

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How to Build Portable, In-Database Feature Engineering Pipelines with Ibis Using Lazy Python APIs and DuckDB Execution

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In this tutorial, we demonstrate how we use Ibis to build a portable, in-database feature engineering pipeline that looks and feels like Pandas but executes entirely inside the database. We show how we connect to DuckDB, register data safely inside the backend, and define complex transformations using window functions and aggregations without ever pulling raw

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3 Questions: How AI could optimize the power grid

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Artificial intelligence has captured headlines recently for its rapidly growing energy demands, and particularly the surging electricity usage of data centers that enable the training and deployment of the latest generative AI models. But it’s not all bad news — some AI tools have the potential to reduce some forms of energy consumption and enable cleaner grids.One

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Decoding the Arctic to predict winter weather

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Every autumn, as the Northern Hemisphere moves toward winter, Judah Cohen starts to piece together a complex atmospheric puzzle. Cohen, a research scientist in MIT’s Department of Civil and Environmental Engineering (CEE), has spent decades studying how conditions in the Arctic set the course for winter weather throughout Europe, Asia, and North America. His research

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Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction

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A team of Stanford Medicine researchers have introduced SleepFM Clinical, a multimodal sleep foundation model that learns from clinical polysomnography and predicts long term disease risk from a single night of sleep. The research work is published in Nature Medicine and the team has released the clinical code as the open source sleepfm-clinical repository on

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A Coding Implementation to Build a Unified Apache Beam Pipeline Demonstrating Batch and Stream Processing with Event-Time Windowing Using DirectRunner

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In this tutorial, we demonstrate how to build a unified Apache Beam pipeline that works seamlessly in both batch and stream-like modes using the DirectRunner. We generate synthetic, event-time–aware data and apply fixed windowing with triggers and allowed lateness to demonstrate how Apache Beam consistently handles both on-time and late events. By switching only the

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TII Abu-Dhabi Released Falcon H1R-7B: A New Reasoning Model Outperforming Others in Math and Coding with only 7B Params with 256k Context Window

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Technology Innovation Institute (TII), Abu Dhabi, has released Falcon-H1R-7B, a 7B parameter reasoning specialized model that matches or exceeds many 14B to 47B reasoning models in math, code and general benchmarks, while staying compact and efficient. It builds on Falcon H1 7B Base and is available on Hugging Face under the Falcon-H1R collection. Falcon-H1R-7B is

TII Abu-Dhabi Released Falcon H1R-7B: A New Reasoning Model Outperforming Others in Math and Coding with only 7B Params with 256k Context Window Read More »

Implementing Softmax From Scratch: Avoiding the Numerical Stability Trap

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In deep learning, classification models don’t just need to make predictions—they need to express confidence. That’s where the Softmax activation function comes in. Softmax takes the raw, unbounded scores produced by a neural network and transforms them into a well-defined probability distribution, making it possible to interpret each output as the likelihood of a specific

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Dummy Variable Trap in Machine Learning Explained Simply

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In machine learning with categorical data, it is common to encode the categories as dummy variables (sometimes called one hot encoding) to encode categories as numerical values. This is a significant step since there are many algorithms that do not operate on other things other than numbers like linear regression. Nevertheless, there is one of the mistakes

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Liquid AI Releases LFM2.5: A Compact AI Model Family For Real On Device Agents

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Liquid AI has introduced LFM2.5, a new generation of small foundation models built on the LFM2 architecture and focused at on device and edge deployments. The model family includes LFM2.5-1.2B-Base and LFM2.5-1.2B-Instruct and extends to Japanese, vision language, and audio language variants. It is released as open weights on Hugging Face and exposed through the

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