Animation of a flying, flipping microrobot

MIT engineers design an aerial microrobot that can fly as fast as a bumblebee

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In the future, tiny flying robots could be deployed to aid in the search for survivors trapped beneath the rubble after a devastating earthquake. Like real insects, these robots could flit through tight spaces larger robots can’t reach, while simultaneously dodging stationary obstacles and pieces of falling rubble. So far, aerial microrobots have only been able to fly slowly along smooth trajectories, far from the swift, agile flight of real insects — until now. MIT researchers have demonstrated […]

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The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier

The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier

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In Day 6, we saw how a Decision Tree Regressor finds its optimal split by minimizing the Mean Squared Error. Today, for Day 7 of the Machine Learning “Advent Calendar”, we switch to classification. With just one numerical feature and two classes, we explore how a Decision Tree Classifier decides where to cut the data, using impurity measures like Gini and Entropy. Even without doing the math, we can visually guess possible split points. But which one is […]

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NVIDIA to reportedly increase production of H200 AI chip, as U.S. eases China export ban

NVIDIA to reportedly increase production of H200 AI chip, as U.S. eases China export ban

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Key Highlights: The past year has been anything but quiet for the global chip industry. While trade policy under the Trump administration has been a major talking point for months, a latest decision this week has suddenly pushed NVIDIA back into the spotlight. After prolonged uncertainty around AI chip exports, the U.S. government has finally become lenient. NVIDIA is reassessing H200 AI chip production capacity after getting green signal from Trump administration According to an exclusive report by […]

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3 Questions: The pros and cons of synthetic data in AI

3 Questions: The pros and cons of synthetic data in AI

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Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources. While concrete numbers are hard to pin down, some estimates suggest that more than 60 percent of data used for AI applications in 2024 was synthetic, and this figure is expected to grow across industries. Because synthetic data don’t contain real-world information, they hold the promise of safeguarding privacy while reducing the cost and increasing the […]

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A Gaming GPU Helps Crack the Code on a Thousand-Year Cultural Conversation

A Gaming GPU Helps Crack the Code on a Thousand-Year Cultural Conversation

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Ceramics — the humble mix of earth, fire and artistry — have been part of a global conversation for millennia. From Tang Dynasty trade routes to Renaissance palaces, from museum vitrines to high-stakes auction floors, they’ve carried culture across borders, evolving into status symbols, commodities and pieces of contested history. Their value has been shaped by aesthetics and economics, empire and, now, technology. This figure visualizes 20 representative Chinese ceramic craftsmanship styles across seven historical periods, ranging from […]

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Understanding XGBoost: A Deep Dive into the Algorithm

Understanding XGBoost: A Deep Dive into the Algorithm

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Author(s): Utkarsh Mittal Originally published on Towards AI. Introduction XGBoost (Extreme Gradient Boosting) has become the go-to algorithm for winning machine learning competitions and solving real-world prediction problems. But what makes it so powerful? In this comprehensive tutorial, we’ll unpack the mathematical foundations and practical mechanisms that make XGBoost superior to traditional gradient boosting methods. This tutorial assumes you have basic knowledge of decision trees and machine learning concepts. We’ll walk through the algorithm step-by-step with visual examples […]

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Watermarking and Forensics for AI Models, Data, and Deep Neural Networks

Watermarking and Forensics for AI Models, Data, and Deep Neural Networks

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In my previous paper posted here, I explained how I built a new class of non-standard deep neural networks, with various case studies based on synthetic data and open-source code, covering problems such as noise filtering, high-dimensional curve fitting, and predictive analytics. One of the models featured a promising universal function able to represent any type of smooth response, while leading to fast convergence. Here I explore weight sensitivity and distillation. The words weight and parameter are used […]

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