Explore the rapidly evolving world of artificial intelligence, machine learning, and automation. From AI ethics to real-world applications, this category delivers insights that matter for today and tomorrow.
Yichao “Peak” Ji is one of MIT Technology Review’s 2025 Innovators Under 35. Meet the rest of this year’s honorees. When Yichao Ji—also known as “Peak”—appeared in a launch video for Manus in March, he didn’t expect it to go viral.…
Deep neural networks have drastically evolved over the years, overcoming common challenges that arise when training these complex models.
Agentic AI is coming of age. And with it comes new opportunities in the financial services sector. Banks are increasingly employing agentic AI to optimize processes, navigate complex systems, and sift through vast quantities of unstructured data to make decisions…
This article provides a summary of and commentary on the recent paper
Earlier this summer, I walked through the glassy lobby of a fancy office in London, into an elevator, and then along a corridor into a clean, carpeted room. Natural light flooded in through its windows, and a large pair of…
Developing machine learning systems entails a well-established lifecycle, consisting of a series of stages from data preparation and preprocessing to modeling, validation, deployment to production, and continuous maintenance.
As brands compete for increasingly price conscious consumers, customer experience (CX) has become a decisive differentiator. Yet many struggle to deliver, constrained by outdated systems, fragmented data, and organizational silos that limit both agility and consistency. The current wave of…
Artificial intelligence is fundamentally reshaping how the world operates. With its potential to automate repetitive tasks, analyze vast datasets, and augment human capabilities, the use of AI technologies is already driving changes across industries. In health care and pharmaceuticals, machine…
Extreme gradient boosting ( XGBoost ) is one of the most prominent machine learning techniques used not only for experimentation and analysis but also in deployed predictive solutions in industry.
In a market flooded with AI promises, health care decision-makers are no longer dazzled by flashy demos or abstract potential. Today, they want pragmatic and pressure-tested products. They want solutions that work for their clinicians, staff, patients, and their bottom line.…