Artificial Intelligence

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Apple names Bangalore University graduate Amar Subramanya new VP of AI – Deccan Herald

Apple names Bangalore University graduate Amar Subramanya new VP of AI  Deccan HeraldFrom Hey Siri to Hey Subbu? Apple hires Amar Subramanya, taking a bite out of Google via Microsoft  Times of IndiaWho is Amar Subramanya, the Google-Microsoft veteran set to lead Apple AI?  Business StandardIndian-origin techie tasked to save Apple’s ‘sinking’ AI ship, he earlier left Google […]

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Satellite image reveals how Jabal Arkanū’s rings survived millions of years in the Sahara Desert

Stunning concentric rings in Libya’s Sahara Desert, Jabal Arkanū, were formed by ancient volcanic activity and millions of years of erosion, not meteorite impacts. Captured from the International Space Station, these formations reveal Earth’s internal dynamics and the remarkable persistence of geological structures in extreme arid climates, offering a unique window into our planet’s history.

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eSafety commissioner probed on Roblox and social media ban following Guardian investigation – video

Independent senator David Pocock asked the eSafety commissioner, Julie Inman Grant, in Senate estimates last night about Guardian Australia’s investigation into Roblox and what children may experience on the platform. With Roblox not subject to the under-16s social media ban, Pocock asked whether Roblox was deemed as a gaming platform or a platform that is

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Stonehenge just got stranger: Archaeologists confirm massive man-made ring of pits underground

New research has confirmed that a vast ring of Neolithic pits, more than a mile wide and over 4,000 years old, was deliberately engineered near Stonehenge, not formed by nature. Using cutting-edge dating and soil-analysis techniques, researchers argue it reflects an ancient belief system inscribed into the land itself. The discovery suggests Stonehenge’s builders shaped

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How We Learn Step-Level Rewards from Preferences to Solve Sparse-Reward Environments Using Online Process Reward Learning

In this tutorial, we explore Online Process Reward Learning (OPRL) and demonstrate how we can learn dense, step-level reward signals from trajectory preferences to solve sparse-reward reinforcement learning tasks. We walk through each component, from the maze environment and reward-model network to preference generation, training loops, and evaluation, while observing how the agent gradually improves

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