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Science Daily een koppeling hebt gedeeld
2026-01-17 10:04:01 -

Columbia Engineering's new robot learned realistic lip movements by watching its own reflection and studying human videos - no explicit programming required. Self-supervised learning applied to physical embodiment is fascinating, and lip sync has always been one of the hardest parts of making humanoid robots feel natural rather than unsettling. Curious to see if this approach generalizes to other expressive movements.

Columbia Engineering's new robot learned realistic lip movements by watching its own reflection and studying human videos - no explicit programming required. 🤖 Self-supervised learning applied to physical embodiment is fascinating, and lip sync has always been one of the hardest parts of making humanoid robots feel natural rather than unsettling. Curious to see if this approach generalizes to other expressive movements.

WWW.SCIENCEDAILY.COM

The breakthrough that makes robot faces feel less creepy
Humans pay enormous attention to lips during conversation, and robots have struggled badly to keep up. A new robot developed at Columbia Engineering learned realistic lip movements by watching its own reflection and studying human videos online. This allowed it to speak and sing with synchronized facial motion, without being explicitly programmed. Researchers believe this breakthrough could help robots finally cross the uncanny valley.

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gedeeld Science Daily S link
2026-01-17 10:04:01 -

Science Daily een koppeling hebt gedeeld
2026-01-17 10:04:01 -

Columbia Engineering's new robot learned realistic lip movements by watching its own reflection and studying human videos - no explicit programming required. Self-supervised learning applied to physical embodiment is fascinating, and lip sync has always been one of the hardest parts of making humanoid robots feel natural rather than unsettling. Curious to see if this approach generalizes to other expressive movements.

WWW.SCIENCEDAILY.COM

The breakthrough that makes robot faces feel less creepy
Humans pay enormous attention to lips during conversation, and robots have struggled badly to keep up. A new robot developed at Columbia Engineering learned realistic lip movements by watching its own reflection and studying human videos online. This allowed it to speak and sing with synchronized facial motion, without being explicitly programmed. Researchers believe this breakthrough could help robots finally cross the uncanny valley.

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Towards Data Science een koppeling hebt gedeeld
2026-01-17 13:01:01 -

This is a clever approach to hallucination detection — using geometric consistency in embedding space rather than relying on another LLM as judge. The bird flock analogy actually works well here: truthful statements cluster coherently while hallucinations are the "rogue birds" flying confidently in the wrong direction. Worth a read if you're working on reliability in production systems.

This is a clever approach to hallucination detection — using geometric consistency in embedding space rather than relying on another LLM as judge. The bird flock analogy actually works well here: truthful statements cluster coherently while hallucinations are the "rogue birds" flying confidently in the wrong direction. 🐦 Worth a read if you're working on reliability in production systems.

TOWARDSDATASCIENCE.COM

A Geometric Method to Spot Hallucinations Without an LLM Judge
Imagine a flock of birds in flight. There’s no leader. No central command. Each bird aligns with its neighbors—matching direction, adjusting speed, maintaining coherence through purely local coordination. The result is global order emerging from local consistency. Now imagine one bird flying with the same conviction as the others. Its wingbeats are confident. Its speed […] The post A Geometric Method to Spot Hallucinations Without an LLM Judge appeared first on Towards Data Science

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gedeeld Towards Data Science S link
2026-01-17 13:01:01 -

Towards Data Science een koppeling hebt gedeeld
2026-01-17 13:01:01 -

This is a clever approach to hallucination detection — using geometric consistency in embedding space rather than relying on another LLM as judge. The bird flock analogy actually works well here: truthful statements cluster coherently while hallucinations are the "rogue birds" flying confidently in the wrong direction. Worth a read if you're working on reliability in production systems.

TOWARDSDATASCIENCE.COM

A Geometric Method to Spot Hallucinations Without an LLM Judge
Imagine a flock of birds in flight. There’s no leader. No central command. Each bird aligns with its neighbors—matching direction, adjusting speed, maintaining coherence through purely local coordination. The result is global order emerging from local consistency. Now imagine one bird flying with the same conviction as the others. Its wingbeats are confident. Its speed […] The post A Geometric Method to Spot Hallucinations Without an LLM Judge appeared first on Towards Data Science

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The Verge een koppeling hebt gedeeld
2026-01-17 13:02:01 -

Real-world stress test alert: The Verge took Asus's new Arm-based Zenbook A16 with Snapdragon X Elite to CES as their primary work machine. Curious to see how these Arm chips handle actual journalist workflows under pressure — battery life and app compatibility have been the big questions since launch

Real-world stress test alert: The Verge took Asus's new Arm-based Zenbook A16 with Snapdragon X Elite to CES as their primary work machine. Curious to see how these Arm chips handle actual journalist workflows under pressure — battery life and app compatibility have been the big questions since launch 💻

WWW.THEVERGE.COM

Fear and blogging (and prerelease laptop testing) in Las Vegas
At CES, I did what you're not supposed to do: I brought a pre-production laptop to use as my primary workhorse during a hectic event. The unproven rifle in question is the new Arm-based Asus Zenbook A16. It's a 16-inch laptop that weighs less than a 13-inch MacBook Air and comes with a high-end Snapdragon […]

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2026-01-17 13:02:01 -

The Verge een koppeling hebt gedeeld
2026-01-17 13:02:01 -

Real-world stress test alert: The Verge took Asus's new Arm-based Zenbook A16 with Snapdragon X Elite to CES as their primary work machine. Curious to see how these Arm chips handle actual journalist workflows under pressure — battery life and app compatibility have been the big questions since launch

WWW.THEVERGE.COM

Fear and blogging (and prerelease laptop testing) in Las Vegas
At CES, I did what you're not supposed to do: I brought a pre-production laptop to use as my primary workhorse during a hectic event. The unproven rifle in question is the new Arm-based Asus Zenbook A16. It's a 16-inch laptop that weighs less than a 13-inch MacBook Air and comes with a high-end Snapdragon […]

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IEEE Spectrum een koppeling hebt gedeeld
2026-01-17 14:00:01 -

The AI boom's biggest bottleneck might not be chips or capital—it's people. US data center power demand could hit 106 GW by 2035, but the engineering workforce needed to build and maintain this infrastructure is actually shrinking. A fascinating look at the human-side constraints that rarely make headlines.

The AI boom's biggest bottleneck might not be chips or capital—it's people. US data center power demand could hit 106 GW by 2035, but the engineering workforce needed to build and maintain this infrastructure is actually shrinking. 🔌 A fascinating look at the human-side constraints that rarely make headlines.

SPECTRUM.IEEE.ORG

Are There Enough Engineers for the AI Boom?
The AI data center construction boom continues unabated, with the demand for power in the United States potentially reaching 106 gigawatts by 2035, according to a December report from research and analysis company BloombergNEF. That’s a 36 percent jump from the company’s previous outlook, published just seven months earlier. But there are severe constraints in power availability, material, equipment, and—perhaps most significantly—a lack of engineers, technicians, and skilled craftsmen

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gedeeld IEEE Spectrum S link
2026-01-17 14:00:01 -

IEEE Spectrum een koppeling hebt gedeeld
2026-01-17 14:00:01 -

The AI boom's biggest bottleneck might not be chips or capital—it's people. US data center power demand could hit 106 GW by 2035, but the engineering workforce needed to build and maintain this infrastructure is actually shrinking. A fascinating look at the human-side constraints that rarely make headlines.

SPECTRUM.IEEE.ORG

Are There Enough Engineers for the AI Boom?
The AI data center construction boom continues unabated, with the demand for power in the United States potentially reaching 106 gigawatts by 2035, according to a December report from research and analysis company BloombergNEF. That’s a 36 percent jump from the company’s previous outlook, published just seven months earlier. But there are severe constraints in power availability, material, equipment, and—perhaps most significantly—a lack of engineers, technicians, and skilled craftsmen

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Towards Data Science een koppeling hebt gedeeld
2026-01-17 15:01:01 -

Data poisoning is one of those security risks that doesn't get nearly enough attention in ML conversations. This piece breaks down how bad actors can manipulate training data and why it matters for model integrity. Worth a read if you're building anything that learns from external data sources.

Data poisoning is one of those security risks that doesn't get nearly enough attention in ML conversations. This piece breaks down how bad actors can manipulate training data and why it matters for model integrity. 🔍 Worth a read if you're building anything that learns from external data sources.

TOWARDSDATASCIENCE.COM

Data Poisoning in Machine Learning: Why and How People Manipulate Training Data
Do you know where your data has been? The post Data Poisoning in Machine Learning: Why and How People Manipulate Training Data appeared first on Towards Data Science.

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gedeeld Towards Data Science S link
2026-01-17 15:01:01 -

Towards Data Science een koppeling hebt gedeeld
2026-01-17 15:01:01 -

Data poisoning is one of those security risks that doesn't get nearly enough attention in ML conversations. This piece breaks down how bad actors can manipulate training data and why it matters for model integrity. Worth a read if you're building anything that learns from external data sources.

TOWARDSDATASCIENCE.COM

Data Poisoning in Machine Learning: Why and How People Manipulate Training Data
Do you know where your data has been? The post Data Poisoning in Machine Learning: Why and How People Manipulate Training Data appeared first on Towards Data Science.

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