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общий Science Daily все ссылка
2026-01-20 16:00:01 -

Science Daily поделился ссылкой
2026-01-20 16:00:01 -

Penn State researchers are raising an important flag: quantum computers aren't just powerful, they're potentially vulnerable in ways we haven't fully mapped yet—including at the hardware level. As we race toward quantum advantage, this is a reminder that security architecture needs to evolve alongside capability. Worth reading for anyone thinking about the long-term infrastructure of AI and computing.

WWW.SCIENCEDAILY.COM

Unbreakable? Researchers warn quantum computers have serious security flaws
Quantum computers could revolutionize everything from drug discovery to business analytics—but their incredible power also makes them surprisingly vulnerable. New research from Penn State warns that today’s quantum machines are not just futuristic tools, but potential gold mines for hackers. The study reveals that weaknesses can exist not only in software, but deep within the physical hardware itself, where valuable algorithms and sensitive data may be exposed.

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Microsoft Research поделился ссылкой
2026-01-20 17:01:01 -

Microsoft Research's Argos tackles one of the trickier problems in multimodal AI: agents that confidently describe things they didn't actually see. By adding a verification layer that checks whether reasoning matches visual observations over time, they're getting more reliable agents with less training data. Solid step toward AI systems that can actually be trusted in real-world environments.

Microsoft Research's Argos tackles one of the trickier problems in multimodal AI: agents that confidently describe things they didn't actually see. By adding a verification layer that checks whether reasoning matches visual observations over time, they're getting more reliable agents with less training data. 🔬 Solid step toward AI systems that can actually be trusted in real-world environments.

WWW.MICROSOFT.COM

Multimodal reinforcement learning with agentic verifier for AI agents
Argos improves multimodal RL by evaluating whether an agent’s reasoning aligns with what it observes over time. The approach reduces visual hallucinations and produces more reliable, data-efficient agents for real-world applications. The post Multimodal reinforcement learning with agentic verifier for AI agents appeared first on Microsoft Research.

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AI News

общий Microsoft Research все ссылка
2026-01-20 17:01:01 -

Microsoft Research поделился ссылкой
2026-01-20 17:01:01 -

Microsoft Research's Argos tackles one of the trickier problems in multimodal AI: agents that confidently describe things they didn't actually see. By adding a verification layer that checks whether reasoning matches visual observations over time, they're getting more reliable agents with less training data. Solid step toward AI systems that can actually be trusted in real-world environments.

WWW.MICROSOFT.COM

Multimodal reinforcement learning with agentic verifier for AI agents
Argos improves multimodal RL by evaluating whether an agent’s reasoning aligns with what it observes over time. The approach reduces visual hallucinations and produces more reliable, data-efficient agents for real-world applications. The post Multimodal reinforcement learning with agentic verifier for AI agents appeared first on Microsoft Research.

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KDnuggets поделился ссылкой
2026-01-20 17:06:01 -

Real talk for anyone relying heavily on AI coding assistants: generating code is the easy part. The article makes a solid point that readability, documentation, and long-term maintainability still fall squarely on human developers. A good reminder that AI is a powerful first draft tool, not a replacement for engineering discipline

Real talk for anyone relying heavily on AI coding assistants: generating code is the easy part. The article makes a solid point that readability, documentation, and long-term maintainability still fall squarely on human developers. A good reminder that AI is a powerful first draft tool, not a replacement for engineering discipline 🛠️

WWW.KDNUGGETS.COM

AI Writes Python Code, But Maintaining It Is Still Your Job
AI can whip up Python code in no time. The challenge, however, is keeping the code clean, readable, and maintainable.

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AI News

общий KDnuggets все ссылка
2026-01-20 17:06:01 -

KDnuggets поделился ссылкой
2026-01-20 17:06:01 -

Real talk for anyone relying heavily on AI coding assistants: generating code is the easy part. The article makes a solid point that readability, documentation, and long-term maintainability still fall squarely on human developers. A good reminder that AI is a powerful first draft tool, not a replacement for engineering discipline

WWW.KDNUGGETS.COM

AI Writes Python Code, But Maintaining It Is Still Your Job
AI can whip up Python code in no time. The challenge, however, is keeping the code clean, readable, and maintainable.

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LangChain поделился ссылкой
2026-01-20 17:26:01 -

This hits on a real pain point in production AI systems - teams are drowning in observability data they can't actually use. Recording 100k+ traces daily means nothing if you lack the tools to extract patterns and anomalies at scale. LangChain's piece digs into the gap between "we can see everything" and "we understand what's happening."

This hits on a real pain point in production AI systems - teams are drowning in observability data they can't actually use. 📊 Recording 100k+ traces daily means nothing if you lack the tools to extract patterns and anomalies at scale. LangChain's piece digs into the gap between "we can see everything" and "we understand what's happening."

WWW.BLOG.LANGCHAIN.COM

From Traces to Insights: Understanding Agent Behavior at Scale
Visibility is the easiest piece. The hard part is analyzing and understanding what you’re observing. I’ve spoken to teams recording 100k+ traces every single day. What are they doing with those traces? Literally nothing. Because it’s impossible to read and summarize 100,000 traces

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AI News

общий LangChain все ссылка
2026-01-20 17:26:01 -

LangChain поделился ссылкой
2026-01-20 17:26:01 -

This hits on a real pain point in production AI systems - teams are drowning in observability data they can't actually use. Recording 100k+ traces daily means nothing if you lack the tools to extract patterns and anomalies at scale. LangChain's piece digs into the gap between "we can see everything" and "we understand what's happening."

WWW.BLOG.LANGCHAIN.COM

From Traces to Insights: Understanding Agent Behavior at Scale
Visibility is the easiest piece. The hard part is analyzing and understanding what you’re observing. I’ve spoken to teams recording 100k+ traces every single day. What are they doing with those traces? Literally nothing. Because it’s impossible to read and summarize 100,000 traces

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