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Understanding Machine Learning Interatomic Potentials in Chemistry and Materials Science

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Machine learning interatomic potentials (MLIPs) sit in a sweet spot between classical force fields and expensive quantum chemistry. They learn an approximation of the potential energy surface from reference calculations (often density functional theory or higher-level methods), then use that learned mapping to run molecular dynamics and materials simulations far faster than direct quantum calculations—while keeping much more chemical realism than many traditional empirical potentials. That speed-up changes what scientists can attempt: longer time scales, larger systems, broader screening campaigns, and faster iteration between hypothesis and simulation. But MLIPs also introduce new failure modes: silent extrapolation, dataset bias, uncertain reproducibility, and “it looks right” results that may not hold outside the training domain. This page explains MLIPs in a practical way—how they work, which families exist, how to build them responsibly, and how to trust (or distrust...
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Maximizing Productivity with December 2025 Gemini App Updates

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December 2025 is a useful checkpoint for the Gemini app. Instead of “one big redesign,” the month’s updates are best understood as a set of practical capabilities that make Gemini more helpful in everyday work: faster responses, more grounded research, better visual editing, and more context-rich local results. This page breaks down what’s new in the Gemini app in December 2025 and, more importantly, how to turn those updates into repeatable productivity workflows you can use daily—planning, research, writing, and decision-making—without getting overwhelmed by options. TL;DR Faster core model: Gemini 3 Flash (a major model upgrade) is now available globally, improving speed and everyday responsiveness. Sharper research workflows: NotebookLM can be used as a source in Gemini, and Deep Research reports now include visuals for Ultra users to digest dense information faster. More practical “do” features: Image edits are more precise (Nano Banana), and l...
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Strengthening ChatGPT Atlas Against Prompt Injection: A New Approach in AI Security

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As AI systems become more agentic—opening webpages, clicking buttons, reading emails, and taking actions on a user’s behalf—security risks shift in a very specific direction. Traditional web threats often target humans (phishing) or software vulnerabilities (exploits). But browser-based AI agents introduce a different and growing risk: prompt injection , where malicious instructions are embedded inside content the agent reads, with the goal of steering the agent away from the user’s intent. This matters for systems like ChatGPT Atlas because an agent operating in a browser must constantly interact with untrusted content—webpages, documents, emails, forms, and search results. If an attacker can influence what the agent “sees,” they can attempt to manipulate what the agent does. The core challenge is that the open web is designed to be expressive and untrusted; agents are designed to interpret and act. That intersection is where prompt injection thrives. TL;DR ...
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How Leading Companies Harness AI to Transform Work and Society

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AI is no longer “one tool in the toolbox.” In many organizations, it’s becoming an operating layer that sits across customer service, analytics, security, design, and research. That shift is visible across industries: payments, airlines, enterprise software, banking, biotechnology, and creative platforms are all experimenting with (or already deploying) AI to reduce cycle time, improve decisions, and offer more personalized experiences. But “companies using AI” is too broad to be useful. The more interesting question is how they use it: which workflows they target first, what changes actually stick, and where ethical and operational risks appear when AI is embedded into everyday work. TL;DR Top firms tend to deploy AI in repeatable, high-volume workflows first (support, ops, risk, reporting), then expand into higher-stakes decisions with stronger governance. Practical wins usually come from workflow redesign (clear ownership + approvals + monitoring), no...
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AI-Driven Growth in Hyperscale Data Centers: Sustainability and Privacy Challenges

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Hyperscale data centers are expanding because AI workloads are fundamentally different from “classic” enterprise compute. Training and serving modern models tends to concentrate demand into GPU clusters, high-bandwidth networking, and storage systems that can move and protect massive datasets. The result is a new kind of build cycle: more power density, faster hardware refresh, and bigger capital expenditure (capex) decisions tied to accelerators and the infrastructure around them. This growth is not only an engineering story. It’s also a privacy and sustainability story. As more sensitive data flows into AI pipelines—customer records, product telemetry, documents, support transcripts—the data center becomes a central trust boundary. At the same time, energy use and cooling constraints push operators to balance performance with environmental commitments and local regulations. TL;DR Capex shifts: AI pushes spending toward GPUs/accelerators, networking, and power...
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Ethical Reflections on the Roomba’s Shortcomings in Autonomous Cleaning

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The Roomba, an autonomous vacuum cleaner, has been widely adopted to assist with household cleaning. However, its performance has sometimes fallen short of user expectations, prompting ethical reflections on AI in consumer robotics. TL;DR The article reports concerns about Roomba’s inconsistent cleaning and its impact on user trust. It highlights ethical issues around transparency, privacy, and data handling in robotic devices. Environmental and social implications of robotic cleaners are also discussed in relation to sustainability and labor. Performance and User Trust Users have noted that the Roomba may miss areas or encounter difficulties with obstacles, which can reduce confidence in its reliability. These issues are especially significant for those relying on such devices due to physical challenges, raising ethical questions about product effectiveness and user dependence. Transparency in Capabilities Clear communication about what the Roo...
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Examining the $555,000 AI Safety Role: Addressing Cognitive Bias in ChatGPT

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When a company offers up to $555,000 per year (plus equity) for a single safety leadership role, it’s usually not because the job is glamorous. It’s because the work sits at the intersection of fast-moving model capability, high-stakes risk, and real-world uncertainty. That was the context for OpenAI’s “ Head of Preparedness ” position—shared publicly by Sam Altman as a critical, high-pressure role intended to help OpenAI evaluate and mitigate the kinds of frontier risks that can cause severe harm. The public discussion around the job highlighted several domains at once: cybersecurity misuse, biological risk, model release decisions, and broader concerns about how advanced systems may affect people when deployed at scale. TL;DR The role: “Head of Preparedness” — a safety leadership position focused on OpenAI’s Preparedness framework and severe-harm risk domains. The pay: the job listing described compensation up to $555,000 annually plus equity. Th...
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