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Science and Technology

How are companies preparing for phishing and deepfake threats at scale?

How do businesses prepare for widespread phishing and deepfake attacks?

Phishing has shifted from simple mass emails to precise, data‑fueled assaults, and deepfakes have progressed from mere curiosities to active operational threats; together, they introduce a rapidly scalable danger capable of eroding trust, draining resources, and steering critical decisions off course, prompting companies to prepare by acknowledging a key fact: adversaries now merge social engineering with artificial intelligence and automation to strike with unmatched speed and scale.Recent industry data shows that phishing remains the most common initial attack vector in major breaches, and the rise of audio and video deepfakes has added a new layer of credibility to impersonation attacks.…
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Fotos de stock gratuitas de alambrado, analytics, artificial brain

Which quantum error correction methods are most promising?

Quantum computers promise exponential speedups for certain problems, but they are exceptionally fragile. Quantum bits, or qubits, are highly sensitive to noise from their environment, including thermal fluctuations, electromagnetic interference, and imperfections in control systems. Even small disturbances can introduce errors that quickly overwhelm a computation.Quantum error correction (QEC) addresses this challenge by encoding logical qubits into entangled states of multiple physical qubits, allowing errors to be detected and corrected without directly measuring and collapsing the quantum information. Over the past decade, several QEC approaches have moved from theory to experimental demonstrations, with measurable improvements in error rates, scalability, and…
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How are reinforcement learning and simulation improving robot dexterity?

The role of reinforcement learning and simulation in robot dexterity

Robotic dexterity refers to a machine’s ability to manipulate objects with precision, adaptability, and reliability in complex, changing environments. Tasks such as grasping irregular objects, assembling components, or handling fragile items require subtle control that has historically been difficult to program explicitly. Reinforcement learning and large-scale simulation have emerged as complementary tools that are reshaping how robots acquire these skills, moving dexterity from rigid automation toward flexible, human-like manipulation.Core Principles of Reinforcement Learning for Skilled Dexterous ControlReinforcement learning describes a paradigm where an agent refines its behavior through interactions with an environment, guided by rewards or penalties. In the context…
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An asteroid discovered days ago will narrowly miss Earth

New asteroid discovery to pass very close to Earth

A newly identified asteroid is set to pass relatively near Earth this Monday, drawing interest from astronomers and space agencies around the globe. Although the cosmic gap is small, specialists highlight that the object poses no threat to the planet and will move along its course safely through space.Astronomers are closely monitoring an asteroid known as 2026JH2, a rocky object expected to glide past Earth at an estimated distance of about 91,593 kilometers, roughly 56,900 miles. According to calculations from the European Space Agency, its trajectory will bring it to nearly one quarter of the usual gap between Earth and…
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How are companies preparing for phishing and deepfake threats at scale?

Companies’ readiness for phishing and deepfake threats on a large scale

Phishing has shifted from simple mass emails to precise, data‑fueled assaults, and deepfakes have progressed from mere curiosities to active operational threats; together, they introduce a rapidly scalable danger capable of eroding trust, draining resources, and steering critical decisions off course, prompting companies to prepare by acknowledging a key fact: adversaries now merge social engineering with artificial intelligence and automation to strike with unmatched speed and scale.Recent industry reports indicate that phishing continues to serve as the leading entry point for major breaches, while the emergence of audio and video deepfakes has introduced a more convincing dimension to impersonation schemes.…
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What ethical debates are emerging around AI-generated scientific results?

Debating the ethics of AI-driven scientific discoveries

Artificial intelligence systems are now being deployed to produce scientific outcomes, from shaping hypotheses and conducting data analyses to running simulations and crafting entire research papers. These tools can sift through enormous datasets, detect patterns with greater speed than human researchers, and take over segments of the scientific process that traditionally demanded extensive expertise. Although such capabilities offer accelerated discovery and wider availability of research resources, they also raise ethical questions that unsettle long‑standing expectations around scientific integrity, responsibility, and trust. These concerns are already tangible, influencing the ways research is created, evaluated, published, and ultimately used within society.Authorship, Attribution,…
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Obesity: why the approach is changing

Obesity care: why it’s changing

Obesity is increasingly understood not as a matter of willpower or aesthetics, but as a multifaceted, long‑term medical condition shaped by biological, behavioral, social, and environmental influences. This broader understanding has prompted major shifts in prevention strategies, clinical practice, public policy, and scientific research. This article outlines the factors behind this change, reviews supporting evidence and examples, presents emerging tools and care models, and examines the challenges and consequences for patients, healthcare professionals, and communities.What obesity is and why it mattersObesity is commonly identified using body mass index thresholds (BMI ≥30 kg/m² for adults), though this metric offers only a…
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What trends are reshaping software development with AI code generation?

Reshaping software development: AI code generation trends

AI code generation has evolved from a cutting‑edge experiment into a core pillar of contemporary software creation, shifting from simple snippet autocompletion to influencing architectural planning, testing approaches, security evaluations, and team operations, ultimately marking a major shift not only in development speed but in how humans and machines now collaborate throughout the entire software lifecycle.Copilots Pervading Everything: Spanning IDEs and the Broader ToolchainEarly AI coding assistants focused on in-editor suggestions. Today, copilots are embedded across the stack, including requirements gathering, code review, testing, deployment, and observability.IDE copilots generate functions, refactor legacy code, and explain unfamiliar codebases in real time.Pull…
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Placebo and nocebo: the power of expectation in health

The impact of expectation: placebo and nocebo phenomena

Expectations influence physiology, and the terms placebo and nocebo describe the corresponding beneficial or adverse results shaped by those expectations. A placebo effect arises when an inert intervention or therapeutic context leads to an improvement in health, whereas a nocebo effect appears when harmful outcomes or unwanted symptoms emerge due to negative expectations. These responses are not imaginary; they trigger observable shifts in symptoms, biological indicators, neural activity, and behavior. Grasping these effects is essential for clinical practice, research design, public health strategies, and responsible communication.Key Definitions and DistinctionsPlacebo: improvement attributable to psychological and contextual factors rather than the specific…
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How are serverless and container platforms evolving for AI workloads?

Evolving Serverless & Containers for AI

Artificial intelligence workloads have reshaped how cloud infrastructure is designed, deployed, and optimized. Serverless and container platforms, once focused on web services and microservices, are rapidly evolving to meet the unique demands of machine learning training, inference, and data-intensive pipelines. These demands include high parallelism, variable resource usage, low-latency inference, and tight integration with data platforms. As a result, cloud providers and platform engineers are rethinking abstractions, scheduling, and pricing models to better serve AI at scale.How AI Processing Strains Traditional Computing PlatformsAI workloads vary significantly from conventional applications in several key respects:Elastic but bursty compute needs: Model training may…
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