Science and technology

Software supply-chain attacks have moved from a niche security concern to one of the most disruptive forces shaping modern software development. By targeting the tools, libraries, and services that developers trust, attackers can compromise thousands of organizations through a single weak link. High-profile incidents over the past few years have fundamentally altered how teams design, build, and maintain software, pushing security earlier and deeper into the development lifecycle.Gaining Insight into Software Supply-Chain AttacksA software supply-chain attack takes place when adversaries penetrate the development or delivery workflow rather than targeting the final application itself, compromising shared elements like open-source libraries, build…

Phishing has evolved from crude email scams into highly targeted, data-driven attacks, while deepfakes have moved from novelty to operational threat. Together, they create a scalable risk that can undermine trust, drain finances, and compromise strategic decisions. Companies are preparing for these threats by recognizing a central reality: attackers now combine social engineering, artificial intelligence, and automation to operate at unprecedented speed and volume.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. Executives have been…

Robotic dexterity describes a machine’s capacity to handle objects with precise, adaptable, and dependable control even in dynamic, unpredictable settings. Activities like grasping uneven items, assembling parts, or managing delicate materials call for nuanced manipulation that has long been challenging to encode directly. By combining reinforcement learning with large-scale simulation, researchers are transforming how robots develop these abilities, shifting dexterity away from rigid automation and toward more flexible, human-like interaction.Core Principles of Reinforcement Learning for Skilled Dexterous ControlReinforcement learning is a learning paradigm in which an agent improves its behavior by interacting with an environment and receiving feedback in the…

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 is a learning paradigm in which an agent improves its behavior by interacting with an environment and receiving feedback in the form…