When AlphaFold2 revolutionized drug discovery in 2020, its success relied entirely on the roughly 170,000 protein structures collected by scientists since 1971...

When AlphaFold2 revolutionized drug discovery in 2020, its success relied entirely on the roughly 170,000 protein structures collected by scientists since 1971 and preserved in the Protein Data Bank. Measured data is the backbone for all AI models and workflows that process data as it’s created, act on what matters in real time, and analyzes data for deep insights. With the current rise of modern…

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Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected. Traditional...

Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected. Traditional safety which was built for structured environments can not work anymore as the spaces become more unstructured and robots move out of cages. AI-driven safety is the key. Marking a major milestone in the arrival of physical AI…

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Climate & Sustainability

NVIDIA Quantum InfiniBand now offers intent-based security profiles in Unified Fabric Manager (UFM) that enable multi-tenant fabric security in a single...

NVIDIA Quantum InfiniBand now offers intent-based security profiles in Unified Fabric Manager (UFM) that enable multi-tenant fabric security in a single click. NVIDIA Quantum InfiniBand supports three profiles: General, Bare Metal Cloud, and Secured Bare Metal Cloud. Network administrators can now auto-configure: This cuts deployment time to minutes from hours or days…

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This post is the third of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Post-Training...

This post is the third of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Post-Training Quantization Using NVIDIA Model Optimizer. Converting a quantized checkpoint into an NVIDIA TensorRT engine bridges the gap between model optimization and production deployment, enabling faster inference, higher throughput…

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Federated learning (FL) research often begins with a deceptively simple question: What should we try next? A new aggregation rule, a FedProx coefficient, a...

Federated learning (FL) research often begins with a deceptively simple question: What should we try next? A new aggregation rule, a FedProx coefficient, a server optimizer setting, a SCAFFOLD variant, or a model architecture tweak may all look promising before an experiment starts. After the run finishes, the harder questions begin: Did the change actually improve the metric?

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Informational and operational technology data have long been treated as separate domains.

But AI changed the game. Today, you need the capacity to regularly ingest OT data into your IT systems without a hitch. (Or a breach.) You risk being left behind as your competitors put all their data to work, or assume the risk of consistently importing data from the edge to your internal systems. 

This problem is an immediate one for any company unwilling to be left behind in the AI era: If you want to take full advantage of AI, you need quick, ready access to relevant data. And if your physical operations have hit a snag, your digital tools need to be kept in the loop regularly.

The solution is not to build a host of custom scripts or depend on legacy FTP or SFTP solutions to bring data in from the edge. Those disparate tools can degrade, leak data, and fail during later, repeated OT data extraction runs.

Instead, engineers looking to free IT and OT data from their respective siloes are turning to a managed solution that offers strong encryption, continuous transfer monitoring, and the ability to fully audit every data handoff across the pipeline

Even more, OT systems — the Programmable Logic Controllers, Supervisory Control and Data Acquisition platforms, and historian databases running protocols like Modbus and OPC UA — were designed for uptime rather than connectivity. In modern architecture, however, no operational data can be left behind.

Getting data out of these environments means working against a connectivity model that was never meant to support the polling frequency or authentication patterns that modern IT infrastructure expects. Adding to the challenge, the more tools you introduce to free the OT data, the more attack vectors they may open.

A breach at the OT boundary can affect the physical systems those networks control. That’s a risk calculus most IT security frameworks weren’t built to handle.

On at 12 p.m. Eastern/9 a.m. On Tuesday, June 23, Fortra’s Jerrod Foster & Michael Barford will join The New Stack to discuss IT and OT systems, why extracting operational technology data is challenging, and how Fortra GoAnywhere MFT can resolve both data movement and data security issues that many engineers face today.

Register here to join the conversation:

What you’ll take away:

• Why the IT/OT boundary is an AI infrastructure problem: How the connectivity gap between operational and information technology creates a hard ceiling for teams building on live operational data — and what becomes possible when that data is reliably accessible inside modern pipelines.
• Where DIY solutions break: Why custom scripts and legacy transfer tools fail under real operational conditions — brittle transfers, no visibility, and attack surfaces you can’t audit.
• What secure OT data movement actually looks like: How Fortra GoAnywhere MFT provides an encrypted, automated, and auditable data movement layer that works with the constraints of real OT environments, not against them.

The post How to get operational data off the factory floor without creating an IT breach appeared first on The New Stack.

AI agents are changing how you interact with your PC. Creators, developers, and AI enthusiasts are already using these agents extensively to assist with...

AI agents are changing how you interact with your PC. Creators, developers, and AI enthusiasts are already using these agents extensively to assist with day-to-day tasks such as coding, video editing, and content management. NVIDIA and Microsoft are teaming up to enable the next generation of developers to build on-device agents on the Windows platform, with easier setup, native security…

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As AI agents move from the digital world to the physical environment, they can readily use NVIDIA Jetson to accelerate real-world deployment with optimized...

As AI agents move from the digital world to the physical environment, they can readily use NVIDIA Jetson to accelerate real-world deployment with optimized memory and performance. NVIDIA JetPack 7.2 directly supports one-command deployment of NVIDIA NemoClaw, an open source stack that adds privacy and security controls to OpenClaw. It introduces NVIDIA agent skills for Jetson—Jetson device…

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Last week, the US government announced $2 billion in investments in quantum computing companies, allocating $100 million each to a range of startups in exchange for equity in the companies. Those could be make-or-break investments for many companies that are likely years away from a product that could see widespread use. But a member of the US Congress is now arguing that those deals are illegal, as Congress did not allocate the money for this purpose—instead, it was meant to support public research in semiconductors.

But the biggest chunk of money would go to a company that likely wouldn't exist if it weren't for the government's backing. Anderon will be set up with a billion dollars each from IBM and the government and will inherit personnel and IP from IBM. It will serve as a foundry for fabricating quantum processing units and will contract its services out to IBM and any other company that wants access to cutting-edge hardware.

Is any of this legal?

Zoe Lofgren (D–Calif.), the ranking member of the House Science, Space, and Technology Committee, made it clear that she is not happy with how the government is using its money to support this technology.

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The US government will take equity stakes worth a total of $2 billion in a slew of quantum computing companies, including a startup backed by a firm with links to the Trump family and one taken public by a Pentagon official.

The announcement by the commerce department that it had signed letters of intent with nine companies—including GlobalFoundries and IBM—sent shares in quantum specialists soaring on Thursday.

Both IBM, which is set to get $1 billion, and GlobalFoundries, which will receive $375 million, were up more than 6 percent in pre-market trading. D-Wave Quantum, an awardee that was taken public in 2022 by Emil Michael—now a top Pentagon official—was up more than 20 percent.

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A massive-scale X-ray free-electron laser (XFEL) enables tracking structural and electron dynamics in novel systems, including fusion materials, semiconductors,...

A massive-scale X-ray free-electron laser (XFEL) enables tracking structural and electron dynamics in novel systems, including fusion materials, semiconductors, batteries, and catalysis. It produces ultrashort X-ray pulses that can record the movements of atoms and electrons. These instruments can detect the smallest change in material structure caused by defects and other influences.

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This post is the second of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Turn FP8 Checkpoints...

This post is the second of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Turn FP8 Checkpoints into High-Performance Inference Engines with NVIDIA TensorRT. Model quantization is an effective method to reduce VRAM usage and improve inference performance on consumer devices such as NVIDIA GeForce RTX GPUs.

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The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and...

The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and acting. In most vehicles on the road today, in-vehicle assistants still rely on fixed command-response patterns: interpret a phrase, trigger an action, reset. While effective for well-defined tasks, this approach doesn’t scale to modern…

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For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU,...

For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU, researchers have had to deconstruct them into isolated fragments—single proteins or small domains. This created a context gap, where larger proteins or complexes could not be folded zero-shot due to GPU hardware memory constraints. Now…

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Federated learning (FL) is no longer a research curiosity—it’s a practical response to a hard constraint: the most valuable data is often the least movable....

Federated learning (FL) is no longer a research curiosity—it’s a practical response to a hard constraint: the most valuable data is often the least movable. Regulatory boundaries, data sovereignty rules, and organizational risk tolerance routinely prevent centralized aggregation. Meanwhile, sheer data gravity makes even permitted transfers slow, expensive, and fragile at scale.

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The boom in open source generative AI models is pushing beyond data centers into machines operating in the physical world. Developers are eager to deploy these...

The boom in open source generative AI models is pushing beyond data centers into machines operating in the physical world. Developers are eager to deploy these models at the edge, enabling physical AI agents and autonomous robots to automate heavy-duty tasks. A key challenge is efficiently running multi-billion-parameter models on edge devices with limited memory. With ongoing constraints on…

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Developing real-time vision AI applications presents a significant challenge for developers, often demanding intricate data pipelines, countless lines of code,...

Developing real-time vision AI applications presents a significant challenge for developers, often demanding intricate data pipelines, countless lines of code, and lengthy development cycles. NVIDIA DeepStream 9 removes these development barriers using coding agents, such as Claude Code or Cursor, to help you easily create deployable, optimized code that brings your vision AI applications to…

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Proteins rarely function in isolation as individual monomers. Most biological processes are governed by proteins interacting with other proteins, forming...

Proteins rarely function in isolation as individual monomers. Most biological processes are governed by proteins interacting with other proteins, forming protein complexes whose structures are described in the hierarchy of protein structure as the quaternary representation. This represents one level of complexity up from tertiary representations, the 3D structure of monomers…

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