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Why Encryption Alone Cannot Protect an AI Application

Why Encryption Alone Cannot Protect an AI Application

Encryption is crucial, but AI introduces new vulnerabilities like data poisoning and model inversion that traditional security methods can't stop.
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Protegrity Addresses AI Data Security Challenges for Enterprises

Protegrity Addresses AI Data Security Challenges for Enterprises

Protegrity Addresses AI Data Security Challenges for Enterprises
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The Hidden Privacy Risk in Uploading Personal Documents to an AI App

The Hidden Privacy Risk in Uploading Personal Documents to an AI App

Uploading documents to AI apps feels simple, but it creates hidden privacy risks when your data is used to train models.
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The Carlyle Group Seeks Security Engineer for AI Platforms and Infrastructure

The Carlyle Group Seeks Security Engineer for AI Platforms and Infrastructure

The Carlyle Group Seeks Security Engineer for AI Platforms and Infrastructure
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The Myth That Every AI Startup Needs a Proprietary Foundation Model

The Myth That Every AI Startup Needs a Proprietary Foundation Model

Debunking the myth that AI startups need to build their own foundation models to succeed and exploring where true competitive advantage lies.
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The One Hardware Metric That Matters More Than Peak AI FLOPS

The One Hardware Metric That Matters More Than Peak AI FLOPS

Peak AI FLOPS are a popular but often misleading metric. Discover the one hardware spec that truly dictates real-world AI performance and why it matters.
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Vertical AI Startups: The Specialized Business Model Worth Watching

Vertical AI Startups: The Specialized Business Model Worth Watching

Beyond the general-purpose AI hype, vertical AI startups are solving specific industry problems with a focused, durable, and valuable business model.
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Why AI Startups Are Shifting From Single-Model Dependencies to Multi-Model Systems

Why AI Startups Are Shifting From Single-Model Dependencies to Multi-Model Systems

AI startups are moving beyond relying on a single large language model, opting for more resilient and cost-effective multi-model systems.
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Ripple-65B Preview Open Coding Model Released on Hugging Face, Offers High Context and Performance

Ripple-65B Preview Open Coding Model Released on Hugging Face, Offers High Context and Performance

The Ripple-65B Preview, an open-weight coding model developed by ricursiveAI, has been released on Hugging Face. This model is a 65-billion parameter Transformer, pretrained on 12 trillion tokens, and supports an extensive context window of up to 1,048,576 tokens. It is available in NVFP4 or BF16 formats, with the NVFP4 variant requiring less memory, making it a more realistic option for self-hosting. The model's maker reports a score of 80.52 on Terminal-Bench 2.1, a command-line task benchmark, which is slightly higher than its nearest rival, Reflection Beam. Access to the model can be obtained by downloading its weights from Hugging Face or by calling a host that already serves it.
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CrowdStrike to Acquire Dreadnode for Automated AI Security Testing, Enhancing Falcon Platform Capabilities

CrowdStrike to Acquire Dreadnode for Automated AI Security Testing, Enhancing Falcon Platform Capabilities

CrowdStrike to Acquire Dreadnode for Automated AI Security Testing, Enhancing Falcon Platform Capabilities
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AI Models Optimized for Edge Applications to Address Resource Constraints

AI Models Optimized for Edge Applications to Address Resource Constraints

The AI industry is increasingly focusing on optimizing AI models for edge applications, where devices have limited area, power, and memory. This contrasts with the demand for large language models (LLMs) in data centers. Designers are employing various compression techniques, such as quantization, distillation, and pruning, to reduce the size of AI models while maintaining their output capability. The primary challenge is to right-size an AI model for an embedded application by first determining its purpose and the target device's functions. Small language models (SLMs) and compact edge models require tight system-level integration to ensure optimal efficiency, moving away from a 'largest model wins' mentality to 'experience per watt.'
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AI-Powered Campaign Analysis Transforms Marketing Strategy and Efficiency

AI-Powered Campaign Analysis Transforms Marketing Strategy and Efficiency

AI-Powered Campaign Analysis Transforms Marketing Strategy and Efficiency