Presented by F5Enterprise AI teams have spent years solving for compute, securing GPU allocations, negotiating cloud capacity, and benchmarking training throughput. The assumption embedded in that work is that the path between storage and compute will keep up. In production, that assumption increasingly does not hold. Real traffic introduces latency spikes, network jitter, and node degradation that controlled benchmarks fail to capture, resulting in pipelines that perform well in the lab but stall in deployment. A growing response is AI data delivery, deploying an application delivery controller (ADC) or application delivery and security platform (ADSP) in front of storage as a resilient and secure control point."Provisioning solves for capacity but not for delivery, and that is where [...]
On Sunday, a team of nine researchers at Sina Weibo — the Chinese social media giant better known for its microblogging platform than for cutting-edge artificial intelligence — quietly posted a 14 [...]
For decades, the IQ test has been one of the most familiar — and most contested — yardsticks for human intelligence. Now, a startup project called AI IQ is applying the same metaphor to artificial [...]
AI agents are now embedded in real enterprise workflows, and they're still failing roughly one in three attempts on structured benchmarks. That gap between capability and reliability is the defin [...]
Alibaba's Qwen team released Qwen-AgentWorld on Tuesday — two models trained not to act inside agent environments, but to predict what those environments return. The release covers seven domain [...]
Chinese e-commerce and cloud giant Alibaba's famed Qwen team of AI researchers last night unveiled Qwen3.8-Max, a new flagship 2.4-trillion-parameter mixture-of-experts (MoE) multimodal large lan [...]
Patronus AI, the artificial intelligence evaluation startup backed by $20 million from investors including Lightspeed Venture Partners and Datadog, unveiled a new training architecture Tuesday that it [...]
There is no shortage of AI benchmarks in the market today, with popular options like Humanity's Last Exam (HLE), ARC-AGI-2 and GDPval, among numerous others.AI agents excel at solving abstract ma [...]
Across 116 enterprises, agents are in production and so are the incidents: A majority have already had a confirmed agent security event or a near-miss. Two-thirds of enterprises enforce scoped permiss [...]
AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology [...]