Robust inference in time series analysis is concerned with developing statistical methods that remain valid under departures from standard model assumptions, such as the presence of heteroskedasticity ...
FriendliAI also offers a unique take on the current memory crisis hitting the industry, especially as inference becomes the dominant AI use case. As recently explored by SDxCentral, 2026 is tipped to ...
Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide. This article details many of ...
“I get asked all the time what I think about training versus inference – I'm telling you all to stop talking about training versus inference.” So declared OpenAI VP Peter Hoeschele at Oracle’s AI ...
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AI inference costs dropped up to 10x on Nvidia's Blackwell — but hardware is only half the equation
New deployment data from four inference providers shows where the savings actually come from — and what teams should evaluate ...
Adding big blocks of SRAM to collections of AI tensor engines, or better still, a waferscale collection of such engines, ...
How can the component elements of an unknown material, such as a meteorite, be determined? X-ray fluorescence analysis can be used to identify an abundance of elements, by irradiating samples with ...
“The rapid release cycle in the AI industry has accelerated to the point where barely a day goes past without a new LLM being announced. But the same cannot be said for the underlying data,” notes ...
PlanVector AI Launches First Project-Domain Foundation Model PWM-1F, a Project World Model (PWM) and Temporal Causal Inference (TCI) Analysis Engine for Enterprise Project Agents and Platforms ...
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