The past few years have taught us new terms to describe how large language models (LLMs) learn and adapt. Two of the most important are in-weight learning and in-context learning. Understanding the difference between them not only clarifies where we are with AI today...
In the 1980s, supercomputers captured the imagination. They could model nuclear reactions or simulate the weather—but only if paired with storage. Without disks to hold results, every run would vanish, forcing scientists to start over. Compute without storage was raw...
Over the last two years, the AI conversation has been dominated by models and compute. Bigger models, faster GPUs, cheaper inference. Necessary—but not sufficient. If we want AI that is genuinely useful at work and trustworthy in the enterprise, we need to confront...
In the fast-evolving landscape of artificial intelligence (AI), where models are growing larger and more complex by the day, the demand for efficient processing of vast amounts of data has ushered in a new era of computing infrastructure. With the advent of...
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