So you want to replace workers with AI? Watch out for retraining fees, they're a killer
with profits. But one of things that's often missed in these conversations is the need to retrain these models regularly or risk them aging into irrelevance, particularly in rapidly evolving environments like the news.
Imagine if you exposed a child to everything the world has to offer. For 18 years they absorb all the knowledge they can, but on the first day of their adult life they're locked away in a cave and isolated from the world. Now imagine you provided that person with art supplies and asked them to draw, paint, and render images based on your prompts.
Most AI training today is done on GPUs each with a relatively small amount of fast memory onboard. Nvidia's A100 and H100 GPUs both sport 80GB of HBM memory, while AMD and Intel's GPUs are now pushing 128GB. While there are other architectures out there with different memory topologies, we're going to stick to Nvidia's A100 because the hardware is well supported, widely available in both on-prem and cloud environments and has been running AI workloads for years at this point.
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