According to IBM, sometimes the problem with artificial intelligence (AI) and automation is that they are too labour intensive. Traditional AI tools, especially deep learning-based ones, require huge amounts of effort to use. There is a need to collect, curate, and annotate data for any specific task one wants to perform. This is often a very cumbersome exercise that takes significant amount of time to field an AI solution that yields business value. And then, IBM states, one needs highly specialised, expensive and difficult to find skills to work the magic of training an AI model – if you want to start a different task or solve a new problem, you often must start the whole process over again—it’s a recurring cost.
But, also according to IBM, that’s all changing thanks to pre-trained, open source foundation models. With a foundation model, often using a kind of neural network called a “transformer” and leveraging a technique called self-supervised learning, one can create pre-trained models for a vast amount of unlabeled data.
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