AI is hot and IBM put it front and center, along with its hybrid cloud strategy, at its yearly IBM Think conference. IBM announced its AI development platform, called watsonx.ai, for hybrid cloud applications.
The Vela architecture is easier and cheaper to build than traditional AI supercomputers using standard Ethernet networking switches and is potentially easier for clients to replicate if they want to run watsonx on their premises. PyTorch was also optimized for IBM Vela AI supercomputer architecture. IBM found that there was only a 5% performance overhead to run virtualization on Vela.
IBM's watsonx supports IBM’s commitment to a hybrid cloud strategy running on Red Hat OpenShift. The watsonx AI development platform runs in the IBM cloud or in other public clouds such as AWS or on customer premises, which allows an enterprise to utilize this latest AI technology even if there are business constraints that do not allow the use of a public AI tool. IBM is truly bringing leading-edge AI and hybrid cloud together with watsonx.
To clarify the naming conventions - watsonx is IBM’s AI development and data platform to deliver AI at scale. The products with the Watson brand name are digital labor products that have an AI expertise. The other Watson branded products are Watson Assistant, Watson Orchestrate, Watson Discovery, and Watson Code Assistant . IBM is bringing more focus to the Watson brand. The company has rolled the product previously known as Watson Studio into watsonx.
The use of the term “LLM” is actually a misnomer for this new class of AI that leverages pretrained models to perform multiple tasks. The use of “language” in the term implies this tech is only suited for test , but models can consist of code, graphics, chemical reactions, etc. The term that IBM uses for these large pretrained models, and which is a more descriptive, is foundation models. With FMs, a large set of data is trained to generate a specific model.
Many LLMs are large, and they’re growing larger, because they attempt to train on every type of data so that they can be used for any possible open domain task. In an enterprise setting, that approach is often overkill and may run into scaling issues . By properly selecting an appropriate data set and applying it the right type of model, a much more efficient final model can be achieved. This new model can also be cleaned of any bias, copyright material, etc. with IBM’s watsonx.governance.
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