During a breakout session at the 2017 VMworld conference, Joel Leichnetz and Michael Gandy spoke about the real definition of artificial intelligence and how the company is using it.
Artificial intelligence (AI) gets paid a lot of lip service in the enterprise, with IT leaders arguing that every aspect of their stack will be revolutionized by AI. However, the real meaning of AI, and its true impact, can be difficult to measure.
At a breakout session during the 2017 VMworld conference, VMware's Joel Leichnetz and Michael Gandy spoke on what AI is and how it is being used today. The pair started by defining AI, which they said is: "The theory and development of computer systems able to perform tasks that normally require human intelligence..."
Breaking that down even further, Leichnetz and Gandy separated deep learning and machine learning from AI. As noted, AI is used to mimic specific human behaviors generally. Machine learning uses algorithms to improve the ability of software to learn with experience that is provided by the operator, while deep learning is a subset of machine learning that uses algorithms allowing the software to train itself.
With these different approaches to AI, there are a host of different ways that the technology can be applied. In their presentation, Leichnetz and Gandy gave three specific examples of AI in action across the portfolio of VMware products.
1. vRealize Log Insight
Vmware's vRealize Log Insight uses machine learning-based automatic data consolidation. This allows for intelligent data summarization and the ability to cluster similar messages together. This product also has automatic schema extraction.
In AppDefense, machine learning is used to create an intent-based manifest for an app running in a VM. That way, it can properly secure the app against malicious behavior with an algorithm that measures the run state against the intended state.
Skyline starts by intelligently collecting information from a customer. Then, it uses machine learning as a rule engine to see if something goes outside of normal behavior so it can raise a red flag and offer proactive support.
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- Understanding the differences between AI, machine learning, and deep learning (TechRepublic)
- AI projects are taking off: What does this mean for the future of work? (ZDNet)
- How to ease your big data experts' fears about AI automating their jobs (TechRepublic)