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Here’s how this bedrock mature set of techniques ends up at four different locations on the maturity curve with three of the four showing still too early for adoption. If you’re a definitional purist you might want to put reinforcement learning in here as well but as Gartner points out and as we practitioners are aware, RL is too immature for wide business adoption. To illustrate, take a look at our foundational techniques of machine learning, predictive and prescriptive models. All these heavily overlap in the content they review and not infrequently would lead you to draw different conclusions about the maturity of each option.Įven within a single hype cycle analysis there is easily confusion over the mix and overlap of tools versus techniques versus application categories. You might be unaware that there is a completely different Hype Cycle for Data Science and Machine Learning (a little more nuts and bolts) or you might come across the Hype Cycle for Emerging Technologies.
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There has been such a proliferation of hype cycles and magic quadrants that you could easily be looking in the wrong place.įor example, in the case of trying to select and prioritize an AI strategy you might logically look at the Hype Cycle for Artificial Intelligence (2019 the most current is above). What you might not know is that you now need an expert just to guide you through the expert literature.
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It’s likely that like many of us the first thing you’d reach for would be one of Gartner’s many hype cycle or magic quadrant analyses. Supposing you’re a business leader and supposing you’re trying to make an intelligent decision about prioritizing your AI adoption plans. Read the research, then consult with your own data scientists for a better evaluation of risk. Summary: If you’re planning your AI/ML business strategy watch out for the confusion in categories and overly risky ratings given by some research and review sources.