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The AI Misinformation Epidemic

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Over the past five years, Google searches for Machine Learning have gone up five times. Andy Stewart, a managing partner at Motive Partners pointed out last week at the International Fintech Conference that 'for anything that has machine learning in it or blockchain in it, the valuation goes up, 2, 3, 4, 5x'. There is undeniably great interest from the public as well as investors in Machine Learning and how it can be applied to different industry sectors.

In a recent article about 'The AI Misinformation Epidemic', Zachary Lipton, incoming assistant professor at Carnegie Mellon University, described how interest in Machine Learning from the wider public audience combined with a lack of understanding of the internals of what is happening, is creating the perfect storm of interest with ignorance, causing a misinformation epidemic in the field. In a follow up post, he clarified some of the outline points made in the first post.

In an outline of future posts to come, Lipton attributed this epidemic to some of the AI influencers, some prophets of futurism and a failure of the press to accurately describe AI in layman’s terms.

From a technical perspective, it’s not easy to understand in layman’s terms what’s happening in a Machine Learning system. It’s easier to describe and visualize procedural, deterministic algorithms, but many Machine Learning algorithms are based on probabilistic theory, statistics and N-dimensional spaces. These are terms that cannot be explained easily and within the length limitations of current publications to the average reader.

Even letting this aside, with the wealth of APIs available around Machine Learning from major tech companies, it’s complicated to explain the difference in orders of magnitude of work required between using a predictive analytics SaaS and rolling out your own implementation.

On the flip side, even if the average tech- or general audience-oriented site’s coverage of AI is lacking, the field has a wealth of information freely available to anyone interested to learn. Most of cutting edge research in the field is published in Arxiv and is available to everyone; there are numerous courses and nanodegrees around Machine Learning and AI from distinguished universities and the open source ecosystem is vibrant and welcoming to anyone who wants to get her hands dirty on the subject.

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