April 2018CIOAPPLICATIONS.COM8Machine Learning: Enabling new capabilities in health and beyonds our ability to generate and collect data from multiple sources has increased rapidly over the past three decades, this has presented new opportunities to use that data to benefit our health, environment, productivity, and most facets of life today. This has also resulted in the rapid growth of technologies that enable the management and analytics of these large datasets including cloud infrastructure and novel machine learning (ML) methods and technologies. This is an exciting but also complex time for every industry, company, and consumer. Exciting because new technologies, together with the data, enable new capabilities and insights which could be powerful. For example, in improving sales, or optimizing our favorite routes on ride sharing apps, and even things like identifying abuse and fake content online. But also complex, because understanding the technologies, knowing how to deploy and use them effectively can be a challenge. In addition to the often grey ethical and policy landscape that can be difficult to navigate.The retail space has been quick to leverage these technologies as ML has made clear financial impact and the policy and ethical landscape is less complicated than many other sectors. Two of the biggest players and technical leaders in the sector are Amazon and Netflix, both generate custom recommendations (recommender models) for their users based on existing buying patterns and other data including demographics.The investments the retail sector has made in ML have resulted in rapid technical advances; however the use of machine learning has not been limited to the retail space. We see the application of machine learning in most sectors today, including biotech, health, aerospace, mining, and media. As someone on the biology and health side of data science, I've witnessed significant growth in the number of `data companies' over the years. Companies and research organizations are recognizing the value of data and related technologies such as machine learning for research, discovery, and health outcomes - all of which can derive significant scientific and financial value. Today, as we see the traditional tech "giants" -- Apple, Amazon, Google, NVIDIA and more -- leaning into health and biology in big ways they can bring their large-scale data and data science platforms to these application areas. As a result, there's a smaller divide between the technology and health industries. For instance, Amazon's recent collaboration with the Fred Hutchinson Cancer Research Center is intended to evaluate "millions of clinical notes to extract and index medical conditions." In fact, most of these companies could now also be classified as biotech and health tech firms in some capacity.ASHIVA AMIRI, PHD, DIRECTOR RESEARCH INFRASTRUCTURE, 23ANDME Shiva AmiriIN MY View
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