Machine learning (ML) is routinely used by countless businesses to assist with decision making. In most cases, however, the predictions and business decisions made by ML systems still require the intuition of human users to make judgment calls. In this post, I show how to combine ML with sensitivity analysis to develop a data-driven business strategy. This post focuses on customer churn (that is, the defection of customers to competitors), while covering problems that often arise when using ML-based analysis. These problems include difficulties with handling incomplete and unbalanced data, deriving strategic options, and quantitatively evaluating the potential impact of those options.

Machine learning (ML) is routinely used by countless businesses to assist with decision making. In most cases, however, the predictions and business decisions made by ML systems still require the intuition of human users to make judgment calls. In this post, I show how to combine ML with sensitivity analysis to develop a data-driven business strategy. This post focuses on customer churn (that is, the defection of customers to competitors), while covering problems that often arise when using ML-based analysis. These problems include difficulties with handling incomplete and unbalanced data, deriving strategic options, and quantitatively evaluating the potential impact of those options.

The much-anticipated draft document released on Thursday by a Pentagon advisory group goes beyond similar lists of principles promulgated by big tech companies. If the military manages to adopt, implement, and follow the guidelines, it would leap into an increasingly rare position as a leader in establishing standards for the wider tech world. After Pentagon leaders asked the Defense Innovation Board to draft a list of principles late last year, the board enlisted "human rights experts, computer scientists, technologists, researchers, civil society leaders, philosophers, venture capitalists, business leaders, and DoD officials," including representatives from Facebook, Microsoft, Google and other similar outfits. The Board voted to adopt the draft on Thursday. "AI is expected to affect every corner of the Department and transform the character of war," the draft document says.

This is an episode from Josh Reppun's "What School Could Be in Hawai'i," a podcast on the people, technology and methodologies pushing the mantle of education in the 50th state. Susannah Johnson is the founder of Individualized Realized, an education consultancy aimed at meeting educators where they are – as she did in the classroom with students for thirteen years – on the path to student-centered, authentic, globally minded, and liberated learning. In the move towards student-centered learning technology is essential for individualized learning. Over ten years developing a fully individualized program, the use of technology not only opens up learning to be multidimensional, but also for the asynchronous management of dozens of curricula. When students own their own learning, technology moves beyond learning tool to become a partner for that learning.

David Feinberg, Google's Vice President of Healthcare, recently described "a search bar on top of ... [ ] your [electronic health records] that needs no training," on stage at a conference in Las Vegas. Google is testing a service that would use its search and artificial intelligence technology to analyze patient records for Ascension, the largest nonprofit health system in the U.S., according to documents about the efforts reviewed by Forbes. Called "'Nightingale," the Google-Ascension project indicates that Google's push into health analysis is farther along than previously believed, even as the company has faced a growing backlash over health-related privacy concerns. Ascension said in a statement that all its work with Google complies with privacy law and is "underpinned by a robust data security and protection effort, which Google echoed in its own blog post later Monday, including that "patient data cannot and will not be combined with any Google consumer data. " The Wall Street Journal first published details of the Ascension partnership earlier on Monday.

Cnvrg.io, which is developing a data science platform through auto-adaptive and continual machine learning, today announced raising $8 million in the completion of its seed and Series A funding rounds. Led by Hanaco VC, the latest round follows the company's seed funding round, led by Jerusalem Venture Partners. The company said the new funding will allow the company to open offices in New York and expand its sales and research and development efforts. It serves companies across several industries, including financial services, insurance, health care, retail, automotive, gaming, manufacturing, and media. "As data scientists and AI consultants ourselves, we understand the frustration data scientists, data engineers and organizations encounter when building machine learning," said Yochay Ettun, CEO and co-founder of cnvrg.io.

Stradigi AI, a Montréal-based AI solutions provider and research lab founded in 2014, today announced that it has raised $53 million CAD ($40.3 million) in a series A round led by Canadian institutional funds Investissement Québec and Fonds de solidarité FTQ, with participation from Holdun Family Office, Segovia Capital, Cossette, and company cofounders Basil Bouraropoulos and Curtis Gavura. CEO Bouraropoulos said the influx of capital will accelerate Stradigi's North American expansion, which will include new offices in the U.S., with 50 new positions in research, software, sales, and marketing. Additionally, he says it will bolster development of the firm's freshly unveiled AI platform, Kepler, on the heels of a recently announced partnership with professional services network KPMG. "Investissement Québec and the Fonds de solidarité FTQ, in addition to all the other amazing investors that contributed to this financing, are great partners for Stradigi AI," said Bouraropoulos. "As two of the most respected institutional funds in Canada, with diverse portfolios and deep experience with preparing companies for international growth, IQ and the Fonds will bring tremendous value as we execute our strategy to become one of the top three leading platforms in North America." It's built on an adaptable environment that leverages a software-meets-service model, where guidance from Stradigi's research scientists is provided in tandem with solutions deployed via a secure service.

Radiologists assisted by deep-learning based software were better able to detect malignant lung cancers on chest X-rays, according to research published in the journal Radiology. "The average sensitivity of radiologists was improved by 5.2% when they re-reviewed X-rays with the deep-learning software," said Byoung Wook Choi, M.D., Ph.D., professor at Yonsei University College of Medicine, and cardiothoracic radiologist in the Department of Radiology in the Yonsei University Health System in Seoul, Korea. "At the same time, the number of false-positive findings per image was reduced." Dr. Choi said the characteristics of lung lesions including size, density, and location make the detection of lung nodules on chest X-rays more challenging. However, machine learning methods, including the implementation of deep convolutional neural networks (DCNN), have helped to improve detection.

Chris Wiggins is an associate professor of applied mathematics at Columbia University and the Chief Data Scientist at The New York Times. At Columbia, he is a founding member of the executive committee of the Data Science Institute, and of the Department of Applied Physics and Applied Mathematics as well as the Department of Systems Biology, and is affiliated faculty in Statistics. He is a co-founder and co-organizer of hackNY (http://hackNY.org), Prior to joining the faculty at Columbia he was a Courant Instructor at NYU (1998-2001) and earned his Ph.D. at Princeton University (1993-1998) in theoretical physics. He is a Fellow of the American Physical Society and is a recipient of Columbia's Avanessians Diversity Award.

Already, the two organizations are testing software that allows medical providers to search a patient's electronic health record by specific data categories and create graphs of the information, like blood test results over time, according to internal documents obtained by The New York Times. The aim is to give medical professionals better access to patient data, to improve patient care and, ultimately, to try to glean insights from the data to help treatment. Google is teaming up with Ascension, a nonprofit, as American consumer tech giants like Amazon, Apple, Google and Microsoft jockey to gain a bigger share of the huge health care market. Apple has expanded into virtual medical research using its iPhone and Apple Watch. Microsoft has introduced cloud-based tools to help health systems share medical data.