Diabetes Data Platform Leader Glooko Raises $35 Million in Series C

#artificialintelligence

WIRE)--Glooko, the leader in diabetes data management, today announced $35 million in new funding to accelerate growth, expand international presence, and deepen expertise in data analytics. This round was led by Georgian Partners, a Toronto-based investor focused on applied analytics and machine learning. Other new investors include Insulet Corporation and Mayo Clinic who join existing investors Canaan Partners, Social Capital, Medtronic and Samsung NEXT in the round. Glooko will use the funds to accelerate growth by expanding its sales, marketing and development teams. The company will also increase commercialization efforts in France, Germany, the U.K., Asia and the Middle East, and further development efforts in data analytics and artificial intelligence to provide personalized insights that drive meaningful behavior change.


Your Guide to Master Hypothesis Testing in Statistics

@machinelearnbot

I started my career as a MIS professional and then made my way into Business Intelligence (BI) followed by Business Analytics, Statistical modeling and more recently machine learning. Each of these transition has required me to do a change in mind set on how to look at the data.



Curatio wants to be Tinder plus Facebook for health and disease communities

Mashable

Anyone going through a health crisis or living with a chronic condition is familiar with the same struggle: you want to find people who understand what's going on in your life, but to find those people, you'd probably have to share information about your health more widely than is comfortable. Curatio is trying to solve both those problems. The social app promises to be a combination of Tinder and Facebook for health--and a way to combat the isolation and stigma that can come with health issues. "It's a pain point every single person has at some point in their lives, for themselves or for a family member or friend," said Curatio founder Lynda Ganzert-Brown. "If you're mid-stride in your career, you're probably not going to go onto a social media platform and say, 'I just got diagnosed with Type 2 diabetes.'


Parameterized Exploration

arXiv.org Artificial Intelligence

We introduce Parameterized Exploration (PE), a simple family of methods for model-based tuning of the exploration schedule in sequential decision problems. Unlike common heuristics for exploration, our method accounts for the time horizon of the decision problem as well as the agent's current state of knowledge of the dynamics of the decision problem. We show our method as applied to several common exploration techniques has superior performance relative to un-tuned counterparts in Bernoulli and Gaussian multi-armed bandits, contextual bandits, and a Markov decision process based on a mobile health (mHealth) study. We also examine the effects of the accuracy of the estimated dynamics model on the performance of PE.