Google, as a search engine as well as a company, has come a tremendously long way since the days of a simple web page and a list of links. Nowadays, there's little that we don't think Google is possible of, and with their recent approach to Artificial Intelligence, the company's historic motto of "don't be evil" could be helping the whole human race in the near future. Google's AI research has focused on training these algorithms to make decisions for themselves, and speed up the process exponentially. The firm has already been training their AI to improve the quality of smaller images when enlarged, but the Google Research Blog is sharing something much more important with us this week, the idea of using AI to catch Diabetic Blindness sooner, rather than later. Diabetes is a serious condition, and while many will know about the dangers surrounding the need to amputate a foot because of the disease, few will be familiar with Diabetic retinopathy (DR), a disease which puts as many as 415 Million diabetic patients at risk of blindness all over the world.

Diabetes is no joke, regardless of what Wilford Brimley memes you've seen. The disease's associated foot ulcers can lead to amputation of the limb while diabetic retinopathy (DR) can rob people of their sight. Some 415 million diabetics worldwide are at risk of this visual affliction and many of those living with it in the developing world lack sufficient health care access to treat it. That's why Google is training its deep learning AI to spot DR before it becomes a problem -- and without the help of an on-site doctor. Since the disease is most readily diagnosed by examining a picture of the back of the eye, the Google team has spent the past few years developing a dataset of 128,000 individual images, each examined by 3-7 ophthalmologists from a panel of 54.

When it comes to artificial intelligence, forget the scary movies about rebellious robots or the dire warnings of a dystopian world of disconnected humanity imagined by some popular writers. AI promises, rather, to change our lives in profound ways we are just beginning to experience, according to a ground-breaking survey produced by Stanford University. Stanford is taking the long view of AI, with a project called One Hundred Study on Artificial Intelligence (AI100). The study, written by a panel of AI experts from multiple fields including healthcare, will continue as an ongoing activity, with periodic reports examining how AI will touch different aspects of daily life. The first of those reports, "Artificial Intelligence and Life in 2030," looks into the effects that AI advancements will have on a typical North American city a little more than a decade from now.

They say what happens in Vegas stays in Vegas, but I keep telling everyone I know about the remarkable innovations I saw at IBM World of Watson 2016 conference, held from Oct. 24 to 27 at beautiful Mandalay Bay, where I was among the 17,000 attendees. As I was "welcomed to the World of Watson," I learned that Watson (yes, the computer that was on Jeopardy) is IBM's researchers' vision "to design an intelligent system that brings man and machine together to create a better world." If that sounds like a utopian fantasy, prepare to be amazed at how real that vision has become: Watson is changing how doctors cure disease, how companies analyze their social media footprints, and how financial services firms adapt to ever-changing regulations. I could write an entire book on all that Watson has to offer, but my focus here is on Watson's ability to help financial services firms meet compliance demands more efficiently and with less cost – a much needed innovation as firms spend $99 billion on addressing compliance, thus limiting their ability to invest in growth, according to Marc Andrews, VP of Industry Analytics Solutions for IBM. If you're scratching your head at why regulatory compliance costs are so high, picture this: Linda, a trader at a high-profile brokerage firm, receives a bad performance review from her supervisor.

London-based BenevolentAI believes it has built the kind of artificial intelligence tech that will allow it to identify and develop drugs faster and better than any group of mere scientific mortals can hope for. And now J&J is handing over some experimental molecules it needs to prove it's right. The upstart joins a long line scrambling to apply vast amounts of computational power towards drug development. Their goal is to usher in the long-awaited "pharma 2.0" and finally bend the expensive curve of late-stage trial failure. It's unclear how BenevolentAI's algorithms are any better at evaluating the potential of any small-molecule than other computationally-taxing approaches developed by other groups -- and it's all driven by the data.

Berg Health's cofounder and chief executive Niven R. Narain is used to being laughed out of rooms, given his interest in bringing artificial intelligence into the drug development process. But things have changed, he says, thanks to growing excitement around the practice known as precision medicine. This afternoon, at the Fast Company Innovation Festival, Narain spoke on a panel on the topic of bringing advanced technologies to medicine with industry experts from Mount Sinai and Columbia University. Precision medicine is an all-encompassing term, which broadly refers to the idea of treating patients in a more personalized, targeted way rather than taking a one-size-fits-all approach to disease. The White House announced a $215 million investment in precision medicine earlier this year; if nothing else, it generated a lot of hype.

Introduction: In the first blog, we decided on the predictors. We knew that different predictive models have different assumptions about their predictors. Random Forest has none, but Logistic Regression requires normality of the continuous variables, and assumes the probability between 2 consecutive unit levels in a series of numbers to stay constant. K Nearest Neighbors requires the predictors to be at least on the same scale. SVM, Logistic Regression, and Neural Networks tend to be sensitive to outliers.

Mark Zuckerberg and his wife, Priscilla Chan, recently announced a $3 billion effort to cure all disease during the lifetime of their daughter, Max. Earlier this year, Silicon Valley billionaire Sean Parker donated $250 million to increase collaboration amongst researchers to develop immune therapies for cancer. Google is developing contact lenses for diabetic glucose monitoring; gathering genetic data to create a picture of what a healthy human should be; and working to increase human longevity. The technology industry has entered the field of medicine and aims to eliminate disease itself. It may well succeed because of a convergence of exponentially advancing technologies such as computing, artificial intelligence, sensors, and genomic sequencing.

AliveCor, the leader in FDA-cleared mobile electrocardiogram (ECG) technology for mobile devices, announced a collaboration with Mayo Clinic to utilize AliveCor's unique measurement technology to unlock previously hidden health indicators in ECG readings. These indicators have the potential to not only improve heart health but also overall health care for a variety of conditions. AliveCor provides the first consumer-ready, clinically validated and FDA-cleared ECG to give patients a more complete view of their heart health, improve proactive monitoring and create a new standard of cardiac care. By using AliveCor's deep machine learning capabilities applied to 10 million of its user ECG recordings, Mayo Clinic and AliveCor will work together to uncover hidden physiological signals to improve heart and overall human health. "Mayo Clinic has pioneered new approaches that may uncover significant measures of physiology that have been hidden in individuals' ECGs," said Vic Gundotra, CEO, AliveCor.

Launched by digital health and weight-loss platform Lose It!, the new feature of an already existing app proposes a simple solution to those who struggle to keep track of their caloric intake: Take a photo of your food, and Snap It will immediately display its calorie count Showing people the caloric value of their foods before they eat them can help modify their eating habits. Some studies have shown that keeping a food journal helps people stick to their diet. And in an effort to fight rising obesity rates, the FDA announced in 2014 that chain restaurants throughout the U.S. will have to post calorie information in their menus (the rule is set to go into effect sometime next year). But the FDA rules won't apply to all restaurants. And food diets are cumbersome, tending to go the way of new year's resolutions.