Machine learning is finding many applications in areas you would never imagine. More recently the artificial intelligence medium has been adapted for use in industry as diverse as travel, art, earthquake detection, healthcare and more. Now, machine learning is even helping fight cancer, one of our world's deadliest and most complex diseases. One way it is doing this is by predicting cancer before it even happens. Yes, in this sense, machine learning is almost revealing psychic-like abilities but there is no magic here.

Do you work in artificial intelligence or cancer research? If you're interested in progress at the intersection of the two fields, this group is for you. AI is improving our ability to detect cancer earlier, expedite cancer drug discovery, predict responses to treatment, avoid unnecessary procedures - the list continues to grow. Venture capital is pouring into startups using AI to attack cancer, and every large cancer research organization is developing an AI strategy. Our meetups include presentations from experts in both the medical/scientific and machine learning fields, as well as an opportunity to socialize with fellow cancer fighters.

It is a centuries-old image of good science: the lab-coated expert, hunched over a microscope analyzing a glass slide. Proscia wants to resign it to history – at least, when it comes to battling cancer. The Baltimore-based startup, founded out of Johns Hopkins University in 2014, wants to bring artificial intelligence to dermatopathology, the study of skin-born diseases. It has developed an AI module that can greatly enhance the accuracy and scope of tissue analysis. That means better results, and lower costs.

This August, I heard the words that no one wants to hear from their doctor: "You have cancer." I was diagnosed with a rare non-Hodgkin's lymphoma. After a tumultuous couple of weeks of testing and second opinions it was clear that my prognosis was good. The months of treatment had me thinking about my luck; even though I had to live with cancer, I was fortunate to have a good prognosis. I found myself pondering the age-old question, "is there some reason for this?"

In this transcript from an interview conducted by insideHPC, Mike Bernhardt discusses the CANDLE project for cancer research with Rick Stevens from Argonne National Lab. The CANcer Distributed Learning Environment (CANDLE) is an ECP application development project targeting new computational methods for cancer treatment with precision medicine. What is CANDLE all about? It has to do with building a scalable deep-learning environment that can be applied to a variety of problems in cancer, initially. CANDLE is designed to run on the big machines that we have at the US Department of Energy (DOE). The goal is to have an easy-to-use environment that can take advantage of the full power of these big systems to search through large combinations of deep-learning models to find optimal models for making predictions in cancer.

Every year the US government spends $5 billion on cancer research. And yet more than 8 million people still die every year from the disease--despite the frequent refrain that a cure is just around the corner. Scientists today are exploring more promising new technologies than ever before: whole-genome sequencing, liquid biopsies, mRNA vaccines, AI-powered imaging analysis. But that doesn't necessarily mean they're more promising. No number of flashy new disruptors can fix cancer research's real problem: much of its data can't be trusted, because it was never validated.

Artificial intelligence (AI) is in the midst of a renaissance. New techniques are producing results, such as the defeat of a Go world champion by an AI system developed by a subsidiary of Google, which would have been unthinkable five years ago. These techniques are not just being used to play games. Today, AI is being applied to one of humanity's most daunting challenges: the hunt for a cure for cancer. AI has huge potential for helping scientists manage the mind-boggling complexities of research and other data, to expedite cancer drug discovery and translate scientific findings into real benefits for patients.

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