Those of us who work in data science and artificial intelligence have a love/hate relationship with hype. We're excited by self-driving cars, machines understanding complex images, and computers beating humans at Go (if not StarCraft). On the other hand, we've heard stories of the last'AI winter' and we fear that hype (and the inevitable trough of disillusionment that follows) is setting us up for another one. We know machine learning is math, not magic, and we don't want to be left holding the bag when someone declares that the AI emperor has no clothes. As always, the truth is more nuanced.

Sometime recently the world of healthcare quietly changed and not many people noticed. IBM's artificially intelligent supercomputer Watson decided that it's (his?) prowess at Jeopardy was nothing more than a neat parlour trick and went to med school. Forbes reported that Watson waded through textbooks, medical databases PubMed and Medline and copious quantities of patient records from leading hospitals. "Watson has analysed 605,000 pieces of medical evidence, 2 million pages of text, 25,000 training cases and had the assist of 14,700 clinician hours fine-tuning its decision accuracy," according to Forbes. Supporters now claim that Dr. Watson is now the world's best diagnostician with consistent, accurate diagnoses based on access to essentially all known medical wisdom.

Storm damage repairs and preventative maintenance on power lines and trees are two of the most important tasks utility companies take on, requiring a large amount of time and budget. Currently, ground crews inspect assets manually or via helicopter, and then based on their observations, they'll identify areas that need attention. New technology on the horizon will help protect the grid from potentially dangerous storms and trees that pose a risk of falling. Affordable drone technology, coupled with big data software, is paving the way for a more detailed, holistic approach to storm damage assessment and utility maintenance. Through the collaboration of Edison Electric Institute and Palo Alto-based drone service company Sharper Shape, the EEI Sharper Utility partnership was formed to fast-track long-distance commercial drone inspections of power lines in the U.S. Drone flights have already found their footing in Europe with help from Sharper Shape's European affiliate and are looking to make an impact on the utility industries in the U.S. and the rest of the world.

So far I have talked about decision trees and ensembles. But I hope, I have made you understand the logic behind these concepts without getting too much into the mathematical details. In this post lets get into action, I will be implementing the concepts that we learned in these two blog posts. The only concept that I haven't discussed about is SVM. I suggest you to watch Professor Andrew Ng's week 7 videos on Coursera.

For the past three years, our smartest engineers at Databricks have been working on a stealth project. Today, we are unveiling DeepSpark, a major new milestone in Apache Spark. DeepSpark uses cutting-edge neural networks to automate the many manual processes of software development, including writing test cases, fixing bugs, implementing features according to specs, and reviewing pull requests (PRs) for their correctness, simplicity, and style. Scaling Spark's development has been a top priority for us. Every year, Spark's popularity reaches new highs.

In this role you will be working within a small focused team tasked with investigating and testing new ideas, building proof of concepts to test technology and the market by getting early feedback. You will build sound concepts and architecture but at the same time expecting to fail often. You should be comfortable working with different technologies on a fast moving and sketchily defined problem domain. We will expect you to learn (a lot) and work well with others within the business and external experts to gather and test the best ideas for growing our business and delighting our customers. We expect a lot of these new ideas to be in the area of machine learning, so interest or experience in this area is a plus.

April 29, 2016 - With an ever-increasing volume of electronic data being collected by the healthcare system, researchers are exploring the use of machine learning--a subfield of artificial intelligence--to improve medical care and patient outcomes. An overview of machine learning and some of the ways it could contribute to advancements in plastic surgery are presented in a special topic article in the May issue of Plastic and Reconstructive Surgery, the official medical journal of the American Society of Plastic Surgeons (ASPS). "Machine learning has the potential to become a powerful tool in plastic surgery, allowing surgeons to harness complex clinical data to help guide key clinical decision-making," write Dr. Jonathan Kanevsky of McGill University, Montreal, and colleagues. They highlight some key areas in which machine learning and "Big Data" could contribute to progress in plastic and reconstructive surgery. Machine learning analyzes historical data to develop algorithms capable of knowledge acquisition.

One of my favorite small projects, EmailFiler, was motivated by a school assignment for Georgia Tech's Intro to Machine Learning class. Basically, the assignment was to pick some datasets, throw a bunch of supervised learning algorithms at them, and analyze the results. But here's the thing: we could make our own datasets if we so chose. And so choose I did - to export my gmail data and explore the feasibility of machine-learned email categorization. See, I learned long ago that it's often best to keep emails around in case there is randomly some need to refer back to them in the future.

No company is silly enough to claim them. I am a mobility and digital transformation analyst, consultant and writer. I work with and have worked with many of the companies mentioned in my articles.

Advances in cloud computing, along with the big data movement, have transformed the business IT landscape. Leveraging the cloud, companies are now afforded on demand capacity and mobile accessibility to their business-critical systems and information. At the same time, the amount of structured and unstructured data created by, and available to, organizational users is a constantly moving target, with IDC estimating that the digital universe will grow by a factor of 10 between 2013 and 2020. But while both of these IT megatrends can be the catalysts for innovation and growth, organizations are facing significant new challenges when trying to seize upon their opportunities. Corporate datasets are growing more diverse, complex and massive in size.