Jeopardy-winning Watson is getting better and better at designing cancer treatments. New data presented this week at the American Society of Clinical Oncology's annual meeting show that IBM's Watson for Oncology suggests cancer treatments that are often in-line with what physicians recommend. The company also announced that the cancer care product, designed to help physicians diagnose and treat their patients, is being used by nine new medical centers around the world. In a handful of studies being presented at ASCO, researchers show that Watson for Oncology is pretty dang good at recommending treatments for a variety of different cancers. From research done in India, Watson's treatment recommendations were in agreement with those of physicians 96 percent of the time for lung cancer, 93 percent of the time for rectal cancer, and 81 percent of the time for colon cancer.

You must understand algorithms to get good at machine learning. The problem is that they are only ever explained using Math. In this mega Ebook written in the friendly Machine Learning Mastery style that you're used to, finally cut through the math and learn exactly how machine learning algorithms work. Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch. I live in Australia with my wife and son and love to write and code.

If you think your job is safe, think again. The looming prevalence of automation has the potential to seize anyone's role, no matter how cushy. This is how the foreseeable future in 21st century looks from the vantage point of 2017, and governments and businesses need to be aware of the changes headed their way. Twenty years ago, I described my day to day tasks as an experienced investment banker in London to an optician. For fun, I decided not to disclose the nature of my role, nor the name of my employer.

Summary: Quantum computing is now a commercial reality. Here's the story of the companies that are currently using it in operations and how this will soon disrupt artificial intelligence and deep learning. Like a magician distracting us with one hand while pulling a fast one with the other Quantum computing has crossed over from research to commercialization almost without us noticing. Has the dream of Quantum computing actually stepped out of the lab into the world of actual application. Well, Lockheed Martin has been using it for seven years.

Now is a great time to be in data analytics. We're on a crest of a wave being propelled by sophisticated data use, including machine learning and artificial intelligence, and attractive salary packages. In Australia the median salary of an analytics professional is A$130,000 – over 60 percent more than the Australian median salary. This salary pressure and skills shortage has created a market where 67 percent of hiring managers report applicants are under-skilled. Equally, specialised skill in analytics is difficult to find and retain – 94% of managers found it the same or harder to hire. It's for all these reasons, and to support analytics role in business, that IAPA recently introduced IAPA-certified via credential – so data analysts could be recognised for the skills they bring to the workplace and employers could be confident in the skills of staff and potential candidates.

A smorgasbord of products and solutions, FinTech describes the dynamic landscape of financial services driven by technology. From startups offering existing services at lower costs, to disruptive innovations shaking up the way we manage our finances, the FinTech industry is set to radically alter consumer behaviour and business processes. Global investment in FinTech hit a whopping $5.3 billion (link) for Q4 of 2016. JP Morgan, alone, invested $600 million (during FY 2016) on Fintech initiatives they said included things like developer API's and "interesting developments" in the bill payments area. Things are continuing to rosy for FinTech in 2017, as financial institutions continue to invest in new and emerging technologies (chart) that will either disrupt or protect them from disruption.

Chatbots are about to change the world in more ways than we can imagine. Already, bots around the globe can complete a diverse set of varying tasks. From ordering pizza online to mashing faces together in Project Murphy, chatbots are about to become a normal element in everyday life. As the scope of chatbots becomes broader every day, there are new applications popping up constantly. Education has traditionally been known as a sector where innovation moves slowly.

Data preprocessing is a fundamental part of any machine learning project and often more time is spent on the data preparation than on the actual machine learning. While some preprocessing tasks are problem specific many others such as partitioning data into training and test folds, stratifying samples or building mini-batches are generic. The following Canonical Pipeline shows the processing steps common for deep-learning in vision. A Reader reads sample data stored in text files, Excel or Pandas tables. The Splitter then partitions data into training, validation and test folds and performs stratification if needed.

Deep beneath the ocean's surface, scientists exploring the Central Pacific Basin have spotted behaviour that's never been seen before outside of the fossil record. Among the many remarkable creatures observed during the mission, researchers with the NOAA's Okeanos Explorer witnessed the moment a snail appeared to be munching on the feces of a'sea lily.' Experts say this behaviour can be traced back hundreds of millions of years to the Palaeozoic Era. Among the many remarkable creatures observed during the mission, researchers with the NOAA's Okeanos Explorer witnessed the moment a snail appeared to be munching on the feces of a'sea lily' (pictured) The expedition kicked off on April 27, in effort to collect new data on the deep-water areas of the Pacific, beginning near the American Samoa capital Pago Pago, and ending near Honolulu, Hawaii. It comes as part of the three-year Campaign to Address the Pacific monument Science, Technology, and Ocean Needs, which aims to explore Pacific marine monuments sanctuaries, the'last relatively pristine marine ecosystems on the planet.' Over 23 days, researchers conducted remotely operated vehicle (ROV) dives in American Samoa, the high seas, and the Jarvis Island, Kingman Reef, and Palmyra Atoll Units of Pacific Remote Islands Marine National Monument.

While many people assume that artificial intelligence is something we'll be seeing well into the future, the truth is, many businesses are already using AI, and are successfully implementing business transformation strategies, connecting with their customers, and scaling faster than ever before. Most of these businesses are located overseas. A recent survey by Vanson Vourne, (commissioned by Infosys) polled business leaders across Australia, the United States, France, Germany, China, the UK, and India with at least US$500m in annual revenue and more than 1,000 staff. Of the 1,600 business leaders surveyed, Australian businesses only invested an average of $7.9m in artificial intelligence last year, and placed last in plans for AI integration and the skills required to implement AI. Andrew Growth, Infosys regional head, says that the results of the survey show that Australia is risking becoming uncompetitive.