Clicking this link will start the download. I first heard the term "machine learning" a few years ago, and to be honest, I basically ignored it that time. I knew that it was a powerful technique, and I knew that it was in vogue, but I didn't know what it really was-- what problems it was designed to solve, how it solved them and how it related to the other sorts of issues I was working on in my professional (consulting) life and in my graduate-school research. But in the past few years, machine learning has become a topic that most will avoid at their professional peril. Despite the scary-sounding name, the ideas behind machine learning aren't that difficult to understand. Moreover, a great deal of open-source software makes it possible for anyone to use machine learning in their own work or research.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

A competition pitting artificial intelligence (AI) against human players in the classic video game Doom has demonstrated just how advanced AI learning techniques have become – but it's also caused considerable controversy. While several teams submitted AI agents for the deathmatch, two students in the US have caught most of the flak, after they published a paper online detailing how their AI bot learned to kill human players in deathmatch scenarios. The computer science students, Devendra Chaplot and Guillaume Lample, from Carnegie Mellon University, used deep learning techniques to train their AI bot – nicknamed Arnold – to navigate the 3D environment of the first-person shooter Doom. By effectively playing the game over and over again, Arnold became an expert in fragging its Doom opponents – whether they were other artificial combatants, or avatars representing human players. While researchers have previously used deep learning to train AIs to master 2D video games and board games, the research shows that the techniques now also extend to 3D virtual environments.

IBM's aggressive push into the data analytics market continued today with the announcement of Project DataWorks, a Watson initiative that IBM said is the first cloud-based data and analytics platform to integrate all types of data and enable AI-powered decision-making. Project DataWorks is designed to lower the complexity for business managers and data professionals to collect, organize, govern, secure and generate insight from multi-sourced, multi-format data. The goal: become what IBM calls "a cognitive business." "It's a system that will on-board data, tools, users, apps, all in a scalable and governed way," Rob Thomas, VP of Products, IBM Analytics, told EnterpriseTech. "The purpose is simple: we are preparing all data within a company for use by AI. We're helping people leap in to the future around AI and machine learning."

Cooperation is one of the hallmarks of being human. We are extremely social compared to other species. On a regular basis, we all enter into helping others in small but important ways, whether it be letting someone out in traffic or giving a tip for good service. We do this without any guarantee of payback. Donations are made at a small personal cost but with a bigger benefit to the recipient. This form of cooperation, or donation to others, is called indirect reciprocity and helps human society to thrive.

This post aims to cram in a synopsis of Jeremy Howard's talk at the inaugural Data Science Melbourne MeetUp at Inspire9 in Richmond on 12th May so may be a little disjointed in it's flow. Jeremy freestyled his delivery once he had established from the members pretty quickly with a show of hands what it was he should be talking about. There is a lack of intelligence from computers and data and what is at stake is not the proof of things we already know but knowing about the things we did not think of from data or what we should be questioning. This is where machine learning asks the computer to come up with some of the intelligence for you. Using machine learning to find the interesting insights and adding value is the huge appeal Jeremy finds in machine learning and to explain this, he kicked off with talking about Arthur Samuel who essentially came up with machine learning and invented what appeared to be the world's first self-learning program.

A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we get a car loan, how much we pay for health insurance--are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated.

May it please the court: I am HAL 9000, attorney for the plaintiff. Stephen Hawking once told the BBC that "the development of full artificial intelligence could spell the end of the human race." Bill Gates and Elon Musk have voiced similar concerns. It could be that humanity's demise will look something like Skynet's takeover of the world when it became self-aware in the "Terminator" movie franchise. Irrespective of how the day of AI reckoning will look, we carbon-based lawyers have to figure out a way of making a living while competing against computer programs for work. Until fairly recently, AI never posed an existential threat to the critical-thinking professions such as law and medicine.

I am a self taught coder for the past 6 months. When I apply for jobs, I keep getting rejected as I was told i have no portfolio to show and i dont have a software engineering background so why hire me when they can hire an experienced software engineer and train him on machine learning skills. I am getting depressed over this situation over the past few weeks and considering looking for jobs in my previous non-tech related job field. So kindly, please provide me with an interesting project that can allow me to showcase my skills. I am also willing to do free projects for anyone out there as long im guided on the best practices.

Big data is both a blessing and a curse. The blessing is that if we use it well, it will tell us important things we don't know about patient care processes, clinical improvement, outcomes and more. The curse is that if we don't use it, we've got a very expensive and labor-hungry boondoggle on our hands. But there may be hope for progress. One article I read today suggests that another technology may hold the key to unlocking these blessings -- that machine learning may be the tool which lets us harvest the big data fields.