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.

Looking at the history of mankind spanning thousands of years, hatred was always present and people have been persecuted for a wide variety of reasons up until this day, but in recent times hate has been digitized and weaponized with the rise of social media. One of the biggest roadblocks law enforcement agencies and private institutions face when attempting to detect then combat hateful material found on social networks, especially Twitter, is how exactly to deal with the massive amount of data, including tons of false positives, sarcastic posts, emerging trends that go viral within hours as well as many other variables. It's a monumental task that no analyst, army of analysts or social media intelligence tools of the past can accomplish. For the past 2 years, the team at Soteria Intelligence has focused on developing technologies to confront online hate, school threats, terrorist propaganda and other challenges we face in a very unorthodox way, and through our research and development it became clear that the only way to solve the complex problem at hand was to use deep learning and machine learning. Taking 10 years of research on social media behavior and 5 years of research on social media threats in particular, along with input from a wide range of subject-matter experts, we've focused on creating machine learning systems with ability to assess social media activity faster and more accurately than humanly possible.

In Joseph Sirosh's keynote presentation at the Data Science Summit on Monday, Wee Hyong Tok demonstrated using R in SQL Server 2016 to detect fraud in real-time credit card transactions at a rate of 1 million transactions per second. The demo (which starts at the 17:00 minute mark) used a gradient-boosted tree model to predict the probability of a credit card transaction being fraudulent, based on attributes like the charge amount and the country of origin. Then, a stored procedure in SQL Server 2016 was used to score transactions streaming into the database at a rate of 3.6 billion per hour. If you'd like to try this yourself, a step-by-step tutorial with code to implement the model and scoring is available here. Later in the keynote (starting at 25:00), John Salch, VP of Technology and Platforms at PROS describes using R to determine prices for airline tickets, hotel rooms, and laptops.