People who apply for a loan from a bank or credit card company, and are turned down, are owed an explanation of why that happened. It's a good idea -- because it can help teach people how to repair their damaged credit -- and it's a federal law, the Equal Credit Opportunity Act. Getting an answer wasn't much of a problem in years past, when humans made those decisions. But today, as artificial intelligence systems increasingly assist or replace people making credit decisions, getting those explanations has become much more difficult. Traditionally, a loan officer who rejected an application could tell a would-be borrower there was a problem with their income level, or employment history, or whatever the issue was.

When I first learned that I would be attending IBM InterConnect to provide feedback on IBM's efforts related to cloud, the Internet of Things (IoT) and Watson, I started to reflect on how much IBM has already influenced the IT and security industries during the span of my career. My first recollection was from my early days of working for a boutique consulting practice where we often lost bids to larger, more established firms. I became familiar with a saying, "Nobody ever got fired for hiring IBM," which I took to mean, "You might not be getting the best, but you're getting a company with a solid reputation, so nobody will hold you accountable for their efforts, good or bad." The more I thought about my experiences with IBM, the more I realized how many IBM technologies I have encountered over the years and how innovative and life-changing they were. I thought back to my college days and remembered how invaluable my IBM Selectric II electric typewriter was for doing homework and writing term papers.

The world population is expected to reach 9.7 billion by 2050. China and India, the two largest countries in the world, have populations totalling around one billion. In four years, by 2022, India is predicted to have the largest population in the world, surpassing China. This means we need new ways to grow food in a way that's smarter and helps regulate our use of land, water and energy in order to avoid a global food crisis and feed the planet. Researchers at Carnegie Mellon University's Robotics Institute believe the answer lies in sensors, artificial intelligence (AI) and robots.

And pretty soon, they'll come for us. That seems to be the story today, whether from Hollywood or in breathless articles in popular tech magazines about artificial intelligence and nanotechnology. TNW Conference won best European Event 2016 for our festival vibe. See what's in store for 2017. In a world where machines can learn, once humans push the "on" button, there's no stopping our robot overlords, right?

The company, which is on a mission to "solve intelligence," has hired some of the brightest minds in the world, including academics from Oxbridge and research scientists from firms like Facebook and Microsoft. Exactly how DeepMind's researchers work together has been something of a mystery but the FT story sheds new light on the matter. Researchers at DeepMind are divided into four main groups, including a "neuroscience" group and a "frontiers" group, according to the report. The frontiers group is said to be full of physicists and mathematicians who are tasked with testing some of the most futuristic AI theories. "We've hired 250 of the world's best scientists, so obviously they're here to let their creativity run riot, and we try and create an environment that's perfect for that," DeepMind CEO Demis Hassabis told the FT.

We live in a fractured world that is increasingly becoming polarised with shrill voices intent on drowning out dissent. We have divided ourselves, created inequalities and are steeped in prejudices that we have carried for years. It's not a perfect world, and how can it be -- we are after all humans, intolerant, and unfair. However when it comes to machines, the expectations change. The first words that usually come to mind are: cold, calculating and unbiased.

In this special guest feature, Bradley Merrill Thompson, a partner in the Washington DC office of law firm Epstein Becker; Green and Chairman of the Board of the firm's consulting affiliate EBG Advisors, starts a compelling and timely conversation on the FDA's approach to regulating machine learning.

Machine learning and deep learning are changing the face of digital marketing, and they are forcing websites to make changes to accommodate them. Google is leading the pack, by using both machine learning and deep learning as part of its algorithms for website rankings in search engine results. Google has publicly stated that RankBrain, its machine learning, artificial intelligence system that is used to help process search queries, is now the third ranking factor in search engine results rankings. Artificial intelligence is an all-encompassing term that describes machines that can demonstrate intelligence. Technically speaking, AI means that a device can perceive its environment and take actions based on that environment, giving the impression that a device is making decisions by itself.

We pick the cell size to be bounded by (r)/( (n)), so that each grid cell will contain at most one sample, and thus the grid can be implemented as a simple n-dimensional array of integers: the default 1 indicates no sample, a non-negative integer gives the index of the sample located in a cell. Step 1. Select the initial sample, x0, randomly chosen uniformly from the domain.

Anyone who's visited the New York Stock Exchange lately knows technology has already taken a toll on Wall Street jobs. And the decimation is only going to continue as the artificial intelligence industry booms. By 2025, AI technologies will reduce employees in the capital markets worldwide by 230,000 people, according to a report from Opimas that came out last week. Financial institutions may see a 28% improvement in their cost-to-income ratios. Additionally, financial firms will spend more than $1.5 billion this year on AI-related technologies and $2.8 billion annually by 2021, not including their investments in AI startups, the Opimas report estimated.