Researchers in the Netherlands claim to have created the world's first "robots that procreate." What does that mean exactly? Well, child, when two robots' fitness evaluation algorithms come to a successful conclusion, something beautiful happens. You'll know when you're older -- or if you scroll down. "This breakthrough is a significant first step in the Industrial Evolution and can play an important role in, for instance, the colonization of Mars," reads the press release for the "Robot Baby" project.

This demo follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning, a paper from NIPS 2013 Deep Learning Workshop from DeepMind. The paper is a nice demo of a fairly standard (model-free) Reinforcement Learning algorithm (Q Learning) learning to play Atari games. In this demo, instead of Atari games, we'll start out with something more simple: a 2D agent that has 9 eyes pointing in different angles ahead and every eye senses 3 values along its direction (up to a certain maximum visibility distance): distance to a wall, distance to a green thing, or distance to a red thing. The agent navigates by using one of 5 actions that turn it different angles. The red things are apples and the agent gets reward for eating them. The green things are poison and the agent gets negative reward for eating them.

In 1959, Marvin Minsky and his colleague John McCarthy founded the MIT Artificial Intelligence Project. The men were convinced computers could be made as smart as humans – and then smarter. McCarthy thought functioning AI systems were only a decade away. Around the same time, another researcher, Herbert Simon of CMU, predicted a computer would be the world chess champion by 1967. McCarthy and Simon were wildly wrong – so much so that in 1972 Hubert Dreyfus, a philosophy professor at the University of California, Berkeley, wrote What Computers Can't Do, citing "game playing, language translating, problem solving, and pattern recognition" as key things humans can do and that computers can't.

As banks, financial services providers and brands predict and plan for the way consumers will manage their money in the future, artificial intelligence (AI) is high on the business development strategy for 2016 and beyond. Gideon Hyde, co-founder of Market Gravity, explains how and why artificial intelligence (AI) could hold the key to standing out in banking and financial services. AI is already around us and used everyday within payments, money management and for robo-advice, particularly in the area of intelligent digital assistants that handle regular customer service enquiries and tasks. It can process "big data" far more efficiently than humans and can recognise speech, images, text, patterns of online behaviour, for example to detect fraud as well as appropriate advertisements for upselling. Smart machines and technology can turn data into customer insights and enhance service provisions, bringing the digital experience closer to the human interaction for consumers.

The next big thing in tech is going to be AI. That's what a lot of people in Silicon Valley has been saying lately. Add one more to the list: former Google CEO Eric Schmidt. "To me the biggest platforms will be the ones that will be driven by artificial intelligence," he told an audience gathered at Bell Labs, in Murray Hill, N.J., last month. Schmidt, now executive chairman of Google's parent company Alphabet, was a speaker at a conference celebrating the life and work of Claude Shannon, the legendary Bell Labs engineer whose ideas revolutionized modern communications.

Israeli startup Twiggle, which is developing next generation e-commerce search technologies, announced today that it secured additional funding from the Alibaba Group as the second tranche of its Series A financing. This follows the announcement in April of a 12.5 million round led by Naspers with participation from YJ Capital, State of Mind Ventures and Sir Ronald Cohen. The funding will be utilized to grow the company's R&D team in Israel and drive the company's global expansion plans. No details were disclosed about the amount Alibaba is investing but "Bloomberg" reported that it is 5-10 million. Twiggle uses advanced techniques in data science, artificial intelligence, machine learning and natural language processing to power the next generation of digital commerce.

For consumers seeking convenience and speed, it all sounds great. But how will any of it make Google money? The Mountain View, Calif., company's apparent transition from go-to search engine to omnipresent virtual assistant probably will require rethinking its advertising business. By tracking Web browsing, emails, chats and more, Google has become a dominant force in digital ads. It mines that wealth of personal data to present ads to the people most likely to care about them.

This is the first article in what will become a set of tutorials on how to carry out natural language document classification, for the purposes of sentiment analysis and, ultimately, automated trade filter or signal generation. This particular article will make use of Support Vector Machines (SVM) to classify text documents into mutually exclusive groups. Since this is the first article written in 2015, I feel it is now time to move on from Python 2.7.x and make use of the latest 3.4.x Hence all code in this article will be written with 3.4.x in mind. There are a significant number of steps to carry out between viewing a text document on a web site, say, and using its content as an input to an automated trading strategy to generate trade filters or signals. In this particular article we will avoid discussion of how to download multiple articles from external sources and make use of a given dataset that already comes with its own provided labels. This will allow us to concentrate on the implementation of the "classification pipeline", rather than spend a substantial amount of time obtaining and tagging documents. In subsequent articles in this series we will make use of Python libraries, such as ScraPy and BeautifulSoup to automatically obtain many web-based articles and effectively extract their text-based data from the HTML.

AI is one of those technologies whose promise is resurrected periodically, but only slowly advances into the real world. I remember the dog-and-pony AI shows at IBM, MIT, Carnie-Melon, Thinking Machines, and the like in the mid-1980s, as well as the technohippie proponents like Jaron Lanier who often graced the covers of the era's gee-whiz magazine like "Omni." AI is an area where much of the science is well established, but the implementation is still quite immature. It's not that the emperor has no clothes -- rather, the emperor is only now wearing underwear. There's a lot more dressing to be done.

Nvidia has staked a big chunk of its future on supplying powerful graphics chips used for artificial intelligence, so it wasn't a great day for the company when Google announced two weeks ago that it had built its own AI chip for use in its data centers. Google's Tensor Processing Unit, or TPU, was built specifically for deep learning, a branch of AI through which software trains itself to get better at deciphering the world around it, so it can recognize objects or understand spoken language, for example. TPUs have been in use at Google for more than a year, including for search and to improve navigation in Google Maps. They provide "an order of magnitude better-optimized performance per watt for machine learning" compared to other options, according to Google. That could be bad news for Nvidia, which designed its new Pascal microarchitecture with machine learning in mind.