Antwood is the alias of Tristan Douglas, a producer, microbiologist, and all-around deep thinker hailing from Nanaimo, British Columbia. Some might remember his EP Work Focus from last year, an under-the-radar gem put out by net label B.YRSLF Division. It's a slice of footwork that's been pummeled and fractured into something that's neither here nor there, which is probably why it grabbed the attention of Planet Mu boss Mike Paradinas. Around that time, Douglas was still recording under the name Margaret Antwood, an admittedly lazy spoonerism on internationally-celebrated poet and novelist Margaret Atwood. "I thought it'd be funny to take some figure that's barely known in the public consciousness and do like a really crappy pun of her name," he says.

When Amazon Web Services announced a new machine learning service for its cloud last week, it was a sort of mini-milestone. Now all four of the top clouds -- Amazon, Microsoft, Google, and IBM -- will offer developers the means to build machine learning into their cloud applications. As InfoWorld's Andrew Oliver has observed, both machine learning and big data will eventually disappear as separate technology categories and insinuate themselves into many, many different aspects of computing. Fraud detection is first among them, because it addresses an urgent problem that would be impractical to solve if machine learning didn't exist. To get a sense of how machine learning is combating fraud, I interviewed Dr. Hui Wang, senior director of risk sciences for PayPal.

In 2014, Facebook acquired WhatsApp for 19 billion. That astronomical number set off waves of speculation as to what value Facebook could possibly see in a company with just 55 employees and roughly 20 million in revenue, although it had 500 million users. At last week's F8 conference, that vision became a lot clearer, and it's big. Chatbots will cause a near-term disruption in how businesses interact with consumers, and a long term paradigm shift in how people will interact with machines. The easiest way to see why chatbots will make a near term impact on everyday consumers is by comparing a modern day customer support call to a chatbot experience.

A Stanford robotics team has built a humanoid robot that can stand in as "the physical representation" of a remote pilot -- a swimming avatar for dangerous aquatic expeditions. The robot, OceanOne, is a two-handed, anthropomorphic'bot that provides haptic feedback, meaning the pilot can "feel" what the robot reaches out and touches. OceanOne's first trip was to the wreck of the La Lune, a ship that sank off the coast of France in 1664, where the robot recovered artifacts. The goal is for OceanOne to help out on missions that are too dangerous for human divers. While OceanOne is still a prototype, the project could eventually become a fleet of robotic divers, working together as human pilots guide them from afar.

Looking at what early-stage startups are working on is not only entertaining -- and sometimes concerning -- it can also be a good indicator of where tech is headed. Since we have data on these young ventures at my company, Startup Tracker, we have the opportunity to glimpse emergent product trends in the startup space. We decided to put together a list of the 100 most interesting little-known startups in existence right now and to analyze the underlying patterns. We specifically focused on companies that are building something unique or unconventional. One interesting trend we spotted was the apparent birth of a startup meta-industry -- startups building products for other startups is becoming a thing.

Another week, another IBM acquisition: This time, a cloud consulting and implementation services specialist called Bluewolf Group. IBM (NYSE: IBM) said Thursday (March 31) the acquisition would help extend its analytics, cloud consulting and "experience design" capabilities. Financial details of the acquisition were not disclosed, but reports pegged the deal at about 200 million. Upon completion of the transaction, which is expected by the end of the second quarter of this year, IBM said Bluewolf would become part of its Interactive Experience unit focusing on offering consulting services for clients adopting Salesforce offerings via the cloud. The deal is intended to boost the IBM unit's customer experience and data integration platforms while adding a cloud consulting capability.

February's batch of Scripts of the Week highlights some of the month's best content produced by Kagglers on our public datasets. It also includes a great getting started script predicting outcomes of the 2016 NCAA basketball tournaments for March Machine Learning Mania 2016. Actually, I'm quite new to Kaggle, and before entering into the jungle of competitions, I wanted to train on datasets. I believe that training on datasets is a good way to start on Kaggle: there is no deadline, no competition. I chose this dataset because it was typically calling for sentiment analysis, which is a classical exercise for text-mining.

Everybody wants to look beautiful. We all like to be well dressed and keep up with fashion trends, but most times this is not possible. We are constrained by time, money and the skill to put together trendy outfits. The problem gets compounded when we go shopping online. Every store has 1000's of items in each category.

This week, Ford and Volvo announced they are forming a "coaliton" – along with Google – to push not only for the development of self-driving cars, but for federal "action" (their term) to force-feed them to us. The reasons are obvious: There's money – and control – in it. To understand what's going on, to grok the tub-thumping for these things, it is first of all necessary to deconstruct the terminology. The cars are not "self-driving." The "self-driving" car does what it has been programmed to do by the people who control it.

The big news on March 12 of this year was of the Go-playing AI-system AlphaGo securing victory against 18-time world champion Lee Se-dol by winning the third straight game of a five-game match in Seoul, Korea. After Deep Blue's victory against chess world champion Gary Kasparov in 1997, the game of Go was the next grand challenge for game-playing artificial intelligence. Go has defied the brute-force methods in game-tree search that worked so successfully in chess. In 2012, Communications published a Research Highlight article by Sylvain Gelly et al. on computer Go, which reported that "Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players." AlphaGo combines tree-search techniques with search-space reduction techniques that use deep learning. Its victory is a stunning achievement and another milestone in the inexorable march of AI research.