In the early nineteen-nineties, a few occurrences sparked something in Ted Chiang's mind. He attended a one-man show in Seattle, where he lives, about a woman's death from cancer. A little later, a friend had a baby and told Chiang about recognizing her son from his movements in the womb. Chiang thought back to certain physical principles he had learned about in high school, in Port Jefferson, New York, having to do with the nature of time. The idea for a story emerged, about accepting the arrival of the inevitable.

In the middle of all of the autonomous car promises, slightly thinner and brighter televisions, and appliances that spy on you in as many different ways as they possibly can were a small handful of social robots. These are robots designed to interact with you at home. People responding to IEEE Spectrum's live twitter feeds as we covered each announcement, pointed out that these little white social home robots all look kinda similar to each other, and they also look kinda similar to that little white social home robot that managed to raise $3.7 million on Indiegogo in September of 2014: Jibo. To show what we're talking about (if you haven't been following along with our CES coverage, and you totally should be), here are three new social home robots (Kuri, Mykie, and Hub) that were announced Wednesday, along with Jibo for comparison. Big heads on small bodies.

It was a Saturday afternoon. I met up with a friend to enjoy the occasional good weather by the lake. It was a day void of serious topics or stress. Life is good, I thought to myself. I headed home after a few drinks to prepare for an upcoming trip.

Last year was an eventful one in terms of all the interviews I conducted. Here are a few highlights of the interviews I did last year for you to browse at your leisure. The latest in digital print technology brings Bruce Lee's face to life with this S.H. Figuarts rendition. I started the year talking with Bandai about their toy and figure output, specifically at the collector's end of the spectrum. In the time since, we've had all manner of major new announcements in terms of Bandai's Tamashii Nation line as well as a move into more worldwide markets.

The tech industry's annual Consumer Electronics Show, in Las Vegas, is known for showing off a ton of crazy gadgets, both useful and fanciful. It's easy to dismiss some innovations out of hand, but even the wackiest products hint at what consumers should expect to see in the future, by offering a read of what trends companies are focusing on. Here is a look at seven wacky CES products -- and what they can tell us about the future of tech. What it is: A black shirt that looks like something a super villain would wear. The garment, made by Japanese firm Xenoma, has embedded silver motion-sensing circuits, and it is supposed to track your movements and the position of your body.

WHAT: TechCode, through a strategic partnership with the new AI Frontiers Conference, will host a demo day on Thursday, January 12, featuring startups with a niche focus on artificial intelligence. The TechCode AI Accelerator Demo Day will include demos from fifteen startups and a Q&A session from a panel of Venture Capitalists. In its first year, the AI Frontiers Conference brings together leaders in applied artificial intelligence from companies such as Google, Facebook, Microsoft and Amazon to share insights into the latest deep learning advancements and products. The conference will cover six major areas including speech-enabled assistants, Internet of Things, natural language processing, computer vision, deep-learning frameworks and autonomous driving. All the startups featured in the demo day will be available for in-person interviews at the event.

Disruption was the buzzword of 2016. It seems like every industry is facing disruption through technology, as the world around us becomes increasingly digitized. I believe that 2016 just showed us a glimpse into what is possible through technology. Within 2017, many more industries will be disrupted as technology such as AI and 3D printing become more commonplace. Here's where I see disruptive trends heading in 2017: We'll remember 2017 as the year when robots ran the world.

In February, I took a job designing the personality of a chatbot called Kai. I ghostwrite the lines it says, and I have thought, while testing it, that talking to myself has rarely been so unpredictable. Kai, which was conceived by my employer, Kasisto, to help customers with online banking, works over text message, Slack, and especially Facebook Messenger, where more than thirty-four thousand other chatbots have joined it since April, when Facebook opened the platform to developers. Many of these bots possess no personality. The ones created by CNN and the Wall Street Journal, for instance, greet first-time users with "we," as if the whole newsroom were on the other side of the screen, and run keyword searches rather than engaging in conversation.

Kaggle has become the premier Data Science competition where the best and the brightest turn out in droves – Kaggle has more than 400,000 users – to try and claim the glory. With so many Data Scientists vying to win each competition (around 100,000 entries/month), prospective entrants can use all the tips they can get. And who better than Kaggle CEO and Founder, Anthony Goldbloom, to dish out that advice? We caught up with him at Extract SF 2015 in October to pick his brain about how best to approach a Kaggle competition. According to Anthony, in the history of Kaggle competitions, there are only two Machine Learning approaches that win competitions: Handcrafted & Neural Networks.

We've heard that it is prohibitively expensive for startups and academics to train machine learning models, and this is due to the rental or purchase costs of hardware. The results from one recent Google paper were estimated to cost $13k to emulate. That's just to reproduce the final model, not to emulate the whole experimentation and hyperparameter optimisation caboodle. Equally, there are intelligence tasks (training, inference, or prediction) that would ideally happen on the cellphone or remote sensor but are too compute constrained locally, so currently rely on uploading data to the cloud for processing. Machine intelligence is the future of computing, so what needs to happen at a hardware level to make it faster and more energy- and cost-efficient? We talked to Simon Knowles, CTO of Graphcore, about hardware acceleration of machine intelligence.