First it was Amazon drones; now Google is rolling out robots. The tech company revealed it is developing humanoid robots focused on automating daily tasks, according to The New York Times Wednesday, right on the heels of Amazon announcing the development of a drone delivery program, PrimeAir. Though Google remained tight-lipped on where the project stands, and what specific tasks its robots might do, the announcement has spurred conversation on what role artificial intelligence and unmanned aerial vehicles (UAVs) may play in our future. The project is spearheaded by Google executive Andy Rubin; better known as the engineer who built Google's Android software. He sees the robots as a way to alleviate daily grunt work, possibly in a consumer goods delivery setting.

Internet algorithms tailor the Web -- but they may be removing randomness "People are not forced to think" as widely, says one expert On the other hand, Internet has opened worlds that didn't exist before The flaws are not necessarily in our machines, but in ourselves "People are not forced to think" as widely, says one expert On the other hand, Internet has opened worlds that didn't exist before "I'd say about 95% of the time Amazon suggests a book to me, it's one I already have," he says. This is not due to a lack of interest on the part of Haufe, a professor at Case Western University who specializes in the history and philosophy of science. But Amazon's vaunted algorithm, embodied in the recommendations page of "Your Amazon.com" and the "customers who bought this item also bought" line on each product page, doesn't cast a net wide enough for Haufe's consideration. In that, he sees a bigger concern. Our reliance on computer algorithms, he observes, may be narrowing our choices. "We're losing something vital to the production of knowledge," he says.

To that end, the Department of Defense wants to seed the oceans with robotic vehicles, leaving them on the seafloor until they're needed. In a time of crisis, a military commander would transmit a signal to the underwater vehicles, which would then float to the surface and either monitor the area, or launch a separate unmanned vehicle into the air to gather intelligence. At the end of the mission the vehicle could be picked up by a submarine or support ship. The system of robotic vehicles and their UAVs would be called Upward Falling Payload. The Defense Advanced Research Projects Agency is asking private companies to come up with designs for the communications systems, the "risers" that will float the UAVs to the surface, the UAVs themselves and the equipment they would carry.

This is a guest post. The views expressed in this article are solely those of the blogger and do not represent positions of Automaton, IEEE Spectrum, or the IEEE. When Microsoft was developing its Kinect 3D sensor, a critical task was to calibrate its algorithms to rapidly and accurately recognize parts of the human body, especially hands, to make sure the device would work in any home, with any age group, any clothing, and any kind of background object. Using a computer-based approach to do the calibration had limitations, because computers would sometimes fail to identify a human hand in a Kinect-generated image, or would "see" a hand where none existed. So Microsoft is said to have turned to humans for help, crowdsourcing the image-tagging job using Amazon's Mechanical Turk, the online service where people get paid for performing relatively simple tasks that computers are not good at.

On lengths of yarn stretched between chairs, sixth-grade math students were placing small yellow squares of paper, making number lines--including everything from fractions to negative decimals--in a classroom at Walsh Middle School. Their teacher, Michele O'Connor, had assigned the number lines in previous years, but this year was different. She, personally, hadn't spent much time leading students through practice problems or introducing the basic math concepts they would use in the project. That had largely been relegated to online math lessons, part of separate periods of learning time when students were free to work through computer-based lessons in any subject they chose, at their own pace. The change at Walsh, located in Framingham, Massachusetts, is part of a nationwide pilot program, one that could indicate just how deeply and how quickly the personalized-learning trend will penetrate the average classroom. Indeed, despite the buzz around personalized learning, there's no simple recipe for success, and the common ingredients -- such as adaptive-learning technology and student control over learning -- can backfire if poorly implemented. A looming question is whether personalized learning that works in, say, a tight-knit, mission-driven charter school can be reliably translated into traditional district schools with many more students, less flexible schedules, keener standardized-test worries and cultures steeped in established ways of teaching and learning.

It's 2025 (give or take), and the long-awaited Big One has hit the San Francisco Bay Area. In the frenetic aftermath, teams of specialized rescue workers begin tearing through piles of wreckage--searching for signs of life, administering care, and calling for backup. As Stanford University's leading AI scientist Fei-Fei Li imagines it, they're robots with the smarts to "see" through their immediate surroundings and respond to humans in need, saving the maximum number of lives they can. The enabling technology behind this scenario is one Li has thought about and researched deeply--and it's not too far off, she argues, if computers can master what is arguably humankind's most complicated cognitive ability: vision. Current research, led by Li and the Stanford Artificial Intelligence Laboratory she directs, has already gotten us partially there, thanks to a database of more than 15 million digital images built in 2009.

The year 2016 was an interesting one for startups. Between high valuations and the glory of reaching unicorn status, entrepreneurs have been frenetically chasing private investors -- who were more than willing to deliver the cash -- and thereby delayed their entry onto the public market. Yet there was significantly less VC funding than in 2015. London-based auditing firm Ernst & Young (EY) reports that, as of Q3, U.S. companies raised $41.3 billion in 2,802 venture capital deals this year. In the San Francisco Bay Area alone, there were 916 deals, representing $16.9 billion.

A robot companion could soon help prevent the elderly from feeling lonely. Elli.Q uses artificial intelligence to slowly learn what its owner likes, and will help those less confident with technology to use social media, video chats and play games online. The robot, which its inventors claim is one of most advanced social companion robots in the world, has now been launched during an exhibition at at The Design Museum in London. Elli.Q uses artificial intelligence to slowly learn what her owner likes, and will help those less confident with technology to use social media, video chats and play games online Using'body language' that conveys emotion, speech interface, sounds, lights and images to express herself, Elli.Q is designed to be easily understood. Using AI, it learns the preferences, behaviour and personality of her owner.

Rising inequality and social polarisation are set to shape world developments for the next decade after contributing to Britain's decision to leave the European Union and the ballot-box success of US president-elect Donald Trump, the World Economic Forum says. Climate change was underlined as the third major global trend in the WEF's annual assessment of global risks, published on Wednesday at an event at Bloomberg's European headquarters in London. It said world leaders must work together to avoid "further hardship and volatility in the coming decade". If everything goes to plan, we should see Australian growth around 2.8% in 2017, but there are a number of risks in both directions. Bellamy's CEO Laura McBain has been widely credited for transforming the family-run local company to a global brand.

Discrete-time hidden Markov models are a broadly useful class of latent-variable models with applications in areas such as speech recognition, bioinformatics, and climate data analysis. It is common in practice to introduce temporal non-homogeneity into such models by making the transition probabilities dependent on time-varying exogenous input variables via a multinomial logistic parametrization. We extend such models to introduce additional non-homogeneity into the emission distribution using a generalized linear model (GLM), with data augmentation for sampling-based inference. However, the presence of the logistic function in the state transition model significantly complicates parameter inference for the overall model, particularly in a Bayesian context. To address this we extend the recently-proposed Polya-Gamma data augmentation approach to handle non-homogeneous hidden Markov models (NHMMs), allowing the development of an efficient Markov chain Monte Carlo (MCMC) sampling scheme. We apply our model and inference scheme to 30 years of daily rainfall in India, leading to a number of insights into rainfall-related phenomena in the region. Our proposed approach allows for fully Bayesian analysis of relatively complex NHMMs on a scale that was not possible with previous methods. Software implementing the methods described in the paper is available via the R package NHMM.