Amazon's Echo Dot is the most cost-effective way to add voice-activated control to your smart home. There's an Alexa skill for just about every smart-home platform, DIY or custom installed, and second-generation Echo Dot's cost just $50 each. I have six deployed in my own house, connected to a Vivint Smart Home system. The Vaux speaker dock changes that and delivers a bonus: A rechargeable battery that lets you take Alexa all over your house--and out onto the porch, too. The Vaux costs $50, so it and an Echo Dot still cost less than Amazon's own portable Alexa solution, the Amazon Tap. The Vaux's speaker not only sounds better than the Tap, it doesn't force you to press a button to get Alexa's attention like the Tap does.

This article was originally published at The Conversation. Arend Hintze is a renowned assistant professor of Integrative Biology and Computer Science from Michigan State University. By day, he work on artificial intelligence technologies, trying to bridge the gap between humanity and machine. But by night, he is haunted by the consequences of his research. When the critics rose voice against the ethical nature of AIs, and were dismissed by so called shallow AI researchers, he came forward and put on his views on the same. And honestly, it does make sense.

The word'cyborg' may bring to mind the terrifying robot from the Terminator film. But in a new study, scientists have created a less scary, and much more useful cyborg, by adapting bacteria. The cyborg bacteria are covered in tiny crystals that act as highly efficient solar panels, producing a range of useful compounds, with zero waste. The cyborg bacteria are covered in tiny crystals that act as highly efficient solar panels, producing a range of useful compounds (artist's impression pictured) The team used a type of bacteria called Moorella thermoacetica, which naturally produces acetic acid from carbon dioxide. Acetic acid is a versatile chemical that can be readily converted to a number of fuels, polymers, pharmaceuticals and chemicals.

Academics from the University of Strathclyde and a commercial estate software specialist have won funding to research the use of Artificial Intelligence (AI) to improve energy efficiency in buildings. The team from Strathclyde will work alongside software provider arbnco to explore how AI can be combined with data to create more accurate and robust energy efficiency simulations. The funding has been provided Edinburgh-based Data Lab, the publicly-backed innovation centre that draws together business, academia and the public sector. The nine-month project will focus on arbnco's Arbn Consult platform software, which provides the ability to quickly and accurately assess the Energy Performance Certificate (EPC) rating of a commercial property. This can then be used to produce fully-costed retrofit packages for delivering improvements in energy performance.

The way that people get around cities is changing dramatically. Technological advances and new transportation services are making it possible for city dwellers to cross town ever more efficiently and safely. These shifts could have profound economic and social effects. McKinsey analysis indicates that in 50 metropolitan areas around the world, home to 500 million people, integrated mobility systems could produce benefits, such as improved safety and reduced pollution, worth up to $600 billion. Because each city is unique, the transition to integrated mobility will also play out differently, and produce different results, from one city to the next.

An increasingly popular concern is that robots will eat up labour's share of income at an accelerating rate, leaving ordinary workers impoverished and unemployed. A common dinner conversation topic in Silicon Valley is universal basic income, and the typical argument advanced for UBI is that we are destined to indefinitely continue losing jobs faster than we replace them. Variants on this theme have circulated since the dawn of the Industrial Revolution. Improvements in farming technology have been greeted with skepticism since ancient times for these reasons. Mechanical contraptions for sewing and other tasks were decried as potentially ruinous to workers in Elizabethan England.

This paper considers applications of trajectory data in transportation, and makes two primary contributions. First, it provides a comprehensive literature review detailing ways in which trajectory data has been used for transportation systems analysis, distilling existing research into the following six areas: demand estimation, modeling human behavior, designing public transit, measuring and predicting traffic performance, quantifying environmental impact, and safety analysis. Additionally, it presents innovative applications of trajectory data for the state of Maryland, employing visualization and machine learning techniques to extract value from 20 million GPS traces. These visual analytics will be implemented in the Regional Integrated Transportation Information System (RITIS), which provides free data sharing and visual analytics tools to help transportation agencies attain situational awareness, evaluate performance, and share insights with the public.

Tiny swarming robots--called Kilobots--work together to tackle tasks in the lab, but what can they teach us about the natural world? More info, videos, and sources below DEEP LOOK: a new ultra-HD (4K) short video series created by KQED San Francisco and presented by PBS Digital Studios. See the unseen at the very edge of our visible world. Get a new perspective on our place in the universe and meet extraordinary new friends. About Kilobots How do you simultaneously control a thousand robots in a swarm?

Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a machine learning context. In this paper, we do so by defining machine learning safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. We then use this definition to examine safety in all sorts of applications in cyber-physical systems, decision sciences, and data products. We find that the foundational principle of modern statistical machine learning, empirical risk minimization, is not always a sufficient objective. Finally, we discuss how four different categories of strategies for achieving safety in engineering, including inherently safe design, safety reserves, safe fail, and procedural safeguards can be mapped to a machine learning context. We then discuss example techniques that can be adopted in each category, such as considering interpretability and causality of predictive models, objective functions beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software and open data.

New drone footage has revealed the latest look of Tesla's Gigafactory located on Electric Avenue in Sparks, Nevada. Once completed in 2020, the factory is set to become one of the biggest buildings in the world, with a final size of 10 million square feet. With production underway at the Gigafactory, the company is churning out lithium ion battery cells by the masses in hopes to ultimately reduce the cost of sustainable energy. Tesla says the factory will be producing 35 gigawatt hours of batteries by 2018, which is crucial for the company in reaching its production target of 10,000 units per week in 2018 for its new Model 3 car. According to electrek, Tesla's goal is on target as Tesla co-founder Elon Musk said this month that the factory is already the biggest battery producing factory in the world.