suggesting, automatically implementing, or both suggesting and automatically implementing, one or more household policies to be implemented within a household environment. The household policies include one or more input criteria that is derivable from at least one smart device within the household environment, the one or more input criteria relating to a characteristic of the household environment, a characteristic of one or more occupants of the household, or both. The household policies also include one or more outputs to be provided based upon the one or more input criteria.
In some embodiments, the private network may include at least one first device that captures information about its surrounding environment, such as data about the people and/or objects in the environment. The first device may receive a set of potential content sent from a server external to the private network. The first device may select at least one piece of content to present from the set of potential content based in part on the people/object data and/or a score assigned by the server to each piece of content. The private network may also include at least one second device that receives the captured people/object data sent from the first device. The second device may also receive a set of potential content sent from the server external to the private network. The second device may select at least one piece of content to present from the set of potential content based in part on the people/object data sent from the first device and/or a score assigned by the server to each piece of content. Using the private network to communicate the people/object data between devices may preserve the privacy of the user since the data is not sent to the external server. Further, using the obtained people/object data to select content enables more personalized content to be chosen.
[…]
urther, although not shown in this particular way, in some embodiments, the client device 134 may collect people/object data 136 using one or more sensors, as discussed above. Also, as previously discussed, the raw people/object data 136 may be processed by the sensing device 138, the client device 134, and/or a processing device 140 depending on the implementation. The people/object data 136 may include the data described above regarding FIG. 7 that may aid in recognizing objects, people, and/or patterns, as well as determining user preferences, mood, and so forth.
[0144]
After the client device 134 is in possession of the people/object data 136, the client device 134 may use the classifier 144 to score each piece of content 132. In some embodiments, the classifier 144 may combine at least the people/object data 136, the scores provided by the server 67 for the content 132, or both, to determine a final score for each piece of content 132 (process block 216), which will be discussed in more detail below.
[0145]
The client device 134 may select at least one piece of content 132 to display based on the scores (process block 218). That is, the client device 134 may select the content 132 with the highest score as determined by the classifier 144 to display. However, in some embodiments, where none of the content 132 generate a score above a threshold amount, no content 132 may be selected. In those embodiments, the client device 134 may not present any content 132. However, when at least one item of content 132 scores above the threshold amount and is selected, then the client device 134 may communicate the selected content 132 to a user of the client device 134 (process block 220) and track user interaction with the content 132 (process block 222). It should be noted that when more than one item of content 132 score above the threshold amount, then the item of content 132 with the highest score may be selected. The client device 134 may use the tracked user interaction and conversions to continuously train the classifier 144 to ensure that the classifier 144 stays up to date with the latest user preferences.
[0146]
It should be noted that, in some embodiments, the processing device 140 may receive the content 132 from the server 67 instead of, or in addition to, the client device 134. In embodiments where the processing device 140 receives the content 132, the processing device 140 may perform the classification of the content 132 using a classifier 144 similar to the client device 134 and the processing device 140 may select the content 132 with the highest score as determined by the classifier 144. Once selected, the processing device 140 may send the selected content 132 to the client device 134, which may communicate the selected content 132 to a user.
[…]
The process 230 may include training one or more models of the classifier 144 with people/object data 136, locale 146, demographics 148, search history 150, scores from the server 67, labels 145, and so forth. As previously discussed, the classifier 144 may include a support vector machine (SVM) that uses supervised learning models to classify the content 132 into one of two groups (e.g., binary classification) based on recognized patterns using the people/object data 136, locale 146, demographics 148, search history 150, scores from the server 67, and the labels 145 for the two groups of “show” or “don’t show.”
One user played Beethoven’s “Ode to Joy” on an Android tablet piano app and later bought some groceries online. Another sent a few texts and then checked the weather forecast. A third browsed through some videos before firing up Stevie Nicks on Pandora.
They didn’t use their fingers to type commands or their voices to navigate the the interface.
They used their noggins, specifically the motor cortex region of their brains where a baby aspirin-size chip had been implanted as part of a new study
[…]
Each participant was asked to try out seven common apps on the tablet: email, chat, web browser, video sharing, music streaming, a weather program and a news aggregator. The researchers also asked the users if they wanted any additional apps, and subsequently added the keyboard app, grocery shopping on Amazon, and a calculator.
Photo: BrainGate CollaborationA BrainGate trial participant who is a musician played a snippet of ‘Ode to Joy’ on a digital piano interface.
The participants made up to 22 point-and-click selections per minute and typed up to 30 characters per minute in email and text programs. What’s more, all three participants really enjoyed using the tablet, says Hochberg.
The first ever “solid state” plane, with no moving parts in its propulsion system, has successfully flown for a distance of 60 metres, proving that heavier-than-air flight is possible without jets or propellers.
The flight represents a breakthrough in “ionic wind” technology, which uses a powerful electric field to generate charged nitrogen ions, which are then expelled from the back of the aircraft, generating thrust.
The plane in flight. Photograph: Nature Video/Youtube
Steven Barrett, an aeronautics professor at MIT and the lead author of the study published in the journal Nature, said the inspiration for the project came straight from the science fiction of his childhood. “I was a big fan of Star Trek, and at that point I thought that the future looked like it should be planes that fly silently, with no moving parts – and maybe have a blue glow. But certainly no propellers or turbines or anything like that. So I started looking into what physics might make flight with no moving parts possible, and came across a concept known as the ionic wind, with was first investigated in the 1920s.
“This didn’t make much progress in that time. It was looked at again in the 1950s, and researchers concluded that it couldn’t work for aeroplanes. But I started looking into this and went through a period of about five years, working with a series of graduate students to improve fundamental understanding of how you could produce ionic winds efficiently, and how that could be optimised.”
In the prototype plane, wires at the leading edge of the wing have 600 watts of electrical power pumped through them at 40,000 volts. This is enough to induce “electron cascades”, ultimately charging air molecules near the wire. Those charged molecules then flow along the electrical field towards a second wire at the back of the wing, bumping into neutral air molecules on the way, and imparting energy to them. Those neutral air molecules then stream out of the back of the plane, providing thrust.
The end result is a propulsion system that is entirely electrically powered, almost silent, and with a thrust-to-power ratio comparable to that achieved by conventional systems such as jet engines.
Machine learning is everywhere now, including recruiting. Take CV Compiler, a new product by Andrew Stetsenko and Alexandra Dosii. This web app uses machine learning to analyze and repair your technical resume, allowing you to shine to recruiters at Google, Yahoo and Facebook.
The founders are marketing and HR experts who have a combined 15 years of experience in making recruiting smarter. Stetsenko founded Relocate.me and GlossaryTech while Dosii worked at a number of marketing firms before settling on CV Compiler.
The app essentially checks your resume and tells you what to fix and where to submit it. It’s been completely bootstrapped thus far and they’re working on new and improved machine learning algorithms while maintaining a library of common CV fixes.
“There are lots of online resume analysis tools, but these services are too generic, meaning they can be used by multiple professionals and the results are poor and very general. After the feedback is received, users are often forced to buy some extra services,” said Stetsenko. “In contrast, the CV Compiler is designed exclusively for tech professionals. The online review technology scans for keywords from the world of programming and how they are used in the resume, relative to the best practices in the industry.”
