Our Watch-Bot watches what a human is currently doing, and uses our unsupervised learning model to detect the human's forgotten actions. Once a forgotten action detected (put-milk-backto-fridge in the example), it points out the related object (milk in the example) by the laser spot in the current scene. Andrew Dalton in Engadget called it "a sort of robo-sentry." Watch-Bot is designed to keep an eye on tasks in the home or office and remind you if one of those tasks is still not done--not with a beep, not with a soothing companion-like voice, but with a laser pointer to nab the object still needing attention. Evan Ackerman in IEEE Spectrum said Watch-Bot can independently learn your household activity patterns in order to come up with its unfinished task reminders. Core components of Watch-Bot are a 3D sensor (a Kinect, in this case), a camera that can pan and tilt, a laptop, and laser pointer, said IEEE Spectrum.

One of Google's latest projects is the Magenta project, an effort to generate music, video and images using machine intelligence. The big question Project Magenta tries to answer is, "Can machines make music and art?" If this is possible, the Google team wants to learn how, and if it cannot, they want to know why. Douglas Eck, one of the research scientists working on Magenta, wrote that before Magenta, he worked on music search and recommendation for Google Play Music. His goal in this area was to "use machine learning and an audio signal processing to help listeners find the music they want when they want it."

The tech world was in a frenzy this weekend, arguing over whether or not Apple would collapse if it failed to catch onto the budding trend of artificial intelligence, which some think could be the next major computing platform after the smartphone. Marco Arment, a well-respected developer and observer of the tech industry, wrote a post on his Friday highlighting how Apple appears to be behind on AI compared to its biggest competitors like Facebook, Amazon, and especially Google, which showed off some interesting AI-powered gadgets and apps at the I/O developers conference last week. Microsoft is doing a lot of cool stuff with machine learning and AI too, racist chatbots excluded, of course.) The argument goes that if AI turns out to be a major platform and we end up communicating with our gadgets through voice more than tapping and touching, then Apple could be in big trouble and suffer a similar fate as BlackBerry did when it failed to adapt to modern touchscreen smartphones. Where Apple suffers is big-data services and AI, such as search, relevance, classification, and complex natural-language queries.

Learning machines are capable of working ever more autonomously on ever more complex tasks. In this blog, Muz Janoowalla explores whether it would be smart for humankind to develop an'intelligent gun'. There are an estimated 875 million civilian, law-enforcement, and military firearms in the world, of which 650 million are in the hands of civilians, either legally or illegally. Given the plethora of high-profile gun attacks in recent months and years – particularly in the US, but also in France, Norway, Pakistan and Tunisia, to name but a few – it is disturbingly easy to imagine gunmen on the loose in a school or at a public event, shooting indiscriminately and leaving casualties in their wake. Imagine how different things could be if a gun had artificial intelligence built into it, turning it into an intelligent gun.

Apple is set to completely refresh its MacBook lineup in 2016 according to a well-placed analyst who says Apple's MacBook Pros will be made thinner and lighter with an OLED panel sitting above the keyboard to support the company's fingerprint sensor technology, Touch ID. The information comes from a new research note published by KGI Securities analyst Ming-Chi Kuo who has deep links within Apple's Chinese supply chain and a strong record of predicting what the company will do next. The information has been backed up by sources speaking to 9to5Mac. The dramatically overhauled MacBook Pro will come in 13-inch and 15-inch models, while Apple will also introduce a new 13-inch MacBook, which will be very similar in terms of design and features to the company's current 12-inch MacBook. Apple is set to continue to sell its hugely popular MacBook Air as the entry level laptop in its range.

You are free to share this article under the Attribution 4.0 International license. Small drones need to stay aloft to do their jobs--whether it's searching for dangerous gas leaks or remotely monitoring atmospheric conditions. But the effort can quickly drain battery power. Now, scientists have created RoboBees, insect-sized flying robots that have a switchable electro-adhesive that allows them to perch on materials such glass, wood, or a leaf, using roughly 1,000 times less power than sustained flight. "One of the biggest difficulties with building insect-sized robots is that the physics change as you go that small. A lot of technologies that have been deployed successfully on larger robots become impractical on a centimeter-sized robot," says coauthor Sawyer Fuller, assistant professor of mechanical engineering at the University of Washington.

Including all the convolutional and the pooling layers, what is the total number of layers?

Norm Jouppi, a distinguished hardware engineer at Google, detailed the company's public disclosure of the Tensor Processing Unit (TPU) last week after the CEO Sundar Pichai's earlier announcement at Google I/O. Several questions came up around how the TensorFlow-optimized chipset could compete with publicly available hardware like Nvidia's Tesla P100 and even PaaS providers like Nervana that provide machine learning services. Google's public disclosure of the TPU may have been related to Nvidia's release of the Tesla P100 in April. Jouppi noted that Google wants to lead the industry in machine learning and make the innovation available to its customers, but didn't disclose specific plans or offerings to do so at this time.

Norm Jouppi, a distinguished hardware engineer at Google, detailed the company's public disclosure of the Tensor Processing Unit (TPU) last week after the CEO Sundar Pichai's earlier announcement at Google I/O. ASIC optimizations, reportedly favoring machine learning with TensorFlow (TF) specifically, include reduced computational precision, thereby requiring fewer transistors per operation. Performance-testing parameters or metrics aren't available at this time, but Google claimed the optimizations help increase the number of operations per second the chip can process. Google noted that the project was started several years ago and that it was fast-forwarding current technology by about seven years but hasn't provided data for the community to analyze. Jouppi noted the time from testing a prototype of the chip to data-center deployment was 22 days and that it was an example of Google putting research into practice.

How difficult is it to build a machine learning model on R or Python? Machine Learning models sometime face the issue of system incompatibility. Specially, when the data set is huge. In such cases, either the model takes longer to compute or the system crashes. Hence, for beginners and experts, the use of machine learning offers untimely challenges as well.