Recurrent Neural Networks (RNNs), which are a powerful scheme for modeling temporal and sequential data need to capture long-term dependencies on datasets and represent them in hidden layers with a powerful model to capture more information from inputs. For modeling long-term dependencies in a dataset, the gating mechanism concept can help RNNs remember and forget previous information. Representing the hidden layers of an RNN with more expressive operations (i.e., tensor products) helps it learn a more complex relationship between the current input and the previous hidden layer information. These ideas can generally improve RNN performances. In this paper, we proposed a novel RNN architecture that combine the concepts of gating mechanism and the tensor product into a single model. By combining these two concepts into a single RNN, our proposed models learn long-term dependencies by modeling with gating units and obtain more expressive and direct interaction between input and hidden layers using a tensor product on 3-dimensional array (tensor) weight parameters. We use Long Short Term Memory (LSTM) RNN and Gated Recurrent Unit (GRU) RNN and combine them with a tensor product inside their formulations. Our proposed RNNs, which are called a Long-Short Term Memory Recurrent Neural Tensor Network (LSTMRNTN) and Gated Recurrent Unit Recurrent Neural Tensor Network (GRURNTN), are made by combining the LSTM and GRU RNN models with the tensor product. We conducted experiments with our proposed models on word-level and character-level language modeling tasks and revealed that our proposed models significantly improved their performance compared to our baseline models.

Given the speed at which researchers are advancing artificial intelligence, the question has become not if A.I. will become smarter than its human creators, but when? A team of researchers from Yale University and Oxford's Future of Humanity Institute recently set off to determine the answer. During May and June of 2016, they polled hundreds of industry leaders and academics to get their predictions for when A.I. will hit certain milestones. The findings, which the team published in a study last week: A.I. will be capable of performing any task as well or better than humans--otherwise known as high-level machine intelligence--by 2060 and will overtake all human jobs by 2136. Those results are based on the 352 experts who responded.

Amazon's best-known side ventures have been consumer electronics devices like the Echo and Fire, but the online retailer is potentially looking into a redo of a product at which it has failed: smartphones. Amazon is developing a smartphone series named Ice, Gadgets360 reports. The series will be powered by Google Android and intended to be a low-cost model for developing markets like India. Internally, the phone's specifications are expected to fit this mold. As Gadgets360 noted, one of the phones potentially will include a 5.2-inch to 5.5-inch display.

In late February, the UN-Secretary General held a press conference, highlighting the risk of starvation in East Africa and the necessity to raise funds to address the emergency situations in Somalia and South Sudan. Drought has been back in these countries and their neighbours since 2016, leading to a huge current food crisis. While governments are trying to handle the situation, how could technology innovations help prevent starvation and improve agriculture management in the future? We met with Laurent Rivière, a French 30 years-old entrepreneur, who shared with us his view on the subject with a combination of engineer pragmatism and changemaker idealism . Founder and CEO at Sunbirds for two years, he explained to us how his "bird of the sun," his solar drone, is addressing the agriculture challenges of the 21st century.

Microsoft researchers have already created technology that can do two difficult tasks about as well as a person: identify images and recognize words in a conversation. Now, the company's leading AI experts are working on systems that can do something even more complex: Read passages of text and answer questions about them. "We're trying to develop what we call a literate machine: A machine that can read text, understand text and then learn how to communicate, whether it's written or orally," said Kaheer Suleman, the co-founder of Maluuba, a Quebec-based deep learning startup that Microsoft acquired earlier this year. Machine reading systems also could help doctors, lawyers and other experts more quickly get through the drudgery of things like reading through documents for specific medical findings or rarified legal precedent. That would leave experts more time to focus on treating patients or formulating legal defenses.

Splatoon 2 is gearing up for its big E3 showcase, but before that happens, Nintendo Japan just revealed a new sub weapon as well as some slightly altered returning favorites. The Robot Bomb just might be your new nightmare. The Robot Bomb, seen in the screenshot below, is a brand new sub weapon that's meant to be paired with Roller weapons like the Octobrush. Once the robots are deployed, they walk toward the nearest opponent and detonate in the best position for a successful splat. From the demo video it's not quite clear if the player controls the robot or if it acts on its own.

In terms of overall bang for your buck, solid-fuel rockets are pretty great: They're dead simple, very reliable, and offer respectable efficiency in a very small form factor, as long as you're prepared to handle a lot of thrust all at once and then never again. While some robots have attempted to use rockets to jump from place to place, controllability has always been an issue, since solid-fuel rockets give you a fixed amount of thrust whether you want it or not, and that thrust isn't always directed in exactly the way you'd like. At ICRA last week, researchers from the Japan Aerospace Exploration Agency (JAXA) introduced a small robotic explorer that uses a single solid-fuel rocket to launch itself into the air. What's new is that their robot includes some braking rockets that help it make pinpoint landings, as well as a clever gyroscopic system to make sure that it flies straight as well as providing a way for the robot to get around after landing. The 450-gram robot consists of a housing with batteries and sensors, a reaction wheel (also inside the housing), a primary solid-fuel rocket engine (an Estes C11 with a total impulse of 10 newton-seconds), and two smaller opposing thrust motors.

As ride-hailing leader Uber struggles to develop robot car technology on its own, arch-foe Lyft is busy teaming up with others. On Tuesday, Lyft said it will work with driverless software company NuTonomy to develop fleets of driverless vehicles. The two will focus not just on the robot sensor system but on routing, booking, and on how the system "interacts and communicates with the rider." On an experimental basis, real ride hailing passengers in Boston will be picked up by driverless cars, although a human will be sitting behind the wheel to take over if the robot has problems. The plan is to build out driverless fleets for on-demand transportation when the technology and regulations are ready.

Apple might have quietly shown off the biggest feature of the iPhone 8. The company took to the stage at its WWDC event to show off new features on the iPad – an augmented reality suite that can magically put virtual cups of coffee onto real tables. It does that using the camera – it can understand what it's looking at, and then place virtual objects onto what the camera is showing. But while it appeared to work surprisingly well on the iPad, there have long been rumours that Apple is working on a phone with hardware specially made for such tasks. And it appears that Apple used the event to suggest to developers that they might want to start making software for it.

Ride service companies like Uber and Lyft are focused on the technology of self-driving cars, but what about everythingn else? Lyft is now exploring the passenger aspect via a partnership with self-driving software firm nuTonomy. The idea, the companies say in a news release, is to gain knowledge on areas like passenger comfort and safety, "from routing and booking to the performance of the driving system and how it interacts and communicates with the rider." Lyft and nuTonomy will be doing R&D in the Boston area at the Raymond L. Flynn marine park and nearby at Seaport and Fort Point. During trials, "an engineer from nuTonomy rides in each of its vehicles during testing to observe system performance and assume control if needed," the company said. The company is also running its own small self-driving trials on a small stretch of road in South Boston.