If you've ever struggled to pair your phone with a Bluetooth speaker or set up a wireless printer, you know that it's often easier to connect to a server halfway around the world than to a gadget across the room. That's a problem as we increasingly use our phones to pay for stuff, unlock doors, and control everything from televisions to thermostats. No one wants to wait for coffee because the cash register can't detect their phone, or shiver in the cold because their watch is trying to connect to their neighbor's door lock instead of their own. Multiple wireless technologies have emerged in recent years to tackle this problem, including Bluetooth, LoRa, and NFC. These technologies are all based on radio frequencies. But a growing number of businesses, from Ticketmaster to Google to nuclear-power plants, are turning to a simpler solution: sound.

After several years of research on machine learning algorithms running on oil and gas production data, Solution Seeker has developed a hierarchical neural network model that improves the predictive power for real-time production optimization. The model leverages the power of neural network learning algorithms combined with domain knowledge in the form of first principle physics and production system logic.

Climate Council Australia and digital agency AKQA today announced a collaboration to launch the Climate Council's first ever chatbot designed to help better engage its followers with questions about climate change. The chatbot was designed to help engage the 25-35 year olds who already follow the Climate Council on its social channels but have low engagement. Housed on the Climate Council's Facebook page, the bot will help the audience access research and statistics across a range of climate-related topics, including extreme weather, heatwaves, bushfires and renewable energy and storage technology solutions. The AKQA Research and Development team worked closely with Climate Council to ensure all their findings and research could be transformed into data for the chatbot. AKQA's executive director of the R&D Lab, Tim Devine, said: "To ensure the bot was highly effective, the AKQA Research and Development Lab ran workshops with The Climate Council to gain an understanding of the challenges the organisation faced and how emerging technology such as bots can overcome these challenges. "In the development phase, the lab first tested the IBM Watson Knowledge Studio as a way to restructure content in a way that would train the bot that could answer any question on climate change.

The field of Multi-Agent System (MAS) is an active area of research within Artificial Intelligence, with an increasingly important impact in industrial and other real-world applications. Within a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as one of the prominent agent architectures to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have enabled them to support MAS in complex, real-time, and uncertain environments. This survey aims at providing an overview of the DCOP model, giving a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

We consider the task of low-multilinear-rank functional regression, i.e., learning a low-rank parametric representation of functions from scattered real-valued data. Our first contribution is the development and analysis of an efficient gradient computation that enables gradient-based optimization procedures, including stochastic gradient descent and quasi-Newton methods, for learning the parameters of a functional tensor-train (FT). The functional tensor-train uses the tensor-train (TT) representation of low-rank arrays as an ansatz for a class of low-multilinear-rank functions. The FT is represented by a set of matrix-valued functions that contain a set of univariate functions, and the regression task is to learn the parameters of these univariate functions. Our second contribution demonstrates that using nonlinearly parameterized univariate functions, e.g., symmetric kernels with moving centers, within each core can outperform the standard approach of using a linear expansion of basis functions. Our final contributions are new rank adaptation and group-sparsity regularization procedures to minimize overfitting. We use several benchmark problems to demonstrate at least an order of magnitude lower accuracy with gradient-based optimization methods than standard alternating least squares procedures in the low-sample number regime. We also demonstrate an order of magnitude reduction in accuracy on a test problem resulting from using nonlinear parameterizations over linear parameterizations. Finally we compare regression performance with 22 other nonparametric and parametric regression methods on 10 real-world data sets. We achieve top-five accuracy for seven of the data sets and best accuracy for two of the data sets. These rankings are the best amongst parametric models and competetive with the best non-parametric methods.

At CES in Las Vegas on Tuesday, Honda officially debuted its four newest mobility and companionship robots, part of the company's 3E (Empower, Experience, Empathy) program. Though they are currently only in the conceptual stage, Honda plans to develop the platforms with a variety of like-minded partners as part of the company's "open innovation" approach. First up, we've got the 3E-D18, an autonomous off-road vehicle designed for rugged applications -- everything from backcountry search and rescue to agriculture -- the more mundane and time-consuming, the better. Based on Honda's existing ATV chassis, the D18 is expected to feature all-wheel drive and virtually indestructible airless tires, enabling it to scrabble over nearly any obstacle. The 3E-C18 is more of a robotic pack mule, albeit less adventurous than the D18.

Technology is a wonderful thing; with those small glimpses of the future from sci-fi films are now realities. We have Artificial Intelligence (AI) managing our homes and businesses and automation software streamlining every process. In fact, there is a gadget out there that can help with almost every bit of our daily lives. However, a lot of technology runs on electricity, a need which presents endless issues for those concerned with global warming, climate change and all things green. This is where the use of smart tech or green tech comes in.

A lot can happen in a year. At the start of 2017 I highlighted 15 disruptive technology trends I expected to see in 2017. As we start 2018 I thought it would be interesting to look back over the last 12 months to see what progress has been made. . . Whilst the use of industrial robots for manufacturing is nothing new, 2017 saw a growth in organisations implementing industrial robots alongside their human colleagues. These lightweight plug and play systems, offer huge flexibility, and with an average cost of around £18,000 are a growing part of the industry.

Industrial revolutions--from mechanization to electrification and mass production to increased automation--have long been about replacing human muscle with machines. For many factory workers who might face the threat of redundancy, that is scary enough. But the fourth revolution, which is more about replacing human brain power with artificial intelligence (AI), presents a change that many more workers are finding difficult to accept.

Physicists commonly classify material phases as one or the other. Machine learning is a powerful tool for pattern recognition and thus could help identify phases of matter. However, machine learning needs a bridge to the quantum world, where the physics of atoms, electrons, and particles differs from that of larger objects or galaxies. Now, scientists have provided a bridge, which they call the quantum loop topography technique. This is a machine-learning algorithm based on neural networks.