Generating sequential decision process from huge amounts of measured process data is a future research direction for collaborative factory automation, making full use of those online or offline process data to directly design flexible make decisions policy, and evaluate performance. The key challenges for the sequential decision process is to online generate sequential decision-making policy directly, and transferring knowledge across tasks domain. Most multi-task policy generating algorithms often suffer from insufficient generating cross-task sharing structure at discrete-time nonlinear systems with applications. This paper proposes the multi-task generative adversarial nets with shared memory for cross-domain coordination control, which can generate sequential decision policy directly from raw sensory input of all of tasks, and online evaluate performance of system actions in discrete-time nonlinear systems. Experiments have been undertaken using a professional flexible manufacturing testbed deployed within a smart factory of Weichai Power in China. Results on three groups of discrete-time nonlinear control tasks show that our proposed model can availably improve the performance of task with the help of other related tasks.

Tune in to this extraordinary edition of S.M.A.C. Talk Technology Podcast in which we examine how to become a disruptor (not disrupted!) in every industry Innovation itself isn't the entire challenge – pockets of innovation can be found in most any company, from the wildly successful to those that have failed spectacularly. The real challenge is being able to innovate at scale across an entire organization, all while creating a mechanism for those innovations to be shared, sustained, and to drive value back into the core of the business. In this series of podcasts, we'll explore how you can make that happen in your business. Listen in on these talks from your fellow innovators to discover how to embrace digital transformation and become a disruptor in your industry. More than 3,100 global executives took part in the most comprehensive global study of its kind, conducted by Oxford Economics.

We present a principled way of extending a classical AI planning formalism with systems of state constraints, which relate - sometimes determine - the values of variables in each state traversed by the plan. This extension occupies an attractive middle ground between expressivity and complexity. It enables modelling a new range of problems, as well as formulating more efficient models of classical planning problems. An example of the former is planning-based control of networked physical systems - power networks, for example - in which a local, discrete control action can have global effects on continuous quantities, such as altering flows across the entire network. At the same time, our extension remains decidable as long as the satisfiability of sets of state constraints is decidable, including in the presence of numeric state variables, and we demonstrate that effective techniques for cost-optimal planning known in the classical setting - in particular, relaxation-based admissible heuristics - can be adapted to the extended formalism. In this paper, we apply our approach to constraints in the form of linear or non-linear equations over numeric state variables, but the approach is independent of the type of state constraints, as long as there exists a procedure that decides their consistency. The planner and the constraint solver interact through a well-defined, narrow interface, in which the solver requires no specialisation to the planning context.

Is artificial intelligence (AI) the solution to all of our critical infrastructure management problems? Or, put another way, is AI reward worth the AI risk? AI, of course, refers to the use of data-driven algorithms and machine learning to make automated decisions. Critical infrastructure means any kind of physical or virtual system that affects your health, well-being or safety. Power plants and hospitals are often given as examples of critical infrastructure.

Digitalisation and the new technological possibilities that artificial intelligence (AI) brings are driving the biggest social and economic changes since the industrial revolution. These are associated with opportunities and risks. Without the right political, economic and ethical framework conditions there is a risk of uncontrolled development and a negative impact of AI. AI ultimately affects all industries more or less heavily. AI or specific forms of it, such as machine learning (ML), can be used in a wide variety of application scenarios.

While several approaches to face emotion recognition task are proposed in literature, none of them reports on power consumption nor inference time required to run the system in an embedded environment. Without adequate knowledge about these factors it is not clear whether we are actually able to provide accurate face emotion recognition in the embedded environment or not, and if not, how far we are from making it feasible and what are the biggest bottlenecks we face. The main goal of this paper is to answer these questions and to convey the message that instead of reporting only detection accuracy also power consumption and inference time should be reported as real usability of the proposed systems and their adoption in human computer interaction strongly depends on it. In this paper, we identify the state-of-the art face emotion recognition methods that are potentially suitable for embedded environment and the most frequently used datasets for this task. Our study shows that most of the performed experiments use datasets with posed expressions or in a particular experimental setup with special conditions for image collection. Since our goal is to evaluate the performance of the identified promising methods in the realistic scenario, we collect a new dataset with non-exaggerated emotions and we use it, in addition to the publicly available datasets, for the evaluation of detection accuracy, power consumption and inference time on three frequently used embedded devices with different computational capabilities. Our results show that gray images are still more suitable for embedded environment than color ones and that for most of the analyzed systems either inference time or energy consumption or both are limiting factor for their adoption in real-life embedded applications.

"That will be our differentiator," said Tan. "Anyone can build a transaction platform." Currently, the Singapore-based company is available only to business customers, but says it will roll out to residential consumers from Fall 2018, in line with the liberalization of Singapore's retail energy market. The company then plans to expand to Japan and Australia, where there are many power-generating households that Tan said will benefit from the sharing model by selling their excess power to other homes. It's a model that has investors interested, too: Electrify said it raised $30 million in an initial coin offering in March. According to industry analyst Mark Hutchinson, that could be a sign of growing demand for energy disruptors. But, as ever, that will likely mean leaving some traditional players behind.

China has 100,000 citizens' DNA records that can be accessed using their face in WeChat, the northern part of the country beats a green energy record, and Baidu's AI lab in Silicon Valley surpasses Harvard and MIT in tumor recognition accuracy. This Week In China Tech stays on top of the most important tech stories coming out of the second fastest growing economy in the world. This week, the Shenzhen Huada Forensic Science and Technology Company announced they are storing DNA from over 100,000 individuals from across China. The company, a wholly-owned subsidiary of Huada Group which was established in 1999, is the world's leading genomics research and development institution. The new DNA database has lots of important applications like helping to identify lost or abandoned children, locating missing persons, identification after natural disasters and lifelong record creation (article in Chinese).

Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA hardware has only just begun. FPGA-based trigger and data acquisition (DAQ) systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. We develop a package based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.

In the future, robots with artificial intelligence will help make life easier for all of us – doing our dull, dirty, difficult jobs, and tackling tasks we simply couldn't do ourselves. One of the key areas where we'll look to robots will be extreme environments where it's dangerous or impossible for humans to go. Several projects recently received funding from the Industrial Strategy Challenge Fund as part of the government's £93 million programme for robotics and AI in extreme environments. The programme aims to develop robotic solutions in industries such as off-shore and nuclear energy, space and deep mining, to increase productivity and open up new cross-disciplinary opportunities. As part of this programme Innovate UK is funding £51m of collaborative R&D and demonstrator projects.