Recently developed machine learning techniques, in association with the Internet of Things (IoT) allow for the implementation of a method of increasing oil production from heavy-oil wells. Steam flood injection, a widely used enhanced oil recovery technique, uses thermal and gravitational potential to mobilize and dilute heavy oil in situ to increase oil production. In contrast to traditional steam flood simulations based on principles of classic physics, we introduce here an approach using cutting-edge machine learning techniques that have the potential to provide a better way to describe the performance of steam flood. We propose a workflow to address a category of time-series data that can be analyzed with supervised machine learning algorithms and IoT. We demonstrate the effectiveness of the technique for forecasting oil production in steam flood scenarios. Moreover, we build an optimization system that recommends an optimal steam allocation plan, and show that it leads to a 3% improvement in oil production. We develop a minimum viable product on a cloud platform that can implement real-time data collection, transfer, and storage, as well as the training and implementation of a cloud-based machine learning model. This workflow also offers an applicable solution to other problems with similar time-series data structures, like predictive maintenance.

In the 90s, software engineering shifted from packaged software and PCs to services and clouds, enabling distributed architectures that incorporate real-time feedback from users. In the process, digital systems became layers of technologies metricized under the authority of objective functions. These functions drive selection of software features, service integration, cloud usage, user interaction and growth, customer service, and environmental capture, among others. Whereas information systems focused on storage, processing and transport of information, and organizing knowledge --with associated risks of surveillance-- contemporary systems leverage the knowledge they gather to not only understand the world, but also to optimize it, seeking maximum extraction of economic value through the capture and manipulation of people's activities and environments. The ability of these optimization systems to treat the world not as a static place to be known, but as one to sense and co-create, poses social risks and harms such as social sorting, mass manipulation, asymmetrical concentration of resources, majority dominance, and minority erasure.

Are killer machines coming to annihilate mankind? Are we headed for a dystopian future where robots are our overlords? Are the Cylons already among us? Are concerns voiced by industry icons such as Elon Musk, who has donated millions to The Future of Life Institute, warranted? Oliver Schabenberger recently added a more measured voice to this debate in this commentary piece that he wrote for InformationWeek, pointing out that machines "are not surpassing us in thinking or learning."

Bonsai's Inkling programming language, which makes it easier to build artificial intelligence applications, is moving closer to a 1.0 release. Part of the Bonsai Platform for AI, Inkling is a proprietary higher level language that compiles down to Google's open source TensorFlow library for machine intelligence. Inkling is designed to represent AI in terms of what a developer wants to teach the system instead of focusing on low-level mechanics. It abstracts away from dynamic AI algorithms that would otherwise require expertise in machine learning. Declarative and strongly typed, the language resembles a cross between Python and SQL from a syntactic perspective, said Bonsai CEO Mark Hammond.

AlphaGo caused a stir by defeating 18-time world champion Lee Sedol in Go, a game thought to be impenetrable by AI for another 10 years. AlphaGo's success is emblematic of a broader trend: An explosion of data and advances in algorithms have made technology smarter than ever before. Machines can now carry out tasks ranging from recommending movies to diagnosing cancer -- independently of, and in many cases better than, humans. In addition to executing well-defined tasks, technology is starting to address broader, more ambiguous problems. It's not implausible to imagine that one day a "strategist in a box" could autonomously develop and execute a business strategy.

In the current paper, we present an optimization system solving multi objective production scheduling problems (MOOPPS). The identification of Pareto optimal alternatives or at least a close approximation of them is possible by a set of implemented metaheuristics. Necessary control parameters can easily be adjusted by the decision maker as the whole software is fully menu driven. This allows the comparison of different metaheuristic algorithms for the considered problem instances. Results are visualized by a graphical user interface showing the distribution of solutions in outcome space as well as their corresponding Gantt chart representation. The identification of a most preferred solution from the set of efficient solutions is supported by a module based on the aspiration interactive method (AIM). The decision maker successively defines aspiration levels until a single solution is chosen. After successfully competing in the finals in Ronneby, Sweden, the MOOPPS software has been awarded the European Academic Software Award 2002 (http://www.bth.se/llab/easa_2002.nsf)