Powered by smart machines, the new industrial revolution is changing how manufacturers operate today and plan for the future, influencing a significant transformation in manufacturing, engineering and factory-floor industries. Adding to this, manufacturers are under pressure to meet the demand for faster delivery of new products, coupled with shorter production lifecycles. Organizations are adopting agile, flexible production plant systems and processes to adapt and evolve, so as to remain competitive and profitable. Going forward plants and machines will have to be smarter, better connected, more efficient, flexible, and safe. Over the past several years, innovation frameworks have emerged in industry organizations worldwide, such as Industry 4.0 (Europe), the Industrial Internet Consortium (America), and the Made-in-China initiative, to name a few.

STEPPING OFF THE PLANE in Russia for the first time in 2013, I collided with a wall of blunt language and was intrigued beyond repair. Five years, countless classes and ten visits to Moscow later, I still claim a distinctly below-average capacity for the Russian tongue and its dense, foreboding components. To fill these gaps ahead of my next adventure abroad, I turned to technology. Late last year, Brooklyn's Waverly Labs released the Pilot ($299, waverlylabs.com), These eavesdropping devices use a cloud-based machine learning technology to pipe dozens of different languages into your brain in your mother tongue.

Industry 4.0 is characterized by applying cloud and cognitive computing to current automated and computerized industrial systems resulting in the ability to create smart factories that monitor physical processes, identify issues or optimizations, and perform iterative refinement or proactive maintenance and updates. A recent study was released by Emory University and Presenso called The Future of IIoT Predictive Maintenance. The study is focused on predictive maintenance current state, implementation, resulting impact, and future needs identified within smart factories. Over 100 operations and maintenance professionals across Europe, North America, and Asia Pacific participated. The results showed that while there was good satisfaction with existing predictive maintenance environments, the modeling and machine learning aspects are lagging behind where spreadsheet based statistical modeling has not been replaced by more advanced capabilities.

Artificial Intelligence (AI) and Machine Learning (ML) have already started making inroads into various industries. Healthcare is emerging as one of the biggest beneficiaries of the AI revolution. The technology is capable of facilitating easy and secure access to patient medical data, understanding and analysing their conditions. This ultimately helps improve accuracy and efficiency in the diagnosis and modernisation of health care practices. An example of an elementary implementation of AI is the use of chatbots and virtual assistants that can take care of basic yet tedious tasks like registering medical records, clinical workflows and monitoring lab results – all in an automated and secure process.

NEW DELHI: The Artificial Intelligence Task Force of the Ministry of Defence led by Tata Sons Chairman N Chandrasekaran on Saturday submitted its final report to Defence Minister Nirmala Sitharaman on using AI for military superiority. "The Task Force handed over the final report to Raksha Mantri Nirmala Sitharaman to accept it and to implement its recommendations," the Ministry of Defence said in a statement. The Task Force was constituted in February 2018 to study the strategic implications of AI in national security perspective and in global context. It is a multi-stakeholder group comprising members from government, services, academia, industry and start-ups. "AI has the potential to have transformative impact on national security. It is also seen that AI is essentially a dual use technology. While it can fuel technology driven economic growth, it also has potential to provide military superiority," the statement said.

On Tuesday this week, the U.S. House of Representatives Subcommittees on Research and Technology and Energy invited prominent academics, tech executives, and scientists to talk about the "game-changing" potential and implications of AI, as the hearing charter put it. It touched on a number of topics. Rep. Barbara Comstock (R-VA) sought suggestions from the panel on ways institutions and government might collaborate on AI systems development. And Rep. Marc Veasey (D-TX) asked earnestly about the potential for "doomsday" scenarios. "To what extent do you think [is it] something we should be concerned about?" he said.

Even amid the majority concerns, only 32 percent of Americans support the creation of a Federal Robotics Commission to regulate development and usage of robots. However, 39 percent of Americans between ages 18 and 34 were in favor of the robotics agency, compared to only 25 percent of older people (55 and over). That's the result that West found most interesting, suggesting that support for the idea may continue to increase. "If young people hold on to those views as they age, that would suggest we're headed towards more government regulation," West said. The Trump administration does have a major reorganization of federal agencies on its agenda, including a proposed combination of the Department of Education and Labor.

Energy is limited in mobile systems, however, so for this possibility to become a viable opportunity, energy usage must be conservative. The Low-Power Image Recognition Challenge (LPIRC) is the only competition integrating image recognition with low power. LPIRC has been held annually since 2015 as an on-site competition. To encourage innovation, LPIRC has no restriction on hardware or software platforms: the only requirement is that a solution be able to use HTTP to communicate with the referee system to retrieve images and report answers. Each team has 10 minutes to recognize the objects in 5,000 (year 2015) or 20,000 (years 2016 and 2017) images.

We survey the AI research carried out in Greece recently. A milestone for AI research in Greece came in 1988, when the Hellenic Artificial Intelligence Society (EETN) was founded as a nonprofit scientific organization devoted to organizing and promoting AI research in Greece and abroad. EETN is an affiliated society of the European Association for Artificial Intelligence (EurAI, formerly known as ECCAI). One of the many roles of EETN is the organization of conferences, workshops, summer schools, and other events, such as the Hellenic Conference on Artificial Intelligence (SETN). The first SETN was Science with a team well grounded in KR.

In this article,  I propose a framework for machine theorem discovery and illustrate its use in discovering state invariants in planning domains and properties about Nash equilibria in game theory. I also discuss its potential use in program verification in software engineering. The main message of the article is that many AI problems can and should be formulated as machine theorem discovery tasks.