Scientists from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a way to use machine learning to dramatically accelerate the design of microbes that produce biofuel. Their computer algorithm starts with abundant data about the proteins and metabolites in a biofuel-producing microbial pathway, but no information about how the pathway actually works. It then uses data from previous experiments to learn how the pathway will behave. The scientists used the technique to automatically predict the amount of biofuel produced by pathways that have been added to E. coli bacterial cells. The new approach is much faster than the current way to predict the behaviour of pathways, and promises to speed up the development of biomolecules for many applications in addition to commercially viable biofuels, such as drugs that fight antibiotic-resistant infections and crops that withstand drought.

Course Description What is our role in the universe as human agents capable of knowledge? What makes us intelligent cognitive agents seemingly endowed with consciousness? This is the second part of the course'Philosophy and the Sciences', dedicated to Philosophy of the Cognitive Sciences. Scientific research across the cognitive sciences has raised pressing questions for philosophers. The goal of this course is to introduce you to some of the main areas and topics at the key juncture between philosophy and the cognitive sciences.

The data and analytics market is shifting from discrete products to unified, cloud-based services that support cognitive businesses. The IBM Watson artificial intelligence (AI) and data platform is the integration of these services and technologies. Our mission is to help organizations augment and accelerate their data-driven problem-solving capabilities. IBM delivers tangible insights in the functional domains of sales and marketing, finance and operations, and the Internet of Things (IoT). Watson Artificial Intelligence and Data Platform Services helps you integrate your structured and unstructured data into a unified platform that gives you access to actionable insights.

Cognitive computing and artificial intelligence are trending in the world of Supercomputing. In this era of digitalization, we are all considered about the 4Cs: Consistency, Convenience, Content, and Contextual. To know the difference between an AI-powered platform and Cognitive powered platform is important for any business. The concept of Artificial intelligence is with us for quite a long time now. Artificial intelligence is a really big term which has now become the reality of our lives.

Although artificial intelligence (as a set of technologies, not in the sense of mimicking human intelligence) is here since a long time in many forms and ways, it's a term that quite some people, certainly IT vendors, don't like to use that much anymore – but artificial intelligence is real, for your business too. Instead of talking about artificial intelligence (AI) many describe the current wave of AI innovation and acceleration with – admittedly somewhat differently positioned – terms and concepts such as cognitive computing or focus on several real-life applications of artificial intelligence that often start with words such as "smart" (omni-present in anything related to the IoT as well), "intelligent", "predictive" and, indeed, "cognitive", depending on the exact application – and vendor. Despite the term issues, artificial intelligence is essential for and in, among others, information management, healthcare, life sciences, data analysis, digital transformation, security (cybersecurity and others), various consumer applications, next gen smart building technologies, FinTech, predictive maintenance, robotics and so much more. On top of that, AI is added to several other technologies, including IoT and big, as well as, small data analytics. There are many reasons why several vendors doubt using the term artificial intelligence for AI solutions/innovations and often package them in another term (trust us, we've been there). Artificial intelligence (AI) is a term that has somewhat of a negative connotation in general perception but also in the perception of technology leaders and firms.

Ensuring flexible and efficient manufacturing of customized products in an increasing dynamic and turbulent environment without sacrificing cost effectiveness, product quality and on-time delivery has become a key issue for most industrial enterprises. A promising approach to cope with this challenge is the integration of cognitive capabilities in systems and processes with the aim of expanding the knowledge base used to perform managerial and operational tasks. In this work, a novel approach to real-time rescheduling is proposed in order to achieve sustainable improvements in flexibility and adaptability of production systems through the integration of artificial cognitive capabilities, involving perception, reasoning/learning and planning skills. Moreover, an industrial example is discussed where the SOAR cognitive architecture capabilities are integrated in a software prototype, showing that the approach enables the rescheduling system to respond to events in an autonomic way, and to acquire experience through intensive simulation while performing repair tasks.

In order to reach higher degrees of flexibility, adaptability and autonomy in manufacturing systems, it is essential to develop new rescheduling methodologies which resort to cognitive capabilities, similar to those found in human beings. Artificial cognition is important for designing planning and control systems that generate and represent knowledge about heuristics for repair-based scheduling. Rescheduling knowledge in the form of decision rules is used to deal with unforeseen events and disturbances reactively in real time, and take advantage of the ability to act interactively with the user to counteract the effects of disruptions. In this work, to achieve the aforementioned goals, a novel approach to generate rescheduling knowledge in the form of dynamic first-order logical rules is proposed. The proposed approach is based on the integration of reinforcement learning with artificial cognitive capabilities involving perception and reasoning/learning skills embedded in the Soar cognitive architecture. An industrial example is discussed showing that the approach enables the scheduling system to assess its operational range in an autonomic way, and to acquire experience through intensive simulation while performing repair tasks.

For robots to successfully perceive and understand their environment, they must be taught to act in a goal-directed way. While mapping environments geometry is a necessary prerequisite for many mobile robot applications, understanding the semantics of the environment will enable novel applications, which require more advanced cognitive abilities. Sven Behnke, Head of Autonomous Intelligent Systems Group at the University of Bonn, is tackling this area of robotics by combining dense geometric modelling and semantic categorization. Through this, 3D semantic maps of the environment are built. Sven's team have demonstrated the utility of semantic environment perception with cognitive robots in multiple challenging application domains, including domestic service, space exploration, search and rescue, and bin picking.

Artificial intelligence (AI), mixed reality, and cognitive science research sound like science fiction for today's classrooms, but this technology is available today. Innovation and technology are as integral to education today as chalkboards were in the past. And with the introduction of emerging, new, and proven technology-enhanced innovations, teachers are creating new ways of teaching and improving student learning, leading to a shift in pedagogy. Here are three of the latest innovations our district is using. Lumilo is a pair of mixed-reality smart glasses that provide teachers with continuous, real-time feedback about their students' learning, metacognition, and behavior, as well as potential effects of their own teaching.

It's never been more important for life insurers to offer their policyholders high-quality service. Digitisation and intensified competition with other financial service verticals have blunted prospects for growth and made customer acquisition and retention ever more challenging. Meanwhile, consumers' overall service expectations continue to rise as retailers in other industries set a higher bar. Insurers have struggled to keep up because of the inherent complexity of both their products and their systems' environments. Bringing cognitive computing to customer service will allow insurers to move past these challenges and deliver a level of digital service comparable to that of any other industry.