White Paper

w w w. m r c y. c o m WHITE PAPER 4 Internet of Things (IoT) The connected world through the Internet of Things (IoT) has grown since Kevin Ashton (creator of RFID) first coined the term in 1999. Compact, powerful batteries, advanced embedded processors and a proliferating wireless networking infrastructures have expanded the world's data recording capabilities from inside the "fixed" computer to anywhere. Mobile devices capture temperature, location, audio (voice) and visual image data, to name a few, "at the edge". In near real-time the collected data is transmitted to the cloud or other central repository where big processing resources can analyze the individual data and as a group, offer learned opinions and calculated predictions through AI. Autonomous vehicles For autonomous vehicles to deliver their promise of safe, efficient travel requires a convergence of extensive sensing and cognitive on and off- platform decision-making capability. This compute challenge is being solved with onboard AI processing of the sensor and environmental data. The enabling software is running on a widening array of powerful processors that are being tailored for AI processing acceleration intended for embedding into mobile platforms. With big financial bets being placed by Google, Apple, Amazon, Uber, Tesla and the automobile industry as a whole, the momentum of AI innovation within the autonomous vehicle domain has become unstoppable. This is producing on-platform processing capabilities that support secure and intrinsically safe AI processing and effector (avionics, vetronics, countermeasures, etc.) initiation, both of which are required for autonomous vehicle deployment. AI-enabled vehicles from many enterprises and nations are fundamentally a technology game changer. These capabilities will not remain within the commercial sector; actors from around the globe will leverage them as force multipliers for good and bad missions. The new digital domain with data as the new currency Traditional computing, or computing before AI may be thought of as the execution of algorithms to calculate answers that are "known" or are "knowable". These calculated answers can be shown to be "correct and precise" based upon the predefined, underlying logic and assumptions. AI and CC is the calculation of "likely" outcomes based upon data- pattern analysis and is unrestrained by the initial program parameters. Output Units Hidden Units Input Units Flow of Activation Logical neural network schematic from data input to most likely scenario outputs. Machine learning (ML) directly observes data patterns and stores correlations in a data representation that is commonly referred to as a neural network (i.e. the "brain" of the algorithm). Specific data is introduced to the algorithm (or pre-processed) and the computer calculates many possible outcomes. The user ranks the outcomes and stores them in the machine learning "brain" to be applied to future encounters with similar objects and situations, or the same object or circumstances observed under different conditions. The greater and more precise the reference data and better assumptions built into the AI algorithms produces predictions with greater accuracy and insight. The future of AI and the US government Governments around the world are pushing forward with their AI agendas. Recently, the US government sent strong signals regarding the importance of investing in AI technology and policy. In 2016, it issued a policy statement, "Preparing for the Future of AI" from the Subcommittee on Machine Learning and Artificial Intelligence that concluded that the nation as a whole would benefit from a steady increase in Federal and private sector AI R&D. The policy stated that government should: • Examine how to leverage AI and machine learning in/across all government agencies • Prioritize open training data and open data standards for AI learning • Explore ways to apply AI for more missions • Invest in automated air traffic management for autonomous and piloted aircraft The new digital domain with data as the new currency Vehicles Equipment Manufacturers Customers Cloud Mobile devices Citizens On-premises Partners Sensors Smart cities Data is the new currency Marketplaces Energy systems Supply Chains

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