Machines Among Us: Today’s Reality of Artificial Intelligence

Machine Learning and Artificial Intelligence (AI) used to be the foils for heroes in science fiction movies. The Day the Earth Stood Still; 2001: A Space Odyssey; I, Robot; The Matrix; RoboCop; and Terminator all show a day when machines take over the world with disastrous consequences for humans. The reality is AI is already here today and it is nowhere near as villainous as the movies portray. AI helps diabetics better manage their sugar; enables driverless, electric vehicles that are better for the environment; supports the efforts of cyber warriors; and makes medicine more personal and precise.[Tweet "Machines Among Us: Today's Reality of Artificial Intelligence. #GovEventsBlog"]

Artificial Intelligence is used to describe the activities of a machine when it mimics the cognitive abilities of humans. This cognition allows the machine to take action toward a stated goal. But how do machines become intelligent? Like humans, they must be taught. Machine Learning is a type of AI focused on giving computers the ability to learn without being explicitly programmed. Using algorithms (designed by humans), machines can make predictions using the massive amounts of data they are able to process.[Tweet "AI = the activities of a machine when it mimics the abilities of humans. #GovEventsBlog"]

With AI available to doctors, scientists, cyber experts, and even the average citizen using speech recognition on their phone, we see that the applications are not, in fact, overtaking humans (as portrayed in the movies), but supplementing what we can do. A different, and maybe more accurate, way to define AI is "augmented intelligence."

AI encourages new ways of thinking as machines can cull through much more data than humanly possible, providing new insights on data that already exists. In the cybersecurity world, machines can handle a volume of data that is impossible for humans. Attacks happen at machine speed and AI helps quickly feed critical information to people who can make sense of it and take action. In this way, AI encourages higher levels of thinking. Mundane sorting tasks are done by machine and humans are freed up to do higher-level thinking and concentrate on more complex issues. If a chatbot can answer basic questions about where to send taxes, IRS contact center staff is then freed up to answer more complex and challenging tax issues.

We've noticed an increase in events addressing Machine Learning and AI on GovEvents and wanted to provide a sample of events that continue the education of humans on the education of machines.[Tweet "GovEvents has seen an increase in events addressing Machine Learning and AI. #GovEventsBlog"]

  • Analytics Supporting National Security (March 21; Arlington, VA) -- This event brings together government and military leaders and experts to explore the evolving role of analytics in national security. Early efforts have demonstrated the ability of analytic tools to collect and sift through large volumes of diverse data and gain better insight into a given problem or environment. Speakers will look at what is next for analytics in defense and how AI and machine learning support the goals of analytical programs and tools.
  • G-TECH (April 24-26; Washington, DC) - This event brings together 1500 of the nation's top innovators, thinkers, and technology leaders with leading government CIOs, and program officers for game-changing connections and conversations. AI and machine learning will be covered heavily in the Emerging Technologies and Innovation track.
  • Government IT Modernization (April 26-28; Washington, DC) - CIOs from across government, as well as industry experts will discuss best practices in modernizing legacy systems and taking better advantage of all of the data these systems hold. AI, machine learning, automation, IoT, and mobile are all critical pieces of this modernization.
  • Machine Learning Developers Conference (April 26-27; Santa Clara, CA) - This event explores the hardware and software challenges of building and debugging complex machine learning and artificial intelligence systems. Topics include machine learning and AI algorithms; machine learning and AI hardware - processors and systems; applying machine learning and AI in automotive, industrial, medical and other applications; and Internet of Things.

We'd love to hear your thoughts on resources and events for getting smart on our smart machines.

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