In a recent article on Autonomous Intelligent Systems (AIS) [1], Ajit Joakar described various features and characteristics of such systems, including associated technologies and research areas, building blocks and core elements, critical factors for success, and cross-cutting enablers. He introduces AIS as an "emerging interdisciplinary field that deals with situations where humans interact with AI systems that are autonomous." From this, we see immediately the synergistic interaction between the intelligent system and its human users. While full autonomy suggests that the system can operate without human interaction, it is useful to leave open the opportunity (even the essential necessity) for human intervention, to provide mid-course corrections that keep the AIS on the right (and ethical) course. Another detailed description of AIS comes from a 2014 report on Autonomous Manufacturing [2].

The explosion of interest in Artificial Intelligence (AI) and Machine Learning has triggered the design and development of all sorts of autonomous systems, including digital marketing & ad campaigns, self-driving cars, drones, self-healing (autonomic) systems, autonomous manufacturing, deep space exploration probes, and more. In all of these systems, safety and risk mitigation are paramount. So, what is an autonomous system? A report on "Autonomous Manufacturing" offers these definitions and features: We apply these concepts to a broader set of autonomous systems, borrowing liberally from the concept of a self-driving car: the self-driving organization, self-driving enterprise, and self-driving city (e.g., Smart Cities). We specifically examine five defining characteristics of the self-driving enterprise, their analytics implications, and their related organizational positioning.

In this show, the first of our PegaWorld 18 series, I'm joined by Kirk Borne, Principal Data Scientist at management consulting firm Booz Allen Hamilton. In our conversation, Kirk shares his views on automation as it applies to enterprises and their customers. We discuss his experiences evangelizing data science within the context of a large organization, and the role of AI in helping organizations achieve automation. Along the way Kirk, shares a great analogy for intelligent automation, comparing it to an autonomous vehicle . We covered a ton of ground in this chat, which I think you'll get a kick out of.

Automation can go far beyond cars. Self-driving company capabilities are closer than we realize. Every day we hear more about how self-driving vehicles will change our lives. Automotive innovators such as Tesla and Waymo have been working to advance this capability for years while legacy companies, including GM and Ford, have more recently joined the chase. Self-driving cars are now shuttling around riders -- although still with human overseers -- in Las Vegas, Boston, and Detroit, among other cities.

Automation can go far beyond cars. Self-driving company capabilities are closer than we realize. Every day we hear more about how self-driving vehicles will change our lives. Automotive innovators such as Tesla and Waymo have been working to advance this capability for years while legacy companies, including GM and Ford, have more recently joined the chase. Self-driving cars are now shuttling around riders -- although still with human overseers -- in Las Vegas, Boston, and Detroit, among other cities.

"Today, AI augments what we do, but in the future you'll see decisions made by (AI) entities," said Bernt Wahl, executive director of the Brain Machine Consortium. Wahl argues a logical progression of technological advances will result in smarter, more proactive AI systems. "With the web we created all these search engines and collected all this information," said Wahl, adding that systems like IBM's Watson now help determine whether all that information being collected is accurate. "In the future we'll have a'wisdom engine' that can take the knowledge we know is accurate and make decisions based on that,' he said. Jack Berkowitz, vice president of products and data science for Oracle's Adaptive Intelligence effort said AI has proven useful in helping companies filter the massive amounts of new data they're accumulating. "We call our program adaptive intelligence because it's about learning and adaptation and keeping pace," said Berkowitz. "Companies that try to keep up using the kind of rules-based systems we've had for years won't be able to.

The explosion of interest in Artificial Intelligence (AI), Machine Learning (ML), and Machine Intelligence (MI) has triggered the design and development of all sorts of autonomous systems, including digital marketing & ad campaigns, self-driving cars, drones, self-healing (autonomic) systems, autonomous manufacturing, deep space exploration probes, and more. In all of these systems, safety and risk mitigation are paramount. So, what is an autonomous system? A report on "Autonomous Manufacturing" offers these definitions and features: We apply these concepts to a broader set of autonomous systems, borrowing liberally from the concept of a self-driving car: the self-driving organization, self-driving enterprise, and self-driving city (e.g., Smart Cities). We specifically examine five defining characteristics of the self-driving enterprise, their analytics implications, and their related organizational positioning.

AI has begun to take hold in the everyday, in the form of Siri, Alexa and autonomous vehicles. But if identifying the nearest Korean BBQ and driving me there is all that the future of AI promises, well that's a damn shame. It's time that we become a lot more ambitious about the future of AI. There's a broad perception that the best that AI has to offer will be in the consumer world. This is not the case, and it demonstrates a severe lack of imagination.