By Russell Ruggiero
There is not a day that goes by when the term “Artificial Intelligence (AI)” is not seen in articles, reports, books, and even on the silver screen. However, what is AI and where does it play in to our day-to-day and in context to robotics, along with neural networks? Related topics also include the Physical vs. Virtual Worlds and Conscious vs. Subconscious, and Information vs. Knowledge. AI is a very broad and often freely thrown around term, which needs to be better defined in what it is and where it can be appropriately applied.
The Future Ecosystem
Fast-forward 100 years into the future, as one would expect, the ecosystem will look much different than the one we know today. It would be logical to assume that Robotics, Neural Networks, and AI will play an ever more important role in our society, but from the outset an “Agnostic” foundation will be needed to enable all these visions to reach their full potential.
• Robotics: A branch of technology which deals with the design, construction, operation, and application of robots.
• Neural networks: A computer system modeled on the human brain and nervous system.
• Artificial Intelligence (AI): A branch of computer science that deals with the simulation of intelligent behavior in computers.
Please Note: One important area that relates to the above concerns Machine Learning which is the study of algorithms and statistical models that computer systems use to perform specific tasks without using explicit instructions, relying on patterns and inference instead. It is not a stretch to say that areas outlined in this section are and will continue to receive a great deal of attention and funding from both the public and private sectors.
Physical vs. Virtual Worlds & Conscious vs. Subconscious
We live in and exist in the physical world, while on occasion interacting with the virtual world as well. For example, a person wants to go out for a walk on a January morning in San Francisco. This person wants to know how to dress (windbreaker or sweater?) before leaving her apartment on Nob Hill. Logging on to her phone, the weather app shows light rain and 56 degrees, which usually means her blue windbreaker and taupe topsiders. In essence, she was able to leverage the information provided by the weather app on her phone to make a rational and logical decision. Now let’s dig a little deeper into both the physical world and the human mind to take on a different path in the decision making process. This person occupies a space within a physical structure which for all purposes is static in nature, but the outside weather pattern is both unpredictable and dynamic. Path One: She leverages the virtual world for information to take the appropriate actions (e.g., clothing, distance, etc.). Path Two: She does not use the weather app, but makes the final decision based on the knowledge gained during her life experiences. She opens a window and peers out to gain a better understanding on what she is dealing with in real-time. A chill, along with a drizzle with ominous clouds in the distance raise some serious red flags. She has seen this type of Bay-Area morning many times before, and feels that keeping the walk short and bringing an umbrella seems like a good idea.
The old expression “think on one’s feet” seems apropos. While virtually sourced information like a weather report is useful in many instances, relying on one’s own intuition can be even more valuable, depending on the situation. Now the debate of Information vs. Knowledge comes into play and is a very important issue when discussing AI.
Information vs. Knowledge
When the word debate is uttered, philosophers that include Thales, Socrates, Plato, and Aristotle from the Golden Age of Greece come to mind. No better work epitomizes this influential genre than the Republic by Plato. This work honestly exposes how information and knowledge are used by humans to make an argument during a casual get together. Question, are their arguments based solely on information gathered and then simply reiterated or is the information acquired synthesized and then applied to make a particular point? In fact it could be both, and that decision is really up to the person making the argument. This question comes into play regarding AI in that the word argument could be replaced by the word action.
A meaningful aspiration revolves around transparency, along with leveraging the countless benefits of technology to help better manage day-to-day activities, while also coping with manmade and natural catastrophic events. Let us for a moment look at comparing Federal Agencies to the Greek City States in regards to Information Technology (IT) and the relation to their respected ecosystems. Most have similar governing structures that follow predefined laws, which are meant to promote order and help form a unified society. It is here where efficient bidirectional and unidirectional information flow plays a critical role. We are currently part of a society where face-to-face interactions are augmented by human to machine – machine to machine – machine to human interactions as well. Nonetheless, the issue of knowledge and how it is managed comes on to center stage.
In theory a Knowledge Management (KM) system should leverage all of the information held within an organization to promote both cohesive internal and external ecosystems. It also, stands to reason that the more transparent the system, the greater the chances of reaching organizational goals and objectives. As a result, not only does there have to be an extensive alignment framework in place, but also a constant and reliable open line of communication at all levels to help steer the effort, which calls for both minor and major adjustments from time to time to reach true KM optimization.
AI Next Steps
Simply, AI is about leveraging relevant information to make logical and rational decisions. These “decisions” or “actions” will be based on critical factors (e.g., Vision, Mission, Value, Goal, Objective, Stakeholder, and Organizations, etc.) that are provided by parties that share similar goals and interests. Once the relevant information is identified and gathered, then an open-standard like XML Schema Definition (XSD), which is a recommendation of the W3C that specifies how to formally describe the elements in an Extensible Markup Language (XML) document may be used.
Another key area revolves around Ontologies which is a set of concepts and categories in a subject area or domain that shows their properties and the relation between them. (Oxford).
Other notable W3C efforts include Resource Description Framework (RDF), which provides the foundation for publishing and the linking of data. In addition, important related W3C efforts like Web Ontology Language (OWL) is designed to represent knowledge about things, groups of things, and relations between things.
How will AI go from concept to reach a reality? Through careful methodical planning and the leveraging of open-standards as well as massive public and private sector investments. Would also like to mention two current important AI related efforts. First, AIKR KAIROS. Second, Strategy Markup Language (StratML). These efforts are both taking an open-standards approach (e.g., ISO, OASIS, W3C, etc.), while aspiring to reach a number of attainable goals that could help to push the AI envelope forward. The AI carrot revolves not only around knowledge, but also on how it is used to make rational and logical decisions.