11 Jun Dawn of The Age of Knowledge
Sunset in The Information Age
We stand in the waning days of the Information Age. Certainly the information already available to the searching mind through printed, recorded, and encoded electronic media spans the universe of fact and imagination. I have been told that we are now entering the Age of Context. This is exciting to me because I think context is a key to knowledge. Can we really be close to the beginning of the Age of Knowledge? If so, we need to look in more than one place to find the transformational elements that bring in new defining capabilities and behaviors.
Maybe the true successor to the information age will be the Era of Online Social Interaction (EOSI). This is not a bad outcome, as long as healthy offline social interactions continue. But judging from the volume of information generated in online social interaction, I think we’ll still need better, smarter tools to manage the information glut. This is especially true for companies that need to reach out to people who need their products and services, and for people who want to get what they need quicker and with less searching around. I have this niggling suspicion that almost everyone needs something that is hard to find.
|Understanding Context Cross-Reference|
|Click on these Links to other posts and glossary/bibliography references|
|Prior Post||Next Post|
|What's the Point (of this blog)?||To Choose or not to Choose|
|cognition constraint||Slagle 1963 Singh 2004|
|information decision||Hawkins 2004 Lucky 1989|
|eclectic knowledge||Singh 1966 Scoble 2014|
|modeling mind||Darwin's Dangerous Idea|
The transformation opportunity has been stated as: “Modern computers lack the ability to innovate when presented with a new situation; more, they lack even the knowledge that we, or they, exist at all. We believe the next epoch in computing systems will arise when we can give machines the capacity for more self-awareness and ‘commonsense’ — the ability to think, learn, and act in the world with the resourcefulness and flexibility exhibited by people” (Singh 2004). Understanding context, I believe, is a key to bringing common sense to computers.
While keeping my observations grounded in fact, I intend to engage in an eclectic study of human information processing, or cognition, as well as a study of techniques for simulating cognitive processes on computers. The umbrella under which this research most completely fits is cognitive science.
Cognitive science is, by nature, an eclectic science, drawing on the body of knowledge and theory of at least six major fields. Some segments of the Understanding Context blog focus on physiology of thought, and others consider psychological and other human factors that affect key decisions in computer simulation of cognition. Finally, the concluding segments discuss computer technologies in hardware and software that will enable us to imitate the human mechanism (i.e. AI).
One outgrowth of cognitive science has been a set of theories around the importance of the way we act, collectively known as behaviorism. “AI philosophy was buttressed by the dominant trend in the first half of the twentieth century, called Behaviorism” (Hawkins 2004). By treating the outcomes of thinking (what we choose to say and do) as the output of the human brain, and the environment, including all incoming stimuli as the input, we can envision intelligent systems that respond to a set of inputs in the same way a human would. The computer would then be said to behave in a way that resembles human cognitive processes.
Is Eclecticism Justified?
Where do we turn in academia to find out how the brain works? The description of processes of human cognition in this blog includes perspectives from both the “hard” science of anatomy and the “soft” science of psychology. In discussions of the human mind, the distinction between hard and soft is rather arbitrary. In the context of this research, the distinction becomes even less relevant because the pages on anatomy are filled with hypotheses. Although all these speculations are backed up by facts, many of them only recently discovered, absolute proof is scarce.
While I was doing my graduate studies at the University of Minnesota, I studied under Dr. James Slagle. Slagle is often credited with having built the first expert system. He guided me to spend a lot of time outside the Computer Science Department so I could develop a better understanding of the mechanics of the brain and cognition. What does an eclectic approach give us? Evidence from multiple disciplines can reinforce or weaken some of our theories and intuitions and provide a firmer foundation for validating or invalidating mathematical and computational models.
I do not want to be guilty of greedy reductionism. This is a common phenomenon among impatient researchers who would like to arrive at conclusions before testing all the premises or even ensuring that they know all the applicable constraints from which to establish premises. To avoid this I will clarify my intent:
- I do not know enough to attempt to explain the brain, cognition or consciousness, just to model some phenomena using computers
- I do not know enough about learning to claim anything, just enough to mimic learning processes using computer software
- I do not know any language well enough to claim to be an expert in that language — I’m just a trained observer of language phenomena
- I do not know enough about computers to solve every automation problem, but I know enough to find a solution given the right constraints
If this blog accomplishes anything, it will be to show people from one of the disciplines assembled for this work that there are important findings in other academic areas that can contribute to building a better mousetrap, so to speak, when the mouse in the crosshairs (unfortunate mixed metaphor) is understanding human intent.
Please take a look at the head pages for each of the sections of this blog:
|Click below to look in each Understanding Context section|
|4||Perception and Cognition||5||Fuzzy Logic||6||Language and Dialog||7||Cybernetic Models|
|8||Apps and Processes||9||The End of Code||Glossary||Bibliography|