09 Jun What’s the Point (of this blog)?
Knowledge Glut?
Too many of us are stuck on the on-ramp of the information superhighway, waiting to get on. The highway seems to be moving from my vantage point, but there is this guy stopped at the top of the ramp. Sometimes you just have to get out and walk. By the way, this photo shows the standard state of traffic when I visited Ulan Bator, Mongolia. Is this the state of information access today? This blog is about solving the information glut problem with knowledge tools based on context.
A fundamental premise of the analyses I intend to post here is that we can build computational systems that use context to be able to determine the intent of users based on their words. This may seem straightforward, but it’s not. Truly understanding the intent of a person based on their communication is terrifically difficult, and requires the most complex circuitry in the known universe: the human brain. And even that is often not enough (witness the challenges people of different cultures and different genders face in making themselves understood).
In each post, I will insert a block that shows where in the blog (Intro + 9 sections) you are, some key definitions and bibliographic references that apply to the post. Click the pentagon to get to the section posts, click the “Table of Context” to see all the sections.
| Understanding Context Cross-Reference |
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| Click on these Links to other posts and glossary/bibliography references |
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| Prior Post | Next Post |
| Intro to Understanding Context | Dawn of the Age of Knowledge |
| Definitions | References |
| thought | Campbell 1989 Lucky 1989 |
| mind cognition | von Neumann 1958 |
| intelligence | Turing 1936 Bailey 1996 |
| Glossary artificial intelligence | Hawkins 2004 Bibliography |
Cognitive Distance
Light years of distance separate computation and thought. In truth, comparing computers to the human mind is sort of like comparing apples and orangutans. That said, the artificial intelligence represented by computers provides a new window through which to view the human mind.
The British journalist Jeremy Campbell says the computer and human intelligence are “similar enough to furnish a metaphor for certain operations of reason, but so radically different in other respects as to make it abundantly obvious that this is not the way the mind is made.” In Campbell’s view, the differences cry out for the creation of more “illuminating theories” of the human mind [Campbell, 1989].
The Understanding Context blog approaches “illuminating theories” from the opposite side, where the inquisitive mind cries out for the creation of more intelligent computing theories. My posts are built on a foundation of such theories. They explore both human intelligence and artificial intelligence, pointing the way to the convergence of cognition and computation.
Understanding Context Posts
I’ve been working on this both in academia and industry, and I’ve accumulated a pile of stuff to write about. There are a bunch of topic areas or sections I will explore and I will do my best to organize them so you can follow the threads most interesting to you. You can always search to find a particular section or post or just go back to the home page or menu. You can also get insight into my thinking by using the Glossary or the Bibliography, in which I will provide links to related posts in the topic areas or sections.
Why blog instead of paper? The intent of the blog is to use hypertext to provide links between bits of information that have some logical associations. Though the word “hypertext” presumes only text, computer technology permits links with numbers, graphics, sound, and other goodies. The Understanding Context blog uses hypertext to connect concepts and graphics through the index and bibliography to places in the posts, letting you reach them with just a click of the mouse.
My posts are intended for readers of diverse backgrounds, so many of the terms used frequently are covered in the glossary. Cross-disciplinary studies can be burdened by technical jargon that is typically well understood only by students and practitioners in that particular field. Hopefully, the hypertext model and the glossary will help bridge whatever terminology/comprehension gaps may exist for you.
Understanding Context Layout
I will try to organize this series from the bottom up — that is, before dealing with the psychological issues of cognition critical to intelligent computing models, I’ll work on physiological issues. Sections 1, 2 and 3 include descriptions of some characteristics of the brain and its cells. These sections also contain hypotheses about the brain’s data capacity and functions. The reason for this approach is to make explicit the author’s assumptions about neurophysiology before discussing psychological theories and modeling.
Sections 1-3 Brain physiology and function
Sections 4-6 The psychology of cognition
Sections 7-9 How to teach computers to think
Having chosen a bottom-up approach, I must now apologize since the first two sections violate this approach by looking at the brain from the top down. (Could this really be an attempt to confuse you from the beginning in hopes that my mistakes and wacky ideas will be overlooked?)
The content I will be posting comes from current observations, recent published materials and analyses I have done over the past 25 years of caring about this topic. I am eager to hear others’ perspectives, and that’s why I am putting this out as a blog. I am also eager to get responses, ideas and links to sites of other people and groups working on the various aspects of solving the challenges of human-computer interaction, especially as they relate to context and human language. Thanks for your input.
| Click below to look in each Understanding Context section |
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| Intro | Context | 1 | Brains | 2 | Neurons | 3 | Neural Networks |
| 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 |
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