Entries by John R

This is a May 2010 lecture given by Professor Robert Sapolsky at Stanford University. The lecture is on schizophrenia, but starts with a very informative lecture on language. Specifically, it's about what is shaping up to be the genetic, bio-molecular correlates of grammar and language.

Warning: For most lecturers you can kind-of do little fast-forward jumps during the video, resynchronizing your cognitive-following groove after each jump. This can shave some time off the lecture.

With this guy, that's not so easy. He really loads you up with information. (I'd love to see him do a lecture on autism).


https://www.youtube.com/watch?v=nEnklxGAmak






Viewing note: This starts with a wrap-up on a previous lecture on language and linguistics. The Schizophrenia lecture begins at around 23:30.

Probably the most repeated synonym for the word Fungible, is "interchangeable," followed closely by "substitutable," but there are some subtle differences in connotations (I think). This entry is an attempt to clarify, at least a little bit, just what the word fungible means.

As always, with writing, it's important to emphasize that I'm NOT an expert by any stretch. I'm just a novice, trying to muddle through these issues, and to invite corrections or comments, both from "real" writers, as well as those who, like me, are just trying to get better.

The glossary now has fields and template codes allowing for the addition of a description, and a list of keywords for each entry.

Of course, it is still necessary to go back and add them to the terms that are there.

Today I've added descriptions and keyword lists for about 80 entries. This is pretty close to my limit for one sitting.

When combined with earlier work, I'm happy to report that four letters have been filled so far. This leaves 23 letters still to do. . .

Aha! Did you notice that this adds up to 27 letters? Good eye! The glossary has a separate "letter'" for terms that start with numbers or symbols.

Developing an A.I. robot?:


Program it to cry when it detects something very bad or very good.




Developing a conscious robot?:


Program it to want to cry when it detects something very bad or very good.

Sources and Resources

• Related Background
  • What is it like to be a bat?
    This classic discussion by Thomas Nagel is one of the best known "explanatory devices." It begins to move us away from Turing's flawed (but necessary, at the time) kludge for explaining what consciousness is.

  • The Chinese Room
    A clarifying paper on the Chinese Room Argument, written by John Searle.

  • [pdf] Facing Up to the Problem of Consciousness
    This is an introductory level explanation of consciousness and where we are in dealing with the hard problem of explaining it. It is authored by one of the premier thinkers in the field David Chalmers

  • Time In Three Parts, Temporality

  • "Through Other Eyes" — A short story from a collection of short stories called Nine Hundred Grandmothers by R. A. Lafferty, 1970
    This is a fictional treatment of the concept of first person knowledge (see below).

  
  
  



• Related Glossary Entries

  
  

  • First Person Knowledge

  • Phenomenal Consciousness

  • John Searle

  
  
  
  

Some recent glossary updates have included:

• Connection Strength [Refreshed]
  Banging away some more on the "basics" drum. Attempting to address some common confusion surrounding weight-values vs. the connection-strengths they represent.

• Catastrophic Forgetting [New]
  A definition of a fairly common term from neural network literature, which labels one of the major problems that have been encountered. The entry also includes a discussion of how the problem is fully resolved by Multitemporal Synapses.

• Interference [New]
  Definition and some resources regarding current understanding and ideas on interference in neuronal memory acquisition.

• The Stability Plasticity Problem [New]
  Definition of another term from neural network literature, which is used as a more general label for the problem that is responsible for catastrophic forgetting. This entry also explains how the problem it labels is resolved by Multitemporal Synapses.

• Memory [Refreshed]
  Added some pics to the section on non-neuronal biological learning, along with a terse discussion of things like herding, schooling, and flocking behaviors. These are used to demonstrate how learning can occur in biological systems via an extra-neuronal mechanism.

• Multitemporal Synapses [Refreshed]
  Better explanations and editing plus a diagram (5.1) from the book (originally from the patent application).

• Kinetic Depth Effect [Refreshed]
  Added content, plus reorganized.

• Multilayer Perceptron (MLP)[New]
  Quick add to support other entries.

Overview

“Certainly, one of the most relevant and obvious characteristics of a present moment is that it goes away, and that characteristic must be represented internally.”

Stated plainly[1], the principle behind multitemporal synapses is that we maintain the blunt “residue” of past lessons in long-term connections, while everything else is quickly forgotten, and learned over again, in the instant. In other words, we re-learn the detailed parts of our responses as we are confronted with each new current situation.[2] One of the primary benefits of applying this principle, in the form of multitemporal synapses, is a neural network construct that is completely free of the usual problems associated with catastrophic forgetting. When you eliminate catastrophic forgetting from your neural network structure, the practical result is the ability to develop networks that continuously learn from their surroundings, just like their natural counterparts.

A Major Problem With Neural Networks

One major challenge with conventional neural network models has been in how to maintain connections that store enough intricate in-the-moment response-details to deal with any contingency that the system may encounter. Conventionally, such details would overwhelm long-term lessons stored in permanent connections-weights. This characteristic of conventional neural network models is known as The Stability Plasticity Problem, and is the underlying cause of "catastrophic forgetting."

When an artificial neural network that has learned a training set of responses, then encounters a new response to be learned, the result is usually ‘catastrophic forgetting’ of all earlier learning. Training on the new detail alters connections that are maintained by the network in a holistic (global) fashion. Because of this, it is almost certain that such a change will radically alter the outputs that were desired for the original training set.

The McGurk effect is a perception illusion, which shows how our perception of reality can be affected by interactions between multiple senses. The presentation of the McGurk effect demonstrated in the following video also shows, convincingly, that our visual processes can completely override our auditory perceptions of speech — at least in certain circumstances.

https://www.youtube.com/watch?v=G-lN8vWm3m0

Hearing With Our Eyes

In the above video, you will see the speaker's lips form an 'f'-sound. You will “hear” an 'f'-sound even though the actual sound being produced is a 'b'-sound (dubbed in over the video).

In this video, the 'f' perception reported by your eyes completely overrides the 'b' perception reported by your ears. Can we conclude, from this, that visual processing in the brain is given full priority over auditory processing?

That may be a bit hasty.