Monday, April 24, 2017

Professor Hubert Dreyfus (1929 - 2017)


UC Berkeley Professor Hubert Dreyfus has passed away at the age of 87. Professor Dreyfus is a hero of mine. He was a fearless rebel at heart, the first to criticise the AI community for their symbolic AI nonsense. They hated him for it but he was right, of course. Did the AI community ever apologise for their personal attacks on him? Of course not. The AI community has always been full of themselves and they still are.

Dreyfus contributed more to the field of artificial intelligence than its best practitioners. His insistence that the brain does not model the world is an underappreciated tour de force. His ability to connect the works of his favorite philosophers (Martin Heidegger, Maurice Merleau-Ponty) to the working of the brain was his greatest intellectual achievement in my opinion. I wrote an article about this topic in July of last year. Please read it to appreciate the depth of Dreyfus' understanding of a field that rejected him.

The World Is its Own Model or Why Hubert Dreyfus Is Still Right About AI

The world owes Professor Dreyfus a debt of gratitude. Thank you, Professor.

Monday, April 10, 2017

Signals, Sensors, Patterns and Sequences

[Note: The following is an excerpt from a paper I am writing as part of the eventual release of the Rebel Speech demo program, the world's first unsupervised audio classifier. I have not yet set a date for the release. Please be patient.]

Abstract

Signals, sensors, patterns and sequences are the basis of the brain’s amazing ability to understand the world around it. In this paper, I explain how it uses them for perception and learning. Although I delve a little into the neuroscience at the end, I restrict my explanation mostly to the logical and functional organization of the cerebral cortex.

The Perceptual System

Four Subsystems

Perception is the process of sensing and understanding physical phenomena. The brain’s perceptual system consists of four subsystems: the world, the sensory layer, pattern memory and sequence memory. Both pattern and sequence memories are unsupervised, feedforward, hierarchical neural networks. As explained later, the term “memory” is somewhat inadequate. The networks are actually high level or complex sensory organs. An unsupervised network is one that can classify patterns, objects or actions in the world directly from sensory data. A feedforward network is one in which input information flows in only one direction. A hierarchical network is organized like a tree. That is to say, higher level items are composed of lower level ones.

The world is the main perceptual subsystem because it dictates how the rest of the system is organized. The brain learns to make sense of the way the world changes over time. Elementary sensors in the sensory layer detect minute changes in the world (transitions) and convert them into precisely timed discrete signals that are fed to pattern memory where they are combined into small concurrent patterns. These are commonly called “spatial” patterns although it is a misleading label because concurrent patterns are inherently temporal and used by all sensory modalities, not just vision.

Signals from pattern detectors travel to sequence memory where sequences (transformations) are detected. Sequence memory is the seat of attention and of short and long-term memory. It is also where actual object recognition occurs. An object is a top-level sequence, i.e., a branch in the sequence hierarchy. A recognition event is triggered when the number of signals arriving at a top sequence detector surpasses a preset threshold. Recognition signals (green arrow) from sequence memory are fed back to pattern memory. They are part of the mechanism used by the brain to deal with noisy or incomplete patterns in the sensory stream.

Sequence memory can also generate motor signals but that is beyond the scope of this paper. What follows is a detailed description of each of the four subsystems.

(to be continued)