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02 Aug Artificial Time

Time and Space Perception

Time is omnipresent – you can’t get away from it. It is woven into everything we do and say and understand. It is an inextricable element of context. I was just speaking of how the connections in our brain develop, grow and evolve over time. Representing and handling this “temporal” element is fundamental to any […]

31 Jul Modeling Non-Random Synaptic Links

Random Hairdo

I have discussed the different meanings of “random” in “The Random Hamlet” and “That’s so Random!” in which the mathematical definition presumes there is some not yet known law that governs the phenomenon, where other definitions suggest that randomness means that the phenomenon is not governed by any law. Remember our reference to Rosenblatt’s early contributions in […]

26 Jul Parallel Distributed Pattern Processing

PDP Networks We have discussed recognition processes in the brain. Connectionism, a fundamentally implicit approach to neural modeling, was championed by the parallel distributed processing (PDP) group. PDP networks use many interconnected processing elements (PEs) that, according to the PDP Group, configure themselves to match input data with “minimum conflict or discrepancy” (Rumelhart & McClelland, 1986, Vol. 2, […]

24 Jul Pattern Classification in Space

Deep Space over the Water

Pattern Classification Visual patterns can be recognized and classified based on prior knowledge: I see that this hairy animal has four legs and is about the same size as my dog, so I’ll assume it is (or classify it as) a dog. This may not be a correct classification, but it’s more correct than classifying it […]

03 Jul Do Yawl do Petri Nets

Reactive vs Transformational Systems

Where do you draw a line? In geometry, digital theory, language and time, patterns tend to be linear: they bear distinct sequences. The sequences in these domains either contribute to the meaningfulness of the patterns, or, in the case of time, are the foundation of the patterns. Any logic that focuses on these sequential patterns is linear logic. Temporal Logic […]

06 May Impulse Waves in Layers

Waves

Layered Model Just as the brain has areas with three to six distinct layers, a typical artificial neural systems (ANS) also has several layers. The example at right shows a network with three layers that illustrate a neural network‘s distributed architecture. The uniform circles connected by lines are symbolic of the state of an ANS at […]

05 May Learning from Errors

Error

If at first you don’t succeed, try – try again. Humans are pretty good at learning from our mistakes. In fact, some suggest that whatever doesn’t kill you makes you stronger. Today I’d like to riff on that theme a bit, and talk about ways in which machines can implement learning from errors. Error Minimization […]

09 Apr Abstract Contexts and Fuzzy Reasoning

The Face of AI

We do not yet know how we remember things, nor do we know how we use remembered things in reasoning. The amazing feedback loops of afferent and efferent fibers between different layers of the cortex give us some amazing clues (Hawkins 2004). Today’s discussion of abstract contexts and fuzzy reasoning is intended as a bridge […]

28 Mar Is Everything Black or White?

Branching Circuit

We have exercised our abstract ideas about that which is too big and chaotic for us to understand (everything), let’s take a glimpse at that which is so small and chaotic that we may never really figure it out: the workings of the mind. In the next few posts, we will examine different forms of logical […]

27 Mar Cognition and Emotion

Emotions

I’m conflicted. I suspect you are too. “Since the time of the ancient Greeks, humans have found it compelling to segregate reason from passion, thinking from feeling, cognition from emotion. These contrasting aspects… have in fact often been viewed as waging an inner battle for control of the human psyche”  (LeDoux, 1996, p. 24). In earlier […]