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Sparse Coherence

Making occasionally thoughtful connections on how the mind works, and how to build intelligence in silicon. What is the probability of this coin landing heads up on the next throw? This article introduces two kinds of probability: variability and uncertainty. Let's say that we have flipped the coin seven times, and we got five heads and two tails. We always have incomplete knowledge - and so there is always both variability and uncertainty. In order to make a rational prediction, you must take into a...

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Sparse Coherence | sparsecoherence.blogspot.com Reviews
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Making occasionally thoughtful connections on how the mind works, and how to build intelligence in silicon. What is the probability of this coin landing heads up on the next throw? This article introduces two kinds of probability: variability and uncertainty. Let's say that we have flipped the coin seven times, and we got five heads and two tails. We always have incomplete knowledge - and so there is always both variability and uncertainty. In order to make a rational prediction, you must take into a...
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Sparse Coherence | sparsecoherence.blogspot.com Reviews

https://sparsecoherence.blogspot.com

Making occasionally thoughtful connections on how the mind works, and how to build intelligence in silicon. What is the probability of this coin landing heads up on the next throw? This article introduces two kinds of probability: variability and uncertainty. Let's say that we have flipped the coin seven times, and we got five heads and two tails. We always have incomplete knowledge - and so there is always both variability and uncertainty. In order to make a rational prediction, you must take into a...

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sparsecoherence.blogspot.com sparsecoherence.blogspot.com
1

Sparse Coherence: World Model

http://sparsecoherence.blogspot.com/2008/07/world-model.html

Making occasionally thoughtful connections on how the mind works, and how to build intelligence in silicon. Here is the Blerpl learning algorithm’s view of the world:. Nodes in0, in1, in2 and in3 are the sensor input nodes, like the input switches on the Predictatron, or the pixels in an image sensor. Of course there could be a lot more than four of them. Nodes a, b, c, d and e are high-level independent causes in the world. They determine what is going on in the world. Because they are independe...The b...

2

Sparse Coherence: Confession

http://sparsecoherence.blogspot.com/2008/07/confession.html

Making occasionally thoughtful connections on how the mind works, and how to build intelligence in silicon. Ok ok, so I haven’t really built a Predictatron in a box. But I really have developed and implemented a very general learning algorithm, called Blerpl. More about that soon. Subscribe to: Post Comments (Atom). View my complete profile.

3

Sparse Coherence: Predictatron

http://sparsecoherence.blogspot.com/2008/07/predictatron.html

Making occasionally thoughtful connections on how the mind works, and how to build intelligence in silicon. The other day I built a computer that predicts its input. I call it the Predictatron. Here’s a picture:. First you switch the clock to Predict. Straight away, the lights display the computer’s prediction for the states of the inputs that you are about to set on the switches. You can write this prediction down if you want. Then you flip the clock back to Predict and so on, repeating the cycle over a...

4

Sparse Coherence: Patterns and Causes

http://sparsecoherence.blogspot.com/2008/02/patterns-and-causes.html

Making occasionally thoughtful connections on how the mind works, and how to build intelligence in silicon. This is an image of a dog. You recognised it because you have seen dogs before, although likely not exactly the same as this one. In fact the image that falls on your retina will probably never be exactly the same twice, even if you spend the rest of your life looking only at dogs. And yet all these possible images of dogs have a certain recognisable dogness in common. People find it easy to recogn...

5

Sparse Coherence: Learning

http://sparsecoherence.blogspot.com/2008/07/learning.html

Making occasionally thoughtful connections on how the mind works, and how to build intelligence in silicon. In Blerpl, learning works like this: To start with, the world model is empty. The network contains only the input sensor nodes. These are initially assumed to be independent causes, because we have not yet discovered any dependency rules. You mean the state of in1 is not enough to always predict the state of in2? This is just an introduction to give you the flavour of how Blerpl works, and we have ...

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Ben Williamson's blog: Blerpl

http://benwilliamson.blogspot.com/2008/07/blerpl.html

For those of you who have had to listen to me go on about brain stuff:. After spending a year in mothballs, the learning algorithm I developed last year might finally see the light of day. I've started blogging about it on my other blog, Sparse Coherence. Subscribe to: Post Comments (Atom). Enter your email address:.

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Sparse Coherence

Making occasionally thoughtful connections on how the mind works, and how to build intelligence in silicon. What is the probability of this coin landing heads up on the next throw? This article introduces two kinds of probability: variability and uncertainty. Let's say that we have flipped the coin seven times, and we got five heads and two tails. We always have incomplete knowledge - and so there is always both variability and uncertainty. In order to make a rational prediction, you must take into a...

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We are sparse creations. We create apps with award winning designs and publish only the best. Among all the mobile applications our noble creations. Like the refreshing oasis along a hot desert trail. The story began in United Arab Emirates. Our team. Was travelling in search of inspiration. For something new to create and share. With people. From the very first view over that magnificent land we all knew that it was that exact place where our project. Of what now sparse creations. Words can't describe w...

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