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  • October 5, 2018

    Neurithmic System’s radical new way of thinking about memory and their program (Sparsey) that implements it

    Professor Rod Rinkus  of Neurithmic Systems came up with a net (he calls it SPARSEY) that is about memory – storing memories and retrieving them.   No matter how many memories are already stored, the time to store a new memory, or to retrieve an old one stays the same.   There are some very promising aspects […]

  • September 19, 2018

    Making Neural Nets more decipherable and closer to Computers

    In an article titled “Neural Turing Machines“, three researchers from ‘Google DeepMind’ Alex Graves, Greg Wayne, and Ivo Danihelka describe a neural net that has a new feature, a memory bank. The system is similar in this respect to a Turing Machine – which was originally proposed by Alan Turing in 1936. His hypothetical machine […]

  • August 13, 2018

    Making Recurrent neural net weights decipherable – new ideas.

    One problem with neural nets is that after training, their inner workings are hard to interpret. The problem is even worse with recurrent neural networks, where the hidden layer sends branches back to feed, along with the inputs in the next time step, back to itself. Before I talk about how the problem has been […]

  • June 16, 2018

    When your character flaw is due to brain chemicals

    Blue Dreams is a new book by Lauren Slater – its describes the drugs that have been invented to treat mental illness. Lauren is a science journalist who has needed some of those drugs. Its a very well written book, with both fascinating history and science in it. But lets concentrate here on the science. […]

  • June 14, 2018

    Unraveling The Mystery of How The Brain Makes The Mind – Michael Gazzaniga’s new book

    Michael Gazzaniga, who directs the SAGE institute for the study of the mind at UC Santa Barbara, has come out with a book on how the brain gives rise to the mind. He believes that consciousness is not tied to a specific neural network. Various functions that take place in the brain each have intrinsic […]

  • June 5, 2018

    Judea Pearls causal revolution – and implications for A.I.

    Judea Pearl recently wrote a book for a popular audience about his life work, called “The Book of Why”. Pearl is the inventor of “Bayesian Networks”, which are graphs whose links are probabilities. Such a graph might have some nodes that represented symptoms of a disease, and other nodes that represented various diseases.  The links […]

  • May 10, 2018

    Using word context in documents to predict brain representation of meaning.

    In an article titled Predicting Human Brain Activity Associated with the Meanings of Nouns, Tom Mitchell of Carnegie Mellon University and associates describe how they demonstrated how meanings of words are mapped in the brain. The first step was to get examples of words in documents. They obtained a trillion-word collection. It has been found […]

  • April 30, 2018

    Finding invariant object representations by using the thalamus as a blackboard.

    Randall C. O’Reilly, Dean R. Wyatte, and John Rohrlich at the Department of Psychology and Neuroscience of the University of Colorado Boulder have come up with a new model of how the brain learns. As they put it in their article titled “Deep Predictive Learning”: where does our knowledge come from? Phenomenologically, it appears to […]

  • April 17, 2018

    Creating Neural Nets based on a Free-Energy principle

    In 1982 John Hopfield invented a neural net that minimized a set of constraints. If two nodes of the net were meant to go together, they would start off with a positive weight between them. If two nodes were incompatible, they would start with a negative weight. Sometimes this would lead to conflict – For […]

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