1. How to Create a Mind The Secret of Human Thought Revealed by Ray Kurzweil Slides by Prof. Tappert

2. 1. Thought Experiments on the World Einstein’s thought (Gedanken) experiments led to the special theory of relativity Can we take the same approach with respect to the human brain to determine how it works?

3. 2. Thought Experiments on Thinking Memories are sequential and in order – sequences of patterns From observing, for example, that reciting the alphabet backwards is not easy We can recognize a pattern even if only part of it is perceived E.g., images of portions of faces Experience of our perceptions is changed by our interpretations We are more likely to perceive what we expect E.g., complete the sentence: Consider that we see what we expect to __ We remember routine procedures as a hierarchy of nested activities Like preparing for sleep And hierarchies are involved in recognizing objects and situations

4. 3. A Model of the Neocortex The Pattern Recognition Theory of Mind The cortical column and the mini-column The neocortex has extraordinary uniformity of column-like structure In 1978, Mountcastle hypothesized the neocortex composed of the cortical column as the basic unit, each containing many mini-columns Kurzweil contends the cortical mini-column is a pattern recognizer The human neocortex contains a half million cortical columns Each containing 60k neurons (the neocortex has about 30 billion neurons) A mini-column contains ~100 neurons, so neocortex has ~300 million pattern recognizers Compare with human experts Chess master Kasparov learned about 100k board positions, Shakespeare used 100k word senses, medical physicians master about 100k concepts With redundancy of 100 to 1 and requirements for general knowledge, we need over 100 million pattern processors

5. 3. A Model of the Neocortex The Pattern Recognition Theory of Mind A model of the neocortex Kurzweil favors the hierarchical hidden Markov model (HHMM) Jeff Hawkins and Dileep George proposed similar model Another model (not from this book) Deep learning neural network models, basically multi-layer perceptrons (MLP), also demonstrate many characteristics of the human neocortex Especially the deep-learning convolutional layers

6. 4. The Biological Neocortex https://en.wikipedia.org/wiki/Neocortex http://www.brainwaves.com/ https://en.wikipedia.org/wiki/Neocortexhttp://www.brainwaves.com/

7. 5. The Old Brain A portion of the human brain is pre-mammalian Located underneath the neocortex Provides motivation for seeking gratification and avoiding danger

8. 6. Transcendent Abilities Our emotional thoughts occur in the neocortex Influenced by regions of the old brain, like amygdala And some evolutionary new brain structures, such as spindle neurons Aptitude Creativity Love

9. 7. The Biologically Inspired Digital Neocortex Brain simulations – Markham’s Blue Brain Project (HBP) – full brain simulation expected 2023 Markham’s Blue Brain Project (HBP) HBP Problem Christopher deCharms HBP ProblemChristopher deCharms Neural nets – Rosenblatt’s perceptron, AI winter, backprop’s resurgence Sparse coding – vector quantization (basically clustering technique) Hidden Markov model (HMM) & Kurzweil’s hierarchical hidden Markov model (HHMM) Genetic algorithms (GA) Hierarchical memory systems – Numenta’s Hierarchical Temporal Model (HTM) Jeff Hawkins Dileep George Dissertation Jeff HawkinsDileep George Dissertation The moving frontier of AI – IBM’s Deep & Watson, etc. A strategy for creating a mind

10. 8. The Mind as Computer Computers are sometimes regarded as thinking machines This chapter compares the computer and the human brain

11. 9. Thought Experiments on the Mind This chapter explores consciousness Usually considered the capacity for self-reflection, the ability to understand one’s thoughts and explain them Some therefore believe a baby and a dog are not conscious because they cannot describe their thinking process IBM’s Watson can be put into a mode where it explains how it comes up with a certain answer. Is Watson therefore conscious?

12. 10. The Law of Accelerating Returns Applied to the Mind Law of accelerating returns – growth of information technology follows predictable exponential trajectories The best example of this is the double exponential growth of the price/performance of computation, steady for 110 years Through paradigms so far: electromechanical relays, electronic switches, vacuum tubes, individual transistors, integrated circuits (Moore’s Law) Applied to the mind Brain scanning technology resolution improving at exponential rate Advances in AI technology, also progressing exponentially, influence our understanding of brain operation principles

13. 11. Objections Human intuition is that advances are linear rather than exponential. Many critics, therefore, have difficulty accepting the exponential projections.