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Reasoning by Similarity on Top of an Associative Memory

Reasoning by Similarity on Top of an Associative Memory

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IBM Research - Almaden Colloquium: The Cognitive Enterprise November 19, 2013 at IBM Research - Almaden in San Jose, CA. IBM Research has convened a stellar list of speakers including the founder of Palm, Jeff Hawkins; Paul Hofmann, CTO of Saffron Technology; John Hollar, President of the Computer History Museum; Dick Karp, Turing Award and Kyoto Prize recipient; Olivier Lictharge, Baylor School of Medicine; and distinguished panelists from the Silicon Valley VC community.

IBM Research - Almaden Colloquium: The Cognitive Enterprise November 19, 2013 at IBM Research - Almaden in San Jose, CA. IBM Research has convened a stellar list of speakers including the founder of Palm, Jeff Hawkins; Paul Hofmann, CTO of Saffron Technology; John Hollar, President of the Computer History Museum; Dick Karp, Turing Award and Kyoto Prize recipient; Olivier Lictharge, Baylor School of Medicine; and distinguished panelists from the Silicon Valley VC community.

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Reasoning by Similarity on Top of an Associative Memory

  1. 1. Reasoning by Similarity on Top of an Associative Memory Fabric Paul Hofmann, PhD, CTO Saffron Technology Talk given at IBM Research - Almaden Colloquium: The Cognitive Enterprise November 19th, 2013 Google Twitter RSS FACEBOOKDATABASE SOCIAL NETWORKS STOCKSEmail DATABASESEXEL WordPDF
  2. 2. Associative Memories  Cognitive Distance
  3. 3. Predictive Maintenance 12/3/202 0 3 ©2013 Saffron Technology, Inc. All rights reserved. I remember this feeling, it means…Temperature was 100F+ with high winds I remember Tail #123 reported a similar issue. The last time the pilot reported this problem… RESULT: 100% recall 1% false alarms Up from 66% recall 16% false alarmsData Sources: Structured and unstructured, Maintenance records, purchase orders, work orders, everything that speaks to these issues Reduce down time of aircraft Use multiple sources of data to learn from collective experience 0% 20% 40% 60% 80% 100% Saffron CBM 1 % false alarms 100% hits Predict before part breaks
  4. 4. Early Warning System Structured and Unstructured Data Strategic Early Warning System – Igor Ansoff Scan environment to detect weak signals & rare events to predict surprises Find the unknown enemy to protect The Foundation Early warning system to score threats from people & groups based on dynamic incremental machine learning Incidence Reporting Metadata + E-mails Harvested Web Pages (Terabytes & growing ) Detect weak signals to predict threat
  5. 5. Pattern Recognition In Healthcare Intelligent Platforms for Disease Assessment Novel Approaches in Functional Echocardiograph, Partho P. Sengupta, in JACC: Cardiovascular Imaging, 11/2013 Automate Echocardiogram Diagnoses Heat maps show separation of disease states. Associations between variables in restrictive cardiomyopathy (red) separate from dominant associations in constrictive pericarditis (green) State of the art C-tree 54% using 7 attributes Best doctor 76% Saffron 90% 90 metrics, 6 locations, 20 time frames 10,000 attributes/beat*patient -> 100 million triples / beat*patient
  6. 6. Match Made in Heaven Cognitive Distance  Associative Memories Universality • Cognitive Distance is universal • C. Bennett, IBM, 1997; M Hutter, IDSIA, 2000 AIXI • Nonparametric, incremental, deterministic weights Context • Cognitive Distance depends on context • AM fabric stores context – complete graph Compression • K Complexity measures compressibility • Associative Memories are perfect compressor
  7. 7. Kolmogorov Complexity – Signal vs. Noise Snake eyes are regular sequence -> regular cause, meaning probability > 0 for snake eyes! 100X Place a huge bet on simple outcomes – fair dice have no pattern
  8. 8. How Do Extract Similarity Automatically? Cognitive Distance based on Kolmogorov Complexity Approximating Kolmogorov Complexity K(x) ~ log x/N we get CD ~ max {log(fx),log(y)}-log(x,y) / ( logN-min{log(x),log(y)}  the saddle is closer to the cowboy x=131M “saddle” y=87M “movie” y=1,890M xy=73M xy=8M What is closer to cowboy? 1. saddle or 2. movie
  9. 9. Not Always So Easy - Context Resolves Ambiguity Cognition Is About Context Cognitive Distance Allows for Condition CD|c ~ max {log(xc|c),log(yc|c)}-log(xc,yc|c) / ( logN-min{log(xc|c),log(yc|c)} )
  10. 10. The Bride: Scaling Associative Memory
  11. 11. NoSQL - Associative Memories Are Truly Asynchronous Computing Connections and counts synapses and strengths Hopfield Network Ising Model for order  disorder phase transition e.g. Ferromagnetism weights are deterministic  parameter free
  12. 12. Saffron’s Solution - Large Scale Machine Learning on Sparse Matrices Why is this so special? • Non-parametric, non- linear & instant incremental learning • Graph & statistics • Millions of features • Saffron stores & queries billions of triple counts refid 1234 1 1 1 1 1 1 1 1 1 1 place London 1 1 1 1 1 1 1 1 1 1 person John Smith 1 1 1 1 1 1 1 1 1 1 person Prime Minister 1 1 1 1 1 1 1 1 1 1 time 14-Jan-09 1 1 1 1 1 1 1 1 1 1 verb flew 1 1 1 1 1 1 1 1 1 1 verb meet 1 1 1 1 1 1 1 1 1 1 keyword rainy 1 1 1 1 1 1 1 1 1 1 keyword day 1 1 1 1 1 1 1 1 1 1 keyword aboard 1 1 1 1 1 1 1 1 1 1 duration 2 hours 1 1 1 1 1 1 1 1 1 1 1234 London JohnSmith PrimeMinster 14-Jan-09 flew meet rainy day aboard 2hours refid place person person time verb verb keyword keyword ketword duration Organization United Airlines refid 1234 1 1 1 1 1 1 1 1 1 1 place London 1 1 1 1 1 1 1 1 1 1 person John Smith 1 1 1 1 1 1 1 1 1 1 organization United Airlines 1 1 1 1 1 1 1 1 1 1 time 14-Jan-09 1 1 1 1 1 1 1 1 1 1 verb flew 1 1 1 1 1 1 1 1 1 1 verb meet 1 1 1 1 1 1 1 1 1 1 keyword rainy 1 1 1 1 1 1 1 1 1 1 keyword day 1 1 1 1 1 1 1 1 1 1 keyword aboard 1 1 1 1 1 1 1 1 1 1 duration 2 hours 1 1 1 1 1 1 1 1 1 1 1234 London JohnSmith UnitedAirlines 14-Jan-09 flew meet rainy day aboard 2hours refid place person organization time verb verb keyword keyword ketword duration Person Prime Minister John Smith flew to London on 14 Jan 2009 aboard United Airlines to meet with Prime Minister for 2 hours on a rainy day. refid& 1234 1 1 1 1 1 1 1 1 1 1 person& John&Smith 1 && 1 1 1 1 1 1 1 1 1 person& Prime&Minster& 1 1 && 1 1 1 1 1 1 1 1 organization& United&Airlines& 1 1 1 && 1 1 1 1 1 1 1 time 14<Jan<09 1 1 1 1 && 1 1 1 1 1 1 verb& flew& 1 1 1 1 1 && 1 1 1 1 1 verb& meet& 1 1 1 1 1 1 && 1 1 1 1 keyword& rainy& 1 1 1 1 1 1 1 && 1 1 1 keyword& day& 1 1 1 1 1 1 1 1 && 1 1 keyword& aboard& 1 1 1 1 1 1 1 1 1 && 1 duration 2&hours& 1 1 1 1 1 1 1 1 1 1 & 1234 John&Smith Prime&&Minster United&&Airlines 14<Jan<09 flew& meet& rainy& day& aboard& 2&hours& refid& person person& organization& time verb& verb& keyword& keyword& ketword& duration Place&&&&&&&&&&&&&&&&& London Build the Brain 1. Unify structured & un-structured data 2. Extract entities 3. Build semantic graph with counts on edges  stored as triples Make the Brain Think • Reason by similarity with cognitive distance
  13. 13. Happy Ending – Offspring of KC & AM  Discovery – Search – Entity ranking and semantic context – Convergence – the distance over time  Classification – Predicting risk (bad, good) – Customer life time value – Echocardiogram diagnosis  Clustering – Evolutionary trees, languages, music – Novelty detection: spare parts, planes, etc.
  14. 14. Convergence: Cognitive Distance over Time
  15. 15. Take Away 12/3/202 0 15 ©2013 Saffron Technology, Inc. All rights reserved. DATABASE SOCIAL NETWORKS Email EXCEL Google Twitterrss FACEBOOK STOCKS DATABASES Word PDF Advanced cognitive computing to perform like super brains By matching Cognitive Distance with Associative Memories we are able to • reason by similarity • learn instantly & incrementally w/o parameters • Discern Context Enterprise proven
  16. 16. 16 Twitter @paul_hofmann Email phofmann@saffrontech.com Homepage www.paulhofmann.net Blog www.paulhofmann.net/blog Slide Share www.slideshare.com/paulhofmann LinkedIn www.linkedin.com/in/hofmannpaul Watch Dr. Sengupta Partho’s video on YouTube http://www.youtube.com/watch?v=rGkyDkDmZts

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