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"Crossing the Event Horizon" by Patrick Ehlen, PhD and Chief Scientist at Loop AI Labs (loop.ai)

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Many organizations today have a supermassive black hole at the center of their Digital Transformation strategy: Around 90% of the data they hold about their customers, employees, and business processes persists as "dark data" -- data that is locked away as ordinary human language written in reports, e-mails, internal documents, and customer service tickets, none of which can be easily understood by a machine. While people can read, and make sense of small batches of such data, the amount of data most organizations now hold has become too massive, and in-depth analysis of them goes beyond their resources. Fortunately, the dawn of a new era is upon us: A branch of artificial intelligence known as "Human Capacity Cognitive Computing" is able to provide human-level understanding of this dark data, giving organizations valuable insights into the contents of their dark data. Moreover, it can connect disparate pieces of the big data puzzle, providing a unified "big data big picture" of an organization's past, present, and future role in the marketplace.

About Patrick

Patrick Ehlen is Chief Scientist at Loop AI Labs. He has pursued artificial intelligence since 3rd grade, after reading Arthur C. Clarke's novel 2001: A Space Odyssey. In 1977, before most people had computers, his econometrician father bought a DEC PDP-11 to use in their home, and Patrick became obsessed with figuring out how to make the machine talk.

Fifteen years later, in 1992, inspired by the Parallel Distributed Processing volumes, he trained his first neural network to learn distributed representations of concepts.

Patrick earned a PhD in Cognitive Psychology from the New School for Social Research in 2005. While completing his PhD, he interned at speech recognition pioneer Dragon Systems (acquired by Nuance) and then at AT&T Labs-Research, trailblazing innovations in speech and multimodal interface technology.

He then went to the Center for the Study of Language and Information (CSLI), an AI mecca at Stanford University. As a research scientist in the Stanford's Computational Semantics Lab, he worked on the DARPA CALO AI project and devised machine learning methods to extract concepts and topics from ordinary, spontaneous conversations.

When approached by his old CALO colleague Bart Peintner about the work they were doing at Loop AI Labs, Patrick saw an opportunity to advance the state-of-the-art using techniques from Deep Learning that were just beginning to emerge.

Patrick has been awarded four U.S. patents and produced 45 research publications in the areas of computational semantics, cognitive linguistics, psycholinguistics, word sense disambiguation, human concept learning, and artificial intelligence. His work is cited in over 430 scientific papers.

LinkedIn: https://www.linkedin.com/in/patrickehlen
Twitter: @patrick_ehlen

Veröffentlicht in: Technologie
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"Crossing the Event Horizon" by Patrick Ehlen, PhD and Chief Scientist at Loop AI Labs (loop.ai)

  1. 1. Patrick Ehlen, Ph.D. Chief Scientist Loop AI Labs CROSSING THE EVENT HORIZON
  2. 2. Digital Transformation Strategy
  3. 3. Digital Transformation Strategy Big Data Moore’s Law
  4. 4. Digital Transformation Strategy Big Data Moore’s Law “Dark Data”
  5. 5. Digital Transformation Strategy Big Data Moore’s Law “Dark Data” Event Horizon ?
  6. 6. DIGITAL SPRAWL
  7. 7. DIGITAL SPRAWL • 2001: 5 Exabtyes
  8. 8. DIGITAL SPRAWL • 2001: 5 Exabtyes • 2011: 5 Exabytes every 2 days • 60% CAGR • IoT / Internet 3.0 will expand even more
  9. 9. CORPORATE SOLUTION • Add capacity, leading to unwieldy data centers • Cost per watt to store & analyze data has become a key issue • Cost for data analytics staff • Already coming to the point where we need to do something else
  10. 10. LEARN FROM ANOTHER “BIG DATA” PROBLEM…
  11. 11. LEARN FROM ANOTHER “BIG DATA” PROBLEM… • 100 billion neurons
  12. 12. LEARN FROM ANOTHER “BIG DATA” PROBLEM… • 100 billion neurons • 16.3 bn cortex • 69 bn cerebellum • 100x1012 synapses
  13. 13. THE BRAIN’S BIG DATA PROBLEM • Sensory neurons • Receptor neurons • touch, pressure, vibration, limb position, heat, cold, and pain mechanoreceptors, photoreceptors, thermoreceptors, nociceptors • 31 spinal cord “nerves” • Firing every ~10ms
  14. 14. THE BRAIN’S BIG DATA PROBLEM • Sensory neurons • Receptor neurons • touch, pressure, vibration, limb position, heat, cold, and pain mechanoreceptors, photoreceptors, thermoreceptors, nociceptors • 31 spinal cord “nerves” • Firing every ~10ms • A lot of data!
  15. 15. THE BRAIN’S BIG DATA PROBLEM • Sensory neurons • Receptor neurons • touch, pressure, vibration, limb position, heat, cold, and pain mechanoreceptors, photoreceptors, thermoreceptors, nociceptors • 31 spinal cord “nerves” • Firing every ~10ms • A lot of data!
  16. 16. THE BRAIN’S BIG DATA PROBLEM
  17. 17. HOW DOES THE BRAIN DO IT? • Doesn’t “store” information • Immediate processing • Plot different types of data into a common representational space • Encodes by compressing different types of information into meaningful chunks • Stores those meaningful chunks, uses them to make predictions
  18. 18. WHAT DO I MEAN BY “MEANINGFUL”? • Compressed chunks of information • Not raw data • Pavlov’s dog
  19. 19. RADICAL PROPOSITION • What is the purpose of the brain?
  20. 20. RADICAL PROPOSITION • What is the purpose of the brain? • …to compress information • A “compression machine” • Learns structure
  21. 21. BRAIN “BIG DATA” SOLUTION RECAP • Compression machine • Fuses information as it comes in • Stores fused information in meaningful chunks • Learns structure • Associative learning • …now we have a good basis from which to build a “cognitive” platform to handle our own data problems • A platform based on “human capacity”
  22. 22. COMMON REPRESENTATIONAL SPACE • Prefrontal cortex • Different types of sensory data • Some pre-processing “grandma”
  23. 23. COMMON REPRESENTATIONAL SPACE • Prefrontal cortex • Different types of sensory data • Some pre-processing • Plot into common space “grandma”
  24. 24. HUMAN CAPACITY COGNITIVE COMPUTING • Take unstructured, raw input from many different types of sensors • IoT: door, windows, lights, mobile phones • Plot into a common representational space • Compress that information, fusing it into meaningful chunks • Store only those meaningful chunks • With the correct compression process, we learn patterns and are able to predict • Because it handles unstructured data this way, we don’t need IoT protocols to tell devices how to communicate with each other
  25. 25. DISCUSSION

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