Steve Jurvetson - Disruptive Innovation
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Steve Jurvetson - Disruptive Innovation

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Steve Jurvetson's presentation on Disruptive Innovation at TTI/Vanguard's [next] in San Francisco on December 10, 2013. http://www.ttivanguard.com

Steve Jurvetson's presentation on Disruptive Innovation at TTI/Vanguard's [next] in San Francisco on December 10, 2013. http://www.ttivanguard.com

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Steve Jurvetson - Disruptive Innovation Presentation Transcript

  • 1. [ ] DISRUPTIVE INNOVATION STEVE JURVETSON
  • 2. DFJ Global Network
  • 3. Evermore Moore Electronics, April 19, 1965
  • 4. Moore’s Law — A Recurring Catalyst Source: Ray Kurzweil (each dot is a computing machine)
  • 5. Why does technology accelerate? “All technologies are combinations of technologies that already exist.” — • Combinatorial Explosion • Creates Economy — “Science quickly became the greatest tool for making new things the world has ever seen. Science was in fact a superior method for a culture to learn.” “Throughout history, the engine of human progress has been the meeting and mating of ideas to make new ideas. The human race will prosper mightily in the years ahead, because ideas are having sex with each other as never before.” • Urbanization • Interdisciplinary Disruption • Globalization —
  • 6. Global Network Effects Further Accelerating Change Users Months from Launch
  • 7. Disrupting Many Industries robots & AI explosive post-CMOS era syn bio agriculture manufacturing new space
  • 8. New Space
  • 9. The Information Systems of Biology  Proteins crystallized  Imaging resolution  Genes mapped
  • 10. Carlson’s Curve Gene Synthesis WRITE Gene Sequencing READ $10,000 genome $100 genome Eve Biomedical Data: Rob Carlson, company forecasts
  • 11. Eve Bio: a possible path to ubiquitous sequencing Sources: “DNA sequencing via motion”, Stanford U. , US Pat No: 7,556,922, Ecoli RNAP data and Optical trap system. W J Greenleaf, S M Block Science 2006;313:801-801, T7-RNAP in Magnetic Trap: SUNY and UMDNJ: McAllister lab: Richard T. Pomerantz et al., Nano Lett., Vol 5, No 9,2005.
  • 12. Synthetic Biology: Gen9 Gene Printer Chip2Gene • Parallel Chip synthesis • Pooled Assembly • Automated QC • Picoliter scale • Mut-S Error Correction • Sequence verified • 250,000 at once Oligo Synthesis Gene Assembly Quality Control
  • 13. What to build? GenBank Release 194  260,000 Organisms  190 Gb (billion base pairs)  Doubling every 1.5 years Human Drug Targets (Proteins) Enzyme Homologs 1.5 Gb 150 Mb Polyketides (Small Molecules) 200 Mb
  • 14. Pleistocene Park
  • 15. GMOs for Food, Chemicals, and maybe Fuels “To support humanity’s needs, we will have to grow more crops in the next 50 years than in the past 10,000 years combined.” Chris Keely, Monsanto AgraCast
  • 16. Design, engineer, evolve Sugars 1,4-Butanediol (BDO)
  • 17. Building Complex Systems • Nanotech • Synthetic Biology • Computer Science • Innovative Organizations …whether to design or evolve?
  • 18. The Problem with Evolution • Subsystem Inscrutability - Black box defined by its interfaces - No “reverse evolution” • No simple shortcuts across the iterations - Simulation ~ Reality - Beauty from irreducibility • Locus of Learning is Process, not Product • Robust, within co-evolutionary islands
  • 19. Robots & AI
  • 20. Google’s First Quantum Computer “We actually think quantum machine learning may provide the most creative problem-solving process under the known laws of physics.” - Google Blog
  • 21. Rose’s Law 1,000 100 Number of Qubits 10,000 512q Vesuvius 4 Faster than the universe 128q Rainier 4 Faster than all computers Competitive Performance with Classic Computers 28q Leda 16q Europa II 10 FOR DISCRETE OPTIMIZATION PROBLEMS 4q Calypso 1 2002 2006 2010 2014 2018 “Quantum computers have the potential to solve problems that would take a classical computer longer than the age of the universe.” — Professor David Deutsch, Oxford
  • 22. Beyond Engineering Danny Hillis, The Pattern on the Stone “The greatest achievement of our technology may well be the creation of tools that allow us to go beyond engineering – that allow us to create more than we can understand.”
  • 23. Summary Efficiency Accelerating Technological Change - Interdisciplinary Renaissance - IT innervates $T markets - More Black Swans - Perpetual driver of disruption  Virtuous cycle for entrepreneurs  a great time for the new
  • 24. THANK YOU Steve@DFJ.com
  • 25. Iterative Algorithms & Evolved Systems • Consider: - Cellular automata - Genetic programming - Analog Circuits & Antennae - Danny Hillis: Sort Algorithms - Neural networks - Quantum computers - Wisdom of Crowds
  • 26. The Problem with Evolution • Subsystem Inscrutability - Black box defined by its interfaces - No “reverse evolution” - Tweak the process not the product • No simple shortcuts across the iterations - Simulation ~ Reality - Beauty from irreducibility
  • 27. The tradition of design… Design • Simple Problems • Control • Brittle • Subsystem Clarity - Portable, Modular • Top Down - Inheritance of Scale
  • 28. Dichotomy Design • Simple Problems Evolution • Complex - Transcendence proof • Control • Brittle • Out of Control • Robust, Resilient, Adaptive - Co-evolutionary islands • Subsystem Clarity • Inscrutable subsystems - Portable, Modular • Top Down - Inheritance of Scale • Bottom Up - Hierarchical subsumption - Layers of abstraction - Path dependence
  • 29. Innovation Management • Cognitive Diversity > Ability • Disagreement > Consensus • Voting Policy > Coherence or Comprehensibility • Communication Tuning > Leadership
  • 30. AI Implications • Cut & Paste Portability? • Locus of learning: Process, not Product - Would we bother to reverse engineer? - No hard take off? • Co-evolutionary islands - accustomed environment (differential immunity) • Path dependence - algorithm survival - AI = Alien Intelligence defined by sensory I/O