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The Future of Intelligent Machines: Insights from Jeff Hawkins' Vision

In this chapter by Melanie Swan, the future of Intelligent Machines (IMs) is explored, based on insights from Jeff Hawkins. The discussion addresses the potential characteristics and applications of IMs, technological challenges, and moral considerations. While IMs will not resemble human intelligence, they may revolutionize tasks such as transportation, security, and complex problem-solving. Key features include hierarchical memory systems, sensory input for pattern recognition, and enhanced speed and capacity beyond human capabilities. The talk highlights ethical discussions around IMs and the significant benefits they could offer society.

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The Future of Intelligent Machines: Insights from Jeff Hawkins' Vision

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  1. On Intelligence – Jeff Hawkins Chapter 8: The Future of Intelligence Presented by: Melanie Swan, Futurist MS Futures Group 650-681-9482 m@melanieswan.com http://www.melanieswan.com NIH BCIG April 27, 2006

  2. Building Intelligent Machines (IMs) • Introduction • What will IMs be like? • Technological challenges and timing • Moral/ethical issues • Potential applications for IMs • Near-term • Longer-term • Conclusion

  3. IMs can be built – but may not be like what we think • Appearance and interaction not human-like • Limited-application robots (smart cars, autonomous mini-submarines, self-guided vacuums/mowers) before androids/robots • Necessity: IMs only require cortex equivalent • Cost: Excessive cost/effort for humanoid robots • Multiplicity of IM physical embodiments: cars, planes, computer room racks • Distributed sensors and memory system

  4. How to create an IM • IM: Sensory input that is connected to a hierarchical memory system that models the world and predicts the future • Recipe for building IMs • Set of senses to extract patterns from the world • Attach a cortex-like hierarchical memory system • Training period builds a model of its world through its senses • Result: With its own model of the world, the IM can analogize to past experiences and make predictions

  5. Largest IM technical challenges: memory capacity and connectivity • Capacity • Cortex’s 32 trillion synapses ~= 80 hard-drives • An entire cortex is not required • IM memory chip error tolerance benefit vs. traditional silicon • Connectivity • Speed and sharing substitutes for complexity • A cortex cell might connect to 5,000 or 10,000 other cells • Electrical pulses are 1 million times faster than neurons • Cortex dedicated axons can be IM shared connections • Many approaches to this brain chip connectivity problem underway • When? 10-50 years / “within my lifetime”

  6. Should we create IMs? • Dark imaginings vs. potential benefits of new technology • Unlikely for IMs to threaten large portions of the population (Matrix, Terminator) • IMs will be defined-capacity human-controlled tools • Being intelligent is being intelligent, not being human • IMs (based on the neocortical algorithm) will not have emotions • Best application: where human intellect has difficulty, tedium • Concludes • IM ethics, easy compared to genetics and nuclear energy • IMs are not self-replicating machines • No contemplated way for humans to copy minds into machines

  7. Why should we build IMs? What will IMs do in the near-term? • Imagine near-term uses for brainlike memory systems • Auditory applications: speech recognition • Multi-year training period • Visual applications: security camera (crowbar vs. gift) • Transportation applications: truly smart car

  8. Why should we build IMs? What will IMs do in the longer-term? • Technique: Identify scalable aspects (cheaper, faster and smaller) • Speed: IMs can do in 10 seconds what a human can do in a month • Capacity: many times human capacity; no biological constraint • Replicability: easy to copy, reprogram vs. human brains • Sensory systems: any natural sense plus new senses • Pattern-recognition: any input with non-random with a richness or statistical structure

  9. Why should we build IMs? Specific applications • Application: any mismatch between human senses and the physical phenomena we want to understand • Protein folding • Mathematics and physics • Distributed, globe-spanning sensory systems • Weather, animal migration, demographics, energy use • Understanding and predicting human motivations and behavior • Minute entity sampling • Pattern representation in cells or large molecules • Meta-IM: Collecting several IMs into one whole

  10. Conclusion, IMs… • Think and learn a million times faster than humans • Remember vast quantities of detailed information • See incredibly abstract patterns • Have (distributed) senses more sensitive than our own • Think in three, four or more dimensions • Are not limited Turing Test concepts but amazing tools that could dramatically expand our knowledge of the universe

  11. Thank you! Presented by: Melanie Swan, Futurist MS Futures Group 650-681-9482 m@melanieswan.com http://www.melanieswan.com NIH BCIG April 27, 2006 Licensing: Creative Commons 3.0

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