Reactive Learning Agents
Presented at the Google Developer Group NYC Meetup on 9/12/16 http://www.meetup.com/gdgnyc/events/231371634/ This talk is focused on the design of learning agents using the techniques of reactive machine learning. We explored the difference between software agents (bots), intelligent agents, and learning agents. As the most complex class of agent, a learning agent has a sophisticated internal architecture, which we broke down into different capabilities. Finally, we examined how the techniques from reactive machine learning can allow us to build learning capabilities into our agents. This talk is not just be about AI concepts, though. It covers a range of pragmatic techniques to aid you in your efforts to implement learning agents. Throughout, we considered where and how we can use external resources like libraries, services, datasets, and even humans to solve sub-problems in the agent design process.
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Reactive Learning Agents
- 2. Bio
- 3. Bio
- 4. Bio
- 7. Agents
- 14. Learning Agents
- 18. Reactive
- 20. Reactive Strategies Replication Containment Supervision
- 22. Reactive Machine Learning Uncertain DataInfinite Data Laziness Pure Functions Immutable Facts Possible Worlds
- 29. Reactive
- 31. Source of AI Components
- 32. Source of AI Components Yours
- 33. Source of AI Components Other People’s Yours
- 34. Sensors
- 35. Sensors •Email •Messaging Platforms •Phone cameras & sensors •Wearables
- 36. Sensors •Email •Messaging Platforms •Phone cameras & sensors •Wearables •Your Data Collection Systems
- 43. Reactive Data Collection •Replication via Distributed databases •Event-sourcing/immutable fact databases •Uncertainty-aware data models •AP Databases https://skillsmatter.com/skillscasts/7038-lightning-talks-2
- 44. Actuators
- 46. Actuators •Messaging platforms •Email •Notifications •External API Calls •Robots •Self-driving Cars •Internal API Calls
- 51. Knowledge
- 52. Knowledge •External Datasets •Knowledge as a Service APIs
- 53. Knowledge •External Datasets •Knowledge as a Service APIs •Your Training corpus •Institutional knowledge
- 54. Elixir •Functional Language •Homoiconic Syntax •Concurrency-oriented •Runs on the bEAM (EVM)
- 56. Reactive Knowledge Systems Adapted from https://github.com/mitchellhenke/circuit_breaker
- 62. Learning
- 63. Learning •Machine Learning as a Service APIs •Open Source Libraries
- 64. Learning •Machine Learning as a Service APIs •Open Source Libraries •Your novel Algorithms •Organizational Learning
- 74. Agent Function
- 76. Agent Function •??? •Your Whole system
- 84. Dialyzer in Action ml_system.ex:3: Function predict/1 has no local return ml_system.ex:6: The call 'Elixir.MLSystem':call_model_b(feature@1::number()) will never return since it differs in the 1st argument from the success typing arguments: (binary()) ml_system.ex:22: Invalid type specification for function 'Elixir.MLSystem':call_model_b/1. The success typing is (binary()) -> binary() ml_system.ex:23: Function call_model_b/1 has no local return
- 85. Dialyzer in Action ml_system.ex:24: The call 'Elixir.String':upcase(feature@1::number()) will never return since the success typing is (binary()) -> bitstring() and the contract is (t()) -> t() ml_system.ex:38: Function ensemble/1 will never be called
- 86. Testing Agent Functions Tool Tests Dialyzer Types QuickCheck Properties Concuerror Interleavings
- 87. Summary
- 90. Reactive Machine Learning Uncertain DataInfinite Data Laziness Pure Functions Immutable Facts Possible Worlds
- 92. Reactive Machine Learning reactivemachinelearning.com @jeffksmithjr Use the code gdgnycmu for 39% off the book!
- 94. x.ai @xdotai hello@human.x.ai New York, New York We’re hiring!
- 95. Thanks!