Healthy Competition: How Adversarial Reasoning is Leading the Next Wave of Innovation
In recent years, machine learning and reinforcement learning algorithms have revolutionized how we tackle problems in pattern recognition, inference and prediction. These learning algorithms are inherently stochastic in nature and collaborative by design. While powerful, they often lead to models that exhibit fragility in noisy real-world domains. A new generation of learning algorithms are evolving to augment robustness by embracing adversarial reasoning. In place of cooperative learning, these algorithms espouse game theoretic concepts of competition, deception, and Nash equilibria. In this talk, John will examine the role of adversarial reasoning in problem solving. Attendees will learn about the principles underpinning adversarial reasoning and their relevance to the new generation of machine learning algorithms including actor-critic A3C methods, generative adversarial networks, and variational autoencoders. In the end, the objective of this talk is to provide an intuitive understanding of the coming learning algorithms that can surmise intent, detect and practice deception, and formulate long-range winning strategies to real world problems.
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Healthy Competition: How Adversarial Reasoning is Leading the Next Wave of Innovation
- 1. HEALTHY COMPETITION: HOW ADVERSARIAL REASONING IS LEADING THE NEXT WAVE OF INNOVATION Nashville Analytics Summit August 8-9, 2017 John Liu, PhD CFA Digital Reasoning
- 2. DIGITAL REASONING Trusted Cognitive Computing For A Better World Machine Learning platform that understands human communication Nashville Washington New York London Goldman Sachs Silver Lake HCA Lemhi Ventures Nasdaq National Security Financial Services Healthcare Data Science DIGITAL REASONING | CONFIDENTIAL Prepared Exclusively for Intel Capital
- 4. 3 big ideas…
- 7. adversarial reasoning to the rescue
- 8. a quick review of AI…
- 9. machine learning learns feature weights
- 11. deep learning automated feature learning
- 13. deep reinforcement learning sparse, delayed rewards value fn deepnet policy deepnet model deepnet
- 15. noisy data
- 16. sparse data
- 17. missing data
- 20. false positives
- 21. bad decisions
- 22. bad policies
- 24. are there robust algorithms that can operate in the wild?
- 26. sometimes, yes, very much…
- 27. what if…
- 29. and is intentionally false…
- 30. what if there is an active adversary...
- 33. or even worse?
- 34. An algorithm that can learns to counter active adversaries is more robust in the wild.
- 38. researchers are turning to adversarial reasoning…
- 40. understanding the intent & actions of your opponent…
- 41. contest of actions & rewards…
- 42. incorporating active deception & diversion…
- 43. adversarial model manipulation of data model adversary environment predic1on ac1on policy true data false data
- 45. intent inference plan inference deception detection countermeasure modeling strategy formulation
- 46. Intent & plan inference Bayesian network graphs A5acker Intent Knowledge Probing Ac1vity Malicious Ac1vity Evidence
- 47. games with deception stochastic, partially observable simultaneous play Nash equilibrium sequen1al play Stackelberg game
- 49. mimicry
- 50. decoy
- 51. masking
- 52. doubleplay
- 54. detecting deception linguistic cues information trajectory map info-theoretic value ranking multi-agent ensembling
- 55. Countering deception predict/ignore bad data fuzzy logic/soft margins risk weighting Bayesian filter abstract info from data
- 59. RL deadly triad bootstrapping off-policy learning DL func1on approxima1on instability & divergence
- 64. generative adversarial network real world images discriminator loss generator noise fake real learns decep1on adversarial generator vs discriminator
- 65. early GAN lacked deep semantics
- 68. generative ideas
- 71. adversarial learning leads to robustness adversarial learning algos rapidly coming to market in summary
- 72. THANKS