The New Generation of Learning Algorithms Beyond Machine Learning
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1. The document discusses emerging machine learning algorithms beyond traditional machine learning, including meta-learning, reinforcement learning, and apprentice learning. 2. These new algorithms aim to enable more autonomous, transparent decision making by modeling desires and goals, in addition to skills and knowledge. 3. Reinforcement learning in particular is highlighted as a leading approach, where agents can learn through trial-and-error interaction with an environment to maximize rewards.
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The New Generation of Learning Algorithms Beyond Machine Learning
- 1. BEYOND MACHINE LEARNING: THE NEW GENERATION OF LEARNING ALGORITHMS COMING TO MARKET John Liu, PhD CFA
- 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
- 3. 4 BIG IDEAS
- 4. 1. BIG DATA + ML = BETTER DECISION-MAKING
- 5. 2. MACHINE/DEEP LEARNING IS COMPLEX, HAS DEFICIENCIES
- 6. 3. AUTONOMOUS, TRANSPARENT DECISION-MAKING IS GOAL Wind = 15.4 mph Direction = NW Humidity = 12.3%
- 7. 4. META LEARNING, RL/AL ARE LEADING PATH FORWARD
- 8. MACHINE LEARNING - HISTORY • 1951 1st Neural Network (Minsky) • 1957 Perceptron (Rosenblatt) • 1980s Expert Systems • 1985 Back Propagation • 1997 Deep-Blue beats Kasparov • 2009 Google Self-Driving Cars • 2011 Watson Wins Jeopardy • 2013 Deep Learning • 2015 Machine Dreaming/Deep Speech • 2016 OpenAI
- 10. GPUS / FPGAS 11 TFLOPS ON A DESKTOP (2016) Source: Nvidia
- 12. DEEP NLP FOR CRM
- 14. IDENTIFYING CAT VIDEOS ON YOUTUBE
- 15. ANGRY CATS TOO
- 17. DEEP INCEPTIONISM: ART PICASSO WOULD BE JEALOUS
- 19. DEEP POETRY “i want to talk to you.” “i want to be with you.” “i don’t want to be with you.” i don’t want to be with you. she didn’t want to be with him. - Google Brain
- 20. SCALING GPUS IS TOUGH
- 22. TOO LONG TRAINING TIMES
- 25. SKILL VS WILL Machine Learning Deep Learning is skill, knowledge, context They do not capture desire, drive, end goals
- 26. ML/DL STILL NEED HUMAN INTERVENTION & DECISION MAKING WE WANT AUTONOMOUS, TRANSPARENT DECISION-MAKING
- 28. META-LEARNING • Adapts with Experience • Data Exploitation –Previous episode –Domain transfer (transfer Learning) • Inductive Transfer (learning-to-learn) • Examples: –Auto-tuning classifiers –Case-based reasoning
- 29. REINFORCEMENT LEARNING Idea: learning system that wants something, and adapts its behavior through experience in order to get it. AKA = Hedonistic Learning System
- 30. REVERSE PAVLOV
- 31. NEUROSCIENCE: LTP NEURONS THAT FIRE TOGETHER, WIRE TOGETHER
- 32. FRAMEWORK OF RL Observations Actions Agent Environment Reward Sequential Decision Making (MDPs) Policy Value
- 33. VS. SUPERVISED/UNSUPERVISED Learns from samples with external truth Learns to aggregate unlabeled samples No concept of “rewards”
- 34. INFANT LEARNING TO STAND UP LEARNS THROUGH INTERACTION
- 36. CUSTOMER CHURN
- 37. CLINICAL TRIALS
- 39. RL APPROACHES • Dynamic Programming • Monte Carlo Tree Search • Temporal Difference Learning • Derivative-Free Optimization • Policy Iteration • Value Iteration • Q-Learning • Policy Gradients
- 40. DEEP REINFORCEMENT LEARNING • Leverage Deep Learning ability to generalize pattern recognition • Use to model Q-function • Beat world champions
- 41. EXAMPLE: FIDO SPRINTING OpenAI Gym & MuJoCo
- 42. FIDO LEARNS TO SPRINT OpenAI Gym & MuJoCo
- 43. EXAMPLE: HUMAN BABY TASK OpenAI Gym & MuJoCo
- 44. LIMITATIONS TO RL How would RL learn not to crash a self-driving car?
- 45. APPRENTICE LEARNING • Expert Demonstration Learning • Imitation Learning • Reward Learning • Policy Learning We are running out of time…
- 47. THANKS