Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Smart Data Webinar: Machine Learning Update

287 views

Published on

Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services.

This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.

Published in: Technology
  • Be the first to comment

Smart Data Webinar: Machine Learning Update

  1. 1. MARCH 8, 2018 Machine Learning Update An Overview of Technology Maturity and Product Vendors Adrian J Bowles, PhD Founder, STORM Insights, Inc. Lead Analyst, AI, Aragon Research info@storminsights.com
  2. 2. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. FIRST, DEFINE TERMS Artificial Intelligence Machine Learning Deep Learning Data Science
  3. 3. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. Machine Learning Deep Learning Artificial Intelligence Data Science Each discipline has algorithms and models. FIRST, DEFINE TERMS
  4. 4. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. #MODERN AI: ARTIFICIAL, AUTOMATED, AUGMENTED, AMPLIFIED…INTELLIGENCE PERCEPTION UNDERSTANDING LEARNING Big Data Classic AI Deep Learning
  5. 5. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. Systems Controls LearnReason Understand Model Data Mgmt Human Machine Input Output Gestures Emotions Language Narrative Generation Visualization Reports Haptics Sensors (IOT) Systems Controls ML IN THE MODERN AI LANDSCAPE
  6. 6. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. Human Input Gestures Language Context Learn Reason Understand Model Data Mgmt Detected by Human Senses Derived ImagesSee Hear Touch Smell Taste Sounds Objects Emotions Meaning Concepts Intent Emotions Meaning Concepts Intent Context ML IN THE MODERN AI LANDSCAPE
  7. 7. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. FUNDAMENTAL DESIGN CHOICE: SYMBOLS VS STATISTICS Symbolic Logic Representations Reasoning Concepts Statistical Models Mechanical Theorem Proving
  8. 8. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. PROXIMITY/DISTANCE ALGORITHMS Mapped with vectors, proximity algorithm based on purpose. Mapping for autocorrect/complete vs Mapping for meaning Boy Bay Map Mop Man Nay May Mope Buy Hop Hope Boy Bay Map Mop Man Nay May Mope BuyHop HopeSimilar structure -> similar meaning in vision, not always in language. Memory-Based Reasoning
  9. 9. MACHINE LEARNING FOCUS CONTINUES TO EVOLVE Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. DATA More Data + Faster HW make Deep Learning Practical Deep Learning Success With Recognition Spurs Investment ALGORITHMS & RULES Caution for Applications Where Transparency is Critical Investment Leads to Investigation Broaden the Scope of Applications New “Explainability” Research Emerges Hybrid Solutions to Augment Intelligence Will Thrive for Critical Applications
  10. 10. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. RECOGNIZING CONCEPTS - DISCOVERY <> UNDERSTANDING Courtesy of LoopAI Labs.
  11. 11. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. Supervised Unsupervised Deep General Reinforcement Learning by example, using training data. Strategies based on performance feedback. Discovers patterns based on experience with data. Biologically-inspired ML approach. Leverages simple processing units - analogous to neurosynaptic elements organized in layers that collaborate to solve complex problems. ML MATURING RAPIDLY - ALREADY WELL OVER THE USABILITY THRESHOLD
  12. 12. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. MACHINE LEARNING - ARTIFICIAL NEURAL NETS Input Output Highly Connected Neural Processors A digital representation of the state of the input domain. Scalars, Vectors, Equations…
  13. 13. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. DEEP LEARNING Visible Layer Hidden Layer Hidden Layer Output Layer Hidden Layer Input: Observable Variables HIGHABSTRACTIONLOW Output Pixels Depth of the Model Edges Object Shapes/Parts Object Class Brightness/ Contrast Geometry Rules Features to Extract Methods
  14. 14. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. LIMITATIONS: HOW IMPORTANT IS IT TO BE ABLE TO EXPLAIN REASONING?
  15. 15. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. LOOKING FOR FEATURES: WHICH ONE IS NOT LIKE THE OTHERS? Edges are easy Objects are easy What are the distinguishing features? Context is King for Discovery
  16. 16. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. WHAT CAN A DL SYSTEM “LEARN” FROM THIS PICTURE?
  17. 17. THE MACHINE LEARNING LANDSCAPE: CAPSULES Transforming Auto-encoders G. E. Hinton, A. Krizhevsky & S. D. Wang Department of Computer Science, University of Toronto Abstract. The artificial neural networks that are used to recognize shapes typically use one or more layers of learned feature detectors that produce scalar outputs. By contrast, the computer vision community uses complicated, hand-engineered features, like SIFT [6], that produce a whole vector of outputs including an explicit representation of the pose of the feature. We show how neural networks can be used to learn features that output a whole vector of instantiation parameters and we argue that this is a much more promising way of dealing with variations in position, orientation, scale and lighting than the methods currently employed in the neural networks community. It is also more promising than the hand- engineered features currently used in computer vision because it provides an efficient way of adapting the features to the domain. This paper argues that convolutional neural networks are misguided in what they are trying to achieve. Instead of aiming for viewpoint invariance in the activities of “neurons” that use a single scalar output to summarize the activities of a local pool of replicated feature detectors, artificial neural networks should use local “capsules” that perform some quite complicated internal computations on their inputs and then encapsulate the results of these computations into a small vector of highly informative outputs.
  18. 18. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. Maturity/ Refinement Initial Neural Networks Rules CapsulesAd Hoc ML TECHNOLOGIES MATURITY OVERVIEW Utility: Demonstrated reliability & validity ML Technologies/Approaches: Arrow Width Indicates Estimated Future Development/Potential
  19. 19. THE MACHINE LEARNING MARKET BIG 4 CLOUD-NATIVE, SCALABLE Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. Amazon AWS - Model, Vision, Language services… IBM Watson. Watson Machine Learning Google Cloud Machine Learning Engine Managed service for ML models Microsoft Azure Machine Learning Studio Ease of Use Breadth of Services Depth of Services LinkedIn Data Weather Data
  20. 20. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. THE MACHINE LEARNING MARKET: NOTEWORTHY ML platform supports business users and “citizen data scientists” Private deployment & subscription models (virtual private cloud on AWS, Azure, Google) H2O Compute Engine - Open Source Platform Cognitive Scale: Augmented Intelligence Platform with industry-optimized “CortexAI Systems” (IBM Watson & Microsoft Partners)
  21. 21. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. Develops custom DL solutions THE MACHINE LEARNING MARKET: NOTEWORTHY Skymind - Skymind Intelligence Layer (SKIL) Leverages Spark to help users “productionize” TensorFlow, Keras, DL4J Skytree - ML platform, MLaaS for data scientists LoopAILabs Loop Q Platform, Natural language-independent reasoning
  22. 22. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. THE MACHINE LEARNING MARKET: NOTEWORTHY ALTERNATIVE MODELS Developer of the Hierarchical Temporal Memory model based on the human neocortex. Intel Saffron - Bio-inspired Associative memory model
  23. 23. adrian@storminsights.com Twitter @ajbowles Skype ajbowles KEEP IN TOUCH Upcoming SmartData Webinar Dates & Topics April 12 Knowledge as a Service: An Introduction to the Emerging Pre-Built Knowledge Market May 10 Case Studies: Transforming Industries with AI (Manufacturing & Retail) June 14 Natural Language Processing: From Chatbots to Artificial Understanding with Affective I/O COMING SOON… AGEOFREASONING.COM BOOK, VIDEOS, PROFESSIONAL SERVICES WWW.AGEOFREASONING.COM
  24. 24. CAPSULE REFERENCES Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved. https://medium.com/ai³-theory-practice-business/understanding-hintons-capsule-networks- part-i-intuition-b4b559d1159b https://openreview.net/pdf?id=HJWLfGWRb https://hackernoon.com/capsule-networks-are-shaking-up-ai-heres-how-to-use-them- c233a0971952

×