Leveraging Machine Learning with Andrew Thomas of SDL
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Artificial intelligence is the Holy Grail of agentive technology. But, getting there involves work. Machine learning requires machine teaching. Attend this session with Andrew Thomas to better understand the machine learning landscape and to discover what's practical today—and where we're heading tomorrow. November 30, 2017
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Leveraging Machine Learning with Andrew Thomas of SDL
- 3. Agenda • Defining AI • Natural Language Processing • Machine Learning
- 5. Word cloud
- 7. Cognitive Abilities Motion & Manipulation Perception Natural Language Reasoning & Planning Memory & Emotion
- 8. • Watson • Einstein • Many, many predictive analytics companies Reasoning & Planning
- 9. • Apple Touch / Face ID • Facebook Photo Tagging • Hey Siri / OK Google Perception
- 10. • iRobot Roomba • Boston Dynamics • Tesla / Self-driving vehicles Motion & Manipulation
- 11. • Sentiment Analysis • Commerce Recommendations • Machine Translation Natural Language
- 12. • OK Google • Hey Siri • Emerging emotion- detecting companies Memory & Emotion
- 13. DATA COMPUTING CAPACITY DATA SCIENCE TIME AI Requirements
- 14. Why does business care?
- 16. Code Base & PlatformExpertise & Skills History: 45 Patents 200+ publications Our background
- 17. Language is hard, ok? Monolingual Bilingual Multilingual Polyglot Trilingual 40%43%13%3%1%
- 18. NLU Natural Language Understanding e.g. Summarising NLP Natural Language Processing e.g. Part of Speech Tagging NLG Natural Language Generation ML Powered by Machine Learning e.g. Neural Networks & Deep Learning Types of Linguistic AI
- 19. Evolution of Machine Translation AI Rule-Based S NP + VP NP + Verb + NP Det + N Verb +NP Det + N + Verb + Det + N Det + N + Aux + V + Det + N the + N + Aux + V + Det + N the + N + Aux + V + the + N the + man + Aux + V + the + N the + man + Aux + V + the + ball the + man + will + V + the + ball the + man will + hit + the + ball NeuralStatistical é = argmaxp(e|ƒ)
- 21. Many types of Machine Learning Machine Learning Supervised Learning Classification Regression Unsupervised Learning Clustering Association Semi- Supervised Learning Reinforcement Learning
- 22. data Overly simplified representation Machine Training + Decoding model input output
- 23. 0.42 A Neuron
- 24. 1.0 0.0 1 1 -5.2 3.7 A Neural Network
- 27. 1.0 0.0 1 1 .90 .15 .78 -5.2 3.7 1.0 x 3.7 + 0.0 x 3.7 + 1 x -1.5 = 2.2 1 1+𝑒−2.2 = 0.90 Sigmoid f(x) = 1 1 + 𝑒−𝑥
- 29. Deep Neural Networks Multiple Layers Millions of Parameters Abstract Representations
- 30. Convolutional Neural Network cat dog 60 million parameters
- 31. Image Classification mite mite black widow cockroach tick starfish container ship container ship lifeboat amphibian fireboat drilling platform motor scooter motor scooter go-kart moped bumper car golf cart leopard leopard jaguar cheetah Snow leopard Egyptian cat grille convertible grille pickup beach wagon fire engine mushroom agaric mushroom jelly fungus gill fungus Dead-man’s-fingers cherry dalmatian grape elderberry Staffordshire bulltierrer currant Madagascar cat squirrel monkey spider monkey titi indri howler monkey
- 32. Neural Machine Translation Input Text Output Text ENCODER DECODER -0.2 -0.1 0.1 0.4 -0.3 1.1 4.3 -0.2 0.5 0.9 1.3 3.4 -5.3 -6.2 4.8 9.3 3.4 … 2.6 4.9 0.1 2.6 8.3 -7.3 5.1 1.5 0.6 9.3 -6.2 2.9 1.4 -1.3
- 33. Recurrent Neural Network ENCODER DECODER Sentence meaning is built up Source sentence Translation generated Feeding in last word
- 34. ƒt = σg(Wƒ xt+Uƒht-1+bƒ) it = σg(Wixr+Uiht-1+bi) ot = σg(Woxt+Uoht-1+bo) ct = ƒt°ct-1+it°σg(Wcxt+Ucht-1+bc) ht = ot°σg(ct) ht xt σ σ tanh σ x + x x tanh
- 35. Word Representations and Word Embedding bank1 bank2 star2 star1 transaction finance banking laundering boundary municipality gap canal plateau republic direction manga venue movie novel fantasy video stars video movies television camera talk mars asteroid constellation constellation galaxy moon sun XKing – XMan + XWoman = XQueen
- 36. Beyond Translation Foreign Text Target Text Document Summary Sentence Paraphrase Partial Text Relevant Text
- 39. Copyright © 2008-2018 SDL plc. All rights reserved. All company names, brand names, trademarks, service marks, images and logos are the property of their respective owners. This presentation and its content are SDL confidential unless otherwise specified, and may not be copied, used or distributed except as authorised by SDL. Software and Services for Human Understanding
Editor's Notes
- The Agenda slide provided you room for an image if you choose. Note how the topics are colored, this is to emphasize points. This could also be animated.
- - Internet allows for big data - Cloud computing allows for massive processing power - Companies (like SDL) have been working on AI challenge for decades - Those decades have led us to the current breakthroughs in AI/ML
- Marketing – enables true personalization Automates (and loops) recommendations, content variations, search optimization, mapping customer data to content, sentiment analysis Support – improve self-service/preemptive support Analyze customer behaviors to predict, chatbots, speech recognition
- SDL has been investing in MT for 15 years. Our mission is to build on this: Build state of the art Machine Translation, Natural Language Processing, and Machine Learning capabilities Grounded in advanced Science and principled Software Engineering Supporting SDL’s Commercial and internal business
- The model evolves with your business over time in an ongoing and iterative process. Companies don’t change over night. Digital transformation is a process. This model can evolve your content supply chain from Ad Hoc to Autonomous at the pace that is right for your goals. Each phase is a different stage of maturity in terms of customer experience, customer journey and digital transformation. Let’s go through the stages.