Rahul Mehrotra, Product Manager, Maluuba at The AI Conference 2017
Rahul Mehrotra is a Product Manager at Maluuba, a Canadian AI company that’s teaching machines to think, reason and communicate with humans (acquired by Microsoft in January 2017). Based in the AI epicenter of Montréal, Maluuba applies deep learning techniques to solve complex problems in language understanding. Rahul works across Maluuba’s three research areas (Machine Comprehension, Dialogue Systems and Reinforcement Learning) and helps advance breakthrough research by providing real-world problems and use cases. Rahul leads product initiatives to bring cutting-edge academic research to robust product pipelines. Rahul holds a B.ASc in Systems Design Engineering from the University of Waterloo. Building Literate Machines Advances in AI research have led to great innovations based on image and voice recognition, and 2017 will see further advances in the field of language, including the creation of more literate machines—those that can comprehend and communicate with humans but also machines that begin to model innate human-like skills. In this talk, Rahul Mehrotra will explore how advances in deep and reinforcement learning are being applied to solve language understanding problems. You will gain a deeper understanding of the research fundamentals as well as implications and opportunities that language understanding AI services will bring. Rahul will outline how researchers are seeking to equip machines with higher level cognitive skills like common-sense reasoning, information seeking, transfer learning, and decision-making. He will explain how these capabilities are being applied in enterprise, using practical examples across a range of business functions. These use cases are transformative. To give just one example, knowledge workers and employees would no longer need to desperately search through an organization’s directories, repositories, emails, and other channels to find a specific document. Instead, the employee would communicate with an AI agent leveraging machine comprehension capabilities. The agent would be capable of answering the question in a security-compliant manner by having a deep understanding of the contents of the organization’s documents instead of simply retrieving based on keywords. The talk will provide audience with key takeaways on the underlying research as well as the current and future applications of using language understanding AI in enterprise.
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Rahul Mehrotra, Product Manager, Maluuba at The AI Conference 2017
- 1. Rahul Mehrotra, Product Manager Montréal, Canada Twitter: @TheRahulM © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization Building Literate Machines San Francisco, California
- 4. Maluuba is Microsoft’s DeepMind with a Commercial Tilt
- 7. A look at the A.I. space. CBInsights, 2016https://www.cbinsights.com/reports/CB-Insights-Artificial-Intelligence-Webinar.pdf
- 8. Speech Recognition Object Recognition and Detection Machine Translation Reinforcement Learning Reasoning and Memory Natural Language Understanding Application areas of Artificial Intelligence
- 9. Speech Recognition Object Recognition and Detection Machine Translation Reinforcement Learning Reasoning and Memory } Perception Natural Language Understanding
- 10. Speech Recognition Object Recognition and Detection Machine Translation Reinforcement Learning Reasoning and Memory } Perception Natural Language Understanding }Intelligence
- 13. Why Language? © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization
- 14. Language is what separates humans from the rest of the animal species. Why Language? Verbal Communication Written Communication allowed humans to express thoughts & ideas to each other. exponentially grew collective intelligence of the human race. © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization
- 15. …basic traits of human beings (reasoning, planning and adaptation) If we took… Language is what separates humans from the rest of the animal species. Why Language? Verbal Communication Written Communication allowed humans to express thoughts & ideas to each other. exponentially grew collective intelligence of the human race. …built-in competencies of machines (Crunch large amounts of data)+ A whole new world with infinite possibilities= © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization
- 16. …basic traits of human beings (reasoning, planning and adaptation) If we took… Let’s do what the printing press did to the human civilization: teach machines to be literate (read, write & speak). Language is what separates humans from the rest of the animal species. Why Language? Verbal Communication Written Communication allowed humans to express thoughts & ideas to each other. exponentially grew collective intelligence of the human race. …built-in competencies of machines (Crunch large amounts of data)+ A whole new world with infinite possibilities= © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization
- 19. Artificial Intelligence in the Enterprise Language understanding is a crucial function to the Enterprise. © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization At least 15% of a worker’s time is spent on knowledge discovery (documents, logs, research)
- 20. Artificial Intelligence in the Enterprise Language understanding is a crucial function to the Enterprise. © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization Knowledge WorkerCustomer Service Clerk Information Clerk We aim to build Information Seeking Agents with Knowledge Discovery and Reading Comprehension Skills Average Salary: $31,720 Average Salary: $30,000 Average Salary: $80,000 Total Workers (USA): 1.5M Total Workers (USA): 3M Total Workers (USA): 1.5M Technology Impact: 20% Technology Impact: 10% Technology Impact: 10% TAM: $18B TAM: $30B Clerical work Research and Discovery At least 15% of a worker’s time is spent on knowledge discovery (documents, logs, research) TAM: $9B Customer Care
- 21. 0 1 2 3 4 Virtual agents are limited in intelligence today Abilities Depth Today’s Performance Users expect this Current assistants support 20 or so abilities, each with limited depth. Limitations of AI
- 22. 1 Trained, pre-programmed models for single, narrow domains 2 Requirement of huge in-flux of training data The reality … 3 Lack of fundamental reasoning or transfer learning capabilities Question Answering: Introduction
- 23. Ontological/ Rule based Predictable and limited keyword based queries The Past Underlying Technology
- 24. Ontological/ Rule based Predictable and limited keyword based queries Statistical Machine Learning Queries with high grammatical diversity The Past The Present Underlying Technology
- 25. Ontological/ Rule based Predictable and limited keyword based queries Statistical Machine Learning Queries with high grammatical diversity Deep Language Learning Models do not require traditional, task-specific feature engineering. The Past The Present The Future Underlying Technology
- 28. Information Seeking © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization Teaching artificial agents how to seek information actively, by asking questions
- 29. Question Generation © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization • Most QA work has focused on extractive answers
- 30. Question Generation © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization • Abstractive answers are more natural, and move us toward machine writing
- 36. © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization Enterprise Applications
- 37. Productivity Tasks © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization Machine Reading in Email
- 38. Productivity Tasks © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization Semi-structured Query What hotel did James say I should book in Redmond? Machine Reading in Email
- 39. © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization Unstructured Query What is the Info Seeking team working on? Semi-structured Query What hotel did James say I should book in Redmond? Productivity Tasks Machine Reading in Email
- 46. Rahul Mehrotra email: rahul.mehrotra@microsoft.com Twitter: @TheRahulM © Maluuba Proprietary and Confidential: Do not reproduce or distribute outside of Maluuba without written authorization Thank you!