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Layla El Asri, Research Scientist, Maluuba

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Layla El Asri is a research Scientist at Maluuba. Her work explores artificial intelligence in the context of language understanding, dialogue and human-machine interaction. Layla leads a team seeking to build artificial intelligence systems that are knowledgeable and can exchange information with users to help users accomplish tasks or gain knowledge. Layla completed her PhD at Université de Lorraine in France.

Abstract Summary:

Teaching AI To Make Decisions and Communicate:
Many advances have been made in the area of artificial intelligence, with the goal of building agents that understand how they can interact with their environments, reason and solve complex tasks, and communicate their findings to humans. In this talk, I will focus on efficient decision-making and communication. For decision-making, I will present some work on building an efficient representation of the environment and breaking down tasks into generalizable subtasks. For communication, I will focus on dialogue through natural language and present some of our work in this area.

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Layla El Asri, Research Scientist, Maluuba

  1. 1. A Microsoft company Teaching AI to Make Decisions and Communicate Layla El Asri, Research Manager with slides by Paul Gray, Harm Van Seijen, and Adam Trischler
  2. 2. Maluuba, a Microsoft company Maluuba’s Vision: Solving AGI by Creating Literate Machines Machine Reading Comprehension Teaching artificial agents to read and understand natural language Advanced Conversational Systems Building knowledgeable systems that can exchange information with users to help users accomplish tasks or gain knowledge Reinforcement Learning Fundamental research in scalability of Reinforcement Learning to allow machines to perform complex tasks in the real world
  3. 3. Maluuba, a Microsoft company Teaching AI to Make Decisions and Communicate • Expectations of AI • Learning to Learn • Learning to Perceive • Learning to Communicate
  4. 4. Maluuba, a Microsoft company Expectations of AI Nice, thanks When is my appointment with Marc? You have a meeting with Marc Villeneuve tomorrow at 10am. Ok, where is it again? At Starbucks on Maisonneuve and Montagne so you should leave the office at 9:40. Ok is it the same Starbucks when I met Harry last week? Yes I see. Do you know what Marc’s been up to lately? Yes, there was an article on MIT Tech review yesterday. His company will start commercializing affordable 3d printers. Learning to Communicate Learning to Learn Learning to Perceive
  5. 5. Maluuba, a Microsoft company Learning to Learn • Human beings decompose tasks into subtasks in an efficient way. • Subtasks are achieved without conscious awareness.
  6. 6. Maluuba, a Microsoft company Learning to Learn: Separation of Concerns • Separation between performance metric and learning objective. • Each agent has its own learning objective. • The goal is to find a reasonable policy efficiently.
  7. 7. Maluuba, a Microsoft company Example of Application
  8. 8. Maluuba, a Microsoft company Collecting the fruits Goal Get all fruits as quickly as possible Reward +1 if all fruits are eaten 0 otherwise Number of fruits: n State space: 100x100n = 102n + n NP-complete problem Using one agent per fruit State space reduced to nx100
  9. 9. Maluuba, a Microsoft company Pac-Boy Reward +1 for eating a fruit -10 for each collision with a ghost The episode ends after all fruits are eaten or after 300 time steps. State space Approximately 1028 states
  10. 10. Maluuba, a Microsoft company Configuration 1 agent per fruit 1 agent per ghost 75 fruit agents with 76 states 2 ghost agents with 76x76 states
  11. 11. Maluuba, a Microsoft company Demo DQN SoC
  12. 12. Maluuba, a Microsoft company Results
  13. 13. Maluuba, a Microsoft company Learning to Perceive • For living creatures, perception is adapted to task achievement • First living creatures: ability to react • Evolution: ability to foresee • Challenge: correlate sensory inputs with events • Modern human beings: ability to focus
  14. 14. Maluuba, a Microsoft company Learning to Perceive: Information Gathering Guessing Game tasks that progress in difficulty • Battleship – sink the enemy’s ships quickly • Hangman – guess the phrase quickly • Blockworld We developed a model that achieves super- human performance on these tasks.
  15. 15. Maluuba, a Microsoft company Blockworld Environmen t Observation s Model’s World Belief Peeking Policy Model’s Answer Belief Is the red sphere above the red cross?
  16. 16. Maluuba, a Microsoft company Information Gathering Model
  17. 17. Maluuba, a Microsoft company Learning to Communicate • Language is the most precise communication tool that we have • … but it is still very imprecise • Easier to give orders and strictly define the meaning of words
  18. 18. Maluuba, a Microsoft company How to Build a Goal-Driven Dialogue System? Inform(city = Rio) State tracker Natural Language Understanding (NLU) Natural Language Generation (NLG) Dialogue Management (DM) City = Rio, budget = $2000, hotel = Hilton, price = $1950 Database city = Rio, budget = $2000 Hotel = Hilton, price = $1950 Offer(name = Hilton, price = $1950) “You can book the Hilton for $1950.” “I want to go to Rio.”
  19. 19. Maluuba, a Microsoft company Going One Step Further: Modelling Memory
  20. 20. Maluuba, a Microsoft company Frames Dataset Overview 15 Turns per Dialogue 268 Hotels 109 Cities 19,986 Turns 1369 Dialogues
  21. 21. Maluuba, a Microsoft company Frame Tracking Curitiba, August 15th – August 26th, 4 stars, $2877.68 Columbus, August 15th, Request(price) “And how much if I were to go to Columbus?” Curitiba, August 15th Curitiba, August 15th Curitiba, August 15th – August 26th, 4 stars, $2877.68 Curitiba, August 15th “And how much if I were to go to Columbus?” Columbus, August 15th, Request(price) State Tracking Frame Tracking
  22. 22. Maluuba, a Microsoft company Frame Tracking Model Input The NLU labels, the list of frames, the previous active frame, and the user utterance Output The current active frame and the frames referred by the dialogue acts Model
  23. 23. Maluuba, a Microsoft company Thank you! Papers discussed • Improving Scalability of Reinforcement Learning by Separation of Concerns • Towards Information-Seeking Agents • Frames: A Corpus For Adding Memory To Goal-Oriented Dialogue Systems
  24. 24. Maluuba, a Microsoft company We’re hiring! • Research Scientists • Research Engineers • Developers • Product/Program Managers www.maluuba.com/careers

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