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AI: Feats, Limits and Caveats - Monojit - Opening Keynote AI Dev Days 2018

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This presentation was given by Monojit Choudhury (Microsoft) as opening keynote in AI Dev Days 2018 on 9th March 2018 in Bangalore. www.aidevdays.com
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Artificial Intelligence is changing the way we live, think and feel. It has instilled hope for a better world, as well as fear of being taken over by robots (even if not literally). How much of this is hype, and how much reality? In this talk, we will present my personal perspective on AI, based on our two decades of research experience in Natural Language Processing (NLP), AI and cognition, primarily as a scientist, but also from what Iwehave seen happening at Microsoft’s development centres and many start-ups with whom we have closely interacted with. Taking the case of Natural language understanding and dialogue systems as a running example, on one hand we will try to explain why Artificial General Intelligence is still a dream quite far away; on the other hand, we will try to argue that the most crucial aspect of building practical and useful AI systems is deep understanding of the domain, the user and the problem that one is trying to solve. Techniques like deep learning and platforms like Tensorflow, AzureML or MS Cognitive Services have improved the accuracy of AI systems and drastically reduced the time to production and scale; nevertheless, there are problems, seemingly very simple ones, that are much harder to solve not because of technology, but for a variety of other circumstantial reasons. In particular, we will highlight some unique and interesting challenges for the Indian market and user.

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AI: Feats, Limits and Caveats - Monojit - Opening Keynote AI Dev Days 2018

  1. 1. ARTIFICIAL INTELLIGENCE FEATS, LIMITS, & CAVEATS Monojit Choudhury Microsoft Research Lab India monojitc@microsoft.com @AIDevDays, 9th Mar 2018
  2. 2. The third wave of AI Deep Learning HumanParityin Speech Human Parity in Vision Games Speech-2-Speech Translation
  3. 3. AI: Hype or Reality? Subbarao Kambhampati: Challenges of Human-Aware AI Systems AAAI 2018 Presidential Address
  4. 4. How long will it take for AI to surpass humans? McKinsey Global institute analysis
  5. 5. • What makes Natural Language, Social, and Emotional Understanding hard problems? • Can Deep Learning (or any futuristic technology) magically solve these problems? • Is it necessary to solve these problems to make practically useful AI systems? Three Pressing Questions LIMITS FEATS CAVEATS
  6. 6. Unlike chess or black gammon, the rules of the LANGUAGE GAME are defined and redefined by the language users every time a game is played. LudwigWittgenstein (1889-1951)
  7. 7. It is not difficult to devise a paper machine which will play a not very bad game of chess… Are there imaginable digital computers which would do well in the imitation game? AlanTuring, 1950 The biggest breakthrough in AI will be when computers can read and understand information like humans do. Bill Gates, 2017
  8. 8. Language Understanding Artificial General Intelligence AI-completeºº
  9. 9. Language Understanding requires Common Sense • Linguistic Knowledge – Words, idioms, proverbs, metaphors • World Knowledge • Contextual Knowledge • Socio-cultural Norms
  10. 10. Two scientists walk into a bar. "I'll have H2O," says the 1st. "I'll have H2O, too," says the 2nd. Bartender gives them water because he is able to distinguish the boundary tones that dictate the grammatical function of homonyms in coda position, as well as pragmatic context.
  11. 11. Why is Pragmatics Important? • Communicative intent • Request, order, negotiation, persuasion, threatening, inspiring, demotivating, … • Social Functions • Politeness, formality, socio-cultural conventions • Non-literal Meanings • Jokes, sarcasm, irony, figurative language • Resolving references • Deixis, topic-focus
  12. 12. Language usage strongly depends on the social context
  13. 13. Has/Will Deep Learning revolutionize Language Understanding? • Neural MachineTranslation Systems are better than the Phrase-Based MT Systems for some language pairs – No gain for German-English – Substantial gain for Japanese-English
  14. 14. Has/Will Deep Learning revolutionize Language Understanding? • Neural Networks are universal approximators, however, language is NOT a continuous function, it is a Discrete Combinatorial System • Two fundamental benefits of deep learning to NLP: – Modeling Infinite context à Long distance dependencies – Discrete to continuous mapping à word2vec
  15. 15. Word Embeddings “You shall know a word by the company it keeps.” - Firth, 1957 Continuous Bag-of-Word Skip grams
  16. 16. However, Deep learning cannot do Common Sense Reasoning Hard but tractable problems • How to integrate external knowledge sources? • How to store learnt information explicitly? • How to reason over knowledge base explicitly? • How to acquire DATA for all these?
  17. 17. 1950s – 1980s: Rule based systems Linguists ruled the world 1990s – 2000s: Basic statistical learning Linguistic insights with some data 2000s – 2010s: Large scale supervised and semi-supervised learning Big data with some linguistic insight 2010s - : Deep learning ONLY DATA A brief history of NLP Linguistic insight Data
  18. 18. How much DATA? POSTagging (#annotated words) Machine Translation (#parallel sentences) Speech Recognition (hours of speech) 1950s-1980s Rule-based As much as needed for linguistic analysis 1990s-2000s Linguistic insights + Statistical 10k – 100k 10k-100k 10-100 2000s – 2010s Big data 100k – 100M 100k – 1M 1o-1000 2010s – Deep Learning 1M – 1B 1M – 1B 1000 – 10k Natural languages are fascinating. Adj Noun Verb Adj Natural languages are fascinating. 自然语言是迷人的。 Na-tu-ral lan-gua-ges are fa-sci-na-ting
  19. 19. All languages are equal, but some are more equal than others. Breaking the Zipfian Barrier of NLP. Invited talk at IJCNLP 2008Workshop Rank of Language #Words(million)inLDC Amount of resources present for various languages on LDC (2008) English Chinese Spanish Arabic
  20. 20. Even if we solve the problem of language understanding for a few languages by next 3 or 4 decades, it will take at least a century for building such technology for most of the major languages of the world, unless brain-reading becomes a reality. Or maybe most of the minority languages will die by then!
  21. 21. • What makes Natural Language, Social, and Emotional Understanding hard problems? • Can Deep Learning (or any futuristic technology) magically solve these problems? • Is it necessary to solve these problems to make practically useful AI systems? Three Pressing Questions CAVEATS
  22. 22. Challenges of Human-Aware AI Systems Our Systems seem happiest • Either far away from humans • Or in an adversarial stance with humans Subbarao Kambhampati: AAAI 2018 Presidential Address
  23. 23. AI is a tool. Not the problem & Almost never the complete solution • Human-aware AI – AI should collaborate, not compete with humans – The strengths of the two are complementary – Users are usually cooperative – leverage!
  24. 24. AI is a tool. Not the problem & Almost never the complete solution • Data is the new electricity – Invest and innovate in collecting data – Beware of privacy issues and biases in data – Contribute back – Invest in low-resource technology
  25. 25. AI is a tool. Not the problem & Almost never the complete solution • Understand the problem, the users and the context – Human-Computer Interaction – Ethnography and Sociology – Market Research
  26. 26. Code-mixing • ni som har möjlighet att delta i missing people's sökande efter snälla snälla gör det!!!! • Adik… sem brape boleh bwak kenderaan? normal parent question – UiTMLendufornia • jit fi la fin du mois de decembre kan ljaw bared ktir wttalj Code-Mixing or Code-Switching is mixing of more than one language in a single conversation or utterance.
  27. 27. MULTILINGUAL SOCIETIES provide unique and interesting challenges • Code-switching • Language preference • Linguistic Accommodation of Language choice 50% of the world’s population is multilingual
  28. 28. The Mélange Team: Kalika Bali, Monojit Choudhury, Sunayana Sitaram, Indrani Medhi Thies, Anshul Bawa, Adithya Pratapa, Brij Srivastava Past members: Ashutosh Baheti, Shruti Rijhwani, Royal Sequeira, Chandra Maddila and a bunch of interns Project Mélange https://www.microsoft.com/en-us/research/project/melange/
  29. 29. Thank you! monojitc@microsoft.com

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