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The State of AI 2016


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What's holding back Artificial Intelligence? While researchers rightly focus on better algorithms, there are a lot more things to be done. We'll discuss why AI needs a new, interdisciplinary approach, how it will be used, and what we've learned from our recent State of AI industry survey.

Published in: Technology
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The State of AI 2016

  1. 1. The State of AI 2016 Ines Montani Matthew Honnibal Explosion AI
  2. 2. custom algorithms, applications and data assets makers of spaCy, the fastest-growing open- source NLP library our backgrounds are computer science, linguistics and front-end development Explosion AI is a digital studio specialising in Artificial Intelligence and Natural Language Processing.
  3. 3. We are publishing... Embed, encode, attend, predict The new deep learning formula for state-of-the-art NLP models The State of AI An open-data industry survey on the state of Artificial Intelligence in 2016 ARTICLES PROJECTS spaCy Industrial-Strength Natural Language Processing in Python OPEN SOURCE
  4. 4. 434 practitioners told us about the state of AI. industry survey launched on October 10, 2016 collaboration with Nathan Benaich (Playfair Capital) results will be 100% open-source you can still take part:
  5. 5. The AI hype: about 90% justified.
  6. 6. AI systems are already going into production. Where is your company in the AI adoption cycle? The State of AI 1 % 19 % 28 % 21 % 31 % We have a profitable system in production. We're currently building our first system. We've rolled out our system, but it's not profitable yet. We're AI-curious and experimenting. Other
  7. 7. Young companies already have profitable systems. Early-Stage Startup Venture-Backed Startup Established Company 38 % 16 % 54 % 19 % 36 % 21 % 42 % 48 % 25 % Rolled out and profitable Rolled out but not profitable yet Currently building first system The State of AI
  8. 8. NLP is improving faster than ever. IBM Watson 75 % 100 % 2004 2013 2015 APR 2016 OCT 2016 84.8% 93.7% 93.1%92% 87.3% Example: Speech recognition accuracy
  9. 9. Researchers deliver blueprints, not products. neural networks really took off when ImageNet data set was created research runs on benchmark tasks benchmarks demonstrate solution in principle solutions in practice require different data
  10. 10. © Stanford Dogs Dataset Chesapeake Bay RetrieverCurly-Coated Retriever ImageNet: mostly dogs
  11. 11. © IBTimes / Google Deep Dream
  12. 12. The bottleneck in AI is data, not algorithms.
  13. 13. Data problems are still the biggest problems. high accuracy problems high data quality problems high data quantity problems 20 % 40 % 60 % 80 % 100 % The State of AI How much do the following problems keep you up at night? (4/5 and 5/5)
  14. 14. © GATE Also the state of AI 2016...
  15. 15. You can’t get quality data by boring the shit out of underpaid people. machine learners need machine teachers Amazon Mechanical Turk is the most popular way to create annotated data bad incentives: below minimum-wage payment, outdated tools, zero investment, zero satisfaction
  16. 16. We don’t just need innovation on the back-end.
  17. 17. It’s time to apply what we know about UX to AI. the most valuable knowledge is in decisions humans don't have to think about intuitive interfaces, fast decisions gamification: take inspiration from what humans enjoy doing (and even pay for!)
  18. 18. © 94%, SCIMOB Don’t assume your task can’t be fun.
  19. 19. AI isn’t a closed shop – there’s a lot to contribute. 7 % 14 % 18 % 22 % 39 % < 1 year 3 - 5 years > 7 years 5 - 7 years 1 - 3 years The State of AI How long have you been working with AI?
  20. 20. Thanks! 📊 Take part in the survey 📲 Follow us on Twitter
 @honnibal 💥 Explosion AI