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The Future of IT is Artificial Intelligence (presented by Florian Goossens of radix.ai at #thefutureofit)

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Florian Goossens is the co-founder and project lead at radix.ai, which brings the power of AI to business, from strategy to execution.

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The Future of IT is Artificial Intelligence (presented by Florian Goossens of radix.ai at #thefutureofit)

  1. 1. RADIX.A I I The Future of IT is AI
  2. 2. RADIX.AI I Today 1. AI – ML – DL 2. How to recognise ML opportunities 3. Matching job seekers with jobs at VDAB
  3. 3. RADIX.AI I Artificial Intelligence DL AI ML Computers showing human-like intelligence 3
  4. 4. RADIX.AI I AI Algorithms Data Computing Power Why now?
  5. 5. RADIX.AI I Narrow AI General AI Super AI Where are we today? Voorbeeldjes
  6. 6. RADIX.AI I Machine Learning DL AI ML Algorithms that learn from data to make predictions 6
  7. 7. RADIX.AI I Old way: Write software with explicit rules to follow: if email contains V!agrå then mark is-spam; if email contains … if email contains … New way: Write software to learn from examples: try to classify some emails; change self to reduce errors repeat; 7 Machine Learning is a new way of programming
  8. 8. RADIX.AI I input → model → output Andrew Ng
  9. 9. RADIX.AI I Step 1: collect inputs and outputs input → output 70 m2 → € 260.000 91 m2 → € 330.000 102 m2 → € 400.000 150 m2 → € 448.000 120 m2 → € ? Real Estate Prices, Ixelles, Belgium, 2018
  10. 10. RADIX.AI I Step 2: train the modelReal Estate Prices, Ixelles, Belgium, 2018 € 400.000 € 300.000 € 200.000 output 70m2 100m2 130m2 input model
  11. 11. RADIX.AI I Step 3: predictReal Estate Prices, Ixelles, Belgium, 2018 € 400.000 output 120 m2 input model
  12. 12. RADIX.AI I input → model → output Appartement 2 Bedrooms 1050 Ixelles 140 m2 1980 Gas ...has a large terrace with a nice view on the parc ...
  13. 13. RADIX.AI I input → model → output income Decision Trees Neural Networks savings income savings income savings Logistic Regression
  14. 14. RADIX.AI I House price Days until sale Number of candidates input → model → output Chance of being sold within a year Chance of defaulting on the loan Chance of flooding Loan yes/no Picture is interior or exterior Best channel to advertise a house The output of a model can be a number, a probability, or a category. 123,45 83% A / B / C
  15. 15. RADIX.AI I Look at your goal. Look at your data. Map. How to recognize machine learning opportunities. Goal: - Telecom company - Wants to Customer Lifetime Value Data: - User behaviour - User demographics - User billing - User network - User social media Use Cases: - Churn prediction - Personalised offers Output: - 10% most likely churners - Reasons for churn
  16. 16. RADIX.AI I
  17. 17. RADIX.AI I Why ML? Better performance: forecasting, job matching object recognition, translation Easy to adapt: spam filtering, evolving job market
  18. 18. RADIX.AI I Why not ML? Simple: when the results are good enough without ML accounting
  19. 19. RADIX.AI I Deep Learning DL AI ML 19 Neural Networks
  20. 20. RADIX.AI I 20
  21. 21. RADIX.AI I Why DL? Feed any data type! We are looking for an educationally focused Governess for a family who live between Dubai and London. The summer months are spent in London and Europe and there is travel throughout the year. This is a sole charge position as both parents work. You will be responsible for two children aged 18 months & 3 years old. Never gonna give you up Male dancing Traveling nanny
  22. 22. RADIX.AI I Why DL? Scale improves performance! 22 Large Neural Network Amount of data ModelPerformance Traditional ML models E.g., SVM, GP, RF, LR ML DL
  23. 23. RADIX.AI I Why DL? Useful embeddings! 23 Useful word embedding h1 ⋯ ⋯h2 hNBabysitter word
  24. 24. RADIX.AI I Why DL? Useful embeddings! 24 Babysitter Nanny Kinderverzorger
  25. 25. RADIX.AI I Why DL? Useful embeddings! 25 h1 h2 h3 king – man + woman =
  26. 26. RADIX.AI I How to create word embeddings
  27. 27. RADIX.AI I DL AI ML Summary
  28. 28. RADIX.AI I Matching jobs seekers & jobs with deep learning
  29. 29. RADIX.AI I It’s a great time for job seekers 29 Jobs Job Seekers
  30. 30. RADIX.AI I New technologies = new possibilities 30 1050 Ixelles Babysitter EN License B 1000 Brussels Nanny … speaks English … … drive the kids to school …
  31. 31. RADIX.AI I Deep Learning DL AI ML Neural Network 31
  32. 32. RADIX.AI I Summarize jobs and jobs seekers into embeddings 32 Useful word embeddings Useful document embeddings Useful location embeddings Useful job (seeker) embeddings h1 ⋯ ⋯h2 hN h1 ⋯ ⋯h2 hN h1 ⋯ ⋯h2 hN h1 ⋯ ⋯h2 hN h1 ⋯ ⋯h2 hN h1 ⋯ ⋯h2 hN
  33. 33. RADIX.AI I DL reason 1: useful embeddings Why embeddings are so cool
  34. 34. RADIX.AI I Job Recommender Architecture 34 Job embedding Job Seeker embedding .9 Similarity Complex data Embedding 300 numbers Score 1 number h1 ⋯ ⋯h2 hN h1 ⋯ ⋯h2 hN RADIX.AI I 34
  35. 35. RADIX.AI I Embeddings are steered by click data 35 Job embedding Job Seeker embedding .9 Similarity Complex data Embedding 300 numbers Score 1 number h1 ⋯ ⋯h2 hN h1 ⋯ ⋯h2 hN RADIX.AI I 35
  36. 36. RADIX.AI I In production soon at vdab.be! 36 1050 Ixelles Babysitter EN, FR License B 1000 Brussels Nanny … speaks English and French … … drive the kids to school …
  37. 37. RADIX.AI I hello@radix.ai 37 Deep Learning At our core, we are a team of Machine Learning engineers. We make machines learn. 3 Natural Language Processing Understanding natural language is not easy. It’s our favorite challenge. Job Matching Matching jobs and job seekers. 1 2
  38. 38. Contact us: https://www.linkedin.com/in/itworks https://twitter.com/itworks www.itworks.be Presented at: The Future of IT 20 September 2018 in Brussels

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