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Big Data, Smart Algorithms, and Market Power - A Computer Scientist's Perspective

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What makes companies based on AI powerful - the data they own or the algorithms they use?

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Big Data, Smart Algorithms, and Market Power - A Computer Scientist's Perspective

  1. 1. 3/28/19 Heiko Paulheim 1 Big Data, Smart Algorithms, and Market Power A Computer Scientist’s Perspective Heiko Paulheim Chair for Data Science University of Mannheim Heiko Paulheim
  2. 2. 3/28/19 Heiko Paulheim 2 Introductory Example: GPS vs. Smart Phones • Tests show: smart phones do the job better – with smart phones on the rise, GPS sales decline 0 5 .0 0 0 1 0 .0 0 0 1 5 .0 0 0 2 0 .0 0 0 2 5 .0 0 0 3 0 .0 0 0 G P S s a le s S m a rt p h o n e s a le s Source: Statista
  3. 3. 3/28/19 Heiko Paulheim 3 Computer Science Interlude: Navigation • Problem: find the shortest path through a network • Solution: known since the 1950s – can be written down in less than 20 lines End Start 2km 2km 1km 1km 1km 3km 2km 1km
  4. 4. 3/28/19 Heiko Paulheim 4 Computer Science Interlude: Navigation • Usually, we do not want the shortest way – but the fastest • We need to estimate times End Start 0:05 0:15 0:10 0:10 0:15 0:15 0:05 0:10
  5. 5. 3/28/19 Heiko Paulheim 5 Estimating Times for Edges • Static: path length and speed limit • Dynamic: live car movements • Google Maps: owned by Google – So is Android – 57M smart phones in Germany, market share of Android: 80% ● i.e., one android phone in every other car
  6. 6. 3/28/19 Heiko Paulheim 6 Visual Depiction • One Android phone in every other car Image: Bing Maps
  7. 7. 3/28/19 Heiko Paulheim 7 Improving Navigation • Ingredients: – A simple standard textbook algorithm from the 1950s – A lot of data • Better navigation – Usually: not by smarter algorithms – But by better (=bigger) data! End Start 0:05 0:10 0:15 0:10 0:25 0:10 0:15 0:15 0:05 Image: https://neo4j.com/blog/top-13-resources-graph-theory-algorithms/
  8. 8. 3/28/19 Heiko Paulheim 8 A.I. Winters and A Paradigm Shift • AI has a massive uptake since the 2010s – But using very different paradigms 1st AI Winter 2nd AI Winter Fast & Horvitz (2016): Long-Term Trends in the Public Perception of Artificial Intelligence
  9. 9. 3/28/19 Heiko Paulheim 9 An Example for AI: Go • 1990s – Using handcrafted rules ● i.e., smart algorithms – Often defeated by children • 2010s – Using data from millions of games ● i.e., big data – AlphaGo: Beat some of world’s best players in 2016
  10. 10. 3/28/19 Heiko Paulheim 10 AI in the Big Data Age (1) • Algorithms are fairly simple and well known • Data matters Banko & Brill (2001): Scaling to Very Very Large Corpora for Natural Language Disambiguation smarter algorithm more data
  11. 11. 3/28/19 Heiko Paulheim 11 AI in the Big Data Age (2) • Algorithms are fairly simple and well known • Data matters Banko & Brill (2001): Scaling to Very Very Large Corpora for Natural Language Disambiguation more data: trivial baseline beats smart algorithms
  12. 12. 3/28/19 Heiko Paulheim 12 Big Data: Long vs. Wide Data • Long data = more records of the same kind – e.g., GPS data from more users • Wide data = more information about the same records – e.g., additional information about users Lehmberg & Hassanzadeh (2018): Ontology Augmentation Through Matching with Web Tables
  13. 13. 3/28/19 Heiko Paulheim 13 Big Data: Long vs. Wide Data • Example: YouTube (owned by Google) – Display videos to the user that are as interesting as possible • Long data: users’ interaction histories • Wide data: users’ interaction histories + Google Web searches + visited places + Google Play music preferences + ...
  14. 14. 3/28/19 Heiko Paulheim 14 Big Data: Long vs. Wide Data • Example: Facebook – Display as much content of interest as possible • Long data: user profile and interactions • Wide data: user profile and interactions + WhatsApp chats In Germany, OVG Hamburg prohibits this combination! Image: https://www.instagram.com/p/Bt3OG4DFOsK/
  15. 15. 3/28/19 Heiko Paulheim 15 It’s All about Patterns in Data • Examples – Traffic movements – Online user behavior – Cliques in social networks – … • Methods: – Data Mining – Machine Learning – … → Intensively researched since the 1980s Image: https://factordaily.com/balaraman-ravindran-reinforcement-learning/
  16. 16. 3/28/19 Heiko Paulheim 16 Patterns in Long Data
  17. 17. 3/28/19 Heiko Paulheim 17 Patterns in Long Data
  18. 18. 3/28/19 Heiko Paulheim 18 Patterns in Wide Data
  19. 19. 3/28/19 Heiko Paulheim 19 Take Aways • Modern AI Systems – Rely on massive amounts of data – Processed with fairly simple algorithms • Algorithms are often well known – e.g., textbooks, research papers – It is hard to own an algorithm • Data is crucial – Longer data (e.g., acquiring more customers) – Wider data (e.g., merging businesses) – It is easy to own data
  20. 20. 3/28/19 Heiko Paulheim 20 Big Data, Smart Algorithms, and Market Power A Computer Scientist’s Perspective Heiko Paulheim Chair for Data Science University of Mannheim Heiko Paulheim

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