Big Data was de verzamelnaam voor alles wat je nog niet deed, maar al wel door Google of Amazon was uitgevonden. Inmiddels doen we al die dingen wel dus heet productaanbevelingen weer gewoon productaanbevelingen, fraudebestrijding weer fraudebestrijding, en spraakherkenning nog steeds spraakherkenning; geen Big Data. Geeft niet, want nu is er AI. Deze keynote legt uit of dat anders is, en waarom.

1. Vroeger Noemden we dat Big Data…

2. About this Person
Friso van Vollenhoven
CTO at fashionTrade
Previously working on Applied Data Innovation ™ at
GoDataDriven
Twitter: @fzk
I’m also on LinkedIn

3. What Happened to Big Data?

4. Things that Happened in 2016 - 2018 that
Would Have been Called Big Data in 2012

11. A Quick Reminder of the History of
Computing Power

16. Evolution of AI techniques

17. “It is the science and engineering of making intelligent
machines, especially intelligent computer programs.” - John
McCarthy, 2007
A computer system able to perform tasks that normally
require human intelligence, such as visual perception,
speech recognition, decision-making, and translation
between languages.
Artificial Intelligence?

18. Machine Learning?
Field of study that gives computers the ability to learn
without being explicitly programmed.
Coined by Arthur Samuel in 1959.
ML is a means to achieve AI.

19. Mostly linear models
Big Data for better exploratory analysis
More complex feature engineering
Works well in closed domain with large training sets
Well suited for online learning / online model updates
Machine Learning in Big Data

20. Still Big Data, very compute intensive (specialised
hardware)
Still benefits from large training sets
Better generalisation beyond training context
Allows for transfer learning, due to inherent learning
hierarchy in deep neural networks
Deep Learning

21. Deep Learning
1960s: Neural networks
1960-70s: Backpropagation
1974-80: First AI Winter
1980s: Convolution
1987-93: Second AI Winter
1990s: Unsupervised deep learning (cat experiment)
1990s-2000s: Supervised deep learning back en vogue
2006s-present: Modern deep learning

22. Big Data as Enabler

23. Getting Better at the Games
1992: Neural network plays backgammon (Gerald
Tesauro)
1997: Garry Kasparov loses to IBM’s Deep Blue in a
six-game series
2011: IBM’s Watson wins on Jeopardy
2016: Google’s AlphaGo beats Lee Sedol of Korea
2017: Google’s AlphaGo beats Ke Jie of China

24. Deep Learning is often the missing piece to
a complete AI solution, but rarely the only
technique used

25. Contains hardware optimisations for use case of speech
to text (e.g. seven directional microphones, not one)
Speech to text requires vast amounts of (manually)
annotated training data
Open ended question answering requires continuous
tuning and human intervention combined with rule sets
to prevent worst cases
Substantial amount of cloud resources required for
training and evaluation that fails on device
All within a commercially viable energy budget
Amazon Echo

26. Uses one or more lidar sensors, combined with short
distance radar and anywhere between 5 and 20 cameras;
humans have two eyes (in most, but not all cases)
Still doesn’t generalise to unknown situations / context (a
car trained for San Francisco cannot drive in the snow)
Self Driving Tech

27. Powered by AI!

28. (the above is actual website
content from a chatbot company;
blockchain AI FTW!)

30. The Reality of AI in Products
Is the
product
(hugely)
successful?
AI likely
hidden
behind a real
product
feature
Does the AI
work as
advertised?
yes no
Technology
looking for a
use case
Not AI
yes no

31. The Reality of AI in Products

32. The Reality of AI in Products
https://chatbot.fail/

33. Thanks for Listening