AI is advancing rapidly and will have wide-ranging impacts. It is learning to perform tasks beyond perception like complex algorithms, databases, and user interfaces. For algorithms, neural networks can now replicate existing algorithms like Photoshop filters with constant time and memory usage, and their performance can be adjusted based on accuracy or speed needs. For databases, learned indexes can provide faster lookups and use less storage than traditional indexes. For user interfaces, AI is starting to automatically generate UI elements. To stay relevant, people should learn AI through available online courses.

1. Software is eating the world.
AI is going to eat software.
Tambet Matiisen
Mobile Monday
14.01.2019

2. AI success stories
Image recognition Speech recognition Machine translation
But what about?
Algorithms? Databases? UI design?

3. Beyond perception: algorithms

4. Learning to imitate Photoshop filters
Xu et al. Deep Edge-Aware Filters (2015)

5. Learning to imitate any existing costly algorithm
● No shortage of training data - you can always generate more!
● The algorithm now runs in constant time and memory.
● Easy to make tradeoffs between accuracy and speed:
○ Want more accuracy? Add more layers!
○ Want faster running time? Remove some layers!
● Easy to port to new platforms or even bake into silicon!
● Examples: weather prediction, fluid simulation, ...
● Caveat: may need network architecture tuning.
Andrej Karpathy. Software 2.0. https://medium.com/@karpathy/software-2-0-a64152b37c35

6. Beyond perception: databases

7. Learned indexes
f(x) = p
x1 x2 ... xp-1 xp xp+1 ... xn
p - position of the record in sorted array
x > xpx < xp
Kraska et al. The Case for Learned Index Structures (2017)

8. Learned indexes
Kraska et al. The Case for Learned Index Structures (2017)
● Increased lookup speed.
● Vastly less storage needed.
● From O(logn) to O(1)? Actually more like O(sqrt(n)), which is > O(logn).

9. Beyond perception: UI

10. Automatic generation of UI elements
https://twitter.com/TeleportHQio/status/1043245039261044736

11. What you can do about it?

12. Learn AI!
Stanford course “Convolutional Neural Networks for Visual Recognition”
http://cs231n.github.io/
fast.ai course “Practical Deep Learning For Coders”
https://course.fast.ai/
Andrew Ng deep learning courses
https://www.deeplearning.ai/

13. Thank you!
tambet@ut.ee