I talk about what interesting work that startups are doing in machine learning. #startups #machinelearning #artificianintelligence

1. Machine learning developments
beyond Facebook, Google,
Microsoft and Twitter
Rudradeb Mitra
https://www.linkedin.com/in/mitrar

2. Briefly about myself
• AI researcher. Published 10 paper on Document
understanding, Semantic web, 5th generation
programming languages
• Masters from University of Cambridge, UK
• Entrepreneur for last 5 years. Currently 3
startups using machine learning algorithms.
• Mentor of Founders Institute, MIT Enterprise.

3. What I am going to talk
about?
Objective: Focus more on the application of machine
learning and show the huge opportunities that lie ahead.
• 5 startups from 3 verticals using machine learning.
• Are current approaches really intelligent?
• 2 startups that are doing things differently
• Future

4. 5 startups in 3 verticals
across 4 countries
• Directly spoke with the founders / Senior
management of these companies
References:
http://thinkapps.com/blog/tag/machine-intelligence/

5. Vertical I:
Using machine learning to
identify Human emotions

6. Affectiva - Understanding
human emotions through face
• Recently raised $14M and was
featured in Tech Crunch.
• Identify face and key points in
the face. Based on the
positions of the points measure
facial muscle movement.
• Map facial expressions to
emotions. DB with 4 M faces
from 75 countries.
• Future: Make API available for
devs to make engaging apps
Full interview with VP of marketing and product:
http://thinkapps.com/blog/development/machine-intelligence-affectiva-interview/

7. Beyond verbal - Understanding
human emotions through voice
• From Israel. Uses 1.61 M voice
samples from 170 countries. Have
8 granted patents.
• Have pre-labelled voice data and
then identify patterns. Magic
sauce: High quality data
• Combining emotions with content
for ex, siri plays music based on
emotions.
• Future: Launched cloud based
API. Medical applications to
detect conditions based on voice.
Full interview with Director of marketing:
http://thinkapps.com/blog/development/machine-intelligence-beyond-verbal-interview/

8. Vertical II: Cars/
Vehicles

9. Preteckt: Vehicle
maintenance
• Tennessee based startup.
• Predicting vehicle breakdown
• Data from previous
breakdowns and see patterns.
• Working with 800 trucks, in
pipeline around 30000 trucks
• Future: Make vehicle
breakdowns a thing of past
Full interview with Cofounder and COO:
http://thinkapps.com/blog/development/machine-intelligence-preteckt-interview/

10. Motosmarty/VivaDrive:
Predicting accidents
• Belgian startup. EU funded
$350k. Recently got acquired
partly by another organization.
• Analyze driver behavior and
give personalized incentives
to drive better.
• 2M kms driver data.
• Future: Use social media to
identify groups and identify
patterns in group to predict
accidents.

11. Vertical III: Document
understanding

12. Beagle - Non lawyers
navigate complex contract
• One of the most innovative
Canadian startup - featured in
SXSW.
• Learns patterns in things you
are looking in a contract
• Identifies parts of contract that
are of your interest and highlight
those in a new contract
• Future: Expand to document
understandability. Medical,
engineering reports etc.
Full interview with founder and CEO :
http://thinkapps.com/blog/development/machine-intelligence-beagle-interview/

13. What is common in all the
previous approaches?

14. Are current approaches
really intelligent?
• Do humans learn through lots
of data?
• 100 pictures of cat to identify
next cat, will we call the
person intelligent?
• Autonomous vehicles to learn
to drive - cannot give millions
of examples of failing over

15. 2 startups taking
approach similar to how
humans learn

16. Geometric intelligence
• Cofounders - Prof of psychology NYU and
Prof of Information engineering in Cambridge
• Human brain is capable to do more than
recognize patterns in a large set of data. Tries
to abstract concepts from relatively small
amount of data.
• Intelligence?: Object similar to car but bigger
-> truck.
• One shot or zero shot learning: Identify
objects with no or small data.
References:
https://www.technologyreview.com/s/601551/algorithms-that-learn-with-less-data-could-expand-ais-power/#/set/id/601579/
https://www.technologyreview.com/s/544606/can-this-man-make-aimore-human/
References:
https://en.wikipedia.org/wiki/Cognition

17. Vicarious: The secretive AI startup
• SV based startup raised in total $75M from very impressive investors.
Using cognitive sciences to develop new ways of processing data
• Broke human captcha 2 years ago.
• Human brain learns from experiences (causal) and tries to make
predictions. Focussed at learning through cause and effect. ‘What
makes car a car’, ‘Can something with 2 wheels is a car’
References:
https://www.technologyreview.com/s/601496/inside-vicarious-the-secretive-ai-startup-bringing-imagination-to-computers/

18. Future
• In 2-3 years. Opportunities in every single vertical.
Pick any vertical and use existing machine learning
algorithms to add intelligence on top of existing tools.
• In 4-5 years. New AI algorithms based on cognitive
sciences which will have imagination and able to
learn like humans.
• In 10 years. A world with autonomous vehicles,
softwares exactly know what you feel and want,
predicts what will happen.

19. Thank You for listening.
Go out and build something great!
PS: Always happy to meet over coffee