The document discusses the applications of machine learning across various startups and verticals, emphasizing opportunities beyond major tech companies. It highlights innovative approaches in understanding human emotions, vehicle maintenance, and document interpretation. The future vision includes advancements in AI algorithms based on cognitive sciences, leading to autonomous systems and enhanced predictive capabilities.

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