Application of Artificial Intelligence in the real World
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twentybn Quick Introduction
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● TECHNOLOGY PROVIDER, enabler of A.I. applications, and DEEP LEARNING innovator ● We love progress-by-engineering and believe in RESEARCH-LEAD DEVELOPMENT ● Founded in 03/2015 by 4 MACHINE LEARNING EXPERTS with 15 years of experiences each ● By end of the year 12 incredible A.I. engineers, researchers, and code ninjas ● $ 2.5M seed round from a US-based investor in 03/2016 ● BERLIN OFFICE: Headquarter, Product ● TORONTO OFFICE: R&D
In A Nutshell
● Advisors: ○ PROF. YOSHUA BENGIO (Univ. Montreal)
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Applications
A.I. that works
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A.I. that works
Applications
On audio data
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Application:
Applications
#1 Speech Recognition
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Application:
Applications
#1 Speech Recognition
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Application:
Applications
#1 Speech Recognition
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A.I. that works
Applications
On image data
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Application:
Applications
#2 Object Detection
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Application:
Applications
#3 Visual Search
slyce.it 11
Application:
Applications
#4 (Semi-)Autonomous Driving
MobileEye 12
A.I. that works
Applications
On text data
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Application:
Applications
#5 Machine Translation
AirBnB
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Application:
Applications
#5 Machine Translation (getting better and better ;)
AirBnB / Google 15
Application:
Applications
#6 Image to Text
Carnegie Mellon University16
A.I. that works
Applications
Beyond “simple” processing
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Application:
Applications
#7 Image Generation
University of Freiburg 18
Application:
Applications
#8 Game playing
Google DeepMind 19
A.I. that does not work
Applications
(yet)
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Application:
Applications
#9 Chatbots
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Application:
Hype Alert!
Applications
#9 Chatbots
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Application:
Applications
#10 Visually Guided Robotics
FANUC
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Application:
Applications
#11 Surveillance
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So what’s the link between all this?
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Machine Learning
Machine Learning!
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Machine Learning! Machine Learning
(More specifically: Deep Learning)
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Principles of Machine Learning 2nd Step: Online Prediction with a (Neural Network) Model
Machine Learning
Input
Trained Model
Output
Elephant
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Principles of Machine Learning 1st Step: Offline (Supervised) Training of a Model Learning Algorithm
Machine Learning
Labeled Dataset
Trained Model
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A bit of history of Deep Learning Connecting old and new technology is the key
Machine Learning
Biology
Big Data
1958 Perceptron
1986 Backprop-Algorithm
2011 “Deep Learning”
Mathematic
Hardware (GPUs)
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A new software development process (Employing Machine Learning)
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Traditional Software Development Process
ML Software Development Process
Domain experts are highly involved in programming.
Designing
Programming
Integrating
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Machine Learning Software Development Process
New Software Development Process
Domain and ML experts define data requirements.
Designing
Data Acquisition
Training
Integrating
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Summary Take home messages
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Complex Audio, Image and Text Analysis is possible. Machine Learning makes it intelligent. Deep Learning is the driving force. Data acquisition is a requirement and ML expertise is key.
In short:
A.I. works!
Summary
Domain expert programmers are replaced by ML-knowable data curators.
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Thank you! DR. FLORIAN HOPPE MANAGING DIRECTOR
Contact
[email protected] @Florian_Hoppe
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THE FOUNDERS
Additional Slides
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THE FOUNDERS THE FOUNDERS
The humans behind the A.I.
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DR. CHRISTIAN THURAU CHIEF TECHNICAL OFFICER
DR. FLORIAN HOPPE MANAGING DIRECTOR
DR. INGO BAX
MANAGING DIRECTOR
THE FOUNDERS
DR. ROLAND MEMISEVIC CHIEF SCIENTIST
*from left to right 39
SAAS/DEMO PLATFORM
OUR SAAS/DEMO PLATFORM: powered by the twentybn CORTEX
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SAAS/DEMO PLATFORM
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Access our pretrained Neural Networks via REST-API
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Train your own classifiers
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Build your own applications on top of our API
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Wrappers for common languages in development
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The power of transfer learning: tailor-made solutions for your company using the twentybn CORTEX
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Ready to be used on-premise and (soon) embedded system 41
