The difference between Machine Learning and Artificial Intelligence. A discussion on the various future scenarios of working with Big Data and how human can compliment machines to solve more complex challenges

1. Machine Learning / Artificial
Intelligence
OUR FUTURE WITH THE MACHINE
ALEX POON
2/27/2014

2. ALEX POON
•
VP Engineering @ Outbrain (JUST QUIT)
•
Co-founder of Visual Revenue (Acquired by Outbrain 2013)
•
Love scalable systems, machine learning; Fascinated by Robots
•
Used to build signal intelligence software for Unmanned Aerial Vehicle (UAV)
•
Play Zerg in Starcraft II

3. ARTIFICIAL INTELLIGENCE

4. MACHINE LEARNING
INPUT
BLACK BOX
OUTPUT
TRAINING
DATA

5. ARTIFICIAL INTELLIGENCE
The INTELLIGENCE exhibited by machines or software, and the
branch of computer science that develops machines and software
with INTELLIGENCE.
MACHINE LEARNING
A branch of artificial intelligence, concerns the construction and
study of systems that can LEARN from data
SOURCE WIKIPEDIA

6. intelligence
REASONING
Human Only
Human +
Data Informed
algorithmic
Human +
Machine Assisted
Machine Only
http://x.ai/2014/01/23/our-future-relationship-with-robots/
manual
ACTION
automatic

7. intelligence
REASONING
Human Only
Human +
Data Informed
algorithmic
Human +
Machine Assisted
Machine Only
http://x.ai/2014/01/23/our-future-relationship-with-robots/
manual
ACTION
automatic

8. What about the future?
HUMAN ONLY
MACHINE ONLY

9. FUTURE: A little bit of both
More Data Sources
Working with Machines
Natural Languages
Now
H Only
H + Data
Informed
H+M
Assisted
M Only
Future
H
Only
H + Data
Informed
H + M Assisted
M Only

10. Working with Machines
1
INPUT
2
INPUT
Input
3
Input
INPUT
Analyze
Analyze
Analyze
Decide
Decide
Decide

11. Working with Machines
1
INPUT
2
INPUT
Input
3
Input
INPUT
Analyze
Analyze
Analyze
Decide
Decide
Decide

12. SUMMARY
•
Strong Machine Learning techniques != Intelligence
•
In the near future, more data, sensors, natural language input
•
Machine and human cooperate to solve complex problems
•
Human judgments can improve algorithms when done right
•
We can partner with machines if we understand them

13. ALEX POON
alexpoonnyc@gmail.com
@alexpoon06
Excited about Machine Learning, NLP, and Data Engineering?
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