Review on current state of Machine Learning

1. MACHINE LEARNING
機械学習
ON THE RIGHT PATH?
正しい道に？
Silicon Brains
www.si-brains.com

2. Silicon Brains
www.si-brains.com
Machine learning: field of AI that gives computer
systems the ability to "learn" (progressively improve
performance on a specific task) with data, without being
explicitly programmed.
DEFINITION
WHAT IS OUT THERE?
ANN (Artificial Neural Networks): Fixed structure of an
interconnected group of functions with a number of
unknown parameters to be found in order to model
complex, multi-variable functions, find patterns in data or
capture the statistical structure of an unknown
probability function.
NOT MUCH DIFFERENT FROM REGRESSION METHODS

3. Silicon Brains
www.si-brains.com
WHAT IS THE PROBLEM?
• Current systems do not learn, are “trained” (not much
different than fitting regression coefficients)
• Current systems are static, do not change. Only the
parameters (data) contain the learning
• Current systems are made of arbitrary layers, without
any justification or proof of being optimal
• Current systems use mostly functions that allow an
easy cost function
• Current systems need huge amounts of training data
and time.

4. Silicon Brains
www.si-brains.com
HOW SHOULD MACHINE LEARNING BE?
• Systems should evolve and change incrementally and
only if they improve with the change
• Learning resides in parameters, functions, nodes and
connections
• Systems can be hierarchically more complex than a
number of layers
• Systems self-optimize continuously
• Learning happens during normal usage
• Learning uses no much more data and effort than
during normal usage

5. Silicon Brains
www.si-brains.com
WHAT DO SILICON BRAINS PURSUE?
A true Machine Learning system that:
• Learns on the go
• Never stop optimizing itself
• Builds itself based on global optimization
• Contains the minimum or no a priori structure
• Focus is on system self-building, not on problem
solving

6. Silicon Brains
www.si-brains.com
AND WE BASE OURSELVES ON:
LIFE, the force that has made living beings from plants to
humans along millions of years of evolution
• We learn as we try things, not before
• Life continuously improves (*)
• Life and performance determines success and failure
• Starts from scratch (*)
• Focus is on system self-building, not on problem
solving
(*) Living beings inherit evolution, systems are copied

7. Silicon Brains
www.si-brains.com
ありがとうございます
Thank you