Novelty-Organizing Team of Classifiers (NOTC) is the first method to join both direct policy search and value function approaches into one method.
In fact, it is part of a new class of algorithms called Self-Organization Classifiers which is the first and perhaps the only type of algorithm that can adapt on the fly when a given problem changes (e.g., maze changes in shape).
NOTC's high adaptability is possible because states are automatically updated with experience.
NOTC has also being applied to camera-based autonomous driving and other applications.
It is expected that variations of this algorithm or principles discovered here will be useful in future technologies and/or applications.
SUNA and Unified Neural Model
Evolutionary reinforcement learning uses the design flexibility of evolutionary algorithms to tackle the most general paradigm of machine learning, reinforcement learning.
SUNA is the first and currently the only algorithm that takes this flexibility to the limit, unifying most of the previous proposed features into one unified neuron model.
SUNA is a learning system that evolves its own topology and is able to learn even in non-markov problems.
In other words, SUNA's generality increases robustness to parameters and types of problems that can be solved.
SUNA and its variations were applied to various problems including function approximation, neural based symmetric encryption.
Moreover, a method based on SUNA was also applied to the evolution of Deep Neural Networks.
Hacking AI Systems
Hacking into AI systems is one of the few ways to enter their world.
Attacking learning systems allow us to dive deep into the code learned by a system, revealing what a learning system understands to be, for example, a "horse" or a "ship".
Indeed, in a research that appeared on BBC news, we showed that only one-pixel is necessary to change between classes.
In other words, the learned concepts of, for example, "horse" or "ship" can be changed with only one pixel.
This demonstrates that although such neural networks achieve suprahuman recognition in datasets what the neural networks really "understand" to be a "horse" or "ship" is far from intelligent.
We will continue to hack AI systems to test and understand them.
For there are a lot of unexplored land inside the black-box of AI systems.
SAN is an extremely simple multi-objective optimization algorithm which outperformed all algorithms in the hardest multi-objective benchmark.
When analyzed under the concept of Optimization Forces it was revealed that the reason for SAN's performance derive from avoiding the conflict inside single population of candidate solutions by using independent subpopulations.
Learning algorithms are nothing but optimization algorithms which allow machines to learn and adapt.
The next generation of machine learning will certainly have a stronger synergy between them and models to be evolved.