The next step to Human-Level AI? GoodAI awards grant for exploring how novel behaviors arise in artificial intelligence
Artificial intelligence (AI) research and development company, GoodAI, has awarded a grant to Tomáš Mikolov from the Czech Institute of Informatics, Robotics, and Cybernetics CTU to study how novel behaviors arise in AI.
Learning in Badger experts improves with episodic memory
AI agents often operate in partially observable environments, where only part of the environment state is visible at any given time. An agent in such an environment needs memory to compute effective actions from the history of its actions.
GoodAI Meta-learning & Multi-agent Learning Workshop attracts researchers from across the world
Last week GoodAI organized the first Meta-Learning & Multi-Agent Learning Workshop which throughout the week saw over 60 participants from across the world take part including speakers from Google Brain, DeepMind, OpenAI, University of Oxford, Stanford University and MIT.
Benefits of modular approach – generalization
One of the properties of the Badger architecture is modularity: instead of using one big neural network, the Badger should be composed of many small Experts which solve the whole task in a collaborative manner.
Workshop on Collective Meta-Learning and the Benefits of Deliberation
GoodAI recently hosted a virtual workshop with a number of external collaborators in order to address some of the crucial open questions related to our Badger Architecture.
Trainability of Badger – Why is Badger so hard to train?
To understand why Badger is hard to train, we need to understand first how Badger learns, using a toy task. We try to understand the plateaus, what happens during this period, and why the plateaus are there.
GoodAI’s ToyArchitecture published in PLOS ONE
Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are often uncomputable, or lack practical implementations.
Internal Badger Workshop – Summary
We recently organized an internal workshop with a number of external collaborators to advance the progress of various challenging topics related to the Badger architecture. In this post, we would like to share the outcomes of sessions.
Distributed Evolutionary Computation on Deep Reinforcement Learning Tasks
Currently, we are experimenting with an experimental setup proposed in our Badger paper. One of the areas of explorations is an evaluation of suitability of various training settings: supervised learning, Deep Reinforcement Learning (RL), and evolutionary optimization.
Neural Networks in Unity using Native Libraries
This guide shows how to use Pytorch’s C++ API to use neural networks in Unity. We can use this with existing Python-based models, by freezing the execution trace into a binary file that is loaded by the library at runtime.