AI Research at the Frontier of Innovation
We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.
Exploring the frontiers of language model capabilities with state-of-the-art architectures and training methodologies.
Our research focuses on developing efficient transformer architectures that balance performance and computational requirements. We explore novel attention mechanisms, activation functions, and normalization techniques to enhance model capabilities.
We develop advanced training techniques that improve model convergence, reduce computational requirements, and enhance generalization capabilities. Our methods focus on data efficiency and transfer learning across domains.
Developing techniques to ensure AI systems behave in accordance with human values and intentions, even as they become more capable.
Creating AI systems that can understand and generate content across different modalities including text, images, audio, and video.
Building AI models that achieve state-of-the-art performance while requiring significantly less computational resources.
Designing AI systems that work effectively with humans, augmenting human capabilities rather than replacing them.
Research on building autonomous AI agents that can perceive, reason, plan, and execute tasks in complex environments through a recursive process of thinking, acting, and observing.
Exploring how high-quality human feedback data significantly improves AI model performance, focusing on data collection methodologies and quality assessment frameworks.
class RecursiveRLAgent:"""Recursive Reinforcement Learning Agent"""def __init__(self, state_dim, action_dim): self.state_dim = state_dim self.action_dim = action_dim self.memory = ReplayBuffer(10000) self.meta_policy = Policy(state_dim, action_dim) self.sub_policies = [Policy(state_dim, action_dim) for _ in range(3)]def recursive_learn(self, state, depth=0):if depth > MAX_RECURSION_DEPTH:return self.predict(state) action = self.meta_policy.select_action(state) next_state = self.recursive_learn( self._transform(state, action), depth + 1 ) reward = self._compute_reward(state, next_state) self.meta_policy.update(state, action, reward, next_state)Developing advanced reinforcement learning techniques that recursively apply learning algorithms at multiple levels, enabling more efficient exploration and better generalization.
We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.
Our research focuses on making AI systems more accessible, understandable, and useful for people from all backgrounds and technical skill levels. We believe that AI should augment human capabilities and help people achieve their goals, rather than replacing or diminishing human agency.
Through our work on interpretable models, intuitive interfaces, and inclusive design, we're creating AI systems that can be effectively used and trusted by everyone.
Recent advances in AI systems that can process and generate multiple types of data, from text to images to audio.
2024-09-28 • 6 min read
How our research is focused on democratizing access to advanced AI capabilities through more efficient and affordable systems.
2024-08-17 • 5 min read
Our approach to ensuring that AI research and development is conducted ethically and with consideration for societal impact.
2024-07-22 • 7 min read
Interested in collaborating or learning more about our research? Get in touch with our team.