Teaching AI to make decisions and communicate
The Next Challenges for Reinforcement Learning
We look at cognitive skills such as learning, perception and judgement and how these support our work teaching AI to make decisions and communicate.
Maluuba at AAAI-17 in San Francisco
As reinforcement learning continues to drive breakthroughs in AI, we explore some of the challenges and opportunities for researchers.
Maluuba co-founders reflect on UW Velocity experience
Maluuba research team members will be taking part in the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) from February 4-9.
Maluuba + Microsoft: Towards Artificial General Intelligence
Maluuba founders Sam Pasupalak and Kaheer Suleman reflect on the journey from UW Velocity to building Maluuba into a leading pioneer in AI research.
Creating curious machines: Building information-seeking agents
We are incredibly excited to announce an important milestone on our journey so far. As of today, Maluuba has agreed to be acquired by Microsoft. As we turn the page on this new chapter, we thought we would discuss this exciting development and share our thoughts on what’s next to come.
Maluuba’s AI & Deep Learning predictions for 2017
Maluuba Research has developed a suite of tasks that teach artificial agents how to seek information actively, by asking questions. We’ve also designed a deep neural agent that learns to accomplish these tasks through efficient information-seeking behaviour. Such behaviour is a vital research step towards Artificial General Intelligence.
Infographic: 2016 Review
Maluuba’s research team share their perspectives on the trends, initiatives and applications of AI that they think will be most transformative in 2017 and beyond.
Memory & Machines: A Study in Goal-Oriented Dialogue Systems
Our Year in Review infographic highlights our research work in 2016
Decomposing Tasks like Humans: Scaling Reinforcement Learning By Separation of Concerns
Our research and the development of the new Frames dataset will serve as a valuable tool to help dialogue researchers build goal-oriented dialogue systems that can handle multiple items.
A key tenet of AI research states that intelligent machines should be able to make decisions and learn from environmental feedback similar to humans. In this post, we describe our recent research into task decomposition using multiple agents.