Maluuba + Microsoft: Towards Artificial General Intelligence
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.
What Sci-fi and Philosophy Can Teach Us About AI Ethics
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.
Maluuba partners with McGill University's Reasoning & Learning Lab to Teach Common Sense to Machines
Maluuba Senior Research Scientist Adam Trischler wrote an essay on what Sci-Fi and Philosophy can teach us about AI Ethics.
Maluuba releases two new datasets for Natural Language Understanding research
A new collaboration with McGill University's Reasoning and Learning Lab.
Maluuba at NIPS 2016
Maluuba introduces two sophisticated human-generated datasets to advance research in natural language understanding. These datasets explore fundamental aspects of human capabilities including information-seeking, exploration, curiosity, decision-making and memory.
¡Hola! A team of Maluuba people are heading to Barcelona to take part in the Neural Information Processing Systems conference (NIPS). We’re looking forward to taking part in workshops and tutorials as well as networking with our fellow colleagues.