When Android first burst onto the scene in 2007, it had a rocky start. The world was stuck in the throngs of Apple fever, and the iPhone’s popular “there’s an app for that” marketing campaign became a part […]
An emerging trend in recruiting is the rise of automated interviewing, including the integration of artificial intelligence technology. This transition is a boon for the largely outdated recruiting industry. AI components allow companies to easily sift through dozens (or hundreds) […]
This paper presents a micro-traffic simulation (named “DeepTraffic”) where the perception, control, and planning systems for one of the cars are all handled by a single neural network as part of a model-free, off-policy reinforcement learning process. The paper also […]
This paper presents a study in Distributed Deep Reinforcement Learning (DDRL) focused on scalability of a state-of-the-art Deep Reinforcement Learning algorithm known as Batch Asynchronous Advantage ActorCritic (BA3C). Using synchronous training on the node level (while keeping the local, single […]
As examples like Wikipedia, Google, and Linux illustrate, new information and communication technologies are now enabling dramatically new ways of connecting large numbers of people and computers to produce intelligent behavior. These new forms of “collective intelligence” are already having […]
Much work remains in the area of social conversation as well as free-form conversation over a broad range of domains and topics. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar university […]
The subjectivity associated with evaluating conversations is a key element underlying the challenge of building non-goal oriented dialogue systems. This paper proposes a comprehensive evaluation strategy with multiple metrics designed to reduce subjectivity by selecting metrics which correlate well with […]
