An engineer from the Johns Hopkins Center for Language and Speech Processing has developed a machine learning model that can distinguish functions of speech in transcripts of dialogs outputted by language understanding, or LU, systems in an approach that could eventually help computers “understand” spoken or written text in much the same way that humans do.

Developed by CLSP Assistant Research Scientist Piotr Zelasko, the new model identifies the intent behind words and organizes them into categories such as “Statement,” “Question,” or “Interruption,” in the final transcript: a task called “dialog act recognition.” By providing other models with a more organized and segmented version of text to work with, Zelasko’s model could become a first step in making sense of a conversation, he said.

“This new method means that LU systems no longer have to deal with huge, unstructured chunks of text, which they struggle with when trying to classify things such as the topic, sentiment, or intent of the text. Instead, they can work with a series of expressions, which are saying very specific things, like a question or interruption. My model enables these systems to work where they might have otherwise failed,” said Zelasko, whose study appeared recently in Transactions of the Association for Computational Linguistics.

As robots are gradually introduced into various real-world environments, developers and roboticists will need to ensure that they can safely operate around humans. In recent years, they have introduced various approaches for estimating the positions and predicting the movements of robots in real-time.

Researchers at the Universidade Federal de Pernambuco in Brazil have recently created a new deep learning model to estimate the pose of robotic arms and predict their movements. This model, introduced in a paper pre-published on arXiv, is specifically designed to enhance the safety of robots while they are collaborating or interacting with humans.

“Motivated by the need to anticipate accidents during human-robot interaction (HRI), we explore a framework that improves the safety of people working in close proximity to robots,” Djamel H. Sadok, one of the researchers who carried out the study, told TechXplore. “Pose detection is seen as an important component of the overall solution. To this end, we propose a new architecture for Pose Detection based on Self-Calibrated Convolutions (SCConv) and Extreme Learning Machine (ELM).”

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Amazon showed off a “fully autonomous mobile robot” called Proteus that can move carts around its warehouses, and even interact with humans, all on its own. The company also has other robots it’s working on.

The autonomous robot will initially be deployed in the outbound GoCart handling areas of Amazon’s fulfillment and sort centers. “Our vision is to automate GoCart handling throughout the network, which will help reduce the need for people to manually move heavy objects through our facility and instead let them focus on more rewarding work,” the company said.

Celebrating 10 years of robotic evolution, Amazon unveiled several more automated systems. Cardinal (Opens in a new window) is an AI-based robot arm that can select one package out of a pile of boxes, lift it, read the label, and place it on the appropriate GoCart (for Proteus to collect). Amazon is currently trialing a Cardinal prototype for handling packages up to 50 pounds and expects to deploy the technology in fulfillment centers next year.