Tuesday June 25, 2019

Researchers Design Tool that Customises Caffeine Intake for Alertness

It also enables users to automatically obtain optimal caffeine timing and doses to achieve peak alertness at the desired times, said the study

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Researchers have designed a web-based caffeine optimisation tool with effective strategies to maximize alertness while avoiding excessive caffeine consumption.

Using multiple sleep-deprivation and shift-work scenarios, the researchers have generated a caffeine-consumption guidance, according to findings published in the journal Sleep.

Their analysis found that the solutions suggested by the quantitative caffeine optimisation tool either required on average 40 per cent less caffeine or enhanced alertness by an additional 40 per cent.

“The tool allows an individual to optimize the beneficial effects of caffeine while minimizing its consumption,” said study lead author Jaques Reifman from the US Army Medical Research.

tea
The findings showed that people who were more sensitive to caffeine and were drinking a lot of coffee consumed low amounts of tea. Pixabay

In the tool, the users can input several factors – the desirable peak-alertness periods within a sleep/wake schedule, the minimum desirable level of alertness and the maximum tolerable daily caffeine intake.

The tool allows users to predict the alertness of an “average” individual as a function of sleep/wake schedule and caffeine schedule.

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It also enables users to automatically obtain optimal caffeine timing and doses to achieve peak alertness at the desired times, said the study.

“For example, if you pull an all-nighter, need to be at peak alertness between, say, 9 a.m. and 5 p.m., and desire to consume as little caffeine as possible, when and how much caffeine should you consume?, This is the type of question the tool was designed to answer,” Reifman said. (IANS)

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Researchers Teaching Artificial Intelligence to Connect Senses Like Vision and Touch

The new AI-based system can create realistic tactile signals from visual inputs

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Artificial intelligence, road infrastructure
The fully-automated system is based on AI-powered object detection to identify street signs in the freely available images. Pixabay

A team of researchers at the Massachusetts Institute of Technology (MIT) have come up with a predictive Artificial Intelligence (AI) that can learn to see by touching and to feel by seeing.

While our sense of touch gives us capabilities to feel the physical world, our eyes help us understand the full picture of these tactile signals.

Robots, however, that have been programmed to see or feel can’t use these signals quite as interchangeably.

The new AI-based system can create realistic tactile signals from visual inputs, and predict which object and what part is being touched directly from those tactile inputs.

Teaching, Artificial Intelligence, Researchers
) A team of researchers at the Massachusetts Institute of Technology (MIT) have come up with a predictive Artificial Intelligence (AI). Pixabay

In the future, this could help with a more harmonious relationship between vision and robotics, especially for object recognition, grasping, better scene understanding and helping with seamless human-robot integration in an assistive or manufacturing setting.

“By looking at the scene, our model can imagine the feeling of touching a flat surface or a sharp edge”, said Yunzhu Li, PhD student and lead author from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

“By blindly touching around, our model can predict the interaction with the environment purely from tactile feelings,” Li added.

The team used a KUKA robot arm with a special tactile sensor called GelSight, designed by another group at MIT.

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Using a simple web camera, the team recorded nearly 200 objects, such as tools, household products, fabrics, and more, being touched more than 12,000 times.

Breaking those 12,000 video clips down into static frames, the team compiled “VisGel,” a dataset of more than three million visual/tactile-paired images.

“Bringing these two senses (vision and touch) together could empower the robot and reduce the data we might need for tasks involving manipulating and grasping objects,” said Li.

The current dataset only has examples of interactions in a controlled environment.

Teaching, Artificial Intelligence, Researchers
While our sense of touch gives us capabilities to feel the physical world, our eyes help us understand the full picture of these tactile signals. Pixabay

The team hopes to improve this by collecting data in more unstructured areas, or by using a new MIT-designed tactile glove, to better increase the size and diversity of the dataset.

“This is the first method that can convincingly translate between visual and touch signals”, said Andrew Owens, a post-doc at the University of California at Berkeley.

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The team is set to present the findings next week at the “Conference on Computer Vision and Pattern Recognition” in Long Beach, California. (IANS)