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Robot babysitter for dogs

27.04.2017

Ford designer Itzel Cortez has developed a special nanny robot for dogs. The system controls the pet's food and location through a smartphone app.

Ford designer Itzel Cortes presented her robot project to the world at the Last Mile Mobility Challenge. According to the developer, the collar is inextricably linked with the smartphone. In the application, the owner can independently choose the time for walking and feeding the dog. With such a nanny, a pet can be let out on the street without fear of losing it. The robot can play all the owner's commands, feed the dog and even clean up after it while walking.

According to Itzel Cortes, her invention will be very useful for modern people who spend days at work. The Ford designer was inspired to create a babysitter by her own dog.

“I don’t have tea in my dachshund Mary. Unfortunately, I can’t always give her due attention, so such a robot nanny will come in handy. Now I can send my dog ​​for a walk at any time without worries,” said Itzel Cortes at the Last Mile Mobility Challenge.

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Artificial brain needs sleep too 13.06.2020

It is not known if androids will count sheep, but will most likely need periods of rest that offer benefits similar to those that sleep provides to a living brain, according to new research from the Los Alamos National Laboratory.

“We are studying diving neural networks, which are systems that learn in the same way that a living brain does,” said Los Alamos Lab computer scientist Yizing Watkins. “We were fascinated by the prospect of learning a neuromorphic processor in a way similar to how humans and other biological systems receive information from the environment in the process of development.

Watkins and her research team found that the network simulation became unstable after extended periods of unsupervised training. When they exposed the networks to states similar to the waves that the living brain experiences during sleep, stability was restored.

The discovery came as a research team was working on building neural networks that are close to how humans and other biological systems learn to understand. Initially, the group struggled to stabilize simulated neural networks undergoing dictionaryless training, which involves classifying objects without prior examples for comparison.

“The question of how to prevent instability in learning systems really only comes up when trying to use biologically realistic processors...,” said Los Alamos computer scientist and research co-author Garrett Kenyon. “The vast majority of researchers in machine learning, deep learning, and artificial intelligences never face this problem, because in the artificial systems they study, they can afford to perform global mathematical operations that affect the overall dynamic gain of the system."

The researchers characterize the decision to expose the networks to an artificial analogue of sleep as a near-last effort to stabilize them. They experimented with different types of noise, roughly comparable to the static you might encounter between stations when tuning a radio. The best results were obtained when they used waves of so-called Gaussian noise, which includes a wide range of frequencies and amplitudes. They suggest that the noise mimics the input received by biological neurons during non-REM sleep. The results show that non-REM sleep may in part help ensure that cortical neurons remain stable and do not hallucinate.

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