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Category: robotics/AI – Page 1,805

World’s First Robot Citizen Sophia Said She Wants To Have A Baby

Okay…very odd indeed.

As already mentioned, the Saudi Arabian Government officially granted Sophia citizenship in 2017. She is the first and only robot to be an official citizen of a country. Her citizenship sparked some controversy, and not just from people who don’t think robots deserve rights. Rather, many people pointed out the contrast to women’s rights in the country.

Read: A Never-Before-Seen Type of Signal Has Been Detected in The Human Brain

Sophia spoke on a stage without wearing the required coverings that women have to wear there. Saudi Arabia is one of the countries lagging the furthest behind in women’s rights. Only a few years ago were women given the right to drive. Still, the country operates under the guardianship system which states that women must always be accompanied by men in public. They can’t go anywhere without permission or do most things without permission. Even to report domestic violence or abuse they need permission from male relatives.

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DeepMind Introduces ‘Enformer’, A Deep Learning Architecture For Predicting Gene Expression From DNA Sequence

DNA contains the genetic information that influences everything from eye color to illness and disorder susceptibility. Genes, which are around 20,000 pieces of DNA in the human body, perform various vital tasks in our cells. Despite this, these genes comprise up less than 2% of the genome. The remaining base pairs in the genome are referred to as “non-coding.” They include less well-understood instructions on when and where genes should be created or expressed in the human body.

DeepMind, in collaboration with their Alphabet colleagues at Calico, introduces Enformer, a neural network architecture that accurately predicts gene expression from DNA sequences.

Earlier studies on gene expression used convolutional neural networks as key building blocks. However, their accuracy and usefulness have been hampered by problems in modeling the influence of distal enhancers on gene expression. The proposed new method is based on Basenji2, a program that can predict regulatory activity from DNA sequences of up to 40,000 base pairs.

The Future Of AI-Driven Meeting Technology

Early in 2,021 the Stanford Virtual Human Interaction Lab looked at the psychological consequences of spending long days videoconferencing and in virtual meetings. The popularized term “Zoom fatigue,” is the result of maxing out cognitive load and even reducing effectiveness. For all of that investment in remote work technology, senior managers feel there is very little payoff.

The University of North Carolina surveyed 182 senior managers and 65% of them felt meetings kept them from completing their own work, 71% felt meetings were inefficient and unproductive, and 64% felt meetings undercut deep thinking.

As technology-dependent remote workers proliferate, new solutions are coming to the fore that may make both in-person and virtual meetings more productive.

Enabling AI-driven health advances without sacrificing patient privacy

There’s a lot of excitement at the intersection of artificial intelligence and health care. AI has already been used to improve disease treatment and detection, discover promising new drugs, identify links between genes and diseases, and more.

By analyzing large datasets and finding patterns, virtually any new algorithm has the potential to help patients — AI researchers just need access to the right data to train and test those algorithms. Hospitals, understandably, are hesitant to share sensitive patient information with research teams. When they do share data, it’s difficult to verify that researchers are only using the data they need and deleting it after they’re done.

Secure AI Labs (SAIL) is addressing those problems with a technology that lets AI algorithms run on encrypted datasets that never leave the data owner’s system. Health care organizations can control how their datasets are used, while researchers can protect the confidentiality of their models and search queries. Neither party needs to see the data or the model to collaborate.

Georgia Tech Researchers Create Wireless Brain-Machine Interface

Researchers from Georgia Tech University’s Center for Human-Centric Interfaces and Engineering have created soft scalp electronics (SSE), a wearable wireless electro-encephalography (EEG) device for reading human brain signals. By processing the EEG data using a neural network, the system allows users wearing the device to control a video game simply by imagining activity.

Predicting gene expression with AI

Based on Transformers, our new architecture advances genetic research by improving the ability to predict how DNA sequence influences gene expression.

When the Human Genome Project succeeded in mapping the DNA sequence of the human genome, the international research community were excited by the opportunity to better understand the genetic instructions that influence human health and development. DNA carries the genetic information that determines everything from eye colour to susceptibility to certain diseases and disorders. The roughly 20,000 sections of DNA in the human body known as genes contain instructions about the amino acid sequence of proteins, which perform numerous essential functions in our cells. Yet these genes make up less than 2% of the genome. The remaining base pairs — which account for 98% of the 3 billion “letters” in the genome — are called “non-coding” and contain less well-understood instructions about when and where genes should be produced or expressed in the human body.

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