A $100 million AI robot that can analyze a human’s brain to predict whether it will be prone to depression or anxiety is now on the market.
Researchers from New York University have developed a neural network that is able to predict the likelihood of depression and anxiety and can predict which patients will benefit from an antidepressant medication.
This AI machine-learning system can be used in the medical field for the first time to predict and optimize the cost-effectiveness of new treatments.
It can also be used to evaluate new treatments and assess their effectiveness.
The system is based on a neural net that is already used to analyze the brain of people with autism spectrum disorders.
It was developed by the Neural Network Lab at NYU and uses an artificial neural network to analyze how an individual’s brain reacts to stimuli and reactivate their immune system.
“The machine learning that we are developing to do that is very sophisticated,” said Alex Shum, an assistant professor in the department of electrical engineering and computer science at NYU.
“It has very sophisticated features, and we have a lot of the capabilities of machine learning already in our toolboxes.
But to get a lot more sophisticated features like deep learning, that we have to develop a new toolbox, it’s really difficult,” said Shum.
Shum said this AI system is the first one of its kind that can understand how a human brain works, so it can predict what kind of symptoms are likely to be triggered by certain types of stimulation.
Shums team has also developed a way to analyze brain activity that is highly sensitive to what’s happening in the brain and how it affects the immune system, and it has shown that the neural network is able the to make predictions on the emotional state of an individual.
Shu Zhang, an associate professor of neuroscience at NYU, said the team is now looking for partners to build the AI robot and use it in clinical settings.
“We have been developing neural nets for the last few years.
But we never imagined that this is a real thing,” said Zhang.”
So now we have this really, really powerful machine-to-machine, machine-based neural network,” said the professor.
This new AI system was built using an artificial intelligence system developed by NYU and the University of Wisconsin.
“The technology is very similar to the one that is used in neurophysiology and neuroimaging,” said NYU computer science professor and co-founder of the Neural Networks Lab, Dr. Shum.
“This neural network has a very high level of accuracy.
The best example is predicting the rate at which a brain waves are oscillating, and this is based, in part, on the brain activity of an autistic person.”
Shum, who is also the director of the NYU’s Institute for the Study of Brain and Cognitive Functions, said that the AI system has been used in clinical applications for several years.
“Our current work is very much focused on developing neural networks for neuroimagers in the laboratory, so that they can help neuroimaged patients to improve their quality of life, or the way they function, or their cognitive functioning,” said Professor Shum said.
“If the neural networks can be applied to real-world settings, then it could lead to much better personalized medicine.”
Shu said that this AI machine learning system is more advanced than any existing artificial neural net, because it uses sophisticated hardware, a large amount of computing power and is capable of performing deep learning operations on a very large dataset of data.
Shumar added that there are a lot in the pipeline for this AI neural network, which could eventually lead to a robot that could analyze patients’ brain activity and diagnose them in minutes.
Shuma and his team are currently working with medical organizations and academia to develop and test these neural nets in the field, so they could be in clinical trials in as little as a few years, Shum added.
“There’s so much research going on in neuroscience, and AI has the potential to revolutionize medicine, but the challenges are enormous,” Shum noted.
“One of the biggest challenges is to make sure that the technology is right for the patient and the patient is right,” he said.