California


Brain Implants and AI Model Used To Translate Thought Into Text

#artificialintelligence

Researchers at the University of California, San Francisco have recently created an AI system that can produce text by analyzing a person's brain activity, essentially translating their thoughts into text. The AI takes neural signals from a user and decodes them, and it can decipher up to 250 words in real-time based on a set of between 30 to 50 sentences. As reported by the Independent, the AI model was trained on neural signals collected from four women. The participants in the experiment had electrodes implanted in their brains to monitor for the occurrence of epileptic seizures. The participants were instructed to read sentences aloud, and their neural signals were fed to the AI model.


Complete Machine Learning and Data Science: Zero to Mastery

#artificialintelligence

HIGHEST RATED Created by Andrei Neagoie, Daniel Bourke English [Auto-generated] Students also bought Learn Data Wrangling with Python Machine Learning A-Z: Hands-On Python & R In Data Science Python for Data Science and Machine Learning Bootcamp The Data Science Course 2020: Complete Data Science Bootcamp R Programming A-Z: R For Data Science With Real Exercises! Preview this course GET COUPON CODE Description Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 200,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. This is a brand new Machine Learning and Data Science course just launched January 2020! Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies.


Turning brain activity to text with AI: Time for nations to debate what is ethical, and what isn't

#artificialintelligence

A few years ago, when researchers successfully demonstrated the use of augmented reality (AR) to treat PTSD by examining which parts of the brain it impacted, no one would have thought that scientists would be able to able to use artificial intelligence (AI) to turn brain activity to text. According to The Guardian, scientists at the University of California (UC) have been able to do so using electrode arrays implanted in the brain. Although the results are not too revolutionary, and AI commits mistakes more often than not, the fact that this is now possible is an achievement in itself. While, in the AR experiment, scientists were looking at which part of the brain gets impacted by certain images and videos to decode neural response, in the UC experiment, AI converted brain activity to numbers related to aspects of speech. The AI, with great difficulty, could only do this for the 50 sentences in which it was trained.


Automation May Take Jobs--but AI Will Create Them

#artificialintelligence

Chances are you've already encountered, more than a few times, truly frightening predictions about artificial intelligence and its implications for the future of humankind. The machines are coming and they want your job, at a minimum. Scary stories are easy to find in all the erudite places where the tech visionaries of Silicon Valley and Seattle, the cosmopolitan elite of New York City, and the policy wonks of Washington, DC, converge--TED talks, Davos, ideas festivals, Vanity Fair, the New Yorker, The New York Times, Hollywood films, South by Southwest, Burning Man. The brilliant innovator Elon Musk and the genius theoretical physicist Stephen Hawking have been two of the most quotable and influential purveyors of these AI predictions. AI poses "an existential threat" to civilization, Elon Musk warned a gathering of governors in Rhode Island one summer's day.


Online Pie & AI: Real-world AI Applications in Medicine

#artificialintelligence

AI is transforming the practice of medicine. It's helping doctors diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments. To help make this transformation possible worldwide, you need to gain practical experience applying machine learning to concrete problems in medicine. We've gathered experts in the AI and medicine field to share their career advice and what they're working on. We'll also be celebrating the launch of our new AI For Medicine Specialization!


Artificial intelligence decodes the facial expressions of mice

#artificialintelligence

Mice move their ears, cheeks and eyes to convey emotion.Credit: Getty Researchers have used a machine-learning algorithm to decipher the seemingly inscrutable facial expressions of laboratory mice. The work could have implications for pinpointing neurons in the human brain that encode particular expressions. Their study "is an important first step" in understanding some of the mysterious aspects of emotions and how they manifest in the brain, says neuroscientist David Anderson at the California Institute of Technology in Pasadena. Nearly 150 years ago, Charles Darwin proposed that facial expressions in animals might provide a window onto their emotions, as they do in humans. But researchers have only recently gained the tools -- such as powerful microscopes, cameras and genetic techniques -- to reliably capture and analyse facial movement, and investigate how emotions arise in the brain.


Machine Learning for Smarter 3D Printing

#artificialintelligence

However, one issue that still persists is how to avoid printing objects that don't meet expectations and thus can't be used, leading to a waste in materials and resources. Scientists at the University of Southern California's (USC's) Viterbi School of Engineering has come up with what they think is a solution to the problem with a new machine-learning-based way to ensure more accuracy when it comes to 3D-printing jobs. Researchers from the Daniel J. Epstein Department of Industrial and Systems Engineering developed a new set of algorithms and a software tool called PrintFixer that they said can improve 3D-printing accuracy by 50 percent or more. The team, led by Qiang Huang, associate professor of industrial and systems engineering and chemical engineering and materials science, hopes the technology can help make additive manufacturing processes more economical and sustainable by eliminating wasteful processes, he said. "It can actually take industry eight iterative builds to get one part correct, for various reasons," said Qiang, who led the research.


ModelOps Is The Key To Enterprise AI

#artificialintelligence

In the last two years, large enterprise organizations have been scaling up their artificial intelligence and machine learning efforts. To apply models to hundreds of use-cases, organizations need to operationalize their machine learning models across the organization. At the center of this scaling up effort is ModelOp, the company that builds solutions to scale the processes that take models from the data science lab into production. Even before their recent $6 million Series A funding led by Valley Capital Partners with participation from Silicon Valley Data Capital, they are already the leader providing ModelOps solutions to Fortune 1000 companies. ModelOps is a capability that focuses on getting models into 24/7 production.


Training AI To Transform Brain Activity Into Text

#artificialintelligence

Back in 2008, theoretical physicist Stephen Hawking used a speech synthesizer program on an Apple II computer to "talk." He had to use hand controls to work the system, which became problematic as his case of Lou Gehrig's disease progressed. When he upgraded to a new device, called a "cheek switch," it detected when Hawking tensed the muscle in his cheek, helping him speak, write emails, or surf the Web. Now, neuroscientists at the University of California, San Francisco have come up with a far more advanced technology--an artificial intelligence program that can turn thoughts into text. In time, it has the potential to help millions of people with speech disabilities communicate with ease.


Hunt for George Clooney's face explains how stress affects decisions

New Scientist

Stress can lead to poor decision-making, and people hunting for George Clooney's face could help us understand why. Thackery Brown at Stanford University, California, and his colleagues asked 38 people, with an average age of 23, to navigate looping paths around 12 different virtual towns in a simulated environment. Each town had just a few streets and took about a minute to navigate. The researchers also placed the face of a celebrity – George Clooney, for example – at a point along the route. The team then asked the participants to navigate the simulation again while lying inside a functional magnetic resonance imagine (fMRI) machine.