If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Researchers have recently developed an artificial intelligence-powered solution capable of covering brain waves into text. According to the New England Journal of Medicine, the research was conducted at the University of California, where a paralyzed man's brain waves were analyzed and were translated into text, enabling the man to communicate with others. This new groundbreaking artificial intelligence technology will enable individuals who have complete body paralysis to interact with everyone. Edward Chang, a senior author, said, "To our knowledge, this is the first successful demonstration of direct decoding of full words from the brain activity of someone who is paralyzed and cannot speak." He also mentioned that this study would help scientists restore communication using the human brain's natural speech machinery.
In a medical first, researchers harnessed the brainwaves of a paralyzed man unable to speak and turned what he intended to say into sentences on a computer screen. It will take years of additional research but the study, reported Wednesday, marks an important step toward one day restoring more natural communication for people who can't talk because of injury or illness. "Most of us take for granted how easily we communicate through speech," said Dr Edward Chang, a neurosurgeon at the University of California, San Francisco, who led the work. "It's exciting to think we're at the very beginning of a new chapter, a new field" to ease the devastation of patients who have lost that ability. Today, people who can't speak or write because of paralysis have very limited ways of communicating.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. In a medical first, researchers harnessed the brain waves of a paralyzed man unable to speak -- and turned what he intended to say into sentences on a computer screen. It will take years of additional research but the study, reported Wednesday, marks an important step toward one day restoring more natural communication for people who can't talk because of injury or illness. "Most of us take for granted how easily we communicate through speech," said Dr. Edward Chang, a neurosurgeon at the University of California, San Francisco, who led the work.
We like to think of brains as computers: A physical system that processes inputs and spits out outputs. But, obviously, what's between your ears bears little resemblance to your laptop. Computer scientists know the intimate details of how computers store and process information because they design and build them. But neuroscientists didn't build brains, which makes them a bit like a piece of alien technology they've found and are trying to reverse engineer. At this point, researchers have catalogued the components fairly well.
AI researchers are constantly coming up with ways to make the technology serve us better. However, while these services are impressive they can be a tad creepy. A time when social media will be able to like pictures on our behalf, based on how our brain reacts to the picture sounds quite uncomfortable. Although Instagram isn't working on an algorithm that tracks brain waves, researchers from the University of Helsinki and Copenhagen University are. This AI matchmaker can tell when you find someone attractive.
An artificial intelligence system has been developed that can delve into your mind and learn which faces and types of visage you find most attractive. Finnish researchers wanted to find out whether a computer could identify facial features we find attractive without any verbal or written input guiding it. The team strapped 30 volunteers to an electroencephalography (EEG) monitor that tracks brain waves, then showed them images of'fake' faces generated from 200,000 real images of celebrities stitched together in different ways. They didn't have to do anything - no swiping right on the ones they like - as the team could determine their'unconscious preference' through their EEG readings. They then fed that data into an AI which learnt the preferences from the brain waves and created whole new images tailored to the individual volunteer.
They say beauty lies in the eye of the beholder, but in actuality, it goes far deeper than that. The concept of physical beauty resides in the mind, defined by whatever features we find attractive in other people's faces. These subtle preferences represent some of our most private inner thoughts – but that doesn't mean they can't be monitored, and perhaps even predicted. In a new study, researchers used electroencephalography (EEG) measurements to identify what kind of facial features people found to be attractive, and then fed the results to an artificial intelligence (AI) program. The machine learning system – termed a generative adversarial neural network (GAN) – was first able to familiarise itself with what sorts of faces individual people found desirable, and then fabricate entirely new ones specifically designed to please: tailored visions of synthesised beauty, as unattainable as they were perfect.
At a sleep research symposium in January 2020, Janna Lendner presented findings that hint at a way to look at people's brain activity for signs of the boundary between wakefulness and unconsciousness. For patients who are comatose or under anesthesia, it can be all-important that physicians make that distinction correctly. Doing so is trickier than it might sound, however, because when someone is in the dreaming state of rapid-eye movement (REM) sleep, their brain produces the same familiar, smoothly oscillating brain waves as when they are awake. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research develop ments and trends in mathe matics and the physical and life sciences. Lendner argued, though, that the answer isn't in the regular brain waves, but rather in an aspect of neural activity that scientists might normally ignore: the erratic background noise.
Hiking the Franconia Ridge Loop is an intimidating proposition. The trail, in the heart of New Hampshire's White Mountain National Forest, is close to 9 miles long, and peaks at over 5,000 feet above sea level. The ridge connects several of New Hampshire's highest peaks and offers stunning views of the surrounding mountains. The ridge itself is a ragged, narrow path flanked by alpine tundra, with low-standing bushes and virtually no trees. My partner and I found ourselves on the Franconia trail on a recent Sunday morning, our backpacks full of trail mix, sandwiches, and hot tea, our minds ready to take on the arduous hike. We entered the forest and discovered a path covered on all sides with beech, birch, and fir trees. Ferns littered the forest floor, while moss covered downed trees like a short but scraggly beard. The path appeared to ascend into infinity, like the Penrose stairs. The hike was a welcome escape from the city, and as we walked, the conversation turned to the power of nature.
X, Alphabet's experimental R&D lab, today detailed Project Amber, a now-disbanded project which aimed to make brain waves as easy to interpret as blood glucose. The goal was to develop objective measurements of depression and anxiety that could be used to support diagnoses, treatment, and therapies. An estimated 17.3 million adults in the U.S. have had at least one major depressive episode, according to the U.S. National Institutes of Health. Moreover, the percentage of adults in the U.S. experiencing serious thoughts of suicide increased 0.15% from 2016-2017 to 2017-2018 -- 460,000 more people than last year's dataset. Today's assessments mostly rely on conversations with clinicians or surveys like the PHQ-9 or GAD-7. The Amber team sought to marry machine learning techniques with electroencephalography (EEG) to measure telling electrical activity in the brain.