Intel CEO Brian Krzanich speaks at a 2016 AI event. Intel might be an old-school computing company, but the chipmaker thinks the latest trends in artificial intelligence will keep it an important part of your high-tech life. AI technology called machine learning today is instrumental to taking good photos, translating languages, recognizing your friends on Facebook, delivering search results, screening out spam and many other chores. It usually uses an approach called neural networks that works something like a human brain, not a sequence of if-this-then-that steps as in traditional computing. Lots of companies, including Apple, Google, Qualcomm and Nvidia, are designing chips to accelerate this sort of work.
These distinct neural signatures could guide scientists as they study the underlying neurobiology of how we both learn motor skills and work through complex cognitive tasks, says Earl K. Miller, the Picower Professor of Neuroscience at the Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences, and senior author of a paper describing the findings in the Oct. 11 edition of Neuron. When the MIT researchers studied the behavior of animals learning different tasks, they found signs that different tasks might require either explicit or implicit learning. During explicit learning tasks, there was an increase in alpha2-beta brain waves (oscillating at 10-30 hertz) following a correct choice, and an increase delta-theta waves (3-7 hertz) after an incorrect choice. By contrast, delta-theta rhythms only increased with correct answers during an implicit learning task, and they decreased during learning.
Yes, there is some evidence that brain games help to boost attention, executive function and short-term memory skills. It requires you to read and process visual information, but you're not actively challenging multiple areas of the brain, nor are you increasing the connections between them or engaging any personal or historical islands of consciousness. The sort of games the brain needs to build cognitive reserve are complex activities which directly strengthen the bridges and roads that lead to central islands of consciousness. We use 100 per cent of our brains 100 per cent of the time -- even when we're sleeping -- but some activities present specific challenges to different brain functions, making them particularly effective at building a cognitive reserve.
This is one of the analogies Jimmy Soni and Rob Goodman use to explain information theory in A Mind at Play, the excellent new biography of information age founder Claude Shannon. From a very young age, as the authors explain, Shannon displayed a lively and curious mind. Besides his mathematical theory of communication, during that time, he also laid the groundwork for modern signal processing and important aspects of cryptography (such as the one-time pad). One of the pleasures of modern biography-reading is that when the authors mention Shannon's filmed 1950 demonstration of Theseus at Bell Labs, one can pause to find it on YouTube.
AI algorithms have been successfully tested in pinpointing healthy brains and those with the disease with 86 per cent accuracy, leading to hopes it could ultimately be used by the NHS to predict Alzheimer's. La Rocca's algorithm was tested on 38 scans of patients with Alzheimer's and 29 of those without the disease, with it then tested on another 148 people. Of those, 48 people had Alzheimer's, 52 people were healthy, and 48 had mild cognitive impairment. The damage caused to the brain by Alzheimer's disease causes the symptoms commonly associated with dementia.
Recent applications of machine learning with big data are able to predict diseases--such as Alzheimer's and diabetes--with incredible accuracy, years before the onset of symptoms. To assess the likelihood of a patient developing a certain condition, physicians have traditionally relied on risk calculators such as this one. Bringing together the data collected in many large-scale studies across diverse medical specialties, together with information from our medical records and other sources, doctors can accurately calculate the likelihood of suffering from a disease, a patient's possible outcome, and even figure out what the main predictors for each illness are. The CS experts have brought to the table the capacity to identify, develop, and fine-tune machine learning algorithms and techniques to predict conditions with better accuracy and speed.
Doctors already use MRI scans to look for changes characteristic of Alzheimer's but scientists believe artificial intelligence could help specialists to diagnose the conditions before changes are clearly visible. Researchers believe the new technology could be used by doctors to predict Alzheimer's and other diseases within a decade. Dr La Rocca told New Scientist that AI-based testing would also be cheaper and more comfortable than invasive techniques, which look for "sticky" plaques and tangles of protein in the brain that have been linked to the disease.
In a study, published earlier this month, researchers developed a machine-learning algorithm to detect Alzheimer's in brain scans 86 percent of the time. Even more impressively, it identified changes in the brain that showed mild cognitive impairment (MCI) 84 percent of the time. It might be able to identify these changes even earlier, but the researcher's only tested it on individual's who developed Alzheimer symptoms within nine years.
"We've learned that you cannot make a definite statement about a particular gene," Winston Hide, Professor in computational biology at The University of Sheffield, explains. According to Winston Hide, data reproducibility and unraveling the differential contribution of multiple genes in relevant biological pathways are some of the big issues in this field. According to Barry, the solution might come from imitating the brain's own ability to process information through deep learning. However, can we really make good models of the brain by using deep learning?
The AI was trained to correctly spot the difference between diseased and healthy brains, before being tested on its accuracy abilities on a second set of 148 scans – 52 of which were healthy, 48 had Alzheimer's and the other 48 had a mild cognitive impairment that was known to develop into Alzheimer's within 10 years. The algorithm correctly distinguished between healthy and diseased brains 86% of the time, according to the researchers, who added that it was also able to spot the difference between a healthy brain and a mild impairment with an 84% accuracy rating. Last month mobile game Sea Hero Quest – which uses navigation challenges to gather data about spatial movement as part of research into the disease – was expanded to virtual reality for the first time. The game sets users navigation challenges, and they can opt-in to share their data with the researchers behind the game, who can use player performance data to plot spatial navigation skills of different ages groups and genders.