Now, researchers are using AI scans to detect Alzheimer's almost a decade earlier than doctors making a diagnosis based on symptoms alone. 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. Nicola Amoroso, Marianna La Rocco, and colleagues from the University of Bari, Italy, taught AI software to tell the difference between healthy and unhealthy brains using MRI scans from the Alzheimer's Disease Neuroimaging Initiative. The researchers discovered that the algorithm was most effective at analyzing brain regions of 2,250 to 3,200 cubic millimeters – which just so happens to be the same size as anatomical structures associated with the disease (e.g.
It is critical to our mission to enable machine learning researchers with the most powerful training scenarios, and for us to give back to the gaming community by enabling them to utilize the latest machine learning technologies. At Unity, we wanted to design a system that provide greater flexibility and ease-of-use to the growing groups interested in applying machine learning to developing intelligent agents. The ML-Agents SDK allows researchers and developers to transform games and simulations created using the Unity Editor into environments where intelligent agents can be trained using Deep Reinforcement Learning, Evolutionary Strategies, or other machine learning methods through a simple to use Python API. As mentioned above, we are excited to be releasing this open beta version of Unity Machine Learning Agents today, which can be downloaded from our GitHub page.
In a recent paper in Nature Communications, Nithin Mathews, Anders Lyhne Christensen, Rehan O'Grady, Francesco Mondada, and Marco Dorigo from universities in Lisbon, Brussels, and Switzerland, present the idea of a "mergeable nervous systems for robots," with a framework for fully modular robotic systems: We present robots whose bodies and control systems can merge to form entirely new robots that retain full sensorimotor control. Our control paradigm enables robots to exhibit properties that go beyond those of any existing machine or of any biological organism: the robots we present can merge to form larger bodies with a single centralized controller, split into separate bodies with independent controllers, and self-heal by removing or replacing malfunctioning body parts. MNS robots thus constitute a new class of robots with capabilities beyond those of any existing machine or biological organism: An MNS robot can split into separate autonomous robots each with an independent brain unit, absorb robotic units with different capabilities into its body, and self-heal by removing or replacing malfunctioning body parts--including a malfunctioning brain unit. Nithin Mathews: The sensorimotor system that physically connects a robot's central processing unit to its sensors and actuators can be seen as a robotic nervous system--quite similar (at a conceptual level at least) to a biological nervous system of an higher order animal with a single brain controlling its host body.
A few months ago, Andy McAfee and Erik Brynjolfsson published Machine, Platform, Crowd: Harnessing Our Digital Future, - their third book on the impact of the 21st century digital revolution on the economy and society, - following the publication of The Second Machine Age in 2014 and Race Against the Machine in 2011. The book is organized into three sections, each focused on a major trend that's reshaping the business world: the rapidly expanding capabilities of machines; the emergence of large, asset-light platform companies; and the ability to now leverage the knowledge, expertise and enthusiasm of the crowd. The Deep Mind team trained AlphaGo using deep learning algorithms, which are partly modeled on the way a young child learns a human language: by listening, speaking, repetition and feedback. Deep learning is part of the broad class of machine learning systems that enable computers to acquire capabilities by ingesting and analyzing large amounts of data instead of being explicitly programmed, - thus getting around Polanyi's pervasive paradox.
Feng Zhang, a pioneer of the revolutionary CRISPR gene-editing technology, TAL effector proteins, and optogenics, is the recipient of the 2017 $500,000 Lemelson-MIT Prize, the largest cash prize for invention in the United States. Prior to harnessing CRISPR-Cas9, Zhang engineered microbial TAL effectors (TALEs) for use in mammalian cells, working with colleagues at Harvard University, authoring multiple publications on the subject and becoming a co-inventor on several patents on TALE-based technologies. Zhang was also a key member of the team at Stanford University that harnessed microbial opsins for developing optogenetics, which uses light signals and light-sensitive proteins to monitor and control activity in brain cells. Zhang's numerous scientific discoveries and inventions, as well as his commitment to mentorship and collaboration, earned him the Lemelson-MIT Prize, which honors outstanding mid-career inventors who improve the world through technological invention and demonstrate a commitment to mentorship in science, technology, engineering and mathematics (STEM).
The team from the University of Bari trained the AI by feeding in 67 MRI scans - 38 from Alzheimer's patients and 29 healthy patients - then asked it to analyse the neuronal connectivity to form an algorithm. Following the training, the AI was then asked to process brains from 148 subjects - 52 were healthy, 48 had Alzheimer's disease and 48 had mild cognitive impairment (MCI) but were known to have developed Alzheimer's disease two and a half to nine years later. Although there is no cure for Alzheimer's disease, early diagnosis can allow people to start making lifestyle choices to slow the progression of the disease. The Bari University research team now intends to extend the technique to help with the early diagnosis of neurodegenerative conditions such as Parkinson's disease.
Two years ago, Shao, a mechanical engineer with a flair for biology, was working with embryonic stem cells, the kind derived from human embryos able to form any cell type. The work in Michigan is part of a larger boom in organoid research--scientists are using stem cells to create clumps of cells that increasingly resemble bits of brain, lungs, or intestine (see "10 Breakthrough Technologies: Brain Organoids"). Scientists have started seeking ways to coax stem cells to form more complicated, organized tissues, called organoids. Following guidelines promulgated last year by Kimmelman's international stem-cell society, Fu's team destroys the cells just five days after they're made.
In this case the frontal lobe of cerebrum makes a self aware (introspective) decision to reject either information or modify each information to eliminate conflict. Ans) Anterior prefrontal cortex has been associated with top-level processing abilities that are thought to set humans apart from other animals. This brain region has been implicated in planning complex cognitive behaviour, personality expression, decision making, and moderating social behaviour. Ans) Medial prefrontal cortex (mPFC) is considered to be a part of the brain's reward system.
A recent study from Northwestern University in Chicago uses machine learning to shed some light on the matter. Each monkey was strapped up to devices that could measure their neuronal activity -- whether in the primary motor cortex, the dorsal premotor cortex, or the primary somatosensory cortex. The machine learning algorithm the team developed to do that job aimed to analyze the horizontal and vertical distance that the mouse cursor moved during each test, purely by looking at the neurological data. The team developed a number of different algorithms to test, varying from standard statistics based algorithms to machine learning based approaches, such as the Long Short-Term Memory Network.
Aggression and sexual behaviour are controlled by the same brain cells in male mice – but not in females. The brain regions that contain these cells look similar in mice and humans, say the researchers behind the study, but they don't yet know if their finding has relevance to human behaviour. They discovered a set of cells within this region in male mice that controlled both aggressive and sexual behaviours. Plus other regions that are known to look different in male and female mouse brains have "considerable overlap" in the brains of women and men, she says.