New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Following Oliver Sacks, Antonio Damasio may be the neuroscientist whose popular books have done the most to inform readers about the biological machinery in our heads, how it generates thoughts and emotions, creates a self to cling to, and a sense of transcendence to escape by. But since he published Descartes' Error in 1994, Damasio has been concerned that a central thesis in his books, that brains don't define us, has been muted by research that states how much they do. To Damasio's dismay, the view of the human brain as a computer, the command center of the body, has become lodged in popular culture. In his new book, The Strange Order of Things, Damasio, a professor of neuroscience and the director of the Brain and Creativity Institute at the University of Southern California, mounts his boldest argument yet for the egalitarian role of the brain. In "Why Your Biology Runs on Feelings," another article in this chapter of Nautilus, drawn from his new book, Damasio tells us "mind and brain influence the body proper just as much as the body proper can influence the brain and the mind.
An amazing new video shows a thought racing across the surface of the human brain in less than a second. Experts tracked the path of singular thoughts through people's minds as they underwent open brain surgery. Electrodes were hooked directly to the surface of each patient's white matter, taking readings while they completed a simple call-and-response task. The scans show clearly how the brain acts in response to sight and sounds, which scientists say could explain'why we say things before we think'. Experts asked people to repeat the word'humid'.
Scientists at the ATR Computational Neuroscience Labs in Japan have created an AI-based system that's capable of performing deep image reconstruction from human brain activity. In simple words, it's reading our minds without actually knowing what we're thinking. Now, what that means is the AI system can't see inside our brain or the things we're picturing. It takes help of the brain waves (MRI data) to guess what we are thinking and draws an image out of it. To train their AI, the researchers fed it with recorded brainwaves of human subjects after showing them images.
Professor Digby Tatum, pictured, believes he has uncovered the secret behind human'gut feelings' Scientists believe they have uncovered the secret behind'gut feeling' claiming the human brain has a form of wi-fi which is constantly gathering information on other people by simply looking at them. Professor Digby Tatum of the University of Sheffield has been researching the human brain and how people communicate. He believes his work shows that language only plays a limited role when it comes to communication. Professional poker players believe they can pick up'tells' from their opponents by picking up on visual clues, or slight movements. Prof Tatum, who is the university's Clinical Professor of Psychotherapy, said people can pick up on subliminal information.
Experts who want to build a better robot are calling for brain scientists and artificial intelligence programmers to work together, saying it will benefit both the advancement of AI technology and our understanding of the human mind. It's not about making an exact replica of the human brain and placing it into a robot. Neuroscientist-turned-AI researcher Pascal Kaufmann told International Business Times that the focus should be on understanding how the brain works as a whole, rather than piece by piece, and then using the principles that govern it in an artificial mind. He compares the development of artificial intelligence to the invention of the airplane: Human beings could not replicate a bird wing with all its nuances, but they created a plane by using the scientific principles by which a bird flies and it worked just as well. Some programmers are trying to mimic the human brain but "I think that's pointless … to copy [and] paste nature," Kaufmann said.
Reports from some of these first projects make up the majority of the book, with the balance of the book providing an overview of neuroinformatics. The book's foreword provides interesting history and perspective on the incubation of neuroinformatics. The preface and first two chapters of the book explain neuroinformatics and the motivation for it. As with so many other fields, there has been an information explosion in neuroscience research. Data are produced by tens of thousands of investigators in hundreds of journals.
Vast information from the neurosciences may enable bottom-up understanding of human intelligence; that is, derivation of function from mechanism. This article describes such a research program: simulation and analysis of the circuits of the brain has led to derivation of a detailed set of elemental and composed operations emerging from individual and combined circuits. The specific hypothesis is forwarded that these operations constitute the "instruction set" of the brain, that is, the basic mental operations from which all complex behavioral and cognitive abilities are constructed, establishing a unified formalism for description of human faculties ranging from perception and learning to reasoning and language, and representing a novel and potentially fruitful research path for the construction of human-level intelligence. Attempts to construct intelligent systems are strongly impeded by the lack of formal specifications of natural intelligence, which is defined solely in terms of observed and measured human (or animal) abilities, so candidate computational descriptions of human-level intelligence are necessarily underconstrained. This simple fact underlies Turing's proposed test for intelligence: lacking any specification to test against, the sole measures at that time were empirical observations of behavior, even though such behaviors may be fitted by multiple different hypotheses and simulated by many different proposed architectures.
The 1956 Dartmouth summer research project on artificial intelligence was initiated by this August 31, 1955 proposal, authored by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. The original typescript consisted of 17 pages plus a title page. Copies of the typescript are housed in the archives at Dartmouth College and Stanford University. The first 5 papers state the proposal, and the remaining pages give qualifications and interests of the four who proposed the study. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
A team of researchers at Wits University in Johannesburg, South Africa have made a major breakthrough in the field of biomedical engineering. According to a release published on Medical Express, for the first time ever, researchers have devised a way of connecting the human brain to the internet in real time. It's been dubbed the "Brainternet" project, and it essentially turns the brain " … into an Internet of Things (IoT) node on the World Wide Web." The project works by taking brainwave EEG signals gathered by an Emotiv EEG device connected to the user's head. The signals are then transmitted to a low cost Raspberry Pi computer, which live streams the data to an application programming interface and displays the data on an open website where anyone can view the activity.
In this last module, we explore supervised learning and reinforcement learning. The first lecture introduces you to supervised learning with the help of famous faces from politics and Bollywood, casts neurons as classifiers, and gives you a taste of that bedrock of supervised learning, backpropagation, with whose help you will learn to back a truck into a loading dock.The second and third lectures focus on reinforcement learning. The second lecture will teach you how to predict rewards à la Pavlov's dog and will explore the connection to that important reward-related chemical in our brains: dopamine. In the third lecture, we will learn how to select the best actions for maximizing rewards, and examine a possible neural implementation of our computational model in the brain region known as the basal ganglia.