Avoid injury and improve efficiency with tips from a physical therapist. Don't be a snow hero. Breakthroughs, discoveries, and DIY tips sent every weekday. You know, for life's most essential resource, water knows a hundred ways to kill you if you're not careful. When it's not trying to drown you in its pools and coastlines during the summer, it shape-shifts to snow in the winter, piling up emergency room visits for those forced to shovel it.

Assessment is a crucial part of education. Traditional marking is a source of inconsistencies and unconscious bias, placing a high cognitive load on the assessors. An approach to address these issues is comparative judgement (CJ). In CJ, the assessor is presented with a pair of items and is asked to select the better one. Following a series of comparisons, a rank is derived using a ranking model, for example, the BTM, based on the results. While CJ is considered a reliable method for marking, there are concerns around transparency, and the ideal number of pairwise comparisons to generate a reliable estimation of the rank order is not known. Additionally, there have been attempts to generate a method of selecting pairs that should be compared next in an informative manner, but some existing methods are known to have created their own bias within results inflating the reliability metric used. As a result, a random selection approach is usually deployed. We propose a novel Bayesian approach to CJ (BCJ) for determining the ranks of compared items alongside a new way to select the pairs to present to the marker(s) using active learning (AL), addressing the key shortcomings of traditional CJ. Furthermore, we demonstrate how the entire approach may provide transparency by providing the user insights into how it is making its decisions and, at the same time, being more efficient. Results from our experiments confirm that the proposed BCJ combined with entropy-driven AL pair-selection method is superior to other alternatives. We also find that the more comparisons done, the more accurate BCJ becomes, which solves the issue the current method has of the model deteriorating if too many comparisons are performed. As our approach can generate the complete predicted rank distribution for an item, we also show how this can be utilised in devising a predicted grade, guided by the assessor.

Consciousness is a thriving industry. Consciousness is a buzzing business in neuroscience labs and brain institutes. Just a few decades ago, consciousness barely registered as a credible subject for science. Perhaps no one did more to legitimize its study than Francis Crick, who launched a second career in neurobiology after cracking the genetic code. In the 1980s Crick found a brilliant collaborator in the young scientist Christof Koch.

From Star Trek's Data and 2001's HAL to Columbus Day's Skippy the Magnificent, pop culture is chock full of fully conscious AI who, in many cases, are more human than the humans they serve alongside. But is all that self-actualization really necessary for these synthetic life forms to carry out their essential duties? In his new book, How to Grow a Robot: Developing Human-Friendly, Social AI, author Mark H. Lee examines the social shortcomings of the today's AI and delves into the promises and potential pitfalls surrounding deep learning techniques, currently believed to be our most effective tool at building robots capable of doing more than a handful of specialized tasks. In the excerpt below, Lee argues that the robots of tomorrow don't necessarily need -- nor should they particularly seek out -- the feelings and experiences that make up the human condition. Although I argue for self-awareness, I do not believe that we need to worry about consciousness.

A new method to accurately record brain activity at scale has been developed by researchers at the Crick, Stanford University and UCL. The technique could lead to new medical devices to help amputees, people with paralysis or people with neurological conditions such as motor neurone disease. The research in mice, published in Science Advances, developed an accurate and scalable method to record brain activity across large areas, including on the surface and in deeper regions simultaneously. Using the latest in electronics and engineering techniques, the new device combines silicon chip technology with super-slim microwires, up to 15-times thinner than a human hair. The wires are so thin they can be placed deep in the brain without causing significant damage.

In 1976, Francis Crick arrived at the Salk Institute in La Jolla, California, overlooking a Pacific Shangri-La with cotton candy skies and a beaming, blue-green sea. He had already won the Nobel Prize for co-discovering the double-helix structure of DNA, revealing the basis of life to be a purely physical, not a mystical, process. He hoped to do the same thing for consciousness. If matter was strange enough to explain a creature's life code, he thought, maybe it's strange enough to explain a creature's mind, too. For something that everybody walks around with everyday, consciousness wouldn't seem to be as immense a puzzle as the origin of the universe.

The platform, HRMAn ('Herman'), which stands for Host Response to Microbe Analysis, is open-source, easy-to-use and can be tailored for different pathogens including Salmonella enterica. Pioneered by scientists at the Francis Crick Institute and UCL, HRMAn uses deep neural networks to analyse complex patterns in images of pathogen and human ('host') cell interactions, pulling out the same detailed characteristics that scientists do by-hand. The research is published in the open access journal eLife, which includes a link to download the platform and access tutorial videos. "What used to be a manual, time-consuming task for biologists now takes us a matter of minutes on a computer, enabling us to learn more about infectious pathogens and how our bodies respond to them, more quickly and more precisely," says Eva Frickel, Group Leader at the Crick, who led the project. "HRMAn can actually see host-pathogen interactions like a biologist, but unlike us, it doesn't get tired and need to sleep!"

Backpropagation over deep neural networks has as much to do with the way the brain learns as modern jet airplanes have to do with the way birds fly. Both jets and birds fly, but they do so using entirely different principles. Jets do things birds cannot (fly at 500 miles per hour carrying many passengers), birds do things jets cannot (take off instantly). Each neuron is sending out "da dit da" messages like Morse code to neighboring neurons. The transfer functions are entirely different from RLUs or sigmoid.

Francis Harry Compton Crick OM FRS[1][4] (8 June 1916 – 28 July 2004) was a British molecular biologist, biophysicist, and neuroscientist, most noted for being a co-discoverer of the structure of the DNA molecule in 1953 with James Watson, work which was based partly on fundamental studies done by Rosalind Franklin, Raymond Gosling and Maurice Wilkins. Together with Watson and Maurice Wilkins, he was jointly awarded the 1962 Nobel Prize in Physiology or Medicine "for their discoveries concerning the molecular structure of nucleic acids and its significance for information transfer in living material".[3][6] Crick was an important theoretical molecular biologist and played a crucial role in research related to revealing the helical structure of DNA. He is widely known for use of the term "central dogma" to summarize the idea that genetic information flow in cells is essentially one-way, from DNA to RNA to protein.[7] During the remainder of his career, he held the post of J.W. Kieckhefer Distinguished Research Professor at the Salk Institute for Biological Studies in La Jolla, California. His later research centered on theoretical neurobiology and attempts to advance the scientific study of human consciousness. He remained in this post until his death; "he was editing a manuscript on his death bed, a scientist until the bitter end" according to Christof Koch.[8] Crick was the first son of Harry Crick (1887–1948) and Annie Elizabeth Crick (née Wilkins; 1879–1955). He was born and raised in Weston Favell, then a small village near the English town of Northampton, in which Crick's father and uncle ran the family's boot and shoe factory. His grandfather, Walter Drawbridge Crick (1857–1903), an amateur naturalist, wrote a survey of local foraminifera (single-celled protists with shells), corresponded with Charles Darwin,[9] and had two gastropods (snails or slugs) named after him. At an early age, Francis was attracted to science and what he could learn about it from books. As a child, he was taken to church by his parents. But by about age 12, he said he did not want to go anymore, as he preferred a scientific search for answers over religious belief.[10] Walter Crick, his uncle, lived in a small house on the south side of Abington Avenue; he had a shed at the bottom of his little garden where he taught Crick to blow glass, do chemical experiments and to make photographic prints. When he was eight or nine he transferred to the most junior form of the Northampton Grammar School, on the Billing Road.

If consciousness is not algorithmic, then how is it created? Obviously we do not know. Scientists who are interested in subjective awareness study the objective facts of neurology and behaviour and have shed new light on how our nervous system processes and discriminates among stimuli. But although such sensory mechanisms are necessary for consciousness, it does not help to unlock the secrets of the cognitive mind as we can perceive things and respond to them without being aware of them. A prime example of this is sleepwalking.