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AI diagnoses Alzheimer's with more than 95% accuracy


An artificial intelligence (AI) algorithm has produced another significant breakthrough using attention mechanisms and a convolutional neural network to …

Can Neural Networks Show Imagination? DeepMind Thinks They Can - KDnuggets


I recently started a new newsletter focus on AI education. TheSequence is a no-BS (meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Creating agents that resemble the cognitive abilities of the human brain has been one of the most elusive goals of the artificial intelligence(AI) space. Recently, I've been spending time on a couple of scenarios that relate to imagination in deep learning systems which reminded me of a very influential paper Alphabet's subsidiary DeepMind published last year in this subject.

Future of AI Part 2


This part of the series looks at the future of AI with much of the focus in the period after 2025. The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great. This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Some of the classical approaches to AI include (non-exhaustive list) Search algorithms such as Breath-First, Depth-First, Iterative Deepening Search, A* algorithm, and the field of Logic including Predicate Calculus and Propositional Calculus. Local Search approaches were also developed for example Simulated Annealing, Hill Climbing (see also Greedy), Beam Search and Genetic Algorithms (see below). Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959. A non-exhaustive list of examples of techniques include Linear Regression, Logistic Regression, K-Means, k-Nearest Neighbour (kNN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forests, XG Boost, Light Gradient Boosting Machine (LightGBM), CatBoost. Deep Learning refers to the field of Neural Networks with several hidden layers. Such a neural network is often referred to as a deep neural network. Neural Networks are biologically inspired networks that extract abstract features from the data in a hierarchical fashion.

Tempus fugit: How time flies during development


“Fugit irreparabile tempus,” wrote Virgil, a reminder that our lives are defined by the irreversible flow of time. As soon as the egg is fertilized, embryonic cells follow a developmental program strictly organized in time. The sequence typically is conserved throughout evolution, but individual events can occur over species-specific time scales. Such differences can have marked effects. For instance, it takes 3 months to generate cerebral cortex neurons in a human but only 1 week in a mouse. This prolonged neurogenesis likely contributes to evolutionary expansion of the human brain ([ 1 ][1]). But the mechanisms underlying developmental time scales remain largely unknown. On pages 1449 and 1450 of this issue, Rayon et al. ([ 2 ][2]) and Matsuda et al. ([ 3 ][3]), respectively, report an association between species-specific developmental time scales and the speed of biochemical reactions that support protein turnover. Cell differentiation during mammalian development uses two types of timing mechanisms (biological clocks) based on oscillations or unidirectional processes (hourglass clocks). Modeling development in pluripotent stem cells (PSCs) from various species shows that the pace of differentiation of many cell types in an in vitro setting largely recapitulates the species-specific timing observed in embryos ([ 4 ][4], [ 5 ][5]). Even when human neurons are transplanted as single cells in a mouse brain, they follow their own prolonged developmental timeline ([ 6 ][6]). This suggests that cell-intrinsic mechanisms, yet to be discovered, dictate the timing of developmental trajectories in a species-specific manner. Matsuda et al. examined a biological rhythm typical of vertebrate embryos: the “somite segmentation clock,” by which the body is built segment (or somite) by segment, thanks to waves of expression of specific genes (oscillations) in presomitic mesodermal (PSM) cells. Using in vitro modeling with mouse and human PSCs, the authors examined waves of expression of HES7 (hes family bHLH transcription factor 7), a segmental-clock master gene. They found similar waves in PSM cells of both species, but the period of oscillations in human cells was ∼5 hours instead of 2 hours (as in mouse cells), consistent with another recent report ([ 7 ][7]). What might underlie such cell-intrinsic differences? Evolutionary divergence in developmental processes usually occurs as a result of changes in the gene regulatory networks (GRNs) that control them ([ 8 ][8]). The authors examined the GRN of segmental oscillations, and except for the period of oscillation, they found no obvious difference between human and mouse gene expression. They then swapped the mouse and human genome sequences containing the HES7 locus. The human HES7 gene transplanted in mouse cells displayed fast oscillations like the mouse gene, whereas the mouse gene transplanted in the human cells displayed slower, human-like oscillations (see the figure). Thus, even DNA cis-regulatory components of the GRN do not appear to dictate the time scale of HES7 oscillations. However, Matsuda et al. found important species-specific differences in a different mechanism: the speed of biochemical reactions leading to protein turnover (production and decay). Human cells displayed slower kinetics of protein expression (including “expression delays” related to RNA transcription, splicing, and translation) and a slower rate of protein decay, mostly related to degradation. Many examined parameters showed a twofold difference in mouse versus human cells, matching the time differences observed for the segmentation clock. ![Figure][9] Same events, distinct timingGRAPHIC: KELLIE HOLOSKI/ SCIENCE Rather than being dominated by clocklike oscillations, the developmental process is specified mostly by cell-fate transitions, by which embryonic cells gradually an d irreversibly become differentiated cells. Could it be that similar mechanisms regulate these hourglass-like timing events as well? Rayon et al. explored this notion using a motor neuron (MN) developmental model from mouse and human PSCs. Examination of MN development in vitro revealed that the underlying GRN is similar in both species, except that human motoneurogenesis takes 2.5 times longer in the human cell model versus the mouse. The authors then examined the influence of sonic hedgehog, the key morphogen that induces MN fate (by changing timing and intensity of the signal), and the MN-development master gene OLIG2 (oligodendrocyte transcription factor 2) (by inserting the human gene in mouse cells) but found no effects that explained the species-specific time differences. They then analyzed protein stability during MN development and found that the mean protein half-life was doubled in human cells compared with mouse cells, which is consistent with the findings of Matsuda et al. Both studies point to protein turnover as a potential source of variation in developmental time scales. Each group tested this hypothesis further by in silico modeling of their experimental systems, which predicted, in each case, a prominent influence of the delay in protein production and protein decay on developmental time scales. That protein turnover affects the timing of development is provocative and attractive but must be validated by experimental evidence for causal relationship between the two (by altering the production and decay of proteins and mRNA, and then examining the developmental time scale). Such experiments will also help to determine the respective contributions of expression delay versus protein decay, on which each study puts a somewhat different emphasis. The consistent results from both studies also raise questions about the mechanisms upstream of interspecies differences in protein turnover. Metabolism is an attractive candidate. Protein turnover requires a considerable amount of energy ([ 9 ][10]), and metabolic rewiring has emerged as a central instructor of cell fate transitions ([ 10 ][11]), although through epigenetic remodeling rather than changes in proteostasis. Another question is whether the same principles apply to developmental events that display more pronounced time scale differences. For example, GRN divergence might operate through specific genes that modulate the timing of human cortical neurogenesis ([ 11 ][12]). Furthermore, metabolism and protein turnover might display differences depending on the cell context or the specific protein involved. And known correlations between developmental timing, life span, and aging across species ([ 12 ][13]) might all be causally linked to differences in metabolism and protein turnover. 1. [↵][14]1. A. M. M. Sousa et al ., Cell 170, 226 (2017). [OpenUrl][15][CrossRef][16][PubMed][17] 2. [↵][18]1. T. Rayon et al ., Science 369, eaba7667 (2020). [OpenUrl][19][Abstract/FREE Full Text][20] 3. [↵][21]1. M. Matsuda et al ., Science 369, 1450 (2020). [OpenUrl][22][Abstract/FREE Full Text][23] 4. [↵][24]1. J. van den Ameelen et al ., Trends Neurosci. 37, 334 (2014). [OpenUrl][25][CrossRef][26][PubMed][27] 5. [↵][28]1. M. Ebisuya, 2. J. Briscoe , Development 145, dev164368 (2018). [OpenUrl][29][Abstract/FREE Full Text][30] 6. [↵][31]1. D. Linaro et al ., Neuron 104, 972 (2019). [OpenUrl][32] 7. [↵][33]1. M. Diaz-Cuadros et al ., Nature 580, 113 (2020). [OpenUrl][34][CrossRef][35][PubMed][36] 8. [↵][37]1. E. H. Davidson, 2. D. H. Erwin , Science 311, 796 (2006). [OpenUrl][38][Abstract/FREE Full Text][39] 9. [↵][40]1. J. Labbadia, 2. R. I. Morimoto , Annu. Rev. Biochem. 84, 435 (2015). [OpenUrl][41][CrossRef][42][PubMed][43] 10. [↵][44]1. N. Shyh-Chang et al ., Development 140, 2535 (2013). [OpenUrl][45][Abstract/FREE Full Text][46] 11. [↵][47]1. I. K. Suzuki et al ., Cell 173, 1370 (2018). [OpenUrl][48][CrossRef][49][PubMed][50] 12. [↵][51]1. A. A. Fushan et al ., Aging Cell 14, 352 (2015). [OpenUrl][52][CrossRef][53][PubMed][54] Acknowledgments: P.V. is funded by the European Research Council, Belgian Fonds Wetenschappelijk Onderzoek, Excellence of Science Research programme, AXA Research Fund, Belgian Queen Elizabeth Foundation, and Fondation Université Libre de Bruxelles. R.I. was supported by the Belgian Fonds de la Recherche Scientifique. 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Your thoughts can be displayed on this cyborg garment


Have a look at 2020's best robot vacuums -- all tried and tested in my office and home. A new project that forms a data visualization of brain signals in clothing has recently been showcased at the virtual Ars Electronica festival. The robotic dress is coupled to 1,024 channels of a BCI (Brain-Computer Interface) and has 64 outputs for light and movement. The Pangolin Scales' dress components function like animatronic elements that move and light up based on the recordings of the brain waves. The project originated at the Institute for integrated circuits at JKU (Johannes Kepler University, Linz, Austria), in collaboration with the Austrian Neurotechnology company G.tec.

How to Give A.I. a Pinch of Consciousness


In 1998, an engineer in Sony's computer science lab in Japan filmed a lost-looking robot moving trepidatiously around an enclosure. The robot was tasked with two objectives: avoid obstacles and find objects in the pen. It was able to do so because of its ability to learn the contours of the enclosure and the locations of the sought-after objects. But whenever the robot encountered an obstacle it didn't expect, something interesting happened: Its cognitive processes momentarily became chaotic. The robot was grappling with new, unexpected data that didn't match its predictions about the enclosure.

How AI can overcome gender bias in autism diagnosis and treatment - MedCity News


Statistically, boys are four times as likely as girls to receive a diagnosis of autism spectrum disorder (ASD). But that's not because boys are four times as likely to have ASD. According to Autism Speaks, a research and advocacy organization, many girls living with ASD simply "do not fit the stereotypical picture of autism seen in boys." This gender bias often leads parents and clinicians to miss signs of autism in young girls, resulting in later diagnosis and intervention. Researchers at The Rhode Island Consortium for Autism Research and Treatment (RI-CART) found that on average, girls are diagnosed with autism 1.5 years later than boys. This is extremely problematic, as the earlier autism is diagnosed the earlier treatment can begin.

'Bionic eye' linked to chip in brain could cure blindness

Daily Mail - Science & tech

It has been more than 10 years in the making, but scientists are preparing to implant a'bionic eye' in a human subject. Researchers at Monash University have developed wireless implants that sit on the surface of the brain, which are said to restore vision to the blind. Called Gennaris bionic vision system, it includes a custom headgear fitted with a camera and wireless transmitter, a vision processor unit and software and a set of 9x9 millimeter tiles that are implanted into the brain. Studies of the device, used in sheep, were found to be successful and did not produce any adverse health effects. The team is currently seeking funding to ramp up manufacturing and distribution of the implant, which they say could soon be used to cure other ailments including paralysis.

Elon Musk's brain-computer startup is getting ready to blow your mind


Elon Musk couldn't resist a small joke when he gave the world a first look at Neuralink, the brain-computer interface (BCI) project that he's been working on for the past two years. "I think it's going to blow your minds," he said. The aim of his startup is to develop technology to tackle neurological problems, from damage caused by brain or spine trauma to the type of memory problems that can become more common in people as they age. The idea is to solve these problems with an implantable digital device that can interpret, and possibly alter, the electrical signals made by neurons in the brain. "If you can correct these signals you can solve everything from memory loss, hearing loss, blindness, paralysis depression, insomnia, extreme pain, seizures, anxiety, addiction, strokes, brain damage; these can all be solved with an implantable neural link," Musk said at the demonstration of the technology, which also unexpectedly featured live pigs that had actually been implanted with the company's technology.

Dystech using artificial intelligence to help speed screening for learning disorders


French-born Hugo Richard was diagnosed with dyslexia and dysgraphia at the age of 12, but he didn't look into reading and writing disorders until he established Dystech. Dystech is an Australian startup that was co-founded by Richards, Mathieu Serrurier, Jim Radford, and Gilles Richard, and is responsible for developing a screening app for early detection of learning disorders, such as dyslexia and dysgraphia. Speaking to ZDNet, Richard said through research, the company identified that assessing something like dyslexia is often tricky. He explained that having a reading difficulty can often mean many things and so, the diagnosis of someone with dyslexia is not always accurate, nor is there sufficient support for those who do have it. "We've observed there's a big imbalance between supply and demand. There are many more individuals with dyslexia than there are providers who can provide a diagnosis," Richard told ZDNet.