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AI, ML, 5G, IoT will be most important tech in 2021: Study


Bengaluru, Nov 23: Artificial intelligence (AI), Machine learning, 5G and Internet of Things (IoT) would be the most important technologies in 2021, according to a new study by the Institute of Electrical and Electronics Engineers (IEEE). The technical professional organisation on Monday released the results of a survey of Chief Information Officers (CIO) and Chief Technology Officers (CTO) in the US, the UK, China, India and Brazil. The survey was on the most important technologies for 2021, the impact of the COVID-19 pandemic on the speed of their technology adoption and the industries expected to be most impacted by technology. On which would be the most important technologies, nearly one-third of the total respondents (32 per cent) said AI and ML followed by 5G (20 per cent) and IoT (14 per cent), according to an IEEE statement. Manufacturing (19 per cent), healthcare (18 per cent), financial services (15 per cent) and education (13 per cent) are the industries that most believe would be impacted by technology, according to the CIOs and CTOs surveyed.

Artificial Intelligence Against Corruption


Corruption grows when accountability is low -- it is hard to imagine a politician abusing their power for personal gain if they knew for certain that they would get caught and punished. This is why improving accountability is a wining strategy for fighting corruption, and Artificial Intelligence technology can help us do that. Whether we realize it or not, AI technologies that spot wrongdoing are already all around us. Credit card companies, for example, have been using it for years -- if your card is used in strange countries, to buy strange products, in a price range that is strange to your normal behavior, the company's AI models are likely to flag it as suspicious. And it does so incredibly fast for millions and millions of transactions everyday.

Brain mapping, from molecules to networks


CATEGORY WINNER: CELL AND MOLECULAR BIOLOGY William E. Allen William E. Allen received his undergraduate degree from Brown University in 2012, M.Phil. in Computational Biology from the University of Cambridge in 2013, and Ph.D. in Neurosciences from Stanford University in 2019. At Stanford, he worked to develop new tools for the large-scale characterization of neural circuit structure and function, which he applied to understand the neural basis of thirst. After completing his Ph.D., William started as an independent Junior Fellow in the Society of Fellows at Harvard University, where he is developing and applying new approaches to map mammalian brain function and dysfunction over an animal's life span. [ ][1] Charting what the pioneering neuroanatomist Santiago Ramón y Cajal called the “impenetrable jungle” of the brain ([ 1 ][2]) presents one of biology's greatest challenges. How do billions of neurons, wired through trillions of connections, work together to produce cognition and behavior? Like an orchestra, wherein many instruments played simultaneously produce a sound greater than the sum of its parts, thought and behavior emerge from communication between ensembles of molecularly distinct neurons distributed throughout vast neural circuits. Although we know much about the properties of individual genes, cells, and circuits (see the figure, panel A), a vast gap lies between the function of each brain component and an animal's behavior. Bridging this gap has proven technically and conceptually difficult. Inspired by the fact that the development of high-throughput DNA sequencing led geneticists to shift focus from individual genes to the entire genome, I wanted to develop approaches that could simultaneously link multiple levels of the brain, from molecules to neurons to brain-wide neural networks. My goal was to capture a global perspective while maintaining the high resolution and specificity necessary to understand the function of individual components at each level. This new viewpoint, I hoped, would reveal how the collective properties of the brain's building blocks give rise to behavior. During my doctoral studies at Stanford University with Karl Deisseroth and Liqun Luo, I developed new methods to map the architecture and activity of mammalian neural circuits. I applied these approaches to understand the neural basis of thirst, a fundamental regulator of behavior ([ 2 ][3]). Need-based motivational drives, such as hunger and thirst, direct animals to satisfy specific physiological imperatives important for survival ([ 3 ][4]). Despite decades of research, at the beginning of my studies it was unclear how the activity of neurons that sense these needs causes an animal to engage in specific motivated behaviors (e.g., eating or drinking) to maintain homeostasis ([ 3 ][4]). Thirst, a relatively simple yet important drive, thus seemed the perfect model system for investigating multiple levels in the brain. I first traced thirst motivational drive from cellular gene expression to a circuit mechanism. Using a new version of targeted recombination in active populations (TRAP2), a tool to genetically label neurons according to their activity, I found that neurons in the median preoptic nucleus (MnPO) of the hypothalamus became activated in thirsty mice ([ 4 ][5]) (see the figure, panel C). Single-cell RNA sequencing revealed that these neurons formed a single molecularly defined cell type. Artificial activation of these neurons caused mice to drink water within seconds, whereas their inhibition prevented mice from drinking, which suggested that these MnPO neurons were master regulators of thirst. Drinking water also gradually reduced the activity of these neurons. Finally, activation of these neurons was aversive. Together, these results suggested a surprising “drive reduction” model of thirst motivation: Genetically hard-wired thirst neurons become active when mice need hydration, which causes mice to drink water. This ability to ascribe specific functional relevance to genetically defined neurons inspired me to develop new techniques to map cells within their native tissue architecture in even greater molecular detail. To this end, I co-developed STARmap, an approach for highly multiplexed in situ RNA sequencing to measure the expression of hundreds of genes simultaneously within a brain section at the level of single mRNA molecules ([ 5 ][6]) (see the figure, panel B ). In combination with genetic markers of activity, this technique powerfully describes the molecular identity of behaviorally activated neurons and their neighbors at single-cell resolution. ![Figure][7] New large-scale, high-resolution approaches to bridging multiple levels of brain function A new approach to brain function mapping. (A) An illustration of the levels of brain function and how they are interlinked. (B to D) New approaches to bridging levels: (B) STARm ap amplicons barcoding 1020 RNA species simultaneously with single-molecule resolution in the mouse visual cortex. (C) Genetic labeling of neurons according to activity reveals thirst neurons in the median preoptic nucleus of the hypothalamus, used to identify the motivational mechanism of thirst drive. (D) Brain-wide activity map of the response of thousands of neurons across dozens of brain regions to a water-predicting sensory cue, in thirsty or sated mice, reveals widespread broadcasting of thirst state. GRAPHIC: N. DESAI/ SCIENCE FROM W. ALLEN, WANG ET AL . ([ 5 ][6]), ALLEN ET AL . ( 4 ), ALLEN ET AL . ([ 9 ][8]) Despite these insights, a question remained: How do thirst-sensitive neurons deep in the brain coordinate activity in distributed circuits spanning sensory perception, cognition, and motor output to produce motivated behavior? I found that MnPO thirst neurons projected to many brain regions potentially serving different behavioral roles ([ 4 ][5]), but the gap between individual neurons and brain-wide networks was daunting. Earlier in graduate school, I had developed several new microscopy techniques to characterize brain-wide ([ 6 ][9]) or neocortex- wide ([ 7 ][10]) activity, which revealed that global neural activity was present during even simple motivated behaviors. However, because of the mammalian brain's opacity, these approaches were limited in their ability to record fast neural activity throughout the brain at the scale required to understand thirst motivation. Fortunately, however, developments in microelectronics enabled me to construct global maps of neuronal activity with microsecond-level temporal resolution. Using advanced “Neuropixels” probes ([ 8 ][11]), thin silicon needles that can be acutely inserted into the brain to record the electrical signals of hundreds of neurons simultaneously, I developed an experimental approach to record the activity of huge neuronal ensembles across the brain and reconstruct the anatomical location of each recorded cell ([ 9 ][8]). Applying this technique, I mapped the brain-wide flow of activity through ∼24,000 single neurons during thirst-motivated behavior ([ 9 ][8]) (see the figure, panel D). My experiments revealed that this simple behavior produced an unexpectedly global coordination of activity throughout the brain. By observing how activity changed as mice drank water, as well as directly stimulating hypothalamic thirst neurons, I showed that this activity wave was dependent on the animal's motivational state. Surprisingly, the activity of a few hundred thirst neurons instantly modulated the state of the entire brain. Even more surprisingly, I found many neurons, distributed throughout the brain, that directly encoded thirst. These results suggest that even simple behaviors, such as thirst, are emergent properties of the entire brain. I hope these new approaches will at last enable us to comprehend the rules that transform distributed patterns of electrical activity in neural circuits into thoughts, emotions, and perceptions. Understanding how molecules, neurons, and networks interact to shape these rules will have a sweeping impact on our understanding of brain function in health and disease. 1. [↵][12]“Mas, por desgracia, faltábanos el arma poderosa con que descuajar la selva impenetrable de la substancia gris…” ([ 10 ][13]). 2. [↵][14]1. C. A. Zimmerman, 2. D. E. Leib, 3. Z. A. Knight , Nat. Rev. Neurosci. 18, 459 (2017). [OpenUrl][15][CrossRef][16][PubMed][17] 3. [↵][18]1. S. M. Sternson , Neuron 77, 810 (2013). [OpenUrl][19][CrossRef][20][PubMed][21][Web of Science][22] 4. [↵][23]1. W. E. Allen et al ., Science 357, 1149 (2017). [OpenUrl][24][Abstract/FREE Full Text][25] 5. [↵][26]1. X. Wang et al ., Science 361, eaat5691 (2018). [OpenUrl][27][Abstract/FREE Full Text][28] 6. [↵][29]1. L. Ye et al ., Cell 165, 1776 (2016). [OpenUrl][30][CrossRef][31][PubMed][32] 7. [↵][33]1. W. E. Allen et al ., Neuron 94, 891 (2017). [OpenUrl][34][CrossRef][35][PubMed][36] 8. [↵][37]1. J. J. Jun et al ., Nature 551, 232 (2017). [OpenUrl][38][CrossRef][39][PubMed][40] 9. [↵][41]1. W. E. Allen et al ., Science 364, eeav3932 (2019). [OpenUrl][42] 10. [↵][43]1. S. Ramón y Cajal , Recuerdos de mi vida: Historia de mi labor científica (Moya, Madrid, 1917). 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Oracle supercomputer AI glitch impacts elections in Brazil


Technical problems in the artificial intelligence (AI) component of a supercomputer set-up provided by Oracle prompted delays in the processing of votes during the first round of municipal elections in Brazil last weekend, the Superior Electoral Court (TSE, in the Portuguese acronym), has said. In 2020, for the first time, the TSE centralized countrywide totalization of votes on a supercomputer using database platforms with artificial intelligence technology provided by Oracle. Previously, each of the 27 regional electoral courts across all the Brazilian states counted the votes and forwarded them over to the TSE. The problems in the equipment during the elections on Sunday (15) meant the process of vote processing suffered a delay of nearly three hours. Brazil is one of the only countries in the world where the voting process is entirely electronic.



Fabrizio Poltronieri is an artist who explores the relationship technology and deep-rooted philosophical concepts, such as chance. His current artwork involves Artificial Intelligence, applying machine and deep learning techniques to create and design narratives, moving images and objects. He is a self-taught programmer who started to code during his childhood. His first degree was in Maths, he has a Master Degree in Education and Culture and holds a PhD in Semiotics from the Pontifical Catholic University of São Paulo (PUC/SP). Poltronieri is an Associate Professor and permanent member of the IOCT (Institute of Creative Technologies) at De Montfort University, Leicester, UK, supervising PhD students and teaching creative code in the Digital Arts MA.

Brazil sets out plans to boost innovation


The Brazilian government has published a National Innovation Policy (NIP) setting out plans to encourage and develop innovative products, processes and services across the country. The areas include improving skills; widening the innovation talent pool; encouraging international engagement; and stimulating research, development and innovation within the Brazilian private sector. The government says the NIP will promote the coordination and distribution of public funds towards the advancement of innovation. An Innovation Committee, managed by the Ministry of Science, Technology and Innovations (MCTI) and chaired by the presidential office, will oversee the wide-ranging project. It is due to publish a detailed National Innovation Strategy in the near future, the technology website reported.

3D-printed hearts, self-driving buses, and robots


For the fourth straight year, a survey by IEEE presents a snapshot of how the newest generation of parents, whose kids belong to Generation Alpha, think of artificial intelligence and other technologies in relation to their children's health and wellness. This year's survey takes on special relevance given the ongoing coronavirus pandemic and shifting attitudes about technology and automation across demographics. The study surveyed parents in the U.S., U.K., India, China and Brazil with Generation Alpha children (11 years old or younger). Generation Alpha is considered the most tech-exposed in history, and parents and children's wellness experts are watching closely to see what effects that exposure might have. The pandemic brings new relevance to the annual survey.

Brazil creates national AI innovation network


The Brazilian government has announced the launch of a national innovation network focused on artificial intelligence (AI) with the aim of increasing the production capacity and competitiveness of local companies. Described as the largest in the country, the network is the result of the cooperation between the Ministry of Science, Technology and Innovations (MCTI) and the Brazilian Industrial Research and Innovation Company (EMBRAPII). An investment of 70 million reais (US$ 12 million) deriving from government incentives will go towards the MCTI/EMBRAPII network in the next five years, of which 20 million reais (US$ 3.5 million) will be focused on AI applied to the automotive and agribusiness sectors. The model provides for equal contributions from the private sector, which could double the value of individual projects. The goal of the network is to encourage use of advanced technologies in various productive sectors, through the provision of non-refundable resources, as well as access to an innovation ecosystem with complementary technological skills.

African Desert is Home to Abundant Forest Growth


With help from high resolution satellite imagery and some advanced artificial intelligence techniques, European scientists have been counting the trees in a parched African desert. They pored over 1.3 million square kilometres of the waterless western Sahara and the arid lands of the Sahel to the south, to identify what is in effect an unknown forest. This region a stretch of dunes and dryland larger than Angola, or Peru, or Niger proved to be home to 1.8 billion trees and shrubs with crowns larger than three square metres. "We were very surprised to see that quite a few trees actually grow in the Sahara Desert because up till now, most people thought that virtually none existed. We counted hundreds of millions of trees in the desert alone," said Martin Brandt, a geographer at the University of Copenhagen in Denmark, who led the research.

Argentina Police Are Arresting Innocent People Based on Facial Recognition


In July 2019, Guillermo Federico Ibarrola was heading home on the subway when he was stopped by Buenos Aires police. The authorities told Ibarrola that he was being detained for an armed robbery that had happened three years ago in a city about 400 miles away. He said he had never even been to the city where he was accused of committing the crime. On the sixth day in police custody, he was suddenly released. The police officers offered Ibarrola coffee and dinner, and a bus ticket back home. As it turned out, a "Guillermo Ibarrola" had potentially committed a crime, but it wasn't this Guillermo Ibarrola.