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Spain to invest $720M in artificial intelligence

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Spanish Prime Minister Pedro Sanchez unveiled Spain's national artificial intelligence strategy Wednesday, which will see €600 million ($720 million) of public investment in the sector between 2021 and 2023. "Over the next years, artificial intelligence (AI) will grow exponentially and completely change our lives, cities and environments," Sanchez said during the plan's presentation. "This is a new tool that will change Spain's direction, for the better." He hopes the plan will place Spain as an international leader in the technology and mobilize as much as €3.3 billion ($4 billion) in private investment. Sanchez said he was convinced that AI will eventually boost employment in Spain, as opposed to destroying jobs, which has been a leading concern around the technology.


AI and EI as allies

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Both artificial intelligence (AI) and emotional intelligence (EI) have critical roles to play in security. At the same time, Maureen Metcalf of the Forbes Coaches Council published leadership trends for 2021. They involve economic instability, erosion of trust in societal institutions, and decreasing worker privacy as the office moves home. The trick for security professionals is to join the skills and mindsets that constitute the leadership list to the phenomena that make up the security megatrends. The gap between the two -- which threatens to become a chasm during these times of tectonic shifts -- must be bridged for security professionals not to be left behind.


Cloud and AI: The biggest trends in personal and SMB video surveillance

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The global pandemic has put a spotlight on personal safety and security, so it's unsurprising that the video surveillance market is surging as well. Globally, it hit $45.5 billion this year, while AI technology, which is being integrated into video surveillance products at every price point, will hit $100 billion by the year 2025. Both consumers and small- and medium-size businesses are increasingly looking for solutions to manage the safety and security of homes, businesses and assets. More importantly, they're in search of solutions that incorporate sophisticated new video analytics, AI and cloud-based storage technology. Manufacturers are racing to meet the demand, according to a report by IFSEC Global.


The Rise of AI-powered Clinical Surveillance Systems

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Artificial Intelligence (AI) has entered into each sphere of life now. On account of the mobile applications and Internet of Things (IoT) devices, AI helped break the data gap in a never before way. Presently, healthcare systems are likewise embracing AI benefits in a large number of ways. Patient monitoring has advanced from impromptu to continuous monitoring of different parameters, causing a surge in the amount of unprocessed and unorganized information accessible to clinicians for decision-making," as indicated by F&S specialists. "To harness noteworthy information from this data, healthcare providers are going to big data analytics and other analysis solutions.


Artificial intelligence tool cracks code to imagine proteins in 3D

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An artificial intelligence network solved a scientific problem that has stumped researchers for half a century, successfully predicting the way proteins fold into three-dimensional shapes, a process that has typically taken expensive and painstaking lab work that could go on for decades. The way proteins, one of the building blocks of life, fold drives their functionality and behaviour. For instance, SARS-Cov-2 has a protein that folds as a spike. This shape, therefore, is relevant for biologists (including for its ability to find cures for illnesses). It isn't easy to predict the shape of a protein, though, based on the way amino acids come together to form a protein.


Guide To Ensemble Methods: Bagging vs Boosting

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Building a highly accurate prediction model is certainly a difficult task. Noise – Irreducible error i.e. the part of target value which the model is not able to predict / explain. As you know it is impossible to reduce the noise, hence the term irreducible error, we shift our focus on reducing Bias and Variance. So, Ensemble learning methods bring up the technique to reduce the Bias and Variance of the model by using multiple models together (hence the term Ensemble), in order to achieve better predictive performance, instead of a single model for prediction. There are a number of Ensemble methods, in this article I will be discussing about the two widely used Ensemble methods that are Bagging and Boosting. When we use different / single learning algorithm, multiple times for prediction.


New machine learning tool tracks urban traffic congestion

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This display was computed in less than one hour.... view more A new machine learning algorithm is poised to help urban transportation analysts relieve bottlenecks and chokepoints that routinely snarl city traffic. The tool, called TranSEC, was developed at the U.S. Department of Energy's Pacific Northwest National Laboratory to help urban traffic engineers get access to actionable information about traffic patterns in their cities. WATCH: https://www.youtube.com/watch?v 8S4bLv9CtOo (Video by Graham Bourque Pacific Northwest National Laboratory) Currently, publicly available traffic information at the street level is sparse and incomplete. Traffic engineers generally have relied on isolated traffic counts, collision statistics and speed data to determine roadway conditions. The new tool uses traffic datasets collected from UBER drivers and other publicly available traffic sensor data to map street-level traffic flow over time.


KFC introduces self-driving trucks to sell chicken without human contact

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With everyone encouraged to practice social distancing during the pandemic, companies are looking at new ways to keep business going without human contact. Contactless payment and food delivery have boomed in recent months, but the fried chicken expert KFC decided to take it a step further. Its franchises in China are now offering all its products on the streets of Shanghai in self-driving trucks. The chicken trucks, serving socially-distanced food, were first spotted in front of a metro station by users on Twitter, and they caused quite a stir. They are part of a partnership between Chinese tech startup Neolix and Yum Brands, which owns KFC.


Top Data Science Service Providers In India 2020

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India is one of the leading markets for analytics and data science services, and various data science vendors have seen significant growth over the last few years, as organisations across verticals are investing billions of dollars in data science and analytics to optimise their processes. In fact, despite the pandemic and the resulting quarter-long slowdown, the data science functions across organisations have not been significantly impacted, suggesting that data science and analytics are a mainstay of business processes and value generation. The pre-COVID numbers suggest that as of March 2020, the analytics function in India earned consolidated revenues of $35.9 Bn, a 19.5% growth in revenue over last year. In the post-COVID world, companies are veering towards digital transformation to ensure business continuity – and the analytics and data science functions are playing a crucial role in this journey. As companies are looking to establish AI and Data Science capabilities, the market is further maturing, driving the need for data science vendors/service providers to facilitate the booming market. Over the last few years, multiple vendors with unique capabilities in data science, have not just grown from small operations but also matured in terms of capabilities.


Training your Neural Network with Cyclical Learning Rates – MachineCurve

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At a high level, training supervised machine learning models involves a few easy steps: feeding data to your model, computing loss based on the differences between predictions and ground truth, and using loss to improve the model with an optimizer. For example, it's possible to choose multiple optimizers – ranging from traditional Stochastic Gradient Descent to adaptive optimizers, which are also very common today. Say that you settle for the first – Stochastic Gradient Descent (SGD). Likely, in your deep learning framework, you'll see that the learning rate is a parameter that can be configured, with a default value that is preconfigured most of the times. Now, what is this learning rate? Why do we need them?