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Using machine learning to identify different types of brain injuries

AIHub

Researchers have developed an algorithm that can detect and identify different types of brain injuries. The team, from the University of Cambridge, Imperial College London and CONICET, have clinically validated and tested their method on large sets of CT scans and found that it was successfully able to detect, segment, quantify and differentiate different types of brain lesions. Their results, reported in The Lancet Digital Health, could be useful in large-scale research studies, for developing more personalised treatments for head injuries and, with further validation, could be useful in certain clinical scenarios, such as those where radiological expertise is at a premium. Head injury is a huge public health burden around the world and affects up to 60 million people each year. It is the leading cause of mortality in young adults.


'The lads were buzzing to kick a ball about with their mates' - Championship resumes training

BBC News

Players at Championship clubs were allowed to return to training on Monday - the first step towards the potential resumption of the second-tier season. On Friday, the English Football League provided safety protocols and guidance for clubs to follow upon their return. Players took part in non-contact sessions and trained in small groups. A total of 1,014 Championship players and staff were tested for coronavirus towards the end of last week, with two people testing positive. Hull City confirmed on Sunday that the two positive cases were from their club.


How to reverse-engineer a rainforest

Engadget

But 2019 was the year the earth burned. In Australia, the world watched in horror as bushfires destroyed 10.3 million hectares, marking the continent's most intense and destructive fire season in over 40 years. Earlier that fall, California saw more than 101,000 hectares destroyed, with damages upward of $80 billion. Alaska saw nearly a million. Record-breaking fires also hit Indonesia, Russia, Lebanon -- but nowhere saw the sheer mass of media coverage as the fires that tore through the Amazon nearly all last summer. By year's end, thousands of global media outlets had reported that Brazil's largest rainforest played host to more than 80,000 individual forest fires in 2019, resulting in an estimated 906,000 square hectares of environmental destruction. At the time, Brazil's National Institute for Space Research reported it was the fastest rate of burning since record keeping began in 2013. But amid the charred ruins of one of the largest oxygen-producing environments on the planet, a secret lies buried beneath the soil.


Machine Learning with SQL

#artificialintelligence

This post is about Machine Learning with SQL. It makes sense to build/run Machine Learning models where data stays -- in the database. Step by step info on how to get started. Python (and soon JavaScript with TensorFlow.js) is a dominant language for Machine Learning. There is a way to build/run Machine Learning models in SQL.


The Air Force's AI-Powered 'Skyborg' Drones Could Fly as Early as 2023

#artificialintelligence

The U.S. Air Force is finally pushing into the world of robot combat drones, vowing to fly the first of its "Skyborg" drones by 2023. The service envisions Skyborg as a merging of artificial intelligence with jet-powered drones. The result will be drones capable of flying alongside fighter jets, carrying out dangerous missions. Skyborg drones will be much cheaper than piloted aircraft, allowing the Air Force to grow its fleet at a lower cost. The Air Force, according to Defense News, will award a total of $400 million to one or more companies to develop different types of Skyborg drones.



turing test in AI : can machines think?

#artificialintelligence

Turing test in artificial intelligence is supposed to answer the complicated question of "Can machines think?" here we discuss the Turing test and the vision of Alan Turing about thinking machine.


How to Prevent Overfitting in Machine Learning Models

#artificialintelligence

Very deep neural networks with a huge number of parameters are very robust machine learning systems. But, in this type of massive networks, overfitting is a common serious problem. Learning how to deal with overfitting is essential to mastering machine learning. The fundamental issue in machine learning is the tension between optimization and generalization. Optimization refers to the process of adjusting a model to get the best performance possible on the training data (the learning in machine learning), whereas generalization refers to how well the trained model performs on the data that it has never seen before (test set).


Neuro-Symbolic Artificial Intelligence and Potential Impact on Conversational Commerce

#artificialintelligence

In a joint research effort forged in 2017, the MIT-IBM Watson AI Lab has put significant resources into a new approach to AI that could provide CX and digital transformation specialists with more accurate intent recognition. Known as "neuro-symbolic artificial intelligence," this approach could allow companies to do more with less data and provide for greater transparency and privacy. Employing the approach to Conversational AI could give brands the ability to "add common sense" to their chatbots, intelligent virtual agents and to the prompts provided to live agents. The science combines the probabilistic pattern recognition capabilities of today's Deep Neural Networks (DNNs) and "deep understanding" with an approach to AI that is based on representations of problems, logic and search that are considered more "human-readable." In a new report, Dan Miller, lead analyst and founder with Opus Research, presents the possibility for enterprises to improve automated conversational systems with significant implications for customer care, digital commerce and employee productivity.


Councils turn to artificial intelligence to achieve UK£195mn savings - The EE

#artificialintelligence

Councils in the UK expect to save over £195million (€221 million) in 2020 by introducing artificial intelligence technology techniques, according to a national survey of local authorities. Financial savings, faster resolution of enquiries, freeing up staff to focus on citizen engagement and more accurate processing are the four key reasons behind the trend, revealed in a survey of unitary, borough, county and district councils carried out by local government AI and chatbot specialists Agile Datum . Councils each expect to save an average of £300,000 (€340926) in the next 12 months through greater use of artificial intelligence and another £180,000 ( €204556), on average, through the deployment of self-learning chatbots. One in six councils are anticipating savings between £750,000 (€85231 million) and £1m (1.1 million) just around the introduction of artificial intelligence technology. In all, it amounts to savings of £195m (€221 million) across unitary, borough, district and county councils in the UK.