Education


Using artificial intelligence to enrich digital maps

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A model invented by researchers at MIT and Qatar Computing Research Institute (QCRI) that uses satellite imagery to tag road features in digital maps could help improve GPS navigation. Showing drivers more details about their routes can often help them navigate in unfamiliar locations. Lane counts, for instance, can enable a GPS system to warn drivers of diverging or merging lanes. Incorporating information about parking spots can help drivers plan ahead, while mapping bicycle lanes can help cyclists negotiate busy city streets. Providing updated information on road conditions can also improve planning for disaster relief.


Can AI Drive Education Forward?

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This week Bett, the education show that brings together over 800 education providers, takes center stage in London. Educators, developers, and ecosystem players come together to share what is new, connect and learn. Microsoft is the worldwide partner for Bett, but most platform providers and hardware vendors use the event to launch their latest devices and software solutions aimed at education. As in years past, we have announcements aimed at making life in the classroom easier for the teacher, whether it is about saving time on managing students, assets, or content. Microsoft added new indicator lights at the back of the computers the students are using so teachers can quickly glance at the class and make sure all machines are powered and connected.


Deep learning vs. machine learning: Understand the differences

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Machine learning and deep learning are both forms of artificial intelligence. You can also say, correctly, that deep learning is a specific kind of machine learning. Both machine learning and deep learning start with training and test data and a model and go through an optimization process to find the weights that make the model best fit the data. Both can handle numeric (regression) and non-numeric (classification) problems, although there are several application areas, such as object recognition and language translation, where deep learning models tend to produce better fits than machine learning models. Machine learning algorithms are often divided into supervised (the training data are tagged with the answers) and unsupervised (any labels that may exist are not shown to the training algorithm).


How do We Quantify the Quality of Our Predictions? Part I

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We have all worked on different kinds of Machine learning models, and each model needs to be evaluated in different ways. From the initial data that is provided to the outcome and the way, we as the users want to use it. A classification model would require a different metric for model evaluation as compared to a regression model or a Neural Net, and it's important to know and understand which metric to use and when. Here in this series, we go through some of these metrics, starting from the basic and the most commonly used ones to the application-specific and complex metrics that we can use. We will be starting with the basic metrics from sklearn and progress towards the more complicated metrics after that.


Operationalizing AI

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When AI practitioners talk about taking their machine learning models and deploying them into real-world environments, they don't call it deployment. Instead the term that's used is "operationalizing". This might be confusing for traditional IT operations managers and applications developers. Why don't we deploy or put into production AI models? What does AI operationalization mean and how is it different from the typical application development and IT systems deployment?


Operationalizing AI

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When AI practitioners talk about taking their machine learning models and deploying them into real-world environments, they don't call it deployment. Instead the term that's used is "operationalizing". This might be confusing for traditional IT operations managers and applications developers. Why don't we deploy or put into production AI models? What does AI operationalization mean and how is it different from the typical application development and IT systems deployment?


Building a Lie Detector for Images

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The Internet is full of fun fake images -- from flying sharks and cows on cars to a dizzying variety of celebrity mashups. Hyperrealistic image and video fakes generated by convolutional neural networks (CNNs) however are no laughing matter -- in fact they can be downright dangerous. Deepfake porn reared its ugly head in 2018, fake political speeches by world leaders have cast doubt on news sources, and during the recent Australian bushfires manipulated images mislead people regarding the location and size of fires. Fake images and videos are giving AI a black eye -- but how can the machine learning community fight back? A new paper from UC Berkeley and Adobe researchers declares war on fake images.


New surveillance AI can tell schools where students are and where they've been

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As mass shootings at US schools increase in frequency while our country's gun control laws remain weaker than those in any other developed nation, more school administrators across the US are turning to artificially intelligent surveillance tools in an attempt to beef up school safety. But systems that allow schools to easily track people on campus have left some worried about the impact on student privacy. Recode has identified at least nine US public school districts -- including the district home to Marjory Stoneman Douglas High School (MSD) in Parkland, Florida, which in 2018 experienced one of the deadliest school shootings in US history -- that have acquired analytic surveillance cameras that come with new, AI-based software, including one tool called Appearance Search. Appearance Search can find people based on their age, gender, clothing, and facial characteristics, and it scans through videos like facial recognition tech -- though the company that makes it, Avigilon, says it doesn't technically count as a full-fledged facial recognition tool. Even so, privacy experts told Recode that, for students, the distinction doesn't necessarily matter.


Colleges, businesses need to up their game to cope with AI, IoT: Report

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Estimates suggest that only 20 per cent of today's engineers are employable in this age of new technologies like artificial intelligence (AI), internet of things (IoT), blockchain and cyber security. And it's high time that educational institutions, businesses and the government upped their game. These are the findings of a report unveiled by the BML Munjal University, a higher education institution promoted by the Hero Group. The report, titled ÁI & Future of Work: Redefining Future of Enterprise, analyses the opportunities and challenges brought about by new-age tech changes and presents a roadmap for academic institutions, enterprises as well as the government on how to work together to fulfill the demand for qualified professionals in this new age where exponential technologies like AI and blockchain are going to rule the roost. "Today, legacy skills, tools and technologies have become obsolete. New-age digital professionals proficient in AI, IoT are being called upon to enter the talent workforce, with a new set of skills," said Sameer Dhanrajani, CEO of AIQRATE Advisory, who authored the report.


Tensorflow 2.0: Deep Learning and Artificial Intelligence

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