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The School of the Tomorrow: How AI in Education Changes How We Learn

#artificialintelligence

We live in exponential times, and merely having a digital strategy focused on continuous innovation is no longer enough to thrive in a constantly changing world. To transform an organisation and contribute to building a secure and rewarding networked society, collaboration among employees, customers, business units and even things is increasingly becoming key. Especially with the availability of new technologies such as artificial intelligence, organisations now, more than ever before, need to focus on bringing together the different stakeholders to co-create the future. Big data empowers customers and employees, the Internet of Things will create vast amounts of data and connects all devices, while artificial intelligence creates new human-machine interactions. In today's world, every organisation is a data organisation, and AI is required to make sense of it all.


We used peanuts and a climbing wall to learn how squirrels judge their leaps so successfully – and how their skills could inspire more nimble robots

Robohub

Tree squirrels are the Olympic divers of the rodent world, leaping gracefully among branches and structures high above the ground. And as with human divers, a squirrel's success in this competition requires both physical strength and mental adaptability. Two species – the eastern gray squirrel (Sciurus carolinensis) and the fox squirrel (Sciurus niger) – thrive on campus landscapes and are willing participants in our behavioral experiments. They are also masters in two- and three-dimensional spatial orientation – using sensory cues to move through space. In a newly published study, we show that squirrels leap and land without falling by making trade-offs between the distance they have to cover and the springiness of their takeoff perch.


Intel's Artificial Intelligence Blog

#artificialintelligence

Introduction It's early morning and the sun is shining, but where is the birdsong? The new bird feeder should be filled with seeds, but it's empty, and a happy squirrel is scurrying up a nearby tree with the stolen goods. Unfortunately, most modern bird feeders have not been able to prevent this common problem. By bringing the bird feeder into the 21st century, we can examine how deep learning helps keep birdseed for the birds. In the following, we will explore how to design an image classification solution using the Deep Learning Workbench (DL Workbench).


The internet tricked me into believing I can multitask

Mashable

When we spend so much of our time online, we're bound to learn something while clicking and scrolling. Discover something new with Mashable's series I learned it on the internet. I've never felt more like a hungry squirrel than when I spoke to a neuroscientist who's spent decades studying, and trying to expand, human attention spans. Just like a squirrel forages for nuts, he explained, humans forage for information. As a squirrel hops from tree to tree to gather food -- even if the tree it's hanging out in has plenty to offer -- humans hop from information source to information source.


Squirrel: A Switching Hyperparameter Optimizer

arXiv.org Machine Learning

In this short note, we describe our submission to the NeurIPS 2020 BBO challenge. Motivated by the fact that different optimizers work well on different problems, our approach switches between different optimizers. Since the team names on the competition's leaderboard were randomly generated "alliteration nicknames", consisting of an adjective and an animal with the same initial letter, we called our approach the Switching Squirrel, or here, short, Squirrel. The challenge mandated to suggest 16 successive batches of 8 hyperparameter configurations at a time. We chose to only use one optimizer for a given batch, warmstarted with all previous observations.


Adaptive Universal Generalized PageRank Graph Neural Network

arXiv.org Machine Learning

In many important graph data processing applications the acquired information includes both node features and observations of the graph topology. Graph neural networks (GNNs) are designed to exploit both sources of evidence but they do not optimally tradeoff their utility and integrate them in a manner that is also universal. Here, universality refers to independence on homophily or heterophily graph assumptions. We address these issues by introducing a new Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR weights so as to jointly optimize node feature and topological information extraction, regardless of the extent to which the node labels are homophilic or heterophilic. Learned GPR weights automatically adjust to the node label pattern, irrelevant on the type of initialization, and thereby guarantee excellent learning performance for label patterns that are usually hard to handle. Furthermore, they allow one to avoid feature over-smoothing, a process which renders feature information nondiscriminative, without requiring the network to be shallow. Our accompanying theoretical analysis of the GPR-GNN method is facilitated by novel synthetic benchmark datasets generated by the so-called contextual stochastic block model. We also compare the performance of our GNN architecture with that of several state-ofthe-art GNNs on the problem of node-classification, using well-known benchmark homophilic and heterophilic datasets. The results demonstrate that GPR-GNN offers significant performance improvement compared to existing techniques on both synthetic and benchmark data. Graph-centered machine learning has received significant interest in recent years due to the ubiquity of graph-structured data and its importance in solving numerous real-world problems such as semisupervised node classification and graph classification (Zhu, 2005; Shervashidze et al., 2011; Lü & Zhou, 2011). Usually, the data at hand contains two sources of information: Node features and graph topology.


Regina Barzilay wins $1M Association for the Advancement of Artificial Intelligence Squirrel AI award

AIHub

For more than 100 years Nobel Prizes have been given out annually to recognize breakthrough achievements in chemistry, literature, medicine, peace, and physics. As these disciplines undoubtedly continue to impact society, newer fields like artificial intelligence (AI) and robotics have also begun to profoundly reshape the world. In recognition of this, the world's largest AI society -- the Association for the Advancement of Artificial Intelligence (AAAI) -- announced yesterday the winner of their new Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, a $1 million award given to honor individuals whose work in the field has had a transformative impact on society. The recipient, Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science at MIT and a member of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), is being recognized for her work developing machine learning models to develop antibiotics and other drugs, and to detect and diagnose breast cancer at early stages. In February, AAAI will officially present Barzilay with the award, which comes with an associated prize of $1 million provided by the online education company Squirrel AI. "Only world-renowned recognitions, such as the Association of Computing Machinery's A.M. Turing Award and the Nobel Prize, carry monetary rewards at the million-dollar level," says AAAI awards committee chair Yolanda Gil. "This award aims to be unique in recognizing the positive impact of artificial intelligence for humanity."


Regina Barzilay wins $1M Association for the Advancement of Artificial Intelligence Squirrel AI award

#artificialintelligence

For more than 100 years Nobel Prizes have been given out annually to recognize breakthrough achievements in chemistry, literature, medicine, peace, and physics. As these disciplines undoubtedly continue to impact society, newer fields like artificial intelligence (AI) and robotics have also begun to profoundly reshape the world. In recognition of this, the world's largest AI society -- the Association for the Advancement of Artificial Intelligence (AAAI) -- announced today the winner of their new Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, a $1 million award given to honor individuals whose work in the field has had a transformative impact on society. The recipient, Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science at MIT and a member of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), is being recognized for her work developing machine learning models to develop antibiotics and other drugs, and to detect and diagnose breast cancer at early stages. In February, AAAI will officially present Barzilay with the award, which comes with an associated prize of $1 million provided by the online education company Squirrel AI. "Only world-renowned recognitions, such as the Association of Computing Machinery's A.M. Turing Award and the Nobel Prize, carry monetary rewards at the million-dollar level," says AAAI awards committee chair Yolanda Gil.


Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity -- An Interview with Squirrel AI's Richard Tong

Interactive AI Magazine

The Association for the Advancement of Artificial Intelligence (AAAI) and Squirrel AI Learning announced the establishment of a new $1M annual award for societal benefits of AI. The award will be sponsored by Squirrel AI Learning as part of its mission to promote the use of artificial intelligence with lasting positive effects for society. The new Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity was announced jointly by Derek Haoyang Li, Founder and Chairman of Squirrel AI Learning, and Yolanda Gil, President of AAAI, at the 2019 conference for AI for adaptive Education (AIAED) in Beijing. Established in 2014, Squirrel AI Learning Intelligent Adaptive Education by Yixue Group is the first artificial intelligence company in China to apply AI-powered adaptive learning technology to K12 education. Squirrel AI Learning products use a model that combines artificial intelligence and human coaches to provide students with access to individualized and affordable high-quality education. Although the focus of Squirrel AI Learning is on education, Li insisted that the award will recognize AI innovations across all disciplines. The establishment of this award aims to inspire the AI community and draw attention to AI that can benefit humanity. This new international award will recognize significant contributions in the field of artificial intelligence with profound societal impact that have generated otherwise unattainable value for humanity. The award nomination and selection process will be designed by a committee led by AAAI that will include representatives from international organizations with relevant expertise that will be designated by Squirrel AI Learning.


Can computers ever replace the classroom?

The Guardian

For a child prodigy, learning didn't always come easily to Derek Haoyang Li. When he was three, his father – a famous educator and author – became so frustrated with his progress in Chinese that he vowed never to teach him again. "He kicked me from here to here," Li told me, moving his arms wide. Yet when Li began school, aged five, things began to click. Five years later, he was selected as one of only 10 students in his home province of Henan to learn to code. At 16, Li beat 15 million kids to first prize in the Chinese Mathematical Olympiad. Among the offers that came in from the country's elite institutions, he decided on an experimental fast-track degree at Jiao Tong University in Shanghai. It would enable him to study maths, while also covering computer science, physics and psychology. In his first year at university, Li was extremely shy.