Challenge and engage every student with Spiral! (https://spiral.ac)

Spiral is a suite of serious tools for classroom based collaborative learning with 1:1 devices.

See what teachers are saying about Spiral: https://spiral.ac/what-teachers-are-saying

Video of the PlaNet agent learning to solve a variety of continuous control tasks from images in 2000 attempts. Previous agents that do not learn a model of the environment often require 50 times as many attempts to reach comparable performance.

Paper: https://arxiv.org/pdf/1811.04551.pdf
Website: https://danijar.com/planet

BIOTALENT’s promotional video about the e-learning platform and its innovative inquiry-based e-learning course. Discover how the program enhancing students’ comprehension of biodiversity issues with regards to climate change and, providing them with knowledge and competences in biodiversity and its conservation.

At Greene Central High School in Snow Hill, NC, students receive individualized and real-time feedback while working through problems sets in their mathematics class.

Learn more: tech.ed.gov/STEM

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Machine Learning as a system is the means to help AI understand the task and perform it. Jay Yagnik of Google explains whether this technology is an intrusion in human life and what is the best way to evolve this technology.

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The goal of Project-Based Learning (PBL) is to bring real-world projects into the classroom, and, bring the classroom to the real world. PBL provides students with real-world projects, provides the entrepreneur with a resource to help grow their business, all the while adding to our regional economy...one of Rowan University's Pillars of Success.

For more information visit:
business.rowan.edu

Geoffrey Hinton
University of Toronto

Abstract:
Recent advances in machine learning cast new light on two puzzling biological phenomena. Neurons can use the precise time of a spike to communicate a real value very accurately, but it appears that cortical neurons do not do this. Instead they send single, randomly timed spikes. This seems like a clumsy way to perform signal processing, but a recent advance in machine learning shows that sending stochastic spikes actually works better than sending precise real numbers for the kind of signal processing that the brain needs to do. A closely related advance in machine learning provides strong support for a recently proposed theory of the function of sexual reproduction. Sexual reproduction breaks up large sets of co-adapted genes and this seems like a bad way to improve fitness. However, it is a very good way to make organisms robust to changes in their environment because it forces important functions to be achieved redundantly by multiple small sets of genes and some of these sets may still work when the environment changes. For artificial neural networks, complex co-adaptations between learned feature detectors give good performance on training data but not on new test data. Complex co-adaptations can be reduced by randomly omitting each feature detector with a probability of a half for each training case. This random "dropout" makes the network perform worse on the training data but the number of errors on the test data is typically decreased by about 10%. Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov have shown that this leads to large improvements in speech recognition and object recognition.




Bio:
Geoffrey Hinton received his BA in experimental psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He spent five years as a faculty member in the Computer Science Department at Carnegie-Mellon University then moved to the Department of Computer Science at the University of Toronto where he is the director of the program on Neural Computation and Adaptive Perception which is funded by the Canadian Institute for Advanced Research. He has been awarded the David E. Rumelhart prize, the IJCAI award for research excellence, the Killam prize for Engineering and the NSERC Herzberg Gold Medal which is Canada's top award in Science and Engineering.

Geoffrey Hinton designs machine learning algorithms. His aim is to discover a learning procedure that is efficient at finding complex structure in large, high-dimensional datasets and to show that this is how the brain learns to see. He was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts, deep belief nets and dropout.

Adults around the US are choosing to better themselves and further their education by coming to adult literacy programs. As they improve their reading, writing and math skills, they experience an immense increase in self-confidence, self-efficacy, and hope. This video (with interviews conducted by learners at the Springfield City Library's Read/Write/Now program in Massachusetts) will inspire and enlighten anyone of any educational background who loves to learn, grow and change. The film was a project of the B-BALL Squad. (B-BALL stands for Becoming a Better Adult Learner and Leader.)

Universal Design for Learning (UDL) is a set of principles and techniques for creating inclusive classroom instruction and accessible course materials. At its core is the assertion that all students benefit when they are given multiples ways to take in new information, express their comprehension, and become engaged in learning. This video features faculty and students at Colorado State University describing the benefits of UDL. Includes captions.

The world is changing -- and so should education. For every generation, we must all rethink the way we learn so that we can face the challenges of a new world. @UNESCO is launching the "Futures of Education", a new initiative that will rethink education as we know it and reimagine how knowledge and learning can shape the future of humanity. Join LIVE UNESCO Director-General, @Audrey.Azoulay, together with Heads of State, Ministers and education experts, as they launch this initiative at the @UnitedNations Headquarters in New York on Wednesday 25 September. Let's learn how to learn again and build together the #FuturesOfEducation.

Find out more: https://on.unesco.org/FutureEd

#UNGA #GlobalGoals

Blended learning explained.

In this brief video, I summarize a technique called Local Info Max (LIM) that learns speaker identities using mutual information. LIM is based on a neural encoder that converts raw samples into a high-level speaker representation. Training is conducted in a self-supervised way without explicitly using speaker labels.

Learning partners discuss a cooperative text in CSI - Cooperative Strategies Instruction

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www.liquidlearning.com

Liquid Learning provide leadership development opportunities for professionals throughout Australasia.

Established in 2004, Liquid Learning is a leading professional development provider with an expanding international footprint. Currently active in over ten countries, we research, produce and deliver an extensive range of conferences, exhibitions, workshops and in-house training engagements. These events attract the participation of business leaders and professionals from many of the world’s leading brands.

Our diverse portfolio of conferences and training products cater
to the needs of a variety of industries and professional disciplines.

• Corporate / Private Sector
• Government / Public Sector
• Not-for-Profit / Community Sector
• Education Sector

Students at Pine Grove Middle School in East Syracuse, NY improve their ideas about the impact of species on the food chain using feedback from teacher and peer embedded assessments.
Learn more: tech.ed.gov/STEM

Decentralized Machine Learning (DML) protocol is introduced to unleash the full potential of machine learning development. Through DML protocol, data, processing power and algorithms are all decentralized. The crowdsourced algorithms supplied by our machine learning developers' community are run directly on individual devices by accessing their untapped private data and leveraging their idle processing power. Private data will be kept within the devices so privacy is well protected and only the result will be aggregated to form an unbiased, comprehensive and accurate prediction. Blockchain smart contract will be implemented to connect the participants of the DML protocol in a trustless and middleman-free environment.

To learn more about DML Protocol, please visit our:

Website:
https://decentralizedml.com

Telegram:
https://t.me/DecentralizedML

Medium:
https://medium.com/decentralized-machine-learning

http://www.weforum.org/

We don't have the resources to give all learners the rich experience they need, says Emma Brunskill from Carnegie Mellon University, USA. She calls for "black belt ninja teaching", and describes how a new generation of diligent software can help.