MagniLEARN, an Ed-Tech company using artificial intelligence and Natural Language Processing, received second prize for innovation in Artificial Intelligence and was recognized as a promising AI Startup in the third annual China – Israel Innoweek Conference held in Beijing, China. "Combining Natural Language Processing (NLP) with Artificial Intelligence (AI) allowed us to turn the computer into a language-aware personal tutor for each student," said MagniLEARN CEO Howard Cooper in accepting the award. "Our difference lies in presenting personalized exercises that the student answers with free-form responses. Just like a personal tutor teaching language to a child, we understand what is correct, what is nearly correct, and provide feedback and then reinforcement as the student learns proper English. We gave the computer enough language awareness to become an intelligent and efficient language tutor for each student," he concluded.

From social media to speech recognition, warfare to writing articles, coding to customer service – machine learning and artificial intelligence have become part and parcel of our urban lives. In the pursuit to make life smoother and more comfortable, man has tapped into the potential of AI and invented auto-driven cars, smart sensors to capture spectacular photos, and home assistant devices. Similarly, AI is taking the field of education by storm and replacing traditional methods by the minute. Thanks to AI, the academic world has become more personalized, thus changing the way of e-learning. People can now access educational materials with just a click on their phones and laptop.

HELSINKI (AP) - Finland is offering a techy Christmas gift to all European Union citizens - a free-of-charge online course in artificial intelligence in their own language, officials said Tuesday. The tech-savvy Nordic nation, led by the 34-year-old Prime Minister Sanna Marin, is marking the end of its rotating presidency of the EU at the end of the year with a highly ambitious goal. Instead of handing out the usual ties and scarves to EU officials and journalists, the Finnish government has opted to give practical understanding of AI to 1% of EU citizens, or about 5 million people, through a basic online course by the end of 2021. TOP STORIES Ricky Gervais blasts Hollywood figures as unprincipled, ignorant at Golden Globes'We'll do it for half': George Lopez doubles down on Iran's bounty on Trump Black Americans are coming home to the GOP It is teaming up with the University of Helsinki, Finland's largest and oldest academic institution, and the Finland-based tech consultancy Reaktor. Teemu Roos, a University of Helsinki associate professor in the department of computer science, described the nearly $2 million project as "a civics course in AI" to help EU citizens cope with society's ever-increasing digitalization and the possibilities AI offers in the jobs market.

As automation and artificial intelligence continue to transform Canadian workplaces, post-secondary institutions across the country say they are working to prepare students for jobs that may not even exist yet. Climate change, data science and cybersecurity are increasingly in the spotlight at Canadian universities as they adapt their offerings to address "the needs not only of a changing marketplace but of a changing society," Paul Davidson, president of the association Universities Canada, said in a recent interview. Forecasting can prove difficult, however. "There are numbers like 50 per cent of the jobs (of the future) have not yet been defined, and so how does any organization … prepare for that kind of change?" A research paper released in 2018 showed half of Canadian jobs will be affected by automation in the next decade, and so-called "human skills" such as critical thinking and problem solving will be key to remaining competitive and resilient in an era of disruption and artificial intelligence.

This past semester we have been collaborating on a machine learning Capstone Project with Columbia University's Master of Science in Applied Analytics: capstone projects are applied and experimental projects where students take what they have learned throughout the course of their graduate program and apply it to examine a specific area of study. Capstone projects are specifically designed to encourage students to think critically, solve challenging data science problems, and develop analytical skills. Two group of students built an end-to-end data science solution using Azure Machine Learning to accurately forecast sales. Azure Machine Learning is a cloud-based environment that you can use to train, deploy, automate, manage, and track ML models. Azure Machine Learning can be used for any kind of machine learning, from classical machine learning to deep learning, supervised, and unsupervised learning.

It includes both paid and free resources to help you learn Python for Machine Learning and these courses are suitable for beginners, intermediate learners as well as experts. If you are interested in getting started with the field of machine learning then this is an excellent place to begin. Divided into two parts the classes first discuss the importance of this area and how it can be applied to solve some of the most pressing issues of the world. Following this you will explore some of the fundamental topics like supervised and unsupervised learning, algorithms and evaluation of ML model. This comprehensive course uses a practical approach to explain the foundational jargons and the techniques behind the concepts. The initial lectures talk about the dimensions of data, how to perform clustering and apply the different methods of predictive modeling based on the problem.

--Training a neural network model can be a lifelong learning process and is a computationally intensive one. A severe adverse effect that may occur in deep neural network models is that they can suffer from catastrophic forgetting during retraining on new data. T o avoid such disruptions in the continuous learning, one appealing property is the additive nature of ensemble models. In this paper, we propose two generic ensemble approaches, gradient boosting and meta-learning, to solve the catastrophic forgetting problem in tuning pre-trained neural network models. With stationary training resources and various advanced neural network structures, deep learning models have exceeded human performance in many areas. However, a well-known limitation of deep learning models is the so-called "catastrophic forgetting."

Abstract--In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting. We then compare the utility of several RL algorithms through a discussion of experiments performed on Commercial off-the -shelf (COTS) hardware. Each RL technique is evaluated in terms of convergence, radar detection performance achieved in a con gested spectral environment, and the ability to share 100MHz spect rum with an uncooperative communications system. We examine po licy iteration, which solves an environment posed as a Markov Dec ision Process (MDP) by directly solving for a stochastic mapping between environmental states and radar waveforms, as well a s Deep RL techniques, which utilize a form of Q -Learning to approximate a parameterized function that is used by the rad ar to select optimal actions. We show that RL techniques are benefi cial over a Sense-and-A void (SAA) scheme and discuss the conditi ons under which each approach is most effective. The Third Generation Partnership Project (3GPP) has recently received FCC approval to support 5G New Radio (NR) operation in sub-6 GHz frequency bands that are heavily utilized by radar systems [1], [2]. Thus, there is a significa nt need for radar systems capable of dynamic spectrum sharing.

In real-world scenarios, data tends to exhibit a long-tailed, imbalanced distribution. Developing algorithms to deal with such long-tailed distribution thus becomes indispensable in practical applications. In this paper, we propose a novel self-paced knowledge distillation framework, termed Learning From Multiple Experts (LFME). Our method is inspired by the observation that deep Con-volutional Neural Networks (CNNs) trained on less imbalanced subsets of the entire long-tailed distribution often yield better performances than their jointly-trained counterparts. W e refer to these models as'Expert Models', and the proposed LFME framework aggregates the knowledge from multiple'Experts' to learn a unified student model. Specifically, the proposed framework involves two levels of self-paced learning schedules: Self-paced Expert Selection and Self-paced Instance Selection, so that the knowledge is adaptively transferred from multiple'Experts' to the'Student'. In order to verify the effectiveness of our proposed framework, we conduct extensive experiments on two long-tailed benchmark classification datasets. The experimental results demonstrate that our method is able to achieve superior performances compared to the state-of- the-art methods. W e also show that our method can be easily plugged into state-of-the-art long-tailed classification algorithms for further improvements.

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.