The investment is part of an AI strategy laid out by President Emmanuel Macron at the elite College de France research institute in Paris, the French presidency said. The goal is to make better use of the French higher education system that trains computer engineers and mathematicians only to see them leave for jobs at top U.S. tech companies. Some of them have secured high-level positions at Alphabet, the parent company of search engine Google, and Facebook, which opened an AI research centre in Paris in 2015. Macron's AI plan was inspired by a government-commissioned report by Cedric Villani, the self-styled "Lady Gaga of Mathematics" and winner of the mathematics equivalent of the Nobel Prize. Villani, who is also a lawmaker in Macron's party, said in the report the brain drain to Silicon Valley companies showed the excellence of French schools.

This class is a great overview of the types of machine-learning models, and some of the history of how those models came into use. The fundamental explanations of complex ideas are generally excellent and very clear, but the practical equations that are necessary for implementations are difficult to understand for someone like me who is not used to reading abstract mathematical equations. Examples of equations that are worked out with explicit values are few and far between and this doesn't help. This makes the programming assignments exceptionally difficult even though the code they require is simple. Also, the amount of time required for this course is enormous, easily 10x what is predicted when there is a programming assignment.

Online learning algorithms have a wide variety of applications in large scale machine learning problems due to their low computational and memory requirements. However, standard online learning methods still suffer some issues such as lower convergence rates and limited capability to select features or to recover the true features. In this paper, we present a novel framework for online learning based on running averages and introduce a series of online versions of some popular existing offline algorithms such as Adaptive Lasso, Elastic Net and Feature Selection with Annealing. We prove the equivalence between our online methods and their offline counterparts and give theoretical feature selection and convergence guarantees for some of them. In contrast to the existing online methods, the proposed methods can extract model with any desired sparsity level at any time. Numerical experiments indicate that our new methods enjoy high feature selection accuracy and a fast convergence rate, compared with standard stochastic algorithms and offline learning algorithms. We also present some applications to large datasets where again the proposed framework shows competitive results compared to popular online and offline algorithms.

Despite their great success in practical applications, there is still a lack of theoretical and systematic methods to analyze deep neural networks. In this paper, we illustrate an advanced information theoretic methodology to understand the dynamics of learning and the design of autoencoders, a special type of deep learning architectures that resembles a communication channel. By generalizing the information plane to any cost function, and inspecting the roles and dynamics of different layers using layer-wise information quantities, we emphasize the role that mutual information plays in quantifying learning from data. We further propose and also experimentally validate, for mean square error training, two hypotheses regarding the layer-wise flow of information and intrinsic dimensionality of the bottleneck layer, using respectively the data processing inequality and the identification of a bifurcation point in the information plane that is controlled by the given data. Our observations have direct impact on the optimal design of autoencoders, the design of alternative feedforward training methods, and even in the problem of generalization.

I am sure you have heard about the discussion that's going on where machines will replace or alter approximately 35 million jobs worldwide. We don't know how true is that but a report states that robot automation will take over 800 million jobs by 2030. This isn't limited to education space alone but includes every industry. Intelligent machines are the future and it is coming soon to take over the world. Well, let's see how true is this in the higher education space.

The past two decades have seen the workplace transformed by digital advances. Gone are many traditional structures and practices, replaced with new ways of doing business, designed to support collaboration and digitally-enabled remote and flexible working. As the technology behind AI and robotics becomes more sophisticated, the number of jobs that remain untouched by automation will decrease. "To keep pace, businesses must rethink how they organise work, reinvent jobs, redeploy staff and implement robust plans for the future," says Lynda Gratton, professor of management practice at London Business School (LBS). There are also emerging social trends and shifting demographics to consider.

Live call-in discussion: As artificial intelligence continues to develop, concerns grow about its invasive nature and reach. How much are we willing to cede to the machines, and what effect will that have on our lives? The Vermont House recently passed a bill that would create an AI commission to address these subjects. John Quinn, the state's digital services secretary, and Burlington Rep. Brian Cina discuss these issues and what the proposed commission would address. We also hear from Milo Cress, a Champlain Valley Union High School student, who played an important role in the House passage of the bill which would create the commission.

Employees at all stages of their careers are challenged by the technological and socio-economical changes that are limiting the suitability of these employee's current skills and learning. Widening gap between the skills available and skills in demand is certainly alarming and you should not overlook a timely career advice. To brace yourself for a future-ready career you will require advanced technical training or specialized education. Dynamic re-skilling and learning on-the-go are keys to be successful in the competitive job market. Everybody is talking about Artificial Intelligence.

I've found the review on the first three courses by Arvind N very useful in taking the decision to enroll in the first course, so I hope, maybe this can also be useful for someone else. Taking the five courses is very instructive. The content is well structured and good to follow for everyone with at least a bit of an understanding on matrix algebra. Some experience in writing Python code is a requirement. The programming assignments are well designed in general.

We love Festo because every year they invest an entirely appropriate amount of time and money into bio-inspired robots that are totally cool and very functional but have limited usefulness. More often than not, it seems like Festo is able to take some of what it learns from designing and constructing these things and create practical new revenue-generating products. Which is good for them, and means they'll keep making cool stuff. Over the last few years, we've met ants, butterflies, flying jellyfish and penguins, kangaroos, seagulls, and much more. Festo has just announced its two newest bionic learning network robots--one is a very convincing flying fox, and the other is a walking, tumbling robot inspired by a Saharan spider.