Today is International Women's Day, and we're celebrating by highlighting some of the leading ladies in AI, machine learning and deep learning who have spoken at RE•WORK Summits and dinners over the past 12 months. Whilst the technology industry is seeing more and more women in top roles, the gender imbalance is still clear. At RE•WORK, we're passionate about encouraging women and girls into STEM and are proud to host our series of dinners and our Podcast celebrating women in AI. Earlier this year we hosted the AI Assistants Summit in San Francisco, where 50% of our speakers were women. This was a fantastic showcase of diversity, and we are striving to have more and more female experts presenting at our Summits.

It used to be you could spend an afternoon drumming up a home movie with your little sister, soundtrack it with your favorite mixtape cuts, and upload it to the Internet for sharing without a care. What's an amateur-at-best musician to do? I may have marched on a collegiate snare line (and therefore understand rhythm, phrasings, and tempo), but my ability to create melody probably stopped with middle school recorder lessons. Luckily, we musically challenged filmmakers and podcasters now have robots. A number of high-profile AI composition initiatives have surfaced in recent years--perhaps most notably, Sony's Flow Machine released its debut album in January--and slowly but surely these tools are moving from the research labs and professional production studios into publicly available spaces.

From past few decades, our educational system has been slowly adapting itself to the new age of technology. With every day something new is innovating, everyone needs to accept the new change and learn about it. The biggest change that a mankind is right now facing is the new age of technology i.e. As the artificial intelligence is leading its way into the educational system as well it is becoming more important to incorporate the changes in the way the leaning is happening at higher education levels. A recent analysis of artificial intelligence market in U.S educational sector concluded that use of artificial intelligence in this sector has a compound growth rate of 47.5% throughout 2017-2021 forecasted periods.

Deep multitask learning boosts performance by sharing learned structure across related tasks. This paper adapts ideas from deep multitask learning to the setting where only a single task is available. The method is formalized as pseudo-task augmentation, in which models are trained with multiple decoders for each task. Pseudo-tasks simulate the effect of training towards closely-related tasks drawn from the same universe. In a suite of experiments, pseudo-task augmentation is shown to improve performance on single-task learning problems. When combined with multitask learning, further improvements are achieved, including state-of-the-art performance on the CelebA dataset, showing that pseudo-task augmentation and multitask learning have complementary value. All in all, pseudo-task augmentation is a broadly applicable and efficient way to boost performance in deep learning systems.

We introduce a new class of reinforcement learning methods referred to as {\em episodic multi-armed bandits} (eMAB). In eMAB the learner proceeds in {\em episodes}, each composed of several {\em steps}, in which it chooses an action and observes a feedback signal. Moreover, in each step, it can take a special action, called the $stop$ action, that ends the current episode. After the $stop$ action is taken, the learner collects a terminal reward, and observes the costs and terminal rewards associated with each step of the episode. The goal of the learner is to maximize its cumulative gain (i.e., the terminal reward minus costs) over all episodes by learning to choose the best sequence of actions based on the feedback. First, we define an {\em oracle} benchmark, which sequentially selects the actions that maximize the expected immediate gain. Then, we propose our online learning algorithm, named {\em FeedBack Adaptive Learning} (FeedBAL), and prove that its regret with respect to the benchmark is bounded with high probability and increases logarithmically in expectation. Moreover, the regret only has polynomial dependence on the number of steps, actions and states. eMAB can be used to model applications that involve humans in the loop, ranging from personalized medical screening to personalized web-based education, where sequences of actions are taken in each episode, and optimal behavior requires adapting the chosen actions based on the feedback.

Machine Learning mostly requires the fundamental understanding of Linear Algebra, Statistics and Probability. While you can learn how to use all the advanced libraries to accomplish your ML tasks, once something breaks you won't be able to fix it. Even worse, you won't be able to understand any new studies being done in the field, since to understand them, you will need a somewhat deep understanding of mathematics. Also, you won't be able to conduct your own studies or play around with mathematical ML concepts. This being a very huge topic, it is somewhat hard to find good resources which explain the content properly.

There is new code to play around with for those interested in machine learning and space, a model that predicts hilarious search trends for sex site YouPorn, and another funny story about an ostensibly intelligent medical chatbot in New Zealand. Hunting exoplanets with ML – The machine learning code that a Google engineer and an astrophysicist used to detect exoplanets has been published online. Christopher Shallue, a senior software engineer at Google, and Andrew Vanderburg, a postdoctoral fellow studying astrophysics at the University of Texas, USA, discovered another planet lurking in the Kepler-90 system. It was a special find. Not only was it spotted using a convolutional neural network, but it meant that the Solar System was no longer the biggest planetary system found so far.

Another study shows how images that are used to train image-recognition software amplify gender biases. Two large image collections used for research purposes – including one supported by Microsoft and Facebook – were found to display predictable gender biases in photos of everyday scenes such as sport and cooking. Images of shopping and washing were linked to women, while coaching and shooting were tied to men. If a photo set generally associates women with housework, software trained by studying those photos and their labels create an even stronger association with it.

The development of artificial intelligence (AI) has had a huge influence on today's society, as ongoing discussions evaluate the impacts of creating machines and computer systems that can react and perform like humans. These systems can process information in a more cognitive way, making them capable of more human-like functions like learning, decision-making, and visual perception. Hollywood portrayals of hyper-intelligent robots taking over the planet might make artificial intelligence seem intimidating, but there is a lot that can be gained by through these advanced computer systems. Without the element of human error, intelligent machines are capable of unmatched precision and accuracy, and since they don't require fundamental human needs like oxygen or food, they can perform tasks with far fewer limitations. In fact, AI is already popping up everywhere in our daily lives – through social media recommendations, virtual assistants on our smartphones, and even self-driving cars.

Microsoft France proudly opened the doors to its newly unveiled AI School on 6 March. Located at the company's Paris-based offices, the Microsoft AI School will see 24 students from a variety of backgrounds embark on a free, intensive seven month-course, during which they will learn invaluable AI development skills. The course will prepare participants for future careers in AI, and will be followed by 12 months of employment at participating companies. The new AI School is an embodiment of Microsoft's mission to enable every person and organisation on the planet to achieve more, while contributing to the democratization of AI for people from all backgrounds. Carlo Purassanta, President of Microsoft France believes that "France has all the assets needed to be an AI powerhouse, thanks to the quality of its researchers and engineers. If companies want to accelerate the development of strong projects, we must train AI experts."