Udacity, the global lifelong learning platform, together with Bertelsmann, a media, services and education company, announced that applications are now open for 15,000 scholarships in Data, AI, and Cloud-Computing. As Udacity and Bertelsmann shared earlier this year, the new scholarship program is part of a three-year commitment by Bertelsmann to fund 50,000 scholarships. Both companies have partnered to increase learning opportunities in emerging technologies for students across the globe. "There simply aren't enough people who are equipped with Cloud, Data, and Artificial Intelligence skills," said Gabriel Dalporto, CEO of Udacity. "That's why Bertelsmann and Udacity share a commitment to train new talent and diversify the talent pool in these three exciting fields. I'm confident that the Bertelsmann Scholarship Program will empower learners to master new skills and land some of the most exciting and in-demand jobs available today!"

At a 2017 O'Reilly AI conference, Andrew Ng ranked reinforcement learning dead last in terms of its utility for business applications. Compared to other machine learning methods like supervised learning, transfer learning, and even unsupervised learning, deep reinforcement learning (RL) is incredibly data hungry, often unstable, and rarely the best option in terms of performance. RL has historically been successfully applied only in arenas where mountains of simulated data can be generated on demand, such as games and robotics. Despite RL's limitations in solving business use cases, some AI experts believe this approach is the most viable strategy for achieving human or superhuman Artificial General Intelligence (AGI). The recent victory of DeepMind's AlphaStar over top-ranked professional StarCraft players suggests we might be on the cusp of applying deep RL to real world problems with real-time demands, extraordinary complexity, and incomplete information.

Are you looking for the best online masters programs? Here is the list of the Best Edureka Online Masters Programs will make you proficient in tools, systems, and skills required to build specific professional expertise like Data Scientist, DevOps Engineers, Big Data Architect, Could Architect, Full Stack Web Development, Business Intelligence, Data Analyst or as a Machine Learning expert. According to Edureka, they stand by you all the way to ensure that you achieve your learning goals. Edureka provides instructor-led Live Online Classes as per your convenience. You will have a Personal Learning Manager with Lifetime Access in your enrolled courses.

AI is being built into more systems and software as organizations attempt to compete in the algorithmic age. With the level of machine intelligence reaching new heights, the number of experts is not growing proportionally. To compensate, AI libraries, APIs, systems and software are becoming easier to use so more people can take advantage of them. However, ease of use does not necessarily diminish risks. At present, there's no minimum competence level one must have to operate an AI system, except perhaps data scientists with graduate degrees in math, statistics or computer science who use the most sophisticated tools.

Antoinette Ellis's five-year-old daughter, Zariah, already knows what she wants to be when she grows up. Her main gig will be scientist, but she plans on earning extra income as an opera singer and a part-time DJ. This future may not sound particularly realistic, but Ellis is nonetheless doing all she can to foster her daughter's interests. Zariah watches videos of opera singers, she's taking music lessons and she's often conducting little experiments, as she did this past summer, when she planted strawberries and vegetables in her grandfather's backyard. Whether Zariah ultimately becomes a multi-talented scientist-entertainer remains to be seen, but Ellis is confident she'll succeed at whatever she chooses to do.

September 05, 2019: The Central Board of Secondary Education (CBSE) has announced that it will conduct Capacity Building Programs for high school teachers in association with Microsoft India with an aim to integrate cloud-powered technology in K12 teaching. Meant for teachers of grades 8-10, the program will be conducted in 10 cities across the country, starting September 11, 2019. AI and intelligent technologies are becoming all-pervasive today, transforming organizations across sectors and redefining the way we work. To equip the workforce of tomorrow, it is critical to the ramp up the institutional set-up and build capability among educators as well as integrate advanced technologies into the teaching process. This program will provide teachers better access to the latest Information and Communication Technology (ICT) tools and help them to integrate technology into teaching in a safe and secure manner, thereby enhancing the learning experience and 21st century skills of all students.

Andrew Yan-Tak Ng (Chinese: 吳恩達; born 1976) is a Chinese-American computer scientist and statistician, focusing on machine learning and AI. Also a business executive and investor in the Silicon Valley, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company's Artificial Intelligence Group into a team of several thousand people. Ng is an adjunct professor at Stanford University (formerly associate professor and Director of its AI Lab). Also a pioneer in online education, Ng co-founded Coursera and deeplearning.ai. With his online courses, he has successfully spearheaded many efforts to "democratize deep learning."

It was written as an advanced text to serve as a basis for a graduate course, and/or as a reference to the researcher diving into this fascinating world at the intersection of optimization and machine learning. Such a course was given at the Technion in the years 2010-2014 with slight variations from year to year, and later at Princeton University in the years 2015-2016. The core material in these courses is fully covered in this book, along with exercises that allow the students to complete parts of proofs, or that were found illuminating and thought-provoking. Most of the material is given with examples of applications, which are interlaced throughout different topics. These include prediction from expert advice, portfolio selection, matrix completion and recommendation systems, SVM training and more.

Most research on lifelong learning (LLL) applies to images or games, but not language. Here, we introduce LAMAL, a simple yet effective method for LLL based on language modeling. LAMAL replays pseudo samples of previous tasks while requiring no extra memory or model capacity. To be specific, LAMAL is a language model learning to solve the task and generate training samples at the same time. At the beginning of training a new task, the model generates some pseudo samples of previous tasks to train alongside the data of the new task. The results show that LAMAL prevents catastrophic forgetting without any sign of intransigence and can solve up to five very different language tasks sequentially with only one model. Overall, LAMAL outperforms previous methods by a considerable margin and is only 2-3\% worse than multitasking which is usually considered as the upper bound of LLL. Our source code is available at https://github.com/xxx.

This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from existing policy gradient methods which employ single actor-critic but cannot realize satisfactory tracking control accuracy and stable learning, our proposed algorithm can achieve high-level tracking control accuracy of AUVs and stable learning by applying a hybrid actors-critics architecture, where multiple actors and critics are trained to learn a deterministic policy and action-value function, respectively. Specifically, for the critics, the expected absolute Bellman error based updating rule is used to choose the worst critic to be updated in each time step. Subsequently, to calculate the loss function with more accurate target value for the chosen critic, Pseudo Q-learning, which uses sub-greedy policy to replace the greedy policy in Q-learning, is developed for continuous action spaces, and Multi Pseudo Q-learning (MPQ) is proposed to reduce the overestimation of action-value function and to stabilize the learning. As for the actors, deterministic policy gradient is applied to update the weights, and the final learned policy is defined as the average of all actors to avoid large but bad updates. Moreover, the stability analysis of the learning is given qualitatively. The effectiveness and generality of the proposed MPQ-based Deterministic Policy Gradient (MPQ-DPG) algorithm are verified by the application on AUV with two different reference trajectories. And the results demonstrate high-level tracking control accuracy and stable learning of MPQ-DPG. Besides, the results also validate that increasing the number of the actors and critics will further improve the performance.