Recent news on Sophia the robot getting citizenship in the Saudi Arabia has widely attracted daily news and social media. Despite the debates and agitations on a robot getting recognition as humans, experts view this event as a phenomenal milestone in the research of AI. The current level of Artificial Intelligence is achieved through years of research in Machine Learning, Deep Learning and other related fields. With a lot of hype and investments around, Deep Learning technology – a subdivision of Machine Learning is now successfully applied in our daily life from speech recognition apps in smartphones to YouTube recommendations. One of the pioneers of the Deep Learning, Andrew Ng feels that AI is the new form of electricity where every AI application in future electronic devices will be fuelled by Deep Learning models.

The Facebook AI Research (FAIR) Residency Program is a one-year research training program with Facebook's AI Research group, designed to give you hands-on experience of machine learning research. The program will pair you with a senior researcher or engineer in FAIR, who will act as your mentor. Together, you will pick a research problem of mutual interest and then devise new deep learning techniques to solve it. We also encourage collaborations beyond the assigned mentor. The research will be communicated to the academic community by submitting papers to top academic venues (NIPS, ICML, ICLR, CVPR, ICCV, ACL, EMNLP etc.), as well as open-source code releases.

While the scientific community continues looking for new breakthroughs in artificial intelligence, Andrew Ng believes the tech we need is already here. In his keynote speech Friday at the AI Frontiers Conference, the founder of Coursera & Deeplearning.ai Stop publishing, and start transforming people's lives with technology!" The three-day conference drew over 1,400 attendees from 17 different countries to the Santa Clara Convention Center. Ng's keynote speech was titled "AI is the new electricity".

Before a Google machine learning program discovered an eighth planet in an exoplanet system using NASA spacecraft observations, the only other known system with as many planets was our own. Kepler has been scanning for planet systems beyond our solar system since 2009 and has made countless discoveries of planets orbiting stars, but so far none of those systems have contained as many planets as our own. That changed recently with the help of a Google AI program that found an an eighth planet orbiting the sun-like star Kepler 90, NASA scentists announced Thursday. Like Earth, this new planet, Kepler-90i, is the third rock from its sun. But it's much closer to its sun -- orbiting in just 14 days -- and therefore is a scorching 800 degrees Fahrenheit (427 Celsius) at the surface. In fact, all eight planets are scrunched up around this star, orbiting closer than Earth does to our sun.

We've been talking about data science and data scientists for a decade now. While there's always been some debate over what "data scientist" means, we've reached the point where many universities, online academies, and bootcamps offer data science programs: master's degrees, certifications, you name it. The world was a simpler place when we only had statistics. But simplicity isn't always healthy, and the diversity of data science programs demonstrates nothing if not the demand for data scientists. As the field of data science has developed, any number of poorly distinguished specialties have emerged.

TensorFlow is an open-source machine learning software built by Google to train neural networks. TensorFlow's neural networks are expressed in the form of stateful dataflow graphs. Each node in the graph represents the operations performed by neural networks on multi-dimensional arrays. These multi-dimensional arrays are commonly known as "tensors", hence the name TensorFlow. TensorFlow is a deep learning software system.

To compile this list, we explored deep learning MOOCs (Massive Open Online Courses) published by top universities, colleges, and leading tech companies. Dedicated to beginners, intermediate, and advanced learners, and covering most concepts of Deep Learning, from the most basic to the cutting-edge, all of these courses are free and self-paced, and some of them even offer certificates. It goes without saying that all of these courses come with some prerequisites: basic knowledge of mathematics, how to manipulate GitHub repositories, and a good command of programming languages like Python. Google has published an online course dedicated to deep learning via Udacity, the online course platform. Google's MOOC trains intermediate to advanced developers free of charge for 12 weeks on many aspects of deep learning, such as how to build and optimize deep neural networks.

The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, we consider these requirements and present Ray---a distributed system to address them. Ray implements a dynamic task graph computation model that supports both the task-parallel and the actor programming models. To meet the performance requirements of AI applications, we propose an architecture that logically centralizes the system's control state using a sharded storage system and a novel bottom-up distributed scheduler. In our experiments, we demonstrate sub-millisecond remote task latencies and linear throughput scaling beyond 1.8 million tasks per second. We empirically validate that Ray speeds up challenging benchmarks and serves as both a natural and performant fit for an emerging class of reinforcement learning applications and algorithms.

Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification. Despite the success, the huge number of parameters blocks its deployment to situations with light computing resources. Researchers resort to the redundancy in the weights of DNNs and attempt to find how fewer parameters can be chosen while preserving the accuracy at the same time. Although several promising results have been shown along this research line, most existing methods either fail to significantly compress a well-trained deep network or require a heavy fine-tuning process for the compressed network to regain the original performance. In this paper, we propose the \textit{Block Term} networks (BT-nets) in which the commonly used fully-connected layers (FC-layers) are replaced with block term layers (BT-layers). In BT-layers, the inputs and the outputs are reshaped into two low-dimensional high-order tensors, then block-term decomposition is applied as tensor operators to connect them. We conduct extensive experiments on benchmark datasets to demonstrate that BT-layers can achieve a very large compression ratio on the number of parameters while preserving the representation power of the original FC-layers as much as possible. Specifically, we can get a higher performance while requiring fewer parameters compared with the tensor train method.

In Japan, famous for the longevity of its people, their endearing use of engrish, and their fetish for girls in Catholic school uniforms, the care of the elderly is a particularly acute problem. A third of the Japanese population is reportedly above the age of 60, and the number of people over 90 years of age just topped two million for the first time. Add in a rapidly shrinking population, and you have a country where you have more people eating the early bird special than not. So it's no surprise that Japan is leading the world in robotic elder care, offering a glimpse into our geriatric future.