Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This paper provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. The reader is assumed to be familiar with basic machine learning concepts.

The overall deep learning market is estimated to be valued at USD 3.18 Billion in 2018 and is expected to be worth USD 18.16 Billion by 2023 – MarketsandMarkets There is a lot of buzz around the deep learning technology and the remarkable market growth it has made. A deep learning solution, unlike AI and ML, needs a huge amount of embedded data and computational power to make machines learn human behavior, store knowledge, and make predictions using a trained neural network. To many people, machines behaving like humans and sharing an enormous amount of private data to it sounds creepy because it can harm their privacy. Apart from that, deep learning also has some deep problems like deepfakes and the black box models, which makes business owners reluctant to adopt the technology. But then, whether you know it or not, this technology has a lot of potential to deal with business-specific challenges and the benefits of deep learning outnumber its drawbacks.

There is a renaissance happening in the world of artificial intelligence. Using deep learning, researchers are producing systems that can recognize objects, understand spoken language, and even simulate the human voice. The quality of these systems is advancing at a blistering pace. Just three months months ago, Chinese search giant Baidu showed off Deep Voice, a system for turning text into speech. It could produce speech which was nearly indistinguishable from an actual human voice on the first listen, and do it in near real time.

The past year has been a great one for AI and Machine Learning. Many new high-impact applications of Machine Learning were discovered and brought to light, especially in healthcare, finance, speech recognition, augmented reality, and more complex 3D and video applications. We've seen a big push towards more application driven research, rather than theoretical. Although this can have its drawbacks, it has for the time being made some great positive impacts, generating new R&D that can rapidly be turned into business and customer value. This trend is strongly reflected in much of the ML open source work.

Another artificial intelligence-related exhibition will be held in Osaka next week. This is the third AI boom in Japan. Unlike the first two booms in the 1950s and '80s, however, this time seems to be real. Recent technological breakthrough in image recognition, deep learning and big-data processing capacity is making this happen. Unfortunately, in the case of Japan it only happens in the private sector.

Lunit is a medical AI company devoted to developing advanced medical image analytics and novel imaging biomarkers with its cutting-edge deep learning technology. The company has been internationally acknowledged for its advanced, state-of-the-art technology and application in analyzing medical images. Lunit specializes in developing medical AI software products that can foster accurate and cost-efficient diagnosis and treatment. By providing an accurate quantitative assessment of disease, products are designed to significantly reduce unnecessary tests, treatment, especially expensive invasive tests, such as biopsies. Currently, the company has released two products that provide AI analysis for chest X-rays and mammograms. The accuracy level of their diagnostic products currently ranges from 97% to 99%, depending on the area of diagnosis.

Dynamic Programming Languages are quite popular because they increase the programmer's productivity. However, the absence of types in the source code makes the program written in these languages difficult to understand and virtual machines that execute these programs cannot produced optimized code. To overcome this challenge, we develop a technique to predict types of all identifiers including variables, and function return types. We propose the first implementation of $2^{nd}$ order Inside Outside Recursive Neural Networks with two variants (i) Child-Sum Tree-LSTMs and (ii) N-ary RNNs that can handle large number of tree branching. We predict the types of all the identifiers given the Abstract Syntax Tree by performing just two passes over the tree, bottom-up and top-down, keeping both the content and context representation for all the nodes of the tree. This allows these representations to interact by combining different paths from the parent, siblings and children which is crucial for predicting types. Our best model achieves 44.33\% across 21 classes and top-3 accuracy of 71.5\% on our gathered Python data set from popular Python benchmarks.

Sequential decision-making (SDM) plays a key role in intelligent robotics, and can be realized in very different ways, such as supervised learning, automated reasoning, and probabilistic planning. The three families of methods follow different assumptions and have different (dis)advantages. In this work, we aim at a robot SDM framework that exploits the complementary features of learning, reasoning, and planning. We utilize long short-term memory (LSTM), for passive state estimation with streaming sensor data, and commonsense reasoning and probabilistic planning (CORPP) for active information collection and task accomplishment. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that our framework performs better than its no-learning and no-reasoning versions in a real-world office environment.