This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.

Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy's own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order, while achieving competitive performance with conventional left-to-right generation.

Quantization of neural networks has become common practice, driven by the need for efficient implementations of deep neural networks on embedded devices. In this paper, we exploit an oft-overlooked degree of freedom in most networks - for a given layer, individual output channels can be scaled by any factor provided that the corresponding weights of the next layer are inversely scaled. Therefore, a given network has many factorizations which change the weights of the network without changing its function. We present a conceptually simple and easy to implement method that uses this property and show that proper factorizations significantly decrease the degradation caused by quantization. We show improvement on a wide variety of networks and achieve state-of-the-art degradation results for MobileNets. While our focus is on quantization, this type of factorization is applicable to other domains such as network-pruning, neural nets regularization and network interpretability.

The changing parameters of opportunity and risk from the emerging AI revolution run much deeper than might be generally supposed, say Professor Anthony Elliott and Julie Hare. From personal virtual assistants and chatbots to self-driving vehicles and tele-robotics, AI is now threaded into large tracts of everyday life. It is reshaping society and the economy. Klaus Schwab, founder of the World Economic Forum, has said that today's AI revolution is "unlike anything humankind has experienced before". AI is not so much an advancement of technology, but rather the metamorphosis of all technology.

Medical Imaging 2019 is an addition to the successful series of Medical Imaging and Clinical Research conferences; it is with immense pleasure and pride that we announce our upcoming "5th World Congress on Medical Imaging and Clinical Research" during June 17-18th, 2019 at Rome, Italy. Medical imaging is a technical process which creates Visual representation of interior body for clinical analysis and medical intervention, as well as visual representation of the function of some organs. Medical imaging seeks to reveal internal structures hidden by the skin and bones. Medical imaging is often perceived to designate the set of techniques that noninvasively produce images of the internal aspect of the body. Medical imaging also diagnoses and treats disease.

Typical sensors for object detection include cameras, radars,and LiDARs. In general, different sensors have their unique sensing properties, which brings each type of sensor an advantage overothers when performing object detection. For instance, cameras are able to capture rich texture information of objects in normal light conditions, which makes it possible to identify and distinguish objectsfrom background. Radars attempt to detect objects by continuously transmitting microwaves and then analyzing the received signalsreflected by the objects, which allow the sensors to work regardless of bad weather conditions or dark environments. In recent years, object detection based on cameras has made significant progressby using deep learning framework. The basic idea is to design and train a deep neural network (DNN) by feeding a large number of annotated image samples. The training process enables theDNN to effectively capture informative image features of interested objects via multiple neural layers [2]. As a result, the trained DNN is able to produce impressive performance for visual object detection and other similar tasks such as object classification and segmentation (e.g., Mask R-CNN [3], YOLO [4], and U-Net [5]). Researchon exploiting DNNs for analyzing radar signals is still at an early stage.

We consider the problem of making machine translation more robust to character-level variation at the source side, such as typos. Existing methods achieve greater coverage by applying subword models such as byte-pair encoding (BPE) and character-level encoders, but these methods are highly sensitive to spelling mistakes. We show how training on a mild amount of random synthetic noise can dramatically improve robustness to these variations, without diminishing performance on clean text. We focus on translation performance on natural noise, as captured by frequent corrections in Wikipedia edit logs, and show that robustness to such noise can be achieved using a balanced diet of simple synthetic noises at training time, without access to the natural noise data or distribution.

As if anyone needed reminding that a federal election looms, a war of words has broken out between the offices of Industry Minister Karen Andrews and shadow human services minister Ed Husic over a briefing on, of all things, artificial intelligence. Late last year, Mr Husic approached Ms Andrews' office seeking a briefing on the progress of an AI technology roadmap report being prepared by the CSIRO unit Data61 and the Department of Industry, and to get an understanding of the thinking in the report. The request was knocked by the Minister's office – not once but repeatedly – according to Ed Husic and he is not happy about it. These briefings are quite routine and rarely rejected, he says. While there are no specific rules around such briefings, by convention they are commonplace – although the understanding is that they are done in the background, quietly and without any resulting overtly politicisation. Even in the hyper-partisan times we live in, governments see merit in ensuring both the government and opposition benches the opportunity to understand the detail of evolving policy – particularly where there is complexity.

The idea of'Automata' originates from the mythologies of many cultures across the globe. Early inventors and engineers from ancient civilisation such as Greek, Chinese or Ptolemaic Egyptian attempted to develop a self-operating or automated machine resembling humans and animals. The term'Robot' comes from the Czech word "Robota" refers to "Forced Work or Labor" which was first used to refer the word'Artificial Automata' in a 1920 play R.U.R (Rossum's Universal Robots) by the Czech interwar writer'Karl Capek.' In 1928, one of the first'Humanoid Robots' invented by W.H.Richards, delivered a speech in the annual event of the'Model Engineers Society' in London. The brief history shows that'Robots' are not a new innovation but is a'Thinking Machine' which is programmed by a computer and is capable of doing complex series' of actions automatically.

Machine learning is a growing area of computer science and several programming languages support ML framework and libraries. Among all of the programming languages, Python is the most popular choice followed by C, Java, JavaScript, and C#.