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#.

Accurate classification of seizure types plays a crucial role in the treatment and disease management of epileptic patients. Epileptic seizure type not only impacts on the choice of drugs but also on the range of activities a patient can safely engage in. With recent advances being made towards artificial intelligence enabled automatic seizure detection, the next frontier is the automatic classification of seizure types. On that note, in this paper, we undertake the first study to explore the application of machine learning algorithms for multi-class seizure type classification. We used the recently released TUH EEG Seizure Corpus and conducted a thorough search space exploration to evaluate the performance of a combination of various pre-processing techniques, machine learning algorithms, and corresponding hyperparameters on this task. We show that our algorithms can reach a weighted F1 score of up to 0.907 thereby setting the first benchmark for scalp EEG based multi-class seizure type classification.

In experimental design, we are given a large collection of vectors, each with a hidden response value that we assume derives from an underlying linear model, and we wish to pick a small subset of the vectors such that querying the corresponding responses will lead to a good estimator of the model. A classical approach in statistics is to assume the responses are linear, plus zero-mean i.i.d. Gaussian noise, in which case the goal is to provide an unbiased estimator with smallest mean squared error (A-optimal design). A related approach, more common in computer science, is to assume the responses are arbitrary but fixed, in which case the goal is to estimate the least squares solution using few responses, as quickly as possible, for worst-case inputs. Despite many attempts, characterizing the relationship between these two approaches has proven elusive. We address this by proposing a framework for experimental design where the responses are produced by an arbitrary unknown distribution. We show that there is an efficient randomized experimental design procedure that achieves strong variance bounds for an unbiased estimator using few responses in this general model. Nearly tight bounds for the classical A-optimality criterion, as well as improved bounds for worst-case responses, emerge as special cases of this result. In the process, we develop a new algorithm for a joint sampling distribution called volume sampling, and we propose a new i.i.d. importance sampling method: inverse score sampling. A key novelty of our analysis is in developing new expected error bounds for worst-case regression by controlling the tail behavior of i.i.d. sampling via the jointness of volume sampling. Our result motivates a new minimax-optimality criterion for experimental design which can be viewed as an extension of both A-optimal design and sampling for worst-case regression.

Contrary to popular Anglocentric belief, English isn't the world's most-spoken language by the total number of native speakers -- nor is it the second. In fact, the West Germanic tongues rank third on the list, followed by Hindi, Arabic, Portuguese, Bengali, and Russian. Surprisingly, Google Assistant, Apple's Siri, Amazon's Alexa, and Microsoft's Cortana recognize a relatively narrow slice of those. It wasn't until this fall that Samsung's Bixby gained support for German, French, Italian, and Spanish -- dialects collectively spoken by 616 million people worldwide. And it took years for Cortana to become conversant in Spanish, French, and Portuguese.