We describe a method for training accurate Transformer machine-translation models to run inference using 8-bit integer (INT8) hardware matrix multipliers, as opposed to the more costly single-precision floating-point (FP32) hardware. Unlike previous work, which converted only 85 Transformer matrix multiplications to INT8, leaving 48 out of 133 of them in FP32 because of unacceptable accuracy loss, we convert them all to INT8 without compromising accuracy. Tested on the new-stest2014 English-to-German translation task, our INT8 Transformer Base and Transformer Big models yield BLEU scores that are 99.3% to 100% relative to those of the corresponding FP32 models. Our approach converts all matrix-multiplication tensors from an existing FP32 model into INT8 tensors by automatically making range-precision tradeoffs during training. To demonstrate the robustness of this approach, we also include results from INT6 Transformer models. 1 Introduction We report a method for training accurate yet compact Transformer machine-translation models [ V aswaniet al., 2017 ] . Specifically, we aim these models at hardware with 8-bit integer (INT8) matrix multipliers. Compared to single-precision floating-point (FP32) matrix multiplications, INT8 matrix multiplications not only reduce both storage and bandwidth four times, but they also consume 15 times less energy [ Horowitz, 2014 ] .

Tourism information is scattered around nowadays. To search for the information, it is usually time consuming to browse through the results from search engine, select and view the details of each accommodation. In this paper, we present a methodology to extract particular information from full text returned from the search engine to facilitate the users. Then, the users can specifically look to the desired relevant information. The approach can be used for the same task in other domains. The main steps are 1) building training data and 2) building recognition model. First, the tourism data is gathered and the vocabularies are built. The raw corpus is used to train for creating vocabulary embedding. Also, it is used for creating annotated data. The process of creating named entity annotation is presented. Then, the recognition model of a given entity type can be built. From the experiments, given hotel description, the model can extract the desired entity,i.e, name, location, facility. The extracted data can further be stored as a structured information, e.g., in the ontology format, for future querying and inference. The model for automatic named entity identification, based on machine learning, yields the error ranging 8%-25%.

Robotic Process Automation (RP A) is a technology for automating repetitive routines consisting of sequences of user interactions with one or more applications. In order to fully exploit the opportunities opened by RP A, companies need to discover which specific routines may be automated, and how. In this setting, this paper addresses the problem of analyzing User Interaction (UI) logs in order to discover routines where a user transfers data from one spreadsheet or (Web) form to another. The paper maps this problem to that of discovering data transformations by example - a problem for which several techniques are available. The paper shows that a naive application of a state-of-the-art technique for data transformation discovery is computationally inefficient. Accordingly, the paper proposes two optimizations that take advantage of the information in the UI log and the fact that data transfers across applications typically involve copying alphabetic and numeric tokens separately. The proposed approach and its optimizations are evaluated using UI logs that replicate a real-life repetitive data transfer routine.

Robotic Process Automation (RPA) is a fast-emerging automation technology that sits between the fields of Business Process Management (BPM) and Artificial Intelligence (AI), and allows organizations to automate high volume routines. RPA tools are able to capture the execution of such routines previously performed by a human users on the interface of a computer system, and then emulate their enactment in place of the user by means of a software robot. Nowadays, in the BPM domain, only simple, predictable business processes involving routine work can be automated by RPA tools in situations where there is no room for interpretation, while more sophisticated work is still left to human experts. In this paper, starting from an in-depth experimentation of the RPA tools available on the market, we provide a classification framework to categorize them on the basis of some key dimensions. Then, based on this analysis, we derive four research challenges and discuss prospective approaches necessary to inject intelligence into current RPA technology, in order to achieve more widespread adoption of RPA in the BPM domain.

The history of the government of people is superficially a history of the tensions between personal liberty and social responsibility for the greater good. This is what we're taught about as children and yet I personally believe the major purpose of education is not literacy or numeracy but social conditioning to this mythical golden thread that runs through the polite fabric of societies. At a deeper level, this tension between liberty and responsibility realizes itself in the western economic model as a race for acquiring capital. If we have a lot of money we will be free to do what we want. Similarly to Turing who thought in 1950 that the Turing Test would already be passed, Keynes's view in the 1930s that automation would liberate us from work in 2030 has not come to pass.

-- Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out other diseases with similar symptoms. New technologies, such as smartphone monitoring in free-living conditions, could potentially aid in objectively assessing the symptoms of MS by quantifying symptom presence and intensity over long periods of time. Here, we present a deep-learning approach to diagnosing MS from smartphone-derived digital biomarkers that uses a novel combination of a multilayer perceptron with neural soft attention to improve learning of patterns in long-term smartphone monitoring data. Using data from a cohort of 774 participants, we demonstrate that our deep-learning models are able to distinguish between people with and without MS with an area under the receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our experimental results indicate that digital biomarkers derived from smartphone data could in the future be used as additional diagnostic criteria for MS. UL TIPLE sclerosis (MS) is a neurological disease that affects around 2 million people worldwide [1].

Delivering useful hydrological forecasts is critical for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among others. In this work, we present and appraise a new methodology for hydrological time series forecasting. This methodology is based on simple combinations. The appraisal is made by using a big dataset consisted of 90-year-long mean annual river flow time series from approximately 600 stations. Covering large parts of North America and Europe, these stations represent various climate and catchment characteristics, and thus can collectively support benchmarking. Five individual forecasting methods and 26 variants of the introduced methodology are applied to each time series. The application is made in one-step ahead forecasting mode. The individual methods are the last-observation benchmark, simple exponential smoothing, complex exponential smoothing, automatic autoregressive fractionally integrated moving average (ARFIMA) and Facebook's Prophet, while the 26 variants are defined by all the possible combinations (per two, three, four or five) of the five afore-mentioned methods. The findings have both practical and theoretical implications. The simple methodology of the study is identified as well-performing in the long run. Our large-scale results are additionally exploited for finding an interpretable relationship between predictive performance and temporal dependence in the river flow time series, and for examining one-year ahead river flow predictability.

Thanks to popular science fiction, almost any person on Earth has some knowledge of AI. For business purposes and utilizing advanced technologies there are deeper reasons to look into Artificial Intelligence. Saving time and money, increasing productivity and revenues, avoiding human factor errors is what you get from AI right off the top of a hat. Hundreds of artificial intelligence companies are already conquering markets. The main purpose of our article is not reviewing pros and cons, rather offering AI development companies which can assist your business strategy for consideration. On one hand, our list does not include big-guns like Amazon, Alexa or Apple, and on the other, we also discarded plenty of startups with questionable reputation. We handpicked only those tech companies who, in our opinion, are able to cope with the most challenging AI projects and have positive customer feedback.

Cyberlaw as a discipline saw some massive advances in 2019. These advances were seen in different thrust areas of this discipline. The first significant element of 2019 was the determined focus of sovereign governments across the world, to come up with strong national cybersecurity legislations and legislative frameworks. Consequently, different countries and sovereign governments started moving in the direction of trying to regulate cybersecurity. These regulations normally took two distinctive manifestations.

Temporal abstraction refers to the ability of an agent to use behaviours of controllers which act for a limited, variable amount of time. The options framework describes such behaviours as consisting of a subset of states in which they can initiate, an internal policy and a stochastic termination condition. However, much of the subsequent work on option discovery has ignored the initiation set, because of difficulty in learning it from data. We provide a generalization of initiation sets suitable for general function approximation, by defining an interest function associated with an option. We derive a gradient-based learning algorithm for interest functions, leading to a new interest-option-critic architecture. We investigate how interest functions can be leveraged to learn interpretable and reusable temporal abstractions. We demonstrate the efficacy of the proposed approach through quantitative and qualitative results, in both discrete and continuous environments.