If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
A home office has the potential to cut out an expensive time-consuming commute, provide a more relaxed working environment, and allow more control over the working conditions that suit you. Your workstation is no longer restricted to a single desk; audio is free to roam the air, meaning conference calls aren't confined to a solitary room; and the use of technology now actually augments collaboration with your colleagues rather than hampers it. Among the things to consider are how best to optimise your environment to reduce clutter and unwanted distractions. Internet of Things (IoT) devices can help you do this. The market is full of IoT devices for the home, some useful, some not so useful, and others really rather pointless.
Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose BRITS, a novel method based on recurrent neural networks for missing value imputation in time series data. Our proposed method directly learns the missing values in a bidirectional recurrent dynamical system, without any specific assumption. The imputed values are treated as variables of RNN graph and can be effectively updated during the backpropagation.BRITS has three advantages: (a) it can handle multiple correlated missing values in time series; (b) it generalizes to time series with nonlinear dynamics underlying; (c) it provides a data-driven imputation procedure and applies to general settings with missing data.We evaluate our model on three real-world datasets, including an air quality dataset, a health-care data, and a localization data for human activity. Experiments show that our model outperforms the state-of-the-art methods in both imputation and classification/regression accuracies.
The study intended to understand spatial distribution patterns of odour complaints as well as to investigate socioeconomic* characteristics of the company's operational area. Wastewater treatment works and their impact on environment, particularly on air quality, have been reported since air pollution started to become a serious threat in populated areas such as urban agglomerations. Odour nuisance from wastewater treatment installations can also be linked to concerns on air quality especially since studies on odour effects on human health started to show its impacts on local communities. Symptoms associated to air pollution such as headache, nausea, hoarseness, cough, congestions, shortness of breath, eye, nose, throat irritation have been widely reported by local authorities showing the importance of control and reduction of air pollution.
As machine learning (ML) is being adopted more widely, human lives are being irreversibly transformed. Some ML uses are visible but many others are not even noticeable, already working behind the scenes. First, our daily lives are becoming more influenced by "machine-generated" insights. The web pages you browse seem to know the next questions you would like to ask. Restaurant recommendations are becoming sharper and routes to your destination are optimized based on up-to-date traffic and road conditions -- even more optimized than drivers can intuit based on their experiences.
In recent years Southeast Asia has seen a significant increase in the intensity and frequency of haze events, or days in which visibility falls below 10 kilometers. Caused by airborne particulates known as aerosols, such low-visibility days reduce air quality and endanger human health. The main sources of the pollution are human activities that produce fires -- biomass burning chief among them -- and those that do not, such as fossil fuel combustion, construction, and road dust. Air pollution mitigation measures are urgently needed to address the problem, but will only be effective through a better understanding of the relative contributions of fire and non-fire aerosols to the region's air quality. Toward that end, a team of researchers from MIT and collaborating institutions in Singapore and Hong Kong has developed a model that could provide decision makers with a useful breakdown of air quality impact by emissions source.
Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years. More specially, South Korea has joined the ranks of the world's most polluted countries alongside with other Asian capitals, such as Beijing or Delhi. Much research is being conducted in environmental science to evaluate the dangerous impact of particulate matters on public health. Besides that, deterministic models of air pollutant behavior are also generated; however, this is both complex and often inaccurate. On the contrary, deep recurrent neural network reveals potent potential on forecasting out-comes of time-series data and has become more prevalent. This paper uses Recurrent Neural Network (RNN) with Long Short-Term Memory units as a framework for leveraging knowledge from time-series data of air pollution and meteorological information in Daegu, Seoul, Beijing, and Shenyang. Additionally, we use encoder-decoder model, which is similar to machine comprehension problems, as a crucial part of our prediction machine. Finally, we investigate the prediction accuracy of various configurations. Our experiments prevent the efficiency of integrating multiple layers of RNN on prediction model when forecasting far timesteps ahead. This research is a significant motivation for not only continuing researching on urban air quality but also help the government leverage that insight to enact beneficial policies
For several years, the state of Utah was collecting statistics and feedback on public opinion, but the state didn't really have a plan for what to do with the data. Recently, it decided to use machine learning tools to analyze health, transportation, air quality and geo-based Twitter information to perform sentiment analysis before, during and after Utah's winter inversions and air quality spikes. Utah CIO Michael Hussey explained how the state went about it at the 2018 National Association of State Chief Information Officers (NASCIO) Midyear Conference in Baltimore on Tuesday. Winter inversions in Utah occur when the usual atmospheric conditions become inverted. A dense layer of cold air becomes trapped under a layer of warm air, essentially sealing pollutants closer to the ground.
As machine learning (ML) is being adopted more widely, human lives are being irreversibly transformed . Some ML uses are visible but many others are not even noticeable, already working behind the scenes. First, our daily lives are becoming more influenced by "machine-generated" insights. The web pages you browse seem to know the next questions you would like to ask. Restaurant recommendations are becoming sharper and routes to your destination are optimized based on up-to-date traffic and road conditions, even more optimized than drivers can intuit based on their experiences.
Urban air pollution has attracted much attention these years for its adverse impacts on human health. While monitoring stations have been established to collect pollutant statistics, the number of stations is very limited due to the high cost. Thus, inferring fine-grained urban air quality information is becoming an essential issue for both government and people. In this paper, we propose a generic neural approach, named ADAIN, for urban air quality inference. We leverage both the information from monitoring stations and urban data that are closely related to air quality, including POIs, road networks and meteorology. ADAIN combines feedforward and recurrent neural networks for modeling static and sequential features as well as capturing deep feature interactions effectively. A novel attempt of ADAIN is an attention-based pooling layer that automatically learns the weights of features from different monitoring stations, to boost the performance. We conduct experiments on a real-world air quality dataset and our approach achieves the highest performance compared with various state-of-the-art solutions.