Nowadays with a growing number of online controlling systems in the organization and also a high demand of monitoring and stats facilities that uses data streams to log and control their subsystems, data stream mining becomes more and more vital. Hoeffding Trees (also called Very Fast Decision Trees a.k.a. VFDT) as a Big Data approach in dealing with the data stream for classification and regression problems showed good performance in handling facing challenges and making the possibility of any-time prediction. Although these methods outperform other methods e.g. Artificial Neural Networks (ANN) and Support Vector Regression (SVR), they suffer from high latency in adapting with new concepts when the statistical distribution of incoming data changes. In this article, we introduced a new algorithm that can detect and handle concept drift phenomenon properly. This algorithms also benefits from fast startup ability which helps systems to be able to predict faster than other algorithms at the beginning of data stream arrival. We also have shown that our approach will overperform other controversial approaches for classification and regression tasks.

First days after celebration of the New Year is the time when looking back we can analyze our actions, promises and draw conclusions whether our predictions and expectations came true. As 2018 came to its end, it is perfect time to analyze it and to set trends for the next year. The amount of data generated every minute is enormous. Therefore new approaches, techniques, and solutions have been developed. Looking back to our article Top 10 Technology Trends of 2018 we can say that we were preparing you for the upcoming changes related to aspects of security, changes provoked by the AI in business operations, extensive application of blockchains, further development of the Internet of Things (IoT), growing of NLP, etc. Some of these statements have been implemented in 2018, yet some will remain topical in 2019 as well.

In computational fluid dynamics, it often takes days or weeks to simulate the aerodynamic behavior of designs such as jets, spacecraft, or gas turbine engines. One of the biggest open problems in the field is how to simulate such systems much more quickly with sufficient accuracy. Many approaches have been tried; some involve models of the underlying physics, while others are model-free and make predictions based only on existing simulation data. However, all previous approaches have severe shortcomings or limitations. We present a novel approach: we reformulate the prediction problem to effectively increase the size of the otherwise tiny datasets, and we introduce a new neural network architecture (called a cluster network) with an inductive bias well-suited to fluid dynamics problems. Compared to state-of-the-art model-based approximations, we show that our approach is nearly as accurate, an order of magnitude faster and vastly easier to apply. Moreover, our method outperforms previous model-free approaches.

Epiphanies at innovation conventions are hardly new, but Tim Heinze wasn't expecting his when the light bulb went off. The director of strategic operations for insurance company AXA XL's North American property unit, Heinze had just watched a presentation in which an IBM engineer talked about how Watson, the company's artificial intelligence (AI) software, generates efficiencies in offshore oil drilling operations. The presenter claimed that the Watson technology could "help one engineer think like a thousand." "That caught my attention," Heinze recalls, nodding to the red-hot trend of using sophisticated software to augment work performed by humans. AXA XL is piloting AI and natural language processing (NLP) software to help populate and process information on commercial business properties.

When I reviewed First Alert's Onelink Safe & Sound smoke alarm in mid-2018, I found it to be a powerful entry in the smart smoke detector market. Its inclusion of Bluetooth and an Amazon Echo-compatible smart speaker set it apart from every smoke and carbon-monoxide detector on the market. But its $199 price tag also made it far and away the most expensive device of its type on the market--and that price hasn't budged since its release. Enter the Onelink Smoke Carbon Monoxide Alarm, which lowers the cost of the original product by stripping out its most compelling features: The smart speaker and Bluetooth. Like other products in this category, the Onelink Smoke Carbon Monoxide Alarm is designed to extend the capabilities of a smoke detector by linking it with your smartphone. It still functions by firing off a (rather loud) local siren whenever smoke or CO are detected, but it also (supposedly) sends push alerts to your mobile device, a feature that is most helpful for times when you aren't at home but still want the peace of mind that it's not on fire.

This paper presents a new algorithm, Evolutionary eXploration of Augmenting Memory Models (EXAMM), which is capable of evolving recurrent neural networks (RNNs) using a wide variety of memory structures, such as Delta-RNN, GRU, LSTM, MGU and UGRNN cells. EXAMM evolved RNNs to perform prediction of large-scale, real world time series data from the aviation and power industries. These data sets consist of very long time series (thousands of readings), each with a large number of potentially correlated and dependent parameters. Four different parameters were selected for prediction and EXAMM runs were performed using each memory cell type alone, each cell type with feed forward nodes, and with all possible memory cell types. Evolved RNN performance was measured using repeated k-fold cross validation, resulting in 1210 EXAMM runs which evolved 2,420,000 RNNs in 12,100 CPU hours on a high performance computing cluster. Generalization of the evolved RNNs was examined statistically, providing interesting findings that can help refine the RNN memory cell design as well as inform future neuro-evolution algorithms development.

Principal component analysis is one of the most extensively studied methods for constructing linear low-dimensional representation of high-dimensional data. Modern applications such as privacy presevering distributedcomputations (Hardt and Roth (2013)), covariance estimtion of high-frequency data (Chang et al. (2018),Aït-Sahalia et al. (2010)), detecting power grid attacks (Bienstock and Shukla (2018), Escobar et al. (2018)) etc. require design of sub-linear time algorithms with low storage overhead. Existing workon PCA has focused on design and analysis of single-pass (streaming) algorithms with nearoptimal memoryand storage complexity assuming stationarity of the underlying data-generating process. However, physical systems generating data for such applications undergo rapid evolution. For example, dynamic market behaviour leads to time-series data with volatile covariance matrices. Our understanding of such physical system crucially relies on accurate estimation of the data generating space.

The majority of investors are familiar with artificial intelligence (AI) and the fact that it is currently being injected into nearly every industry. From cars to factories to shopping trips, AI is helping improve experiences all over the globe. But if you delve further into the world of AI, you come across a niche sector called deep learning. With this technology, computers are able to identify patterns and trends and then analyze that data to make educated decisions. This sounds like something human brains allow us to do on our own.

In enterprise AI, C3 (formerly C3 IoT) is amassing an impressive and seemingly unmatched record, one that the company has extended with its latest win, the expansion of a five-year engagement with Enel, Europe's largest power utility, to encompass nearly 50 million smart meters in homes and businesses. This follows C3 contract wins last year with Royal Dutch Shell, the U.S. Air Force and 3M, along with partnerships with AWS, Google Cloud and Microsoft Azure. In the large utilities space, other customers include Con Edison, covering the New York metropolitan area, and Engie, one of the biggest utilities in France. The new contract (dollar amount not disclosed) expands on C3's existing, five-year engagement for Enel in Italy involving 32 million smart meters. C3 will provide the €74.6 billion utility with AI and smart grid analytics applications enabling Enel to deploy the Unified Virtual Data Lake, integrating data across its retail, distribution, trading, renewables and conventional generation businesses. The C3 AI Suite automates elements of data management, data science and AI application building, enabling the entirety of corporate data, regardless of data format or system – such as ERP, HR, financial and operational systems, including SAP Hana, Oracle, Siemens, PostGreSQL, MongoDB, and Cloudera – "to enable and deliver next-generation AI applications across Enel's business," C3 said.

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12% of the average yield and 50% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11% of the average yield and 46% of the standard deviation. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT).