Nippon Telegraph and Telephone Corp. (NTT) said Thursday it will launch a trial for a farming support service using drones and artificial intelligence technology, with a goal of commercializing the service in Japan and other Asian countries. The new system, which connects drones with GPS satellites, is anticipated to help the farm industry in the nation amid a serious labor shortage. NTT aims to raise crop output by up to 30 percent through the new service. The telecommunications giant will conduct the trial service on 8 hectares of a rice field in Fukushima Prefecture from later this month to March 2021. It aims to launch the service on a commercial basis in Japan in two years.

The knowledge is the output of learning through the inseparable combination of theory and practice. It's what remains in one's experience from all the data which got shaped into what we call information. This process can be noticed throughout the different stages of our lives and it's never limited to the academic journey. What I'm aiming to express is that machine learning is nothing but a human logic tailored for more complex problems that surely require more computational capabilities. The last quote represents the nature knowledge acquiring process which, as you may notice, is similar to CRISP-DM Methodology which I detailed in a previous article and which is essential to succeed in your data mining project. To define Machine learning, its is a set of algorithms that are included in the many operations like the Data Mining process and which help you transform your raw data into knowledge, the layer that hides under the obvious information.

Classification using multimodal data arises in many machine learning applications. It is crucial not only to model cross-modal relationship effectively but also to ensure robustness against loss of part of data or modalities. In this paper, we propose a novel deep learning-based multimodal fusion architecture for classification tasks, which guarantees compatibility with any kind of learning models, deals with cross-modal information carefully, and prevents performance degradation due to partial absence of data. We employ two datasets for multimodal classification tasks, build models based on our architecture and other state-of-the-art models, and analyze their performance on various situations. The results show that our architecture outperforms the other multimodal fusion architectures when some parts of data are not available.

Material recognition methods use image context and local cues for pixel-wise classification. In many cases only a single image is available to make a material prediction. Image sequences, routinely acquired in applications such as mutliview stereo, can provide a sampling of the underlying reflectance functions that reveal pixel-level material attributes. We investigate multi-view material segmentation using two datasets generated for building material segmentation and scene material segmentation from the SpaceNet Challenge satellite image dataset. In this paper, we explore the impact of multi-angle reflectance information by introducing the \textit{reflectance residual encoding}, which captures both the multi-angle and multispectral information present in our datasets. The residuals are computed by differencing the sparse-sampled reflectance function with a dictionary of pre-defined dense-sampled reflectance functions. Our proposed reflectance residual features improves material segmentation performance when integrated into pixel-wise and semantic segmentation architectures. At test time, predictions from individual segmentations are combined through softmax fusion and refined by building segment voting. We demonstrate robust and accurate pixelwise segmentation results using the proposed material segmentation pipeline.

Scientists and researchers have long extolled the extraordinary potential capabilities of universal quantum computers, like simulating physical and natural processes or breaking cryptographic codes in practical time frames. Yet important developments in the technology--the ability to fabricate the necessary number of high-quality qubits (the basic units of quantum information) and gates (elementary operations between qubits)--is most likely still decades away. However, there is a class of quantum devices--ones that currently exist--that could address otherwise intractable problems much sooner than that. These near-term quantum devices, coined Noisy Intermediate-Scale Quantum (NISQ) by Caltech professor John Preskill, are single-purpose, highly imperfect, and modestly sized. Dr. Anton Toutov is the cofounder and chief science officer of Fuzionaire and holds a PhD in organic chemistry from Caltech.

It began, late last year, with Silver boasting about the success of his election models and Taleb shooting back that Silver doesn't "know how math works." Silver said Taleb was "consumed by anger" and hadn't had any new ideas since 2001. The argument has gotten personal, with Silver calling Taleb an "intellectual-yet-idiot" (an insult taken from Taleb's own book) and Taleb calling Silver "klueless" and "butthurt." Here is a recap of what they're fighting about so you can know who's right (Silver, mostly) and who's wrong (Taleb). The origin of Taleb's ire can be found in Silver's success since 2008--and his some-time failures. As I described in Nautilus last month, evaluating probabilistic election forecasts can be conceptually slippery, made especially difficult by the counterintuitive properties of mathematical probability.

Finding the source of a disturbance or fault in complex systems such as industrial chemical processing plants can be a difficult task and consume a significant number of engineering hours. In many cases, a systematic elimination procedure is considered to be the only feasible approach but can cause undesired process upsets. Practitioners desire robust alternative approaches. This paper presents an unsupervised, data-driven method for ranking process elements according to the magnitude and novelty of their influence. Partial bivariate transfer entropy estimation is used to infer a weighted directed graph of process elements. Eigenvector centrality is applied to rank network nodes according to their overall effect. As the ranking of process elements rely on emerging properties that depend on the aggregate of many connections, the results are robust to errors in the estimation of individual edge properties and the inclusion of indirect connections that do not represent the true causal structure of the process. A monitoring chart of continuously calculated process element importance scores over multiple overlapping time regions can assist with incipient fault detection. Ranking results combined with visual inspection of information transfer networks is also useful for root cause analysis of known faults and disturbances. A software implementation of the proposed method is available.

Businesses and nonprofits are finding novel ways to employ artificial intelligence in the developing world, using the tools to improve agriculture yields, infant health care, and entrepreneur earnings, according to speakers at MIT Technology Review's EmTech Digital conference in San Francisco on Tuesday. Solomon Assefa, who oversees IBM's research labs in Kenya and South Africa, said the company has been using AI to more accurately predict crop yields in specific regions, based on shifting weather patterns, soil moisture, and other conditions. This insight into growing conditions has helped local farmers raise financing to expand their operations, or make better decisions about the right seeds, appropriate fertilizer, and ideal times to plant and harvest. Separately, the tech giant's research lab has partnered with a startup, Hello Tractor, that links farmers in need of tractors with owners looking to lease equipment. By forecasting demand for the vehicles, IBM has also helped owners raise money to expand their fleet, boosting their profits, Assefa said.

Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors, and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 15 proprietary industrial datasets spanning a wide variety of chemical endpoints. In addition, we introduce a graph convolutional model that consistently outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows.

A Dutch inventor has developed a'cow toilet' to help cut ammonia emissions from cow urine that cause environmental pollution. Tests on the device, which collects around 26 to 35 pints (15 to 20 litres) of urine produced daily by a single cow, have started on a farm in the country. Inventor Henk Hanskamp claims the device could halve the ammonia emissions from cows, which account for almost half (49 per cent) of agricultural ammonia pollution. This type of contamination has multiple negative impacts on both the environment and human health and can threaten aquatic wildlife and contribute to smog. The small-scale tests are being conducted in the Netherlands, the world's second-biggest agricultural exporter behind the United States.