The Global Artificial Intelligence in Agriculture (AIA) Market is expected to grow at a significant CAGR of 24.3% during the forecast period. The factors driving the growth of the global AIA market are rising adoption of information management systems (IMS), automated irrigation, increasing crop productivity by implementing deep learning techniques, and increasing global population. Furthermore, growing trend of precision farming and increasing adoption of smart sensors are also fueling the demand of the global AIA market. Replacement of human labor is also expected to overcome by AIA, to minimize scarcity of physical labor. However, the high cost of collecting data of agricultural land is a major restraint of the AIA market growth.

I open Google Translate twice as often as Facebook, and the instant translation of the price tags is not a cyberpunk for me anymore. That's what we call reality. It's hard to imagine that this is the result of a centennial fight to build the algorithms of machine translation and that there has been no visible success during half of that period. The precise developments I'll discuss in this article set the basis of all modern language processing systems -- from search engines to voice-controlled microwaves. The story begins in 1933.

IBM and AT&T team up to create robust IoT solutions. This collaboration will provide deeper insights from data collected from connected devices. This partnership will bring forth a fully integrated solution that will provide access to powerful design tools, global connectivity, advance analytics, and cognitive services for analyzing IoT data. These features will help to illuminate business opportunities across many industries. The power of Watson APIs, including visual recognition, personality insights, tradeoff analytics and translation, is captured within the Internet of Things to digest unstructured data through the utilization of cognitive computing, and deep learning approaches.

NVIDIA's (NASDAQ: NVDA) graphics processing unit (GPU)-based approach to high-performance computing and deep learning, a category of artificial intelligence (AI) in which machines are trained to make inferences from data the way humans do, has begun making inroads into the global oil and gas industry. This is great news for investors, as this is a multitrillion-dollar industry that forms the foundation of the global economy. While renewable forms of energy have been steadily displacing fossil fuels to generate electricity and electric vehicles (EVs) have begun lessening the transportation industry's ravenous appetite for petroleum products, full transformations of these realms will take decades. Moreover, beyond being used to produce just about everything, oil derivatives are key ingredients in products ranging from plastics and fertilizers to the asphalt that paves our roads and the synthetic fibers that clothe many of us. In 2018, NVIDIA has announced two wins in the oil and gas space.

In deep learning it is common to see a lot of discussion around tensors as the cornerstone data structure. Tensor even appears in name of Google's flagship machine learning library: "TensorFlow". Tensors are a type of data structure used in linear algebra, and like vectors and matrices, you can calculate arithmetic operations with tensors. A Gentle Introduction to Tensors for Machine Learning with NumPy Photo by Daniel Lombraña González, some rights reserved. Take my free 7-day email crash course now (with sample code).

The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen limited application to sequential data, and, as we demonstrate, existing recurrent VAE models have difficulty modeling sequences with long-term structure. To address this issue, we propose the use of a hierarchical decoder, which first outputs embeddings for subsequences of the input and then uses these embeddings to generate each subsequence independently. This structure encourages the model to utilize its latent code, thereby avoiding the "posterior collapse" problem which remains an issue for recurrent VAEs. We apply this architecture to modeling sequences of musical notes and find that it exhibits dramatically better sampling, interpolation, and reconstruction performance than a "flat" baseline model. An implementation of our "MusicVAE" is available online.

Deep learning is still not a very common tool in speaker verification field. We study deep convolutional neural network performance in the text-prompted speaker verification task. The prompted passphrase is segmented into word states - i.e. digits -to test each digit utterance separately. We train a single high-level feature extractor for all states and use cosine similarity metric for scoring. The key feature of our network is the Max-Feature-Map activation function, which acts as an embedded feature selector. By using multitask learning scheme to train the high-level feature extractor we were able to surpass the classic baseline systems in terms of quality and achieved impressive results for such a novice approach, getting 2.85% EER on the RSR2015 evaluation set. Fusion of the proposed and the baseline systems improves this result.

The group affect or emotion in an image of people can be inferred by extracting features about both the people in the picture and the overall makeup of the scene. The state-of-the-art on this problem investigates a combination of facial features, scene extraction and even audio tonality. This paper combines three additional modalities, namely, human pose, text-based tagging and CNN extracted features / predictions. To the best of our knowledge, this is the first time all of the modalities were extracted using deep neural networks. We evaluate the performance of our approach against baselines and identify insights throughout this paper.

Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administrator and analyst to trust and act on the automated analysis of machine learning models. Deep learning methods have been criticized as black box oracles which allow limited insight into decision factors. In this work we seek to "bridge the gap" between the impressive performance of deep learning models and the need for interpretable model introspection. To this end we present recurrent neural network (RNN) language models augmented with attention for anomaly detection in system logs. Our methods are generally applicable to any computer system and logging source. By incorporating attention variants into our RNN language models we create opportunities for model introspection and analysis without sacrificing state-of-the art performance. We demonstrate model performance and illustrate model interpretability on an intrusion detection task using the Los Alamos National Laboratory (LANL) cyber security dataset, reporting upward of 0.99 area under the receiver operator characteristic curve despite being trained only on a single day's worth of data.

Deep neural networks (DNNs) enable innovative applications of machine learning like image recognition, machine translation, or malware detection. However, deep learning is often criticized for its lack of robustness in adversarial settings (e.g., vulnerability to adversarial inputs) and general inability to rationalize its predictions. In this work, we exploit the structure of deep learning to enable new learning-based inference and decision strategies that achieve desirable properties such as robustness and interpretability. We take a first step in this direction and introduce the Deep k-Nearest Neighbors (DkNN). This hybrid classifier combines the k-nearest neighbors algorithm with representations of the data learned by each layer of the DNN: a test input is compared to its neighboring training points according to the distance that separates them in the representations. We show the labels of these neighboring points afford confidence estimates for inputs outside the model's training manifold, including on malicious inputs like adversarial examples--and therein provides protections against inputs that are outside the models understanding. This is because the nearest neighbors can be used to estimate the nonconformity of, i.e., the lack of support for, a prediction in the training data. The neighbors also constitute human-interpretable explanations of predictions. We evaluate the DkNN algorithm on several datasets, and show the confidence estimates accurately identify inputs outside the model, and that the explanations provided by nearest neighbors are intuitive and useful in understanding model failures.