"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
Deep-Learning-as-a-Service, unveiled at IBM's annual IT industry conference in Las Vegas, seeks to lower barriers to deploying AI and deep-learning tools, a complex and painstakingly repetitive process that requires large amounts of computing power, the company said. The new service allows companies to upload data in Watson Studio, IBM's cloud-native platform for data scientists, developers and business analysts. There, they can create deep-learning algorithms for datasets – known in AI parlance as a "neural network" – using a drag-and-drop interface to select, configure, design and code the network. IBM also has automated the repetitive process of fine-tuning deep-learning algorithms, with successive training runs started, monitored and stopped automatically. For many firms, the complexity of creating smart algorithms from scratch has kept them from leveraging AI to parse massive stores of data for business value, the company said.
Understanding interpretations Establishing what happens inside the "brain" of a neural network has been an ongoing research aim for Google over the past few years. The company first described the internal workings of neural networks in a 2015 paper, explaining how the systems are able to create new images and recognise items. The company has now followed up on its "Inceptionism" paper with a new study into the "Building Blocks of Interpretability." Over the past year, Google has acquired more understanding of the way in which neural networks interpret images. In a blog post, the company said it's now exploring how to understand neural networks in the context of the "bigger picture."
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.
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.
NEW YORK, March 12, 2018 (GLOBE NEWSWIRE) -- 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.
"It seems like every time you turn around, someone is talking about the importance of artificial intelligence and machine learning," said Trey Ideker, PhD, University of California San Diego School of Medicine and Moores Cancer Center professor. "But all of these systems are so-called'black boxes.' They can be very predictive, but we don't actually know all that much about how they work." Ideker gives an example: machine learning systems can analyze the online behaviors of millions of people to flag an individual as a potential "terrorist" or "suicide risk." "Yet we have no idea how the machine reached that conclusion," he said.
Trace3 will play a key role in delivering NVIDIA-based artificial intelligence, machine learning, and deep learning solutions to enterprises worldwide. The NVIDIA partner program will include initiatives to help partners expand capabilities for the integration and deployment of NVIDIA GPU computing solutions, including NVIDIA DGX systems. The initial phase will focus on providing services for deep learning and neural network development for image analysis, natural language processing, and time-series analysis.
The "Industrial Machine Vision Market by Component (Hardware (Camera, Frame Grabber, Optics, Processor), and Software (Deep Learning, and Application Specific)), Product (PC-based, and Smart Camera-based), Application, End-User - Global Forecast to 2023" report has been added to ResearchAndMarkets.com's offering. The overall industrial machine vision market was valued at USD 7.91 Billion in 2017 and is expected to reach USD 12.29 Billion by 2023, at a CAGR of 7.61% between 2017 and 2023. This is because of the increasing need for quality inspection and automation, growing demand for AI and IoT integrated machine vision system, increasing adoption of Industrial 4.0, development of new connected technologies, and government initiatives to support smart factories, among others. Governments of different countries worldwide are encouraging investments in manufacturing, which is necessitating the use of various automation products for structural development. Software component is expected to grow at the highest rate between 2017 and 2023.