"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Bluware Corp, the digital innovation platform that enables the oil and gas industry to accelerate digital transformation initiatives using deep learning, is pleased to announce a new agreement with BP (NYSE: BP). Bluware's technology will help BP to improve quality and speed when delivering seismic interpretation products. "BP recognizes the significant impact advances in digital technology can bring and we are pleased to implement Bluware InteractivAI, a new and innovative deep learning technology, augmenting our geoscientists' ability to accelerate subsurface data interpretation," says Ahmed Hashmi, Upstream Chief Digital and Technology Officer at BP. Large seismic data sets are difficult to move and use in workflows and time consuming to interpret. InteractivAI, powered by Bluware Volume Data Store (VDS) cloud-native data environment, enables the acceleration of detailed interpretation tasks. With this tool geoscientists can now train and correct deep learning results interactively, significantly improving structural interpretation workflows.
Artificial Intelligence (AI) is changing the world. Many industries have already been impacted by the integration of AI technology to improve business processes, but that's only the beginning. Through the use of big data, machine learning, and the Internet of Things (IoT), AI captures the ability to think and make decisions like a human would -- but on a massive scale. Correspondingly, this technology has found a use in almost every sector of industry. Education, healthcare, human resources, marketing, and supply chain management have changed and continue to develop through the use of AI technology.
MIM Software Inc., a leading global provider of medical imaging software, announced it has received 510(k) clearance from the US Food and Drug Administration (FDA) for its deep learning auto-contouring software, Contour ProtégéAI . Contour ProtégéAI is an auto-contouring solution that seamlessly integrates into any department's workflow and can be rapidly implemented into virtually any environment. User feedback and a determination to continuously improve auto-segmentation were key drivers in developing the product. "Our customers are under continual pressure to improve their practices while facing escalating time constraints," said Andrew Nelson, Chief Executive Officer of MIM Software Inc. "Our deep learning auto-segmentation product, Contour ProtégéAI, will play a critical role in reducing the burden of contouring." Auto-contouring is an ideal use case for deep learning algorithms because it is one of the most time-consuming clinical tasks.
Being such an immensely powerful sector, the legal field is definitely not exempt from the power of technology now paving its way steadily across all its areas. The advancement of technology in the law field has definitely led to an evolution in the operations of the legal professionals. As legal operations become increasingly automated, this has propelled legal professionals such as lawyers and paralegals to acquire proficiency in operations such as word processing, telecommunications, presenting data, and so on. Law technology has touched every part of the legal field, be it law firms and corporate practices to courtroom operations and handling of documents. Advancing technologies like artificial intelligence enable modern software to go through legal documents, simplify communications as well as discover suitable casework for law professionals.
Deep neural networks achieve state-of-the-art performance in many domains in signal processing. The main practice is getting pairs of examples, input, and its desired output, and then training a network to produce the same outputs with the goal that it will learn how to generalize also to new unseen data, which is indeed the case in many scenarios.
A browser is an incredibly complex piece of software. With such enormous complexity, the only way to maintain a rapid pace of development is through an extensive CI system that can give developers confidence that their changes won't introduce bugs. Given the scale of our CI, we're always looking for ways to reduce load while maintaining a high standard of product quality. We wondered if we could use machine learning to reach a higher degree of efficiency. At Mozilla we have around 50,000 unique test files. Each contain many test functions.
In 2019, 53% of global data and analysis established by decision-makers announced that artificial intelligence is set up, or entirely development inside their company. Here are the artificial intelligence forecasts for 2020. It is important to see that these findings are obtained from statistics revealing percentages calculated from the observation of Fortune 500 companies. The Fortune 500 companies are recognized as the absolute most profitable in the United States. The study shows finding that 29% of developers have worked on AI and machine learning in recent years. The findings came from a Forrester study.
Every day, users from all over the world perform hundreds of millions of search queries with Bing in more than 100 languages. Whether this is the first or the millionth time we see a query, whether the best results for a query change every hour or barely change at all, our users expect an immediate answer that serves their needs. Bing web search is truly an example of AI at Scale at Microsoft, showcasing the next generation of AI capabilities and experiences. Over the past few years, Bing and Microsoft Research have been developing and deploying large neural network models such as MT-DNN, Unicoder, and UniLM to maximize the search experience for our customers. The best of those learnings are open sourced into the Microsoft Turing language models.
The global market already seems a wide growth in the incorporation of artificial intelligence (AI) and machine learning (ML). In addition, AI primarily assists in the identification and monitoring of location and classifying objects along with segmenting scenes and defects, as it operates with less sensitivity to image variability or distortion. The companies are collaborating with the AI companies for integration in the process across print, pharmaceutical, consumer/industrial goods, and food inspection applications. Moreover, the are some traditional defect detection applications, where AI can be used to inspect for a wider range of defect types. Suppliers in the food and pharmaceutical marketplace are primarily intended towards the cost-effective way for the hyperspectral imaging deployment, to gain greater product insights. Moreover, hyperspectral imaging for pill inspection enables the ingredients detection to ensure the correct dosage delivery to the end user consumers.
Edit: If you want to see MarkovComposer in action, but you don't want to mess with Java code, you can access a web version of it here. In the following article, I'll present some of the research I've been working on lately. Algorithms, or algorithmic composition, have been used to compose music for centuries. For example, Western punctus contra punctum can be sometimes reduced to algorithmic determinacy. Then, why not use fast-learning computers capable of billions of calculations per second to do what they do best, to follow algorithms?