Sinequa said its neural search function can answer natural language questions, thanks to four deep learning models it developed with Microsoft Azure and Nvidia teams. Enterprise search company Sinequa is adding a neural search option to its platform with the aim of giving improved accuracy and relevance to customers. Sinequa said the new AI function is the first commercially available system to use four deep learning language models. Combined with the platform's natural language processing and semantic search abilities, Sinequa said this will lead to improved question-answering and search relevance. The Sinequa Search Cloud platform is designed to help employees find relevant information and insights from all enterprise sources in any language in the context of their work.
MLCommons director David Kanter made the point that improvements in both hardware architectures and deep learning software have led to performance improvements on AI that are ten times what would be expected from traditional chip scaling improvements alone. Google and Nvidia split the top scores for the twice-yearly benchmark test of artificial intelligence program training, according to data released Wednesday by the MLCommons, the industry consortium that oversees a popular test of machine learning performance, MLPerf. The version 2.0 round of MLPerf training results showed Google taking the top scores in terms of lowest amount of time to train a neural network on four tasks for commercially available systems: image recognition, object detection, one test for small and one for large images, and the BERT natural language processing model. Nvidia took the top honors for the other four of the eight tests, for its commercially available systems: image segmentation, speech recognition, recommendation systems, and solving the reinforcement learning task of playing Go on the "mini Go" dataset. Also: Benchmark test of AI's performance, MLPerf, continues to gain adherents Both companies had high scores for multiple benchmark tests, however, Google did not report results for commercially available systems for the other four tests, only for those four it won. Nvidia reported results for all eight of the tests.
As a person who is involved in mostly the data related activities such as data processing, data manipulation and model predictions, you are also given an additional task as a data scientist or a machine learning engineer to deploy the product in real-time. After doing the heavy lifting of understanding the right parameters for various models and finally coming up with the best model, deploying the model in real-time can have a significant impact in the way it impresses the business and creates monetary impact. Finally, the model is deployed, and it is able to predict and give its decision based on the historical data at which it was trained. At this point, most people consider that they have completed a large portion of the machine learning tasks. While it is true that a good amount of work has been done so that the models are productionized, there is additional step that is often overlooked in the machine learning lifecycle that is to monitor the models and check if they are performing on the future data or the data that the models have not seen before.
Dr. PKS Prakash is a Data Scientist and an author. He has spent last 12 years in developing many data science solution to solve problems from leading companies in healthcare, manufacturing, pharmaceutical and e-commerce domain. He is working as Data Science Manager at ZS Associates. ZS is one of the world's largest business services firms helping clients with commercial success, by creating data-driven strategies using advanced analytics that they can implement within their sales and marketing operations to make them more competitive, and by helping them deliver impact where it matters.
This special issue highlights the applications, practices and theory of artificial intelligence in the domain of cyber security. In the past few decades there has been an exponential rise in the application of artificial intelligence technologies (such as deep learning, machine learning, block-chain, and virtualization etc.) for solving complex and intricate problems arising in the domain of cyber security. The versatility of these techniques have made them a favorite among scientists and researchers working in diverse areas. The primary objective of this topical collection is to bring forward thorough, in-depth, and well-focused developments of artificial intelligence technologies and their applications in cyber security domain, to propose new approaches, and to present applications of innovative approaches in real facilities. AI can be both a blessing and a curse for cybersecurity.
With the ability to revolutionize everything from self-driving cars to robotic surgeons, artificial intelligence is on the cutting edge of tech innovation. Two of the most widely recognized AI services are Microsoft's Azure Machine Learning and IBM's Watson. Both boast impressive functionality, but which one should you choose for your business? Azure Machine Learning is a cloud-based service that allows data scientists or developers to train, build and deploy ML models. It has a rich set of tools that makes it easy to create predictive analytics solutions. This service can be used to build predictive models using a variety of ML algorithms, including regression, classification and clustering.
In this article, we will discuss how data processing is done in DALI and examine the foundational concepts of this capable package, namely, operations, data nodes, and pipelines, and discover how to build a PyTorch data loader with them. Without further ado, let's get coding! DALI's core is nvidia.dali.Pipeline, a class that defines the data processing procedure, for instance, reading image bytes, decoding them, and normalization, as illustrated below. Note that there are two classes of nodes in this figure; one would be operations that transform the data, portrayed by rectangles, and the other would be data nodes that are the inputs/outputs of the operations, denoted by circles. Reading bytes, decoding them, and normalization are therefore operations, and their inputs/outputs are data nodes.
Please note this role is eligible for remote working within Hungary. Black Swan Data is a fast-growing technology and data science business, with offices in the UK, South Africa, Hungary. We build high quality SaaS solutions which automate data science using advanced machine learning and deep learning techniques. We use some of the coolest technology on the planet so you will never get bored of doing the same thing. You'll be part of a dynamic and growing global team As we continue to grow across the world, you'll find every day brings with it fresh challenges and opportunities to try new things.
Let's take a detailed look. This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.
Deep learning uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, image classification and others. Deep learning often uses convolutional neural networks for many or all of its layers.