In a perfect world, what you see is what you get. If this were the case, the job of Artificial Intelligence systems would be refreshingly straightforward. Take collision avoidance systems in self-driving cars. If visual input to on-board cameras could be trusted entirely, an AI system could directly map that input to an appropriate action--steer right, steer left, or continue straight--to avoid hitting a pedestrian that its cameras see in the road. But what if there's a glitch in the cameras that slightly shifts an image by a few pixels? If the car blindly trusted so-called'adversarial inputs,' it might take unnecessary and potentially dangerous action.
Contentsquare, which has developed a digital experience analytics platform that enables businesses to track online customer behavior, has acquired Upstride, a French startup specializing in improving machine-learning performance. Terms of the deal were not released. With the acquisition, Contentsquare gains Upstride's deep-learning experts to help it further drive innovation in ML and artificial intelligence. Fourteen Upstride engineers will join Contentsquare, bringing their experience of working for leading tech companies such as Facebook, Samsung, GoPro, and Nvidia. Meanwhile, Upstride CEO Gary Roth will fill a strategic role on Contentsquare's operations team.
In this blog, I will briefly talk about the different Machine Learning options that are available in Google Cloud Platform and walk through an example project of my own. This will include briefly talking about the older AI Platform service as well as introducing the new Vertex AI service. My project will give an example of how to read data from a GCS bucket, perform exploratory data analysis in a managed Jupyter notebook instance, train a model in that notebook, save the model to a different GCS bucket, and finally use that model in a full-stack application. Here is the repository with the code for this application. AI Platform was GCP's original Machine Learning service.
Anyone with any doubts about the interest in AI and its use across enterprise technologies only needs to look at the example of the Intelligent Document Processing (IDP) market and the kind of verticals that are investing in it to quash those doubts. According to the Everest Group's recently published report, Intelligent Document Processing (IDP) State of the Market Report 2021 (purchase required) the market for this segment alone is estimated at $700-750 million in 2020 and expected to grow at a rate of 55-65% over the next year. Cost impact is now the key driver for intelligent document processing adoption, closely followed by improving operational efficiency and productivity. These solutions blend AI technologies to efficiently process all types of documents and feed the output into downstream applications. Optical character recognition (OCR), computer vision, machine learning (ML) and deep learning models, and natural language processing (NLP) are the key core technologies powering IDP capabilities.
"Andrew is famous for his ability to teach complex topics that blend mathematics and algorithms, and this work I think is his best yet." Andrew Glassner is a research scientist specializing in computer graphics and deep learning. He is currently a Senior Research Scientist at Weta Digital, where he works on integrating deep learning with the production of world-class visual effects for films and television. He has previously worked as a researcher at labs such as the IBM Watson Lab, Xerox PARC, and Microsoft Research. He was Editor in Chief of ACM TOG, the premier research journal in graphics, and Technical Papers Chair for SIGGRAPH, the premier conference in graphics.
In the last post, we discussed an outline of AI powered cyber attacks and their defence strategies. In this post, we will discuss a specific type of attack which is called adversarial attack. Adversarial attacks are not common now because there are not many deep learning systems in production. But soon, we expect that they will increase. Adversarial attacks are easy to describe.
Computer vision technology is increasingly used in areas such as automatic surveillance systems, self-driving cars, facial recognition, healthcare and social distancing tools. Users require accurate and reliable visual information to fully harness the benefits of video analytics applications but the quality of the video data is often affected by environmental factors such as rain, night-time conditions or crowds (where there are multiple images of people overlapping with each other in a scene). Using computer vision and deep learning, a team of researchers led by Yale-NUS College Associate Professor of Science (Computer Science) Robby Tan, who is also from the National University of Singapore's (NUS) Faculty of Engineering, has developed novel approaches that resolve the problem of low-level vision in videos caused by rain and night-time conditions, as well as improve the accuracy of 3D human pose estimation in videos. The research was presented at the 2021 Conference on Computer Vision and Pattern Recognition (CVPR), a top ranked computer science conference. Night-time images are affected by low light and man-made light effects such as glare, glow, and floodlights, while rain images are affected by rain streaks or rain accumulation (or rain veiling effect).
"Fantastic! How fast can we scale?" Perhaps you've been fortunate enough to hear or ask that question about a new AI project in your organization. Or maybe an initial AI initiative has already reached production, but others are needed -- quickly. At this key early stage of AI growth, entesrprises and the industry face a bigger, related question: How do we scale our organizational ability to develop and deploy AI? Business and technology leaders must ask: What's needed to advance AI (and by extension, data science) beyond the "craft" stage, to large-scale production that is fast, reliable, and economical? The answers are crucial to realizing ROI, delivering on the vision of "AI everywhere", and helping the technology mature and propagate over the next five years.
Complete Guide to TensorFlow for Deep Learning with Python, Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Created by Jose Portilla English [Auto], French [Auto]Preview this Course - GET COUPON CODE Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control.
Today, Artificial Intelligence (AI), Machine Learning, and Deep Learning technologies are used in diverse fields as part of the daily life of large organizations across the globe. The rapid speed of AI growth demonstrates that it is a groundbreaking technology designed to transform the way people use devices and conduct business: achievements in unmanned aerial vehicles, the ability to beat people in chess and sporting games, automated customer service, and analytical systems – of course. Talking about the business, development, or marketing field, for instance, it is worth noting that Artificial Intelligence does not apply in a pure form to real self-aware intelligence machines in this sense. Instead, it can be considered a generic term for the number of software powered by automation that is being used by developers of websites and smartphone apps. They include the recognition of images and speech, cognitive computing, automated processing, and machine learning – for that matter. Speaking of AI in app creation, for many years, starting with Apple's Siri, AI has already been influential in app-creation and marketing growth.