"... the research area that studies the operation and design of systems that recognize patterns in data." It includes statistical methods like discriminant analysis, feature extraction, error estimation, cluster analysis.
– Pattern Recognition Laboratory at Delft University of Technology
Leveraging the benefits of effective data preparation to help build a modern ERP system is a vital component in innovating an organization's data workflow systems. Complex pattern matching and parsing of unstructured data requires a great deal of time and effort often utilizing labor-intensive hand coding. Join us for this latest Data Science Central webinar to learn how B/A Products Company has managed to cut 6-12 months process time of reformatting, restructuring and preparing data down to only 2 months through automation and simplification. In this webinar you will: • Understand technology trends that simplify your analytics modernization journey • Learn about the challenges and solutions that B/A Products Company used to solve their issues with legacy ERP systems • Learn how to accelerate time-to-value for analytics projects with data preparation on AWS • See in action the before / after with the solution live demo Speakers: Jacob S J Joseph, Information Systems Manager - B/A Products Co. Samantha Winters, Director of Marketing and Business Analytics - B/A Products Co. Matt Derda, Customer Marketing Manager - Trifacta Hosted by: Stephanie Glen, Editorial Director - Data Science Central
Security products incorporating artificial intelligence techniques may reduce the workload for human analysts, taking over the time-consuming job of correlating information sources and mining voluminous logs to uncover suspicious patterns of activity. Vendors, seeing the hype around AI, are quick to slap the label on almost any technology for a cutting-edge veneer. AI and machine learning techniques detect patterns in data and use those patterns to make predictions about the future. But those models are only as good as the data used to train them. Make sure it's clear how the security system's models were created.
Now tell me: How much time has passed since you first logged on to your computer...READ MORE An efficient pattern recognition of a lion makes perfect evolutionary sense. If you see a large feline shape moving in some nearby brush, it is unwise to wait until you see the yellows of the lion's eyes before starting to run up the nearest tree. You need a brain that quickly detects entire shapes from fragments of the total picture and provides you with a powerful sense of the accuracy of this recognition. One need only think of the recognition of a new pattern that is so profound that it triggers an involuntary "a-ha!" to understand the degree of pleasure that can be associated with learning. It's no wonder that once a particular pattern-recognition-reward relationship is well grooved into our circuitry, it is hard to shake.
Sometimes recognition software is excellent at correctly categorizing certain types of images but totally fails with others. Some image recognition engines prefer cats over dogs, and some are far more descriptive with their color knowledge. But which is the best overall? Perficient Digital's image recognition accuracy study looked at image recognition -- one of the hottest areas of machine learning. It looked at Amazon AWS Rekognition, Google Vision, IBM Watson, and Microsoft Azure Computer Vision to compare images.
Outside of their ability to understand a company's fundamentals, one of the skills Raj Lala appreciates most about his portfolio managers is their ability to interpret body language. Sitting across from management teams before making a decision to either invest or divest from their companies, Lala, the CEO of Evolve ETFs, said his portfolio managers can learn a lot from simply reading the room. Maybe they spot a nervous twitch after a question on guidance or a CEO unable to make eye contact when responding to a question about declining revenues. That very human capability was at the forefront of Lala's mind when he was recently pitched on two types of artificial intelligence that he could incorporate into his portfolio management processes. And it's one of the reasons he said no. "I can't see AI getting to that point where it replaces human interaction and, quite honestly, I would say god bless our world if that's the case," Lala said.
We investigate the robustness properties of ResNeXt image recognition models trained with billion scale weakly-supervised data (ResNeXt WSL models). These models, recently made public by Facebook AI, were trained on 1B images from Instagram and fine-tuned on ImageNet. We show that these models display an unprecedented degree of robustness against common image corruptions and perturbations, as measured by the ImageNet-C and ImageNet-P benchmarks. The largest of the released models, in particular, achieves state-of-the-art results on both ImageNet-C and ImageNet-P by a large margin. The gains on ImageNet-C and ImageNet-P far outpace the gains on ImageNet validation accuracy, suggesting the former as more useful benchmarks to measure further progress in image recognition. Remarkably, the ResNeXt WSL models even achieve a limited degree of adversarial robustness against state-of-the-art white-box attacks (10-step PGD attacks). However, in contrast to adversarially trained models, the robustness of the ResNeXt WSL models rapidly declines with the number of PGD steps, suggesting that these models do not achieve genuine adversarial robustness. Visualization of the learned features also confirms this conclusion. Finally, we show that although the ResNeXt WSL models are more shape-biased than comparable ImageNet-trained models in a shape-texture cue conflict experiment, they still remain much more texture-biased than humans and their accuracy on the recently introduced "natural adversarial examples" (ImageNet-A) also remains low, suggesting that they share many of the underlying characteristics of ImageNet-trained models that make these benchmarks challenging.
The sophisticated technology that powers face recognition in many modern smartphones someday could receive a high-tech upgrade that sounds--and looks--surprisingly low-tech. This window to the future is none other than a piece of glass. University of Wisconsin-Madison engineers have devised a method to create pieces of "smart" glass that can recognize images without requiring any sensors or circuits or power sources. "We're using optics to condense the normal setup of cameras, sensors and deep neural networks into a single piece of thin glass," says UW-Madison electrical and computer engineering professor Zongfu Yu. Yu and colleagues published details of their proof-of-concept research today in the journal Photonics Research.
Machine Learning (ML) represents a massive change in the computing industry. It is a long-term trend that offers the potential for significant advantages for many enterprises. Accurate prediction is critical for practically all enterprises. Without a degree of confidence in business forecasting, organizations would have a difficult time delivering successful products and services in a cost-effective manner. Machine Learning provides the capability to offer deep predictive and prescriptive decision-making intelligence.
This week we learned that ICE has searched millions of American driver's license photos, using facial recognition tools; the aim - to look for immigrants who are in this country illegally. Now privacy rights supporters and immigration advocates are calling for more transparency and oversight. But as NPR's Joel Rose reports, some version of all this has happened once before. JOEL ROSE, BYLINE: Dozens of protesters gathered in Manhattan yesterday outside the office of a tech company that's growing but still unknown to many Americans. UNIDENTIFIED PROTESTER: You hear that, Palantir?
Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis. Recently, deep learning based supervised and unsupervised image registration methods have been extensively studied due to its excellent performance in spite of ultra-fast computational time compared to the classical approaches. In this paper, we present a novel unsupervised medical image registration method that trains deep neural network for deformable registration of 3D volumes using a cycle-consistency. Thanks to the cycle consistency, the proposed deep neural networks can take diverse pair of image data with severe deformation for accurate registration. Experimental results using multiphase liver CT images demonstrate that our method provides very precise 3D image registration within a few seconds, resulting in more accurate cancer size estimation.