In data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. (Wikipedia)
From smart infrastructure grids to bot-authored news reports, algorithms and artificial intelligence capabilities are routinely working behind the scenes in various aspects of our day-to-day lives. COVID-19 only accelerated the adoption of automation across industries and Gartner pegged "smarter, responsible [and] scalable AI" as one of its top 2021 data and analytics tech trends. In this roundup, we've highlighted some of the ways AI is transforming everything from animal conversation efforts to matchmaking in the digital age. The agtech company AppHarvest is using a number of transformative practices to reimagine farming in the 21st century, including AI. The company is tapping computer vision and AI to help its robo-harvester, Virgo, pick ripe produce right from the vine.
In the IT Operations world, nowadays you hear the term AIOps frequently. Gartner defines it as that which, "… combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination." In simple words, AIOps is the application of AI and Automation to IT processes, for faster resolution of issues. Very often, we get all excited about the prospects of using AI and ML on IT event data. However, it's also important to consider that Automation is an equal contributor to any AIOps implementation. The role of AI & ML is used for generating the necessary signal or insight about a problem.
Artificial intelligence (AI) applied to healthcare wasn't on the radar 10 years ago. Outside of academic circles, relatively few people were even aware of the potential of the technology. It seemed like a distant fantasy. Today, though, that's all changed. AI is the new trend in innovation, and everyone in the sector is talking about it.
In this post, we look at how we can automate the detection of anomalies in a manufactured product using Amazon Lookout for Vision. Using Amazon Lookout for Vision, you can notify operators in real time when defects are detected, provide dashboards for monitoring the workload, and get visual insights from the process for business users. Amazon Lookout for Vision is a machine learning (ML) service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. Defect and anomaly detection during manufacturing processes is a vital step to ensure the quality of the products. The timely detection of faults or defects and taking appropriate actions is important to reduce operational and quality-related costs. According to Aberdeen's research, "Many organizations will have true quality-related costs as high as 15 to 20 percent of sales revenue, in extreme cases some going as high as 40 percent." Manual inspection, either in-line or end-of-line, is a time-consuming and expensive task.
The rapid advancements in the technology of closed circuit television cameras and their underlying infrastructure has led to a sheer number of surveillance cameras being implemented globally, estimated to go beyond 1 billion by the end of the year 2021 . Considering the massive amounts of videos generated in real-time, manual video analysis by human operator becomes inefficient, expensive, and nearly impossible, which in turn makes a great demand for automated and intelligent methods for an efficient video surveillance system. An important task in video surveillance is anomaly detection, which refers to the identification of events that do not conform to the expected behavior. Abnormal events in the general sense have the characteristics of suddenness,in order to be able to understand the abnormal events in the first time, it usually takes a lot of manpower to stare at the monitoring screen for a long time to observe, so It will not only make people tired, but also easily overlook some inconspicuous events. Therefore, the automatic detection and recognition of abnormal events of surveillance video in complex scenes, as the core subject of intelligent video surveillance systems, is receiving more and more attention from researchers.
This workflow shows how to create a simple convolutional network and use it for image classification. This workflow shows an example of how to detect the fonts of letters using a convolutional network. This workflow shows an example of the View of the DL4J Feedforward Leaner nodes. This workflow shows basic concepts of the KNIME Deeplearning4J Integration. This workflow shows how to do anomaly detection of the MNIST dataset using a convolutional network.
Artificial intelligence (AI) in cybersecurity was a popular topic at RSA's virtual conference this year, with good reason. Many tools rely on AI, using it for incident response, detecting spam and phishing and threat hunting. However, while AI security gets the session titles, digging deeper, it is clear that machine learning (ML) is really what makes it work. ML allows for "high-value predictions that can guide better decisions and smart actions in real-time without humans stepping in." Yet, for all ML can do to improve intelligence and help AI security do more, ML has its flaws.
Detecting and responding to novel situations in open-world environments is a key capability of human cognition. Current artificial intelligence (AI) researchers strive to develop systems that can perform in open-world environments. Novelty detection is an important ability of such AI systems. In an open-world, novelties appear in various forms and the difficulty to detect them varies. Therefore, to accurately evaluate the detection capability of AI systems, it is necessary to investigate the difficulty to detect novelties. In this paper, we propose a qualitative physics-based method to quantify the difficulty of novelty detection focusing on open-world physical domains. We apply our method in a popular physics simulation game, Angry Birds. We conduct an experiment with human players with different novelties in Angry Birds to validate our method. Results indicate that the calculated difficulty values are in line with the detection difficulty of the human players.