If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Mobile devices are popular with hackers because they're designed for quick responses based on minimal contextual information. Verizon's 2020 Data Breach Investigations Report (DBIR) found that hackers are succeeding with integrated email, SMS and link-based attacks across social media aimed at stealing passwords and privileged access credentials. And with a growing number of breaches originating on mobile devices according to Verizon's Mobile Security Index 2020, combined with 83% of all social media visits in the United States are on mobile devices according to Merkle's Digital Marketing Report Q4 2019, applying machine learning to harden mobile threat defense deserves to be on any CISOs' priority list today. Google's use of machine learning to thwart the skyrocketing number of phishing attacks occurring during the Covid-19 pandemic provides insights into the scale of these threats. During a typical week in April of this year, Google's G-Mail Security team saw 18M daily malware and phishing emails related to Covid-19.
The application of convolutional neural network technology continues to expand beyond machine vision use cases to the red-hot drug discovery market. The application of AI to new drug development has moved in fits and starts, including IBM's (NYSE: IBM) decision last year to pull the plug on its Watson AI software for pharmaceutical research. Lately, investors are warming to new small molecule drug discovery efforts, including recent efforts aimed at developing therapies for COVID-19. The latest example comes from Atomwise, a San Francisco-based company that claims to have developed the first convolutional neural network for new drug discovery. Atomwise announced a hefty $123 million Series B funding round this week, bringing its investment total to more than $174 million.
Eigenvector Research, Inc. is pleased to announce our 15th annual Eigenvector University. Theses instructor-led, hands-on courses will be online Tuesdays and Thursdays from September 15 though October 15, 2020. Our Chemometrics Courses are aimed at engineers, chemists and other scientists who want to be able to analyze their own laboratory or process data, develop their own data models or develop instruments that produce analytical data. The courses are especially well suited for those with an interest in process analytical technology (PAT) in the pharmaceutical industries, metabolomics, and systems biology. The courses are also useful for those that manage staff that generate and analyze data.
Quant Insight (Qi), a provider of analytics that focus on macro factors to the buy-side quant community, has developed a client application within the OpenFin desktop operating environment. The move is aimed at reducing overhead while improving client experience and flexibility. According to Mahmood Noorani, Qi's founder and CEO, "Making Qi available through the OpenFin ecosystem will allow us to efficiently deliver continuous quant macro data to users without large overheads. Our customers including wealth managers, hedge funds, pension funds will benefit from the enhanced user experience and ease of access to complement their daily decision making and workflow processes." Qi provides quantitative macro analytics to buy-side quants operating across multiple asset classes and with emphasis ranging from discretionary to systematic, and from equity long/short to absolute return.
Google Cloud is rolling out an "AI Hub" supplying machine learning content ranging from data pipelines and TensorFlow modules. It also announced a new pipeline component for the Google-backed Kubeflow open-source project, the machine learning stack built on Kubernetes that among other things packages machine learning code for reuse. The AI marketplace and the Kubeflow pipeline are intended to accelerate development and deployment of AI applications, Google said Thursday (Nov. The new services follow related AI efforts such as expanding access to updated Tensor processing units (TPUs) on the Google Cloud. The AI Hub is described as a community for accessing "plug-and-play" machine learning content.
WekaIO (Weka), the innovation leader in high-performance and scalable file storage, and an NVIDIA Partner Network Solution Advisor introduced Weka AI, a transformative storage solution framework underpinned by the Weka File System (WekaFS) that enables accelerated edge-to-core-to-cloud data pipelines. Weka AI is a framework of customizable reference architectures (RAs) and software development kits (SDKs) with leading technology alliances like NVIDIA, Mellanox, and others in the Weka Innovation Network (WIN) . Weka AI enables chief data officers, data scientists and data engineers to accelerate genomics, medical imaging, the financial services industry (FSI), and advanced driver-assistance systems (ADAS) deep learning (DL) pipelines. In addition, Weka AI easily scales from entry to large integrated solutions provided through VARs and channel partners. Artificial Intelligence (AI) data pipelines are inherently different from traditional file-based IO applications.
As humans, we've generally been focused on making improvements, regardless of whether it's in our own lives or work. Innovation is one of the vehicles that drive our shared need to improve, and AI and machine learning advancements appear to be skyrocketing our growth, compared with the simple innovation that was accessible only a decade-and-a-half ago. Artificial Intelligence will be so significant in the future that it won't be only a choice any longer. Organizations would need to make this innovation a basic part of their processes. Experts at Accenture believe that AI is fit for expanding efficiency levels by 40% in 2035.
Since its initiation, AI has captured the attention of the world owing to its wide range of capabilities. Even NASA (National Aeronautics and Space Administration) has been planning to enlist AI for future space exploration and other programs. Recently in June 2020, the American space agency announced that it has been training the system of AI that will aid scientists in their quest to look for signs of ancient life on Mars and other planets and moons. The program will be spearheaded by the European Space Agency (ESA) Rosalind Franklin'ExoMars' rover mission. It will be heading for the red planet in 2022/23, before moving beyond to moons such as Jupiter's Europa, and of Saturn's Enceladus and Titan.
Customers often need to identify single objects in images; for example, to identify their company's logo, find a specific industrial or agricultural defect, or locate a specific event, like hurricanes, in satellite scans. In this post, we showcase how to train a custom model to detect a single object using Amazon Rekognition Custom Labels. Amazon Rekognition is a fully managed service that provides computer vision (CV) capabilities for analyzing images and video at scale, using deep learning technology without requiring machine learning (ML) expertise. Amazon Rekognition Custom Labels lets you extend the detection and classification capabilities of the Amazon Rekognition pre-trained APIs by using data to train a custom CV model specific to your business needs. With the latest update to support single object training, Amazon Rekognition Custom Labels now lets you create a custom object detection model with single object classes.