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) …
Today, we're happy to announce that the Deep Graph Library, an open source library built for easy implementation of graph neural networks, is now available on Amazon SageMaker. In recent years, Deep learning has taken the world by storm thanks to its uncanny ability to extract elaborate patterns from complex data, such as free-form text, images, or videos. However, lots of datasets don't fit these categories and are better expressed with graphs. Intuitively, we can feel that traditional neural network architectures like convolution neural networks or recurrent neural networks are not a good fit for such datasets, and a new approach is required. A Primer On Graph Neural Networks Graph neural networks (GNN) are one of the most exciting developments in machine learning today, and these reference papers will get you started.
As a testament to this, Snowflake recently achieved the Amazon Web Services (AWS) Machine Learning (ML) and Artificial Intelligence (AI) Competency status. The AWS Competency Program highlights AWS Partner Network (APN) members that have passed a rigorous audit of their security, architecture and customer adoption, and have exhibited proven success supporting customers in specialized solution areas. Achieving AWS Competency status in these categories differentiates Snowflake as an APN member that delivers highly specialized technical proficiency. This announcement is the latest example of Snowflake's relationship with AWS, which has yielded many important milestones for customers. Customers like Yamaha Corporation, the Japanese multinational enterprise with a wide range of products focused on sound and music, benefit from Snowflake's relationship with AWS.
In this regular column, we'll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we're in close touch with vendors from this vast ecosystem, so we're in a unique position to inform you about all that's new and exciting. Our massive industry database is growing all the time so stay tuned for the latest news items describing technology that may make you and your organization more competitive. Matillion Advances Speed And Simplicity Of Data Integration With Release Of Matillion Data Loader – Matillion, a leading provider of data transformation software for cloud data warehouses (CDWs), announced Matillion Data Loader, a free Software-as-a-Service (SaaS) data integration solution that empowers data analytics professionals and business users to simply and easily load and migrate data with a powerful and scalable product.
Match Group, the largest dating app conglomerate in the US, doesn't perform background checks on any of its apps' free users. A ProPublica report today highlights a few incidents in which registered sex offenders went on dates with women who had no idea they were talking to a convicted criminal. These men then raped the women on their dates, leaving the women to report them to the police and to the apps' moderators. These women expected their dating apps to protect them, or at least vet users, only to discover that Match has little to no insight on who's using their apps. The piece walks through individual attacks and argues that the apps have no real case for not vetting their users.
Amazon is leveraging machine learning to fight fraud, audit code, transcribe calls, and index enterprise data. Today during a keynote at its Amazon Web Services (AWS) re:Invent 2019 conference in Las Vegas, the tech giant debuted Amazon Fraud Detector, a fully managed service that detects anomalies in transactions, and CodeGuru, which automates code review while identifying the most "expensive" lines of code. And those are just the tip of the iceberg. With Fraud Detector (in preview), AWS customers provide email addresses, IP addressees, and other historical transaction and account registration data, along with markers indicating which transactions are fraudulent and which are legitimate. Amazon takes that information and uses algorithms -- along with data detectors developed on the consumer business of Amazon's business -- to build bespoke models that recognize things like potentially malicious email domains and IP address formation.
Description Job Description: The Leidos Innovations Center (LInC) seeks a Machine Learning Research Engineer primarily focused on cognitive signal processing, to work in our Arlington, VA office. The candidate will research & develop new, state-of-the-art machine learning algorithms and implement them across the RF domain (e.g., communications, radar, electronic warfare, spectrum sensing, and signals intelligence [SIGINT]), in both modelling and simulation environments and real time software embed systems. The candidate will also contribute to technology developments in signal processing, optimization, detection & estimation, deep learning, and adaptive decision and control. Requires basic knowledge of and ability to apply machine learning and radar/signal processing principles, theories, and concepts in support of direct programs, IR&D, and marketing efforts. Primary Responsibilities Designs and develops methods, algorithms, and systems that apply machine learning technologies to support advanced signal processing concepts.
Worldpay, in partnership with IntelliQA, has been awarded as a winner in the category of Most Innovative Project by The European Software Testing Awards. Sharad Jain and his team were on hand to collect the award and celebrate: "We looked to robotics to speed up our testing process," says Sharad, "we set up a test lab with four robots doing end to end testing for us and our output increased dramatically...I am delighted to be part of wonderful team Worldpay to see my contribution scaling heights and recognized as the best in Europe. Innovation can't get any sweeter."
The context: The vast majority of Facebook's moderation is now done automatically by the company's machine-learning systems, reducing the amount of harrowing content its moderators have to review. In its latest community standards enforcement report, published earlier this month, the company claimed that 98% of terrorist videos and photos are removed before anyone has the chance to see them, let alone report them. So, what are we seeing here? The company has been training its machine-learning systems to identify and label objects in videos--from the mundane, such as vases or people--to the dangerous, such as guns or knives. Facebook's AI uses two main approaches to look for dangerous content.
The first Model Artificial Intelligence Governance Framework in Asia was released by Singapore in January this year, in collaboration with the World Economic Forum's Centre for the Fourth Industrial Revolution. AI, an emerging frontier in technology that allows technical systems to simulate human intelligence and behaviors, has vast applications in many fields. As part of its «Smart Nation» pursuits, Singapore launched its National Artificial Intelligence (AI) Strategy at the Singapore FinTech Festival and Singapore Week of Innovation and TeCHnology (SFF x SWITCH) conference. Within the next three years, AI-powered chatbots will become the first point of contact for residents to report issues with city services while marking systems in schools will soon be automated. Across the financial landscape, AI has also weaved itself into the workings of the industry.
A report by research firm IDC in September said global spending for AI systems will reach $97.9 billion in 2023, a staggering increase from the projected $37.5 billion that will be spent this year. That means the annual growth rate will be 28.4 percent over the next several years. That's not surprising as BigTech is primed to increase their monopoly status in the 2020s with AI leadership that will boost GDP via machine learning with the emergence of an automation economy. Over the last few years, we have seen an exponential upthrust in the number of platforms, applications, and tools based on machine learning and AI technologies. We are seeing greater mainstream impact of algorithms, and machine learning in regular jobs across a variety of industries.