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) …
D2iQ has added a curated distribution of Kubeflow, open source software that makes it easier to deploy workflows that incorporate machine learning algorithms on a Kubernetes cluster, as an extension to its existing portfolio of automation tools. Jie Yu, chief architect for D2iQ, says KUDO for Kubeflow will make it easier for IT teams to deploy workloads that include frameworks such as Spark and Horovod on Kubernetes clusters. At the core of KUDO for Kubeflow is Kommander, a role-based tool that provides centralized management, governance and visibility into disparate Kubernetes regardless of where they are running. IT organizations that are building and deploying artificial intelligence (AI) applications based on machine learning algorithms have embraced containers to simplify building and managing all the elements of what otherwise would be a massive monolithic application that would be too unwieldy to build, update and deploy. Kubernetes, meanwhile, has become the de facto default standard for orchestrating containers.
Ever trained an image recognition model? What accuracy did you get? 90, 95, or maybe a near-perfect 99 percent? No matter what your answer is, we want to ask for a follow-up. If you get a great accuracy on training as well as test images, does it mean your model is ready to be deployed? Well, even though it once did, now it may not be ready.
Want to be a part of an elite team where our innovative technical solutions are delivered to customers that advance the state of the art while addressing long-term problems of importance to national security? At our Leidos' Multi-Spectrum Warfare Research and Analytics Systems (MSWRAS) Division, an organization in the Leidos Innovation Center (LInC), we are looking for you, our next Scientist who specializes in remote sensing data analytics. Join our team of Ph.D. level peers in designing and developing advanced technology-based solutions for contract research and development projects working in our Arlington, VA office. Fun roles you will have in this job: Describe instances of successful, proven, and demonstrable experience contributing to the technical work as part of cross-discipline teams in the development and integration of software-based solutions for competitive, contract-based applied research programs Work with teams composed of members from industry, small businesses, and academic-based researchers and should have experience working on projects focused on multiple technical fields such as machine learning, artificial intelligence, engineering, and software development and integration Describe how the work products to which they contributed had solved customers' problems in such domains as energy, health, and national security or in the commercial sector Work within the MSWRAS Division and across the LInC, performing basic and applied contract research and development projects both leading and working under the guidance of senior scientists and engineers. Processing, interpreting and analyzing large volumes of data collected by remote sensing platforms but may also include other types of phenomenological data such as field measurements, or weather data Independently design and undertake new research as well as partner in a team environment across organizations Contribute to the development of creative and innovative R&D approaches to solving major remote sensing analytics challenges and work with potential sponsors (customers or internal champions) to secure funding for new research efforts based on those topics Contribute to the productivity of teams composed of fellow researchers, data scientists, data engineers, and software engineers to execute complex R&D programs Under the guidance of a senior scientist or engineer, design and develop or integrate secure and scalable applications that are part of broader solutions, that are applicable across multiple domains.
Artificial intelligence (AI) is transforming how enterprises analyze and process information. It is also shifting from theoretical to real-world technology. Companies are deploying AI technologies to boost efficiency, reduce costs, and grow sales and profitability. The technology can also reduce marketing waste by predicting what works. It is the most impactful innovation of our lifetime, and it will create new winners and losers across entire industries.
Among the other questions being asked as a result of the current pandemic is, "What will the rise of artificial intelligence mean for K-12 education?" It would seem safe to assume that the rush to online learning and the adoption of new technologies will inevitably lead educators to embrace tools powered by artificial intelligence. But according to Robert F. Murphy, that more optimistic vision for AI will probably be tempered for now by budget shortfalls that "may seriously delay" school districts from making those types of investments anytime soon. Murphy is an independent education consultant with over two decades of research experience, including as a senior policy researcher for the international think tank RAND Corporation and as the director for evaluation research at SRI International, a scientific research center. In a paper authored last year for RAND, Murphy addressed the more fundamental issues of AI that need to be considered, regarding its further adoption.
The Sisense analytics platform is known for its augmented analytics capabilities and ease of use, and as it moves forward it will do so with a new leader in charge of its product development. Just over a year after its acquisition of Periscope Data, a purchase that added capabilities aimed at data scientists to the features geared toward business users Sisense was already know for, the New York-based vendor is focused on third-generation analytics in which AI and business intelligence embedded throughout the workflow will be prominent. Most recently, Sisense updated its analytics platform with new natural language query capabilities and introduced Knowledge Graph, a graph analytics engine the vendor developed that was trained on more than 650 billion past analytic events and informs the machine learning capabilities of the query tool. Now, to help shape its vision, Sisense has added Ashley Kramer as its first chief product officer. Kramer began her career as a software engineering manager at NASA.