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) …
IonQ, Inc, the leader in quantum computing, announced the release of a new paper in collaboration with Fidelity Center for Applied Technology (FCAT) that demonstrates how its quantum computers can outperform classical computers to generate high-quality data for use in testing financial models. Financial institutions commonly use models for asset allocation, electronic trading, and pricing, and require testing data to validate the accuracy of these models. The new technique, demonstrated by FCAT on IonQ's latest quantum computers, has the potential to be the first class of quantum machine learning models to be deployed for broad commercial use. "At FCAT, we track new and emerging technologies and trends to help Fidelity meet the changing needs of our customers and ass These classical approaches are often limited because real-world dependencies between variables–for example, in a portfolio of stocks–are too complex for them to model. IonQ and FCAT demonstrated that data generated with quantum machine learning algorithms is more representative of these real-world dependencies and is therefore better at accounting for edge cases like black swan events. The technique invented by IonQ and FCAT leverages copulas, a method often used in statistical models to describe relationships between large numbers of variables. For instance, large financial institutions use copulas to understand relationships between stock prices (if the price of X is within a particular range, then the price of Y tends to go up). By using quantum computers to implement copulas, IonQ and FCAT demonstrated the ability to construct complex models beyond the capability of classical computers. "This research, performed on IonQ hardware, shows quite clearly that leveraging quantum computing can lead to superior financial modeling results.
In new research being undertaken by Flinders University in partnership with conservation charity Koala Life and the State Government, non-invasive koala monitoring techniques are being developed using drones and facial recognition technology to count, identify and re-identify koalas. Minister for Environment and Water David Speirs said this cutting-edge technology will be used as part of a study on koalas at Kangaroo Island and the Adelaide Mount Lofty Ranges to get a better understanding of both their numbers and their movements. "Traditionally, monitoring koala populations has involved capturing and individually marking koalas, a process that is both labour-intensive and poses potential welfare issues," Minister Speirs said. "It is very important for us to develop non-invasive techniques to help monitor animals in a safe way, and facial recognition through drone monitoring is utilising the latest technology to achieve this. "The ability to recognise individual members of a species in the wild will help to grow an understanding of individual movements as well as population estimates, and this understanding will allow the development of meaningful management strategies.
Based on the project experiences working on AI (Artificial Intelligence) & ML (Machine Learning) projects with AML (Azure Machine Learning) platform since 2018 in this article we will share a point of view (the good parts) on bringing your AI models to production in Azure Cloud via MLOps. It is a typical situation when the initial experimentation (a trial and error approach) and the associated feasibility study to produce a certain model takes place in AML/Jupyter Notebook(s) first. Once some promising results have been obtained, analyzed and validated via the feasibility study by the Machine Learning Engineering team "locally", the Application Engineering and DevOps Engineering teams can collaborate to "productionalize" the workload at scale in the Cloud (and/or at the Edge as needed). AML (Azure Machine Learning) is an MLOps-enabled Azure's end-to-end Machine Learning platform for building and deploying models in Azure Cloud. Please find more information about Azure Machine Learning (ML as a Service) here, and more holistically on Microsoft's AI/ML reference architectures and best practices here.
The Quadrilateral Security Dialogue, better known as the Quad, has announced various non-military technology initiatives aimed at establishing global cooperation on critical and emerging technologies, such as AI, 5G, and semiconductors. The various technology initiatives were announced after the leaders of Quad countries -- comprised of Australia, India, Japan, and the US -- met on Friday, which marked the first time the group has come together in person. Among the initiatives announced by the security bloc was the intention to develop new global cybersecurity standards across various technology sectors. "With respect to the development of technical standards, we will establish sector-specific contact groups to promote an open, inclusive, private-sector-led, multi-stakeholder, and consensus-based approach," the Quad said in a joint statement. As part of work to be undertaken towards establishing these global technology standards, the Quad said it would publish a Quad Statement of Principles, which will be a guide for implementing responsible, open, high-standards innovation.
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Techniques, tools, best practices and everything you need to to learn machine learning! This is a comprehensive repository containing 30 notebooks on Python programming, data manipulation, data analysis, data visualization, data cleaning, classical machine learning, Computer Vision and Natural Language Processing(NLP). All notebooks were created with the readers in mind. Every notebook starts with a high-level overview of any specific algorithm/concepts being covered. Wherever possible, visuals are used to make things clear.
Mastering unsupervised learning opens up a broad range of avenues for a data scientist. There is so much scope in the vast expanse of unsupervised learning and yet a lot of beginners in machine learning tend to shy away from it. In fact, I'm sure most newcomers will stick to basic clustering algorithms like K-Means clustering and hierarchical clustering. While there's nothing wrong with that approach, it does limit what you can do when faced with clustering projects. And why limit yourself when you can expand your learning, knowledge, and skillset by learning the powerful DBSCAN clustering algorithm?
Today's office workers spend a lot of time moving between different applications. They often have to reenter or copy and paste the same data – such as a customer's name and address details – from one application to another to complete a specific task. Such manual work is laborious, time-consuming, and prone to error. Robotic process automation (RPA) uses software robots to emulate human users and automate – as far as possible – these tedious, mundane tasks. Automation improves the user experience for both employees and customers. RPA boosts productivity and can typically free up between 15% and 30% of an employee's time to focus on tasks that deliver greater value to the organization and improve the customer experience. Companies which use SAP Intelligent RPA (RPA platform of SAP) achieve business benefits and show significant ROI (Return on Investment).
When it comes to present-day automation and repetitive work, we have traditionally thought of actual physical machines, or an entire line of robotic arms and tools that process items from one end towards another. Today, however, we are now getting used to yet another, more virtual type of automation. This is none other than the concept of robotic process automation (RPA): the automation of processes based directly on software and artificial intelligence (AI). But how exactly can we define RPA? We take a look into its brief development history and a few real-world examples, to see how it works, as well as witness its impact on organizational productivity as a whole.