SQL Server in Simple Words

whitePaper | August 31, 2022

Artificial intelligence and machine learning analyses are driving critical decisions impacting our lives and the economic structure of our society. These complex analytical techniques—powered by sophisticated math, computational power, and often vast amounts of data—are deployed in a variety of critical applications, from making healthcare decisions to evaluating job applications to informing parole and probation decisions to determining eligibility and pricing for insurance and other financial services.

whitePaper | July 20, 2022

The growing diversity of actors and circumstances involved in AI/ML deployment makes establishing a set of ethical participatory practices especially difficult for this field. Even among practitioners working at the same organization, there can be substantial differences in both their knowledge about how AI/ML systems were created or will be used and their ability to incorporate inclusive practices. Additionally, the greater availability of AI development platforms, including “no-code” platforms, means that algorithmic models can be deployed without having deep expertise, expanding the number of circumstances automated systems are deployed in. This means many more instances where automated systems are being deployed without consideration of how the algorithms were developed, the provenance of the datasets and nature of the bias on which they were trained and tested, and the ethical implications of their development and deployment.

whitePaper | December 15, 2019

Artificial Intelligence (AI), and specifically Machine Learning (ML), are being tested in an increasing number of fields, including data-centric environments. Image or text analysis, speech recognition, chatbot interactions, custom machine learning models… all these are elements that could enable the AI journey of a public safety and security organisation. This document dives into different aspects of integrating AI & ML in Public Safety activities, at different levels and in different domains of activity. It presents ethical and regulatory considerations, real examples from Public Safety Answering Points (PSAPs) and Emergency Response Organisations (EROs), and also initiatives that can benefit the public sector greatly, with a series of recommendations at the end.

whitePaper | August 26, 2022

Started in 2013, Arya.ai is the first deep learning (DL) startup in India and has been one of the very early adopters to use DL in Financial Institutions. AryaXAI by Arya.ai, a full stack ML Observability platform, offers multiple components - AI explainability, ML monitoring, ML audit and Policy controls, which organisations require beyond simple ML monitoring tools.

whitePaper | May 15, 2023

The future of AI is hybrid. As generative AI adoption grows at record-setting speeds1 and drives higher demand for compute,2 AI processing must be distributed between the cloud and devices for AI to scale and reach its full potential – just like traditional computing evolved from mainframes and thin clients to today’s mix of cloud and edge devices. A hybrid AI architecture distributes and coordinates AI workloads among cloud and edge devices, rather than processing in the cloud alone. The cloud and edge devices, such as smartphones, vehicles, PCs, and IoT devices, work together to deliver more powerful, efficient, and highly optimized AI.

whitePaper | December 6, 2019

Machine learning (ML)-driven personalization is fast expanding from social media to the wider information space, encompassing legacy media, multinational conglomerates and digital-native publishers: however, this is happening within a regulatory and oversight vacuum that needs to be addressed as a matter of urgency. Mass-scale adoption of personalization in communication has serious implications for human rights, societal resilience and political security. Data protection, privacy and wrongful discrimination, as well as freedom of opinion and of expression, are some of the areas impacted by this technological transformation.