Intel’s Recommendations for the U.S. National Strategy on Artificial Intelligence

whitePaper | July 20, 2020

Accurate, trustworthy threat intelligence is a boon if you have it – but too much of it becomes a management headache. Analyst group 451 Research, surveying security leaders for its report Tackling the Visibility Gap in Information Security, found that 49% of enterprises using SIEM, EDR, and other security tools were overwhelmed by the day-to-day operation of managing and ingesting threat feeds into their growing technology stack.1

whitePaper | December 26, 2022

IBM embraces five foundational pillars of trustworthy AI: Explainability, Fairness, Robustness, Transparency, and Privacy. These pillars underpin the development, deployment and use of AI systems. This document and IBM’s trustworthy AI pillars are meant to help you align on both process and outcomes.

whitePaper | July 28, 2020

The penetration of cloud-based contact center solutions into the contact center market has been growing dramatically as enterprises continue to acquire new customer engagement solutions or replace their existing premises-based contact centers. Cloud solutions are growing in popularity and gaining an increasingly significant share of the market as their feature sets and flexibility begin to match or surpass those of legacy premises-based systems. Cloud contact center vendors are spending R&D dollars to further develop their solution capabilities internally while also making strategic acquisitions in the customer engagement market space to enhance and broaden their offerings.

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 | December 22, 2022

Machine learning, one of the key building blocks of AI, has been a part of the technological world since the 1950s, when the earliest programmers asked computers to make sense of large sets of data. Programmers have increasingly refined the ability of machines to study data in order to detect patterns that allow computers to then organise information, identify relationships, make predictions and detect anomalies. Today, modern applications of AI have already given us self-driving cars and virtual assistants and have helped us detect fraud and manage resources like electricity more efficiently. Sectors as diverse as retail, sports, banking, manufacturing and healthcare have all found applications for machine learning and AI.

whitePaper | June 7, 2022

Telecoms need to be able to incorporate new technologies and next-generation connectivity such as 5G to customers and end users. To achieve these ambitious goals, they need to optimize their networks – make them more intelligent if you will. Some of the tools needed include artificial intelligence (AI), machine learning (ML) and artificial intelligence operations (AIOps). This document will explore what intelligent networking means to telecoms, vendors and customers, and how AI and ML technologies and tools can be used, the cultural shifts the industry needs to make it a success, and what to bear in mind when deploying machine learning across a telecom network.