Hikvision Cybersecurity White Paper

whitePaper | July 15, 2022

Today, the use cases for AI (artificial intelligence) and machine learning are developing at a remarkable pace. We are beginning to see cases where AI is contributing to society through its implementation in systems and services that are in production and consumer-facing. These include the automation of nuanced decisions that until now could only be made by humans, thereby increasing the profitability of corporations, and increasing customer satisfaction through the personalization of services making them tailored for each user. In addition, important discussions have begun on the adverse effects such AI systems have on humans and the resulting ethical issues they can create.

whitePaper | September 23, 2022

According to the research published by Microsoft3 on Global Customer Service, 58% of customers feel the importance of Customer Experience (CX), and 61% stopped doing business with a brand due to poor CX and go for different brands.

whitePaper | December 15, 2019

The debate between experts over how automation will affect the future of work has been one of the most active cottage industries in labor economics in recent years. Numerous scholars forecast major disruptions of human work; others minimize those impacts. And yet, the field has nevertheless managed to generate a number of shared insights, with none more consistent than the finding that least welloff will suffer automation’s greatest shocks on the labor market.

whitePaper | July 5, 2023

Never before has the launch of a new technology so rapidly fired the imagination of enterprises and consumers alike. The arrival of ChatGPT in November 2022 showcased the potential of Generative AI powered by Large Language Models (LLMs) in addressing a vast array of new use cases. These use cases were previously unimaginable with conventional computing and analytical AI (now sometimes described as “traditional” or “classic” AI).

whitePaper | January 12, 2020

“The services market for artificial intelligence technologies is rapidly maturing on the back of several years of learning and a realization—delivering AI is unlike delivering any other technology thus far. Delivering on the promise of AI calls for far more collaboration between service providers, tech vendors, and enterprise clients.” The biggest differentiator in enterprise AI services, as described by most of the customer leaders, is the quality of people—not just the quantity available for a specific skill. Domain understanding and data engineering capabilities are top priorities. A culture of innovation, experimentation, collaboration, and co creation with customers is the key winning formula here.”

whitePaper | January 28, 2022

Since more and more decisions that used to be made by humans are nowadays made either with the help of Artificial Intelligence (AI) or by AI alone, it is essential that the AI algorithms are “fair.” In this whitepaper, we discuss issues surrounding the fairness of AI applications, with a special focus on how it can be assessed independently and subsequently certified. We explain why an AI application cannot be classified – and subsequently certified – as “fair” or “unfair” in a general sense and propose an approach that makes it possible to classify it as “fair” or “unfair” under the (application)-specific definition of fairness.