Author: Frederic Jacquet

As they evolve, quantum computers will be able to break widely used cryptographic protocols, such as RSA and ECC, which rely on the difficulty of factoring large numbers and calculating discrete logarithms. Post-quantum cryptography (PQC) aims to develop cryptographic algorithms capable of withstanding these quantum attacks, in order to guarantee the security and integrity of sensitive data in the quantum era.

Understanding the Complexity and Implementation of PQC

Post-quantum cryptography is based on advanced mathematical concepts such as lattices and polynomial equations. These complex foundations require specialized knowledge to be properly understood and effectively implemented.

Artificial Intelligence (AI) represents a rapidly expanding domain characterized by its swiftly evolving technologies and methodologies. Its transformative potential spans across various sectors of the economy and society. Nonetheless, it’s crucial to acknowledge the ethical dilemmas that AI poses. Companies at the forefront of AI development, alongside businesses, communities, administrations, and individuals incorporating it into their daily lives, must remain mindful of these issues.

The Primary Domains of Artificial Intelligence

AI can be categorized into various domains, each with unique attributes tailored to achieve specific goals in particular applications. These objectives and applications stem from the technical capabilities and specific challenges inherent in the respective domains, addressing diverse use cases.

The incredible growth of artificial intelligence applications is due in no small part to the ever-increasing power of computers and their hardware, which today enables systems to be created and trained according to gigantic data volumes or CPU (or GPU) quantities, managing trillions of parameters.

Beyond all the added value these technologies bring, it's important to be aware of and consider the challenges they represent.

One of the most persistent myths surrounding the advent of Artificial Intelligence lies in the fantasy that advanced new technologies carry the "superpower" of being able to address every subject. For some, AI is providential, while for others, it is nothing less than a threat to humanity.

In reality, artificial intelligence technologies cannot solve problems for which they were not designed.

The use of AI chatbots in healthcare necessitates a comprehensive approach to address vital considerations. From data training to security measures and ethical practices, a wide range of precautions must be implemented. Human monitoring, user education, and mitigating the risks of anthropomorphism are crucial aspects to focus on.

Find out how continuous monitoring and feedback promote transparency, user safety, privacy protection, and the provision of reliable information.

As companies across all industries realize the value of implementing a data-driven strategy, machine learning (ML) is emerging as a transformative force to be reckoned with. While implementing machine learning initiatives at the core of their digital transformation strategy, they are measuring the complexity of bringing them to fruition. 

Navigating Complexity and Avoiding Project Pitfalls

Analysts agree that the failure rate of machine learning projects is around 80%. Indeed, Gartner predicted that by the end of 2022, approximately 85 percent of AI projects would have delivered erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them. IDC's research indicates that while AI/ML adoption is on the rise, cost, lack of expertise, and lack of life-cycle management tools are among the top three inhibitors to realizing AI and ML at scale.

In today's rapidly changing business environment, staying ahead of the competition requires constant innovation and adaptation. Supply Chain Management, a domain that is constantly under stress, has seen significant advances in recent years through the use of artificial intelligence (AI). By using tools and solutions augmented by the technological and methodological capabilities of AI, including machine learning (ML), companies can improve their demand forecasting processes and level up their operational excellence. 

This directly impacts their efficiency and the need for them to achieve cost savings. 

I. Introduction

The term “Conversational AI” invokes the use of artificial intelligence technologies to enable computer solutions to communicate with humans in a natural and interactive way. It can be applied in many different contexts, such as customer service Chatbots, virtual assistants, and communication systems. Its capability to understand and respond to human speech has the potential to transform the way we interact with machines, software, and applications.

Whether through text, voice, or other forms of communication, including video or image, Conversational AI is changing the way we communicate with either the digital or the real world around us.