Confidential computing is transforming the landscape of data privacy and security by enabling the processing of sensitive data in a secure and isolated environment, even while it’s actively in use. In this session, we will explore the key concepts and emerging technologies behind confidential computing, including trusted execution environments (TEEs), hardware-based security measures, and their real-world applications. You will gain an understanding of how confidential computing mitigates the risks of data breaches and ensures compliance with privacy regulations, empowering organizations to leverage sensitive data without compromising security.

We will explore the concerns surrounding AI’s increasing independence in decision-making processes and its potential to outpace human oversight. Proponents argue that as AI becomes more self-reliant, it could evolve beyond human comprehension, leading to unintended consequences and a fundamental shift in societal structures. Opponents, however, maintain that AI’s autonomy will remain ultimately controllable and will empower humans to solve complex global challenges more effectively. This debate delves into the risks of diminished human agency, the ethical implications of machine-led governance, and whether safeguards can be implemented to ensure AI remains a tool for human enhancement, rather than a force that undermines it.

This session explores the critical role of cutting-edge innovations like quantum computing and semiconductors in powering AI solutions. We will delve into the computing power, storage capabilities, and networking infrastructure that businesses must establish to handle the complex demands of AI. From traditional cloud computing to the emerging potential of quantum systems, we’ll examine the key factors driving AI deployment at scale. Additionally, we will explore how advancements in semiconductor technology, such as specialized chips for AI processing, are reshaping the landscape. Attendees will gain insights into the infrastructure requirements, challenges, and opportunities that companies must navigate to harness the full potential of AI and stay ahead in an increasingly competitive landscape.

As technology evolves, ambient agents are emerging as invisible yet powerful facilitators of seamless digital experiences. These intelligent systems, integrated into the fabric of everyday life, work behind the scenes to adapt to user needs, automate tasks, and enhance decision-making without direct interaction. This session will explore the rise of ambient agents, their ability to sense and respond to contextual data, and how they can enhance human-computer interaction in ways that feel intuitive, personalized, and unobtrusive. Attendees will gain insights into the future of ambient intelligence, examining its applications across industries and the potential ethical considerations that come with creating such intelligent, yet invisible, systems.

• Do they offer a step towards harnessing and using more generalized intelligence?
• Examining the necessity of keeping humans in the loop as part of the ambient agent experience

AI has the potential to transform industries, but complex infrastructure and high costs often limit its adoption. By simplifying AI infrastructure, organizations can accelerate deployment, reduce costs, and enable seamless scalability, making AI solutions more accessible to businesses of all sizes. A streamlined AI stack minimizes the need for specialized hardware and technical expertise, lowering barriers to entry while fostering rapid innovation. This session explores how a simplified approach to AI infrastructure can drive efficiency, enhance adaptability, and unlock AI’s full potential across industries.

by GMI CLOUD

Examine how and why we are bringing general-purpose AI into the physical world to power the robots of today and the physically-actuated devices of the future

As artificial intelligence continues to transform industries and society, the debate over open-source versus closed-source AI models has never been more relevant. This panel discussion will explore the key differences, advantages, and challenges associated with both approaches. Open-source AI has the potential to democratize technology, fostering collaboration, transparency, and innovation. However, concerns around security, intellectual property, and quality control remain. On the other hand, closed-source AI models often promise more robust, secure, and commercially viable solutions, but they can also limit accessibility and community-driven progress.

Join industry experts as they delve into topics such as the ethical implications of proprietary AI, the role of transparency in building trust, and how both open and closed systems can coexist in the rapidly evolving AI ecosystem. Gain insights into the strategic decisions shaping the future of AI, and how organizations can navigate the complexities of choosing between these two distinct approaches.