SEALSQ unveils AIoT Strategy with WISeAI.IO at Davos AI Roundtable

SEALSQ Corp (“SEALSQ” or “Company”) (NASDAQ: LAES), a company that focuses on developing and selling Semiconductors, PKI and Post-Quantum technology hardware and software products, today announced its innovative AI strategy during the Artificial Intelligence (AI) Roundtable at Davos. Centered around WISeAI.IO, a state-of-the-art machine-learning tool designed to enhance cybersecurity measures and manage digital identities, the strategy represents a revolutionary step forward by SEALSQ in an everchanging AI environment.

WISeAI.IO employs sophisticated algorithms that meticulously monitor and analyze how objects interact with their digital identities across various devices, such as computers and mobile phones, when connected to the Internet. This advanced approach is crucial in safeguarding users against increasingly sophisticated cyber threats, including ransomware and complex malware.

Leveraging SEALSQ’s Secure Element and backed by the robust WISeKey Root of Trust (RoT), to be bolstered by cutting-edge post-quantum semiconductors technologies in the near future, WISeAI.IO is adept at detecting anomalies in data flow, ensuring strong authentication and unparalleled security.

AIoT: The Fusion of AI and IoT Technologies
SEALSQ’s AIoT strategy represents a seamless integration of semiconductors, smart sensors, IoT systems, AI, and a data cloud, offering customers a fully integrated platform to drive innovation and spearhead digital transformation. By utilizing WISeKey’s advanced cybersecurity technology and IoT network, data is securely collected and processed in real-time to enable immediate and highly secure responses to dynamic situations.

The AIoT system functions as the central brain of the SEALSQ Ecosystem, which currently consists of over 1.6 billion semiconductor powered devices. This network acts like a nervous system, ensuring swift and secure interactions across the IoT landscape.

Generative AI: A Leap in Autonomous IoT Device Capabilities
By integrating Generative AI, WISeAI significantly enhances its learning capabilities, drawing from patterns and data to generate novel and original content or data. This advancement dramatically impacts the functionality of autonomous IoT devices. For instance, self-driving cars using Generative AI can navigate more effectively, adapting to obstacles and changing road conditions with unprecedented precision.

Generative AI also personalizes user experiences with IoT devices. Smart home devices, for example, can adapt to user preferences over time, optimizing performance and efficiency. This leads to smarter energy use, cost savings, and minimized maintenance downtime.

Enhanced Cybersecurity through Generative AI
In the cybersecurity realm, Generative AI elevates the security of autonomous devices. It empowers devices to recognize and react to potential threats and vulnerabilities, enhancing resilience against hacking and other security breaches. By generating adversarial examples and synthetic data, Generative AI improves the accuracy of machine learning models and strengthens defenses against sophisticated cyber-attacks.

AI Roundtable at Davos: A Convergence of Minds
In conjunction with its AI strategy announcement, SEALSQ hosted a successful roundtable discussion titled “Decentralization: AI Unleashed: Ensuring Safety and Leveraging Decentralization” at the Hotel Europe in Davos, featuring a diverse panel of experts, including AI researchers, ethicists, technology professionals, and policymakers. The event focused on addressing the rapid evolution of AI and its implications on safety, ethics, and decentralized frameworks.

Moderated by Carlos Moreira, Founder, Chairman, and CEO at SEALSQ, the roundtable addressed ethical, safety, and privacy concerns posed by decentralized AI frameworks and explored strategies for managing AI’s expanding capabilities responsibly.

Key Resolutions of the SEALSQ Roundtable

  1. Establishment of Ethical AI Guidelines: The roundtable emphasized the need for universal ethical standards in AI development and deployment. It resolved to create a comprehensive set of guidelines that address fairness, accountability, transparency, and privacy in AI systems.
  2. Robust AI Safety Protocols: There was a consensus on the importance of developing robust safety protocols for AI systems. These protocols are intended to prevent unintended consequences and ensure AI systems function reliably and safely in diverse environments and situations.
  3. Impact of Decentralized AI on Privacy and Control: The roundtable recognized the potential of decentralized AI systems to enhance privacy and user control. It resolved to support research and development in decentralized AI technologies while ensuring they align with privacy laws and ethical norms.
  4. Enhancing Public-Policy Interface: The roundtable resolved to strengthen the interface between AI technology and public policy. This involves regular dialogues and collaborations between technologists, policymakers, and other stakeholders to ensure that AI development aligns with public interest and policy objectives.
  5. Promoting AI Literacy and Public Awareness: Recognizing the importance of public engagement and awareness, the roundtable resolved to initiate and support programs aimed at increasing AI literacy. These programs will help the public understand the benefits and risks associated with AI technologies.
  6. Fostering Responsible AI Research: A commitment was made to promote responsible AI research that prioritizes ethical considerations and societal impact. This includes encouraging transparency in AI research and fostering an environment where ethical AI development is recognized and rewarded.
  7. Examining the Power Dynamics in AI: The roundtable acknowledged the need to closely examine how decentralization in AI might shift power structures. It resolved to conduct further studies and discussions on how AI can democratize technology access and power distribution, rather than concentrate it.
  8. Regular AI Ethics Audits: There was a resolution to implement regular ethics audits for AI systems, especially those used in critical sectors. These audits will be aimed at ensuring ongoing compliance with ethical standards and adapting to emerging challenges and societal needs.
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