Wild Intelligence by Yael Rozencwajg

Wild Intelligence by Yael Rozencwajg

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Wild Intelligence by Yael Rozencwajg
Wild Intelligence by Yael Rozencwajg
πŸš€ Embarking on the journey to safer AI
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AI unbundled

πŸš€ Embarking on the journey to safer AI

Advancing AI safely and responsibly | A 12-week executive master program for busy leaders

Feb 03, 2025
βˆ™ Paid
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Wild Intelligence by Yael Rozencwajg
Wild Intelligence by Yael Rozencwajg
πŸš€ Embarking on the journey to safer AI
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πŸš€ Embarking on the journey to safer AI
πŸš€ Embarking on the journey to safer AI

Embarking on the journey to safer AI

The transformative power of AI is undeniable.

It's reshaping industries, accelerating scientific discovery, and promising solutions to humanity's most pressing challenges.

Yet, this remarkable potential is intertwined with significant threats.

As AI systems become more complex and integrated into critical aspects of our lives, ensuring their safety and reliability is paramount.

We cannot afford to observe AI's evolution passively; we must actively shape its trajectory, guiding it toward a future where its benefits are maximized, and its risks are minimized.

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A 12-week executive master program for busy leaders

I am thrilled to introduce the 12-week master plan from the AI unbundled section.

A program designed specifically for decision leaders who recognize the critical importance of responsible AI development and deployment.

This program isn't about technical jargon or complex equations.

It's about empowering you with the knowledge and insights to champion AI safety within your organization and contribute to building a future where AI serves humanity responsibly.

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Fundamental principles for a collaborative ecosystem

At Microsoft, they believe that "advancing AI safely and responsibly is essential to unlocking its potential."

Their AI safety policies emphasize a multi-faceted approach, encompassing everything from robust testing and validation to ongoing monitoring and mitigating potential harms.

Similarly, Google's commitment to AI safety highlights the importance of building safety into AI systems from the ground up, focusing on areas like fairness, privacy, and security.

IBM emphasizes the importance of trust and transparency in AI development, highlighting the need for explainable AI and robust governance.

Salesforce is actively collaborating with the National Institute of Standards and Technology (NIST) on AI safety initiatives, demonstrating a commitment to establishing industry-wide standards.

Organizations like SAFE.ai are dedicated to advancing AI safety through research, education, and community building. They foster a collaborative ecosystem for responsible AI development.

These principles resonate deeply with our mission, and we join our efforts, combined with the work of Microsoft and Google, to reinforce the growing consensus that AI safety is not just a technical challenge but a societal imperative.

We believe that AI safety is not an afterthought; it's the foundation upon which genuinely beneficial AI systems are built.

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From building systems to engineering robust AI-safe organizations

Our series "Building safe intelligence systems" delves into the critical aspects of AI safety and security, providing the knowledge and tools to navigate this rapidly evolving landscape. We’ve explored:

  • The ethical foundations of AI: Understanding the values and principles that should guide AI development and deployment.

  • Mitigating AI threats: Identifying and addressing potential biases, vulnerabilities, and unintended consequences.

  • Building robust and resilient AI systems: Developing secure AI architectures and implementing safeguards against adversarial attacks.

  • Fostering collaboration and transparency: Creating open and inclusive AI governance and knowledge sharing processes.

πŸš€ Building safe intelligence systems: moving beyond the AI dystopia (new online course)

πŸš€ Building safe intelligence systems: moving beyond the AI dystopia (new online course)

Yael Rozencwajg
Β·
October 7, 2024
Read full story
πŸš€ The recap of the 10-part online course

πŸš€ The recap of the 10-part online course

Jan 27
Read full story

Over the next 12 weeks, we will explore the critical roles of reliable, trustworthy, and robust safety engineering AI in achieving this vision.

We will investigate how techniques (including code and mathematical) can prove the properties of AI systems, ensuring they behave as intended and preventing unintended consequences.

We will examine real-world case studies, identify key industry adoption trends, and provide actionable strategies for implementing AI safety measures within your organizations.

This program is designed to be engaging, informative, and practical.

We will deliver concise, digestible content, interactive exercises, and actionable insights each week, which you can immediately apply to your work.

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A four-phase program

Our curriculum is strategically structured into four distinct phases, each building upon the previous one to provide a comprehensive and progressive learning experience:

Phase 1: Foundations of AI safety and robust safety engineering
(Weeks 1-3)

This phase lays the groundwork by establishing the critical need for AI safety and introducing the core concepts of robust safety engineering.

We'll explore the current landscape of AI risks and demystify the fundamental principles behind formal methods, setting the stage for deeper exploration in subsequent phases.

Phase 2: Hands-on with simplified examples and outlier detection
(Weeks 4-6)

Moving from theory to practice, this phase provides hands-on experience with simplified AI systems and model checkers.
You'll learn to specify system properties, verify them using practical tools, and identify potential "outliers" – deviations from expected behavior – that could indicate safety vulnerabilities.

Phase 3: Applying robust safety engineering to real-world scenarios
(Weeks 7-9)

Here, we'll examine real-world applications of formal verification in diverse domains, such as autonomous systems, healthcare, and finance. We'll analyze case studies, highlighting successes and challenges, and explore key trends in industry adoption, offering valuable insights into practical implementation.

Phase 4: Building an AI safety culture and strategic implementation
(Weeks 10-12)

The final phase focuses on integrating robust safety engineering and other AI safety practices into your organization's workflows.

We'll discuss strategies for building a culture of AI safety, emphasizing the importance of training, communication, and collaboration.

You'll also learn how to develop a comprehensive AI safety policy and roadmap for your organization.

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Premium Member Exclusive:

A customizable AI safety framework

For our premium members, we are offering an exclusive bonus component integrated throughout the 12 weeks: a customizable AI safety framework.

Through these exclusive resources, we want to empower our premium members to learn about robust safety engineering and implement it within their organizations, effectively driving real-world impact.

Wild Intelligence by Yael Rozencwajg is a reader-supported publication. Consider becoming a paid subscriber to receive new posts and support our work.


A glimpse of our weekly agenda

Let's map out our journey to AI safety week by week.

This meticulously designed program is structured in four strategic phases, each building upon the last, to provide you with a comprehensive and actionable understanding of robust safety engineering for AI.

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