Generative AI for Business Transformation Masterclass

Generative AI
for Business Transformation
Masterclass

3rd July 2024 – Virtual and Interactive

Generative AI and large language models (LLMs) , are ushering in a new era of business innovation. This represents a critical shift in how organizations leverage AI technologies to solve real-world problems and drive meaningful outcomes.

Generative AI for Business Transformation is a comprehensive, one-day masterclass designed to explore the transformative potential of generative AI. This intensive workshop goes beyond theory to delve into practical implications and real-world applications. Led by distinguished professors from the Massachusetts Institute of Technology (MIT) and globally recognized industry speakers, this unique opportunity offers unparalleled insights into leveraging generative AI and large language models (LLMs) to revolutionize workflows, drive innovation, and enhance customer experiences across diverse industries.

During interactive, live-streamed virtual sessions, participants will engage in hands-on activities and assignments that provide actionable strategies for business growth and success. Join us to unlock the potential of generative AI and LLMs for driving meaningful organizational transformation and achieving sustainable business success.

SPEAKERS

Nouri, a renowned AI leader and Australia’s ICT Professional of the Year, has revolutionized AI perspectives while championing Responsible and inclusive AI, founding a global non-profit initiative. He is also the founder of the largest GenAI community in the world, GenAI Works which is a platform for enthusiasts to Learn, Discover and Grow.

Munther A. Dahleh received his B.S. in Electrical Engineering from TAMU in 1983, Ph.D. degree from Rice University, Houston, TX, in 1987 in Electrical and Computer Engineering. Since then, he has been with the Department of Electrical Engineering and Computer Science (EECS), MIT, Cambridge, MA, where he is now the William A. Coolidge Professor of EECS.

Devavrat Shah is Andrew (1956) and Erna Viterbi Professor of AI and Decisions at MIT where he has been teaching since 2005. Devavrat is CEO & Co-Founder of Ikigai Labs which brings Generative AI for tabular, time-series data to enterprises. Devavrat successfully combines academia and entrepreneurship. Devavrat founded an academic unit in Statistics and Data Science at MIT in 2016 where he served as its founding director till 2020.

WHO SHOULD ATTEND

LEARNING OUTCOMES

WHO SHOULD ATTEND

LEARNING OUTCOMES

Agenda

Steve Nouri (3 modules - 30 minutes each)

Module 1:

Overview of Artificial Intelligence: Gain a comprehensive understanding of AI’s evolution, and the different types of AI, and delve into the foundations of Generative AI.

Part 1: Evolution of AI

Part 2: Foundations of Generative AI

Part 3: Key Technologies and Algorithms

Module 2:

Applications of Generative AI in Business: Explore how generative AI is being applied across various business functions and industries.

Part 1: By Business Function

Part 2: By Industry

Module 3:

Building Generative AI Projects: Understand the strategic steps necessary to initiate and manage generative AI projects within an organization.

Part 1: Project Initiation

Part 2: Strategic Integration and Management

Munther Dahleh (2 modules - 45 minutes each)

Module 1:

Data, Systems, and Society: Explore the intersection of data science, systems thinking, and societal challenges, focusing on key themes such as bias, fairness, privacy, and statistical accuracy.

Part 1: Key Themes in Addressing Societal Challenges
Part 2: Case Studies and Breakthroughs

Module 2:

Harnessing AI for Business: Explore how AI and data science can be leveraged to address urgent business challenges

Part 1: Ethical and Regulatory Considerations
Part 2: Collaborative Efforts and Future Directions

Devavrat Shah (2 modules: 45 minutes each)

Module 1 : Understanding Generative AI: Perspectives and Paradigms
Gain an understanding of Generative AI’s multifaceted definition and historical underpinnings, while contrasting traditional and modern perspectives to inform strategic decisions and applications in diverse contexts.

Part 1: What is Generative AI?

Part 2: Traditional vs Modern view

Module 2: Data Management and Performance Metrics in Generative AI

Part 1: Structured vs Unstructured Data

Part 2: KPIs with Generative AI: accuracy, cost, enterprise-readiness, and governance

For any queries, please reach out to vishal@mitsloanme.com

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