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Applied Generative AI for Digital Transformation

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MIT CEU’s
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Applied Generative AI for Digital Transformation

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MIT CEU’s
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Applied Generative AI for Digital Transformation and the Future of Productivity

Applied Generative AI: Tap into the Future of Technology is an intensive and timely three-week program, crafted meticulously to delve into the depths of Generative AI technologies. It targets the implications and practical applications across various organizational contexts. Delivered through live-virtual online sessions, the course amplifies theoretical learning with hands-on, actionable learning activities and assignments that emphasize real-world application.

This course serves a broad audience, ranging from senior leaders and technology heads to managers and professionals across diverse domains such as innovation, sales, product management, marketing, and customer experience. It also welcomes investors interested in potential opportunities offered by Generative AI.

This course skillfully integrates technical expertise with management insights, ethical considerations, and human factors, fostering a holistic comprehension of digital transformation strategies that harness artificial intelligence as a catalyst for change.

Successful completion earns the participants an MIT Professional Education Certificate of Completion and 4.0 MIT CEU (continuing education unit).

75%

75% of professionals expect that generative AI will cause “significant or disruptive change in the nature of their industry’s competition” over the next 3 years.

Source: McKinsey

$55.7 billion

Spending on generative AI platforms and applications is estimated to hit $55.7 billion by 2027.

Source: International Data Corporation (IDC)

5 hours

Marketers believe generative AI will save them an average of five hours of work per week, which is equivalent to one month per year.

Source: Salesforce, Generative AI Snapshot Series

A live digital course to enhance your AI knowledge.

The interactive learning design incorporates five faculty presentations of 45-minutes, followed by a stimulating 45-minute discussion with faculty members and participants in each session.

LEARN MORE ABOUT THE COURSE SPECIFICS

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Learning objectives

1

Understand generative AI deeply, including its historical development
2

Discover how diverse domains like art, biology, emotional support, and learning apply Generative AI
3

Comprehend and implement prompt engineering to enhance productivity
4

Learn strategies for automating organizational workflows using Generative AI
5

Understand the dynamics of reinforcement learning and the power of data search in Generative AI
6

Navigate the ethical, compliance, and risk aspects associated with Generative AI
7

Understand potential digital transformation opportunities enabled by generative AI for your organization
8

Understand what it will take – from both technology and culture - to make AI work in your organization

Learning outcomes

Upon completing this course, participants will:

  • Understand and explain the history and language of artificial intelligence, specifically Generative AI
  • Employ Generative AI for domain-specific tasks like video summarization, automated research, and conversational data insights
  • Utilize Generative AI tools effectively to automate workflows and increase productivity
  • Understand potential digital transformation opportunities and challenges from using generative AI
  • Apply the principles of prompt engineering to enhance business outcomes

The skills you will develop

1.

Proficiency in using Generative AI tools

2.

Application of prompt engineering

3.

Automation of workflows using Generative AI

4.

Employing Generative AI in domain-specific tasks

5.

Understanding and managing Generative AI’s organizational implications, both positive and negative

Competencies

  • Ability to apply Generative AI to improve productivity and efficiency
  • Understanding the interplay between artificial intelligence and ethics
  • Strategy and implementation of Generative AI in business-specific domains
  • Proficiency in identifying and managing business opportunities and risks associated with Generative AI

In addition, you will receive a Certificate of Completion

All the participants who successfully complete the live virtual course Applied Generative AI for Digital Transformation will receive an MIT Professional Education Certificate of Completion. Furthermore, participants will receive 4.0 MIT Continuing Education Unit (CEU)*.

To obtain CEUs, complete the accreditation confirmation, which is available at the end of the course. CEUs are calculated for each course based on the number of learning hours.

* The Continuing Education Unit (CEU) is defined as 10 contact hours of ongoing learning to indicate the amount of time they have devoted to a non-credit/non-degree professional development program.

To understand whether or not these CEUs may be applied toward professional certification, licensing requirements, or other required training or continuing education hours, please consult your training department or licensing authority directly.

This course is aimed at

  • Senior leaders grappling with the potential opportunities and challenges of generative AI for their businesses
  • Technology leaders who want to learn current best practices for adopting and optimizing generative AI systems to boost business outcomes
  • Senior managers and mid-career executives who want to gain insights into the potential applications of generative AI within their organizations
  • Innovation managers, sales and product managers, and marketing and customer experience professionals who want to learn how to leverage generative AI to create new products, new content, and personalized customer experiences
  • Investors in venture capital, private equity, or hedge funds looking to understand investment opportunities created by generative AI
  • Professionals from all industries and sectors are welcome to create a dynamic learning ecosystem

Prerequisites:
No prior background in analytics, computer science, coding or machine learning is required.

Meet the instructors of this course

* speakers and topics are subject to change without notice.

PROFESSOR OF INFORMATION ENGINEERING, CIVIL AND ENVIRONMENTAL ENGINEERING AND DIRECTOR OF MIT GEOSPATIAL DATA CENTER, AND A FACULTY MEMBER IN THE CENTER FOR COMPUTATIONAL SCIENCE AND ENGINEERING PART OF THE SCHWARZMAN COLLEGE OF COMPUTING SCIENCE

Prof. John R. Williams

"Data engineering builds the corporate nervous system and AI is going to make its reactions smart"

John Williams holds a BA in Physics from Oxford University, a MS in Physics from UCLA, and a Ph.D. in Numerical Methods from University of Wales, Swansea. His research focuses on the application of Exascale computation to problems in cyber-physical systems, security and energy. His work on fault-tolerant computing using container migration won the IEEE, High Performance Extreme Computing Conference award for Best Innovation in 2019. He is director of MIT’s Geospatial Data Center and from 2006-2012, was Director of the MIT Auto-ID Laboratory, where the Internet of Things was invented. He is author or co-author of over 250 journal and conference papers, as well as the books on Rock Mechanics and RFID Technology. He contributed to the 2013 report for the UK Office for Science Foresight Project- The Future of Manufacturing. Alongside Bill Gates and Larry Ellison, he was named as one of the 50 most powerful people in Computer Networks. He consults to companies including Accenture, Schlumberger, Shell, Total, Exxon, SAP Research, Microsoft Research, Kajima Corp, US Lincoln Laboratory, Sandia National Laboratories, US Intelligence Advanced Research Projects Activity, Motorola, Phillip-Morris Inc., Ford Motor Company, Exxon-Mobil, Shell, Total, and ARAMCO. His international collaborations include Oxford and Cambridge Universities, HKUST, KACST, Alfaisal University, PolyU Hong Kong, Imperial College of Science and Technology UK, Malaysia University of Science and Technology (MUST), and Masdar Institute of Science and Technology Abu Dhabi. He organized the first Cyber-Physical Security Conference in the UK (2011) and along with Dr. Sanchez, he runs the MIT Applied Cyber Security Professional Education summer course. At MIT he teaches courses Architecting Software Systems (MIT 1.125) and Engineering Computation and Data Science (MIT 1.00/1.001). .

In data engineering and data science, early work included simulation of Ford's global network, analysis of SAP smart grid billing system. For Altria, he analyzed the performance of item level tagging and also their implementation of an anti-counterfeiting system using the Electronic Product Code (EPC)

In password security, Dr. Williams was PI that developed the algorithms for a negative password authentication system for the Intelligence Advanced Research Projects Activity (IARPA) agency. .

Dr. Williams advises companies in the Americas, Europe, Middle East, and Asia.

Affiliations

  • MIT Department of Civil and Environmental Engineering
  • MIT Center for Computational Science and Engineering (CCSE)
  • MIT Schwarzman College of Computing
  • MIT Geospatial Data Center (GDC)
  • MIT Auto-ID Laboratory
  • MIT Center for Complex Engineering Systems (CCES)
  • MIT Consortium for Improving Critical Infrastructure Cybersecurity (IC3)
  • MIT System Design and Management Program

Areas of Interest and Expertise

  • Information Technology,
  • Cyber/Physical Security
  • Web-Based Education Technology
  • Large Scale Network Simulation
  • GeoNumerics of Granular and Powder Systems
  • Modern Software Architecting and Cyber Security
  • Web Services and Distributed Computing
  • Discrete Element Simulation and Analysis of Discontinua

Courses Taught with MIT Professional Education

  • Applied Generative AI for Digital Transformation
  • Blockchain: Disruptive Technology
  • Cloud & DevOps: Continuous Transformation
  • Data Leadership: Transforming the Corporation´s Operations, Management, and Mindset to Leverage Data, AI, and Cloud Computing
  • Digital Transformation: From AI and IOT to Cloud, Blockchain, and Cybersecurity

https://johntango.github.io

EXECUTIVE DIRECTOR OF MIT’S GEOSPATIAL DATA CENTER, RESEARCH SCIENTIST; CENTER FOR COMPLEX ENGINEERING SYSTEMS, SOCIOTECHNICAL SYSTEMS RESEARCH CENTER, UNDER THE SCHWARZMAN COLLEGE OF COMPUTING

Dr. Abel Sanchez

"Five technologies are redefining both the way we make our products and the types of opportunities that exist in the marketplace"

Dr. Sanchez holds a Ph.D. from the Massachusetts Institute of Technology (MIT). He is the Executive Director of MIT's Geospatial Data Center (GDC). His areas of specialty include the Internet of Things (IOT), Big Data, Cybersecurity, and Digital Innovation. He teaches graduate courses in Data Science, Cybersecurity, and Innovation. For the past eight years his research has focused on architecting large-scale computation.

In IOT, Dr. Sanchez led the global network architecture for the Internet of Things at MIT. The design addresses large-scale computation. Compared to the largest numbers in the world, 2 billion computers, 7 billion phones, and 7 billion people, IOT is orders of magnitude bigger. In similar work, Dr. Sanchez directed simulation of the U.S. critical infrastructure with the National Infrastructure Simulation and Analysis Center (NISAC).

In data science, early work included supply chain information engineering, analytics, simulation, and visualization with Wal-Mart, Kraft, and SAP. Dr. Sanchez extended his work to global anti-counterfeiting efforts with Johnson & Johnson, SAP, and Altria. Helping Altria scale track-and-trace using the Electronic Product Code Information Services standard from RFID. The work produced a global infrastructure used by industry and government to this day.

In enterprise computing, Dr. Sanchez led the design of a global data infrastructure simulator, modeling follow-the-sun engineering, to evaluate the impact of competing architectures on the performance, availability and reliability of the system for Ford Motor Company. The simulator modeled user actions, applications, background processes, network load, servers, storage, and global data centers. The work identified data center reductions opportunities estimated at a billion dollars in savings.

In cyber security, Dr. Sanchez directed impact analysis of large-scale cyber attacks designing Cyber Ranges for the Department of Defense (DOD). Conducting repeatable experiments in impact analysis and the ability to model the cyber environment in a highly portable fashion. Looking at the insider threat Dr. Sanchez led the DOD security study on Enterprise Resource Planning systems across the United States Armed Forces.

In password security, Dr. Sanchez led the design of a password firewall (negative authentication) for the Intelligence Advanced Research Projects Activity (IARPA) agency. The Negative Filtering or Negative Authentication (NA) approach utilizes a form of complement profiles which resembles the censoring and maturation process of T- cells in the immune system.

In machine learning, addressing financial fraud, Dr. Sanchez designed a situational awareness framework that exploits different perspectives of the same financial data and assigns risk scores to entities (e.g. payment documents) to improve false positive ratios and assist the identification of fraudulent activity in huge and unlabeled financial data in collaboration with Accenture.

In physical security, Dr. Sanchez is developing algorithms to assess risk in the integration of information technology (IT) and operations technology (OT).

Dr. Sanchez is the founder and Chief Software Architect of the Open Source RFID platform project. Dr. Sanchez' software systems are used by Samsung, NEC, NTT, Hitachi, Motorola, SAP, IBM, and Microsoft. Other software initiatives are in use by Sandia National Laboratories, MIT, and by several organizations in East Asia and Europe.

Dr. Sanchez advises companies in the Americas, Europe, Middle East, and Asia.

Affiliations

  • MIT Geospatial Data Center (GDC)
  • MIT Sociotechnical Systems Research Center (SSRC)
  • MIT Institute for Data, Systems, and Society (IDSS)
  • MIT Center for Complex Engineering Systems (CCES)
  • MIT Center for Computational Science and Engineering (CCSE)
  • MIT Consortium for Improving Critical Infrastructure Cybersecurity (IC3)
  • MIT Schwarzman College of Computing
  • MIT AutoID Laboratory

Areas of Interest and Expertise:

  • Machine Learning
  • Cyber/Physical Security
  • Enterprise Computing
  • Data Science
  • IOT

Courses Taught with MIT Professional Education

  • Applied Generative AI for Digital Transformation
  • Blockchain: Disruptive Technology
  • Cloud & DevOps: Continuous Transformation
  • Data Leadership: Transforming the Corporation´s Operations, Management, and Mindset to Leverage Data, AI, and Cloud Computing
  • Digital Transformation: From AI and IOT to Cloud, Blockchain, and Cybersecurity

https://abel.mit.edu/

Faculty Contributor

PROFESSOR IN MIT’S DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE; HEAD OF COMPUTER-ASSISTED PROGRAMMING GROUP AND ASSOCIATE DIRECTOR AND COO OF MIT COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LABORATORY

Prof. Armando Solar-Lezama

Armando Solar-Lezama is a professor in MIT’s Department of Electrical Engineering and Computer Science. He is the associate director and COO in the Computer Science and Artificial Intelligence Laboratory (CSAIL), where he leads the Computer Assisted Programming Group. Solar-Lezama and his research group focus on program synthesis, a research area at the intersection of programming systems and artificial intelligence.

He is currently the lead PI of the NSF funded Expeditions project “Understanding the World through Code” and is also the founder of PlaySkript Home , an online platform for creating interactive presentations. Solar-Lezama earned a PhD from University of California, Berkeley.

Affiliations:

  • CSAIL: Vertical AI Community of Research; Applied Machine Learning Community of Research; Computation Structures Group; Center for Deployable Machine Learning (CDML)
    • Associate Director and COO, Computer Science and Artificial Intelligence Laboratory
  • LEAD: Computer-Aided Programming Group

Areas of Interest and Expertise:

  • Programming Systems with a Focus on Software Synthesis
  • Programming Tools for Parallel and High Performance
  • Computing
  • Cybersecurity

https://www.csail.mit.edu/person/armando-solar-lezama

MIT Research Contributor

MIT RESEARCH CONTRIBUTOR

Katie M. Lewis

Katie M. Lewis received her PhD from MIT in Developing Domain-Specific Generative Models. As a research assistant, she developed the GIST method to generate fine-grained image-specific text descriptions using LLMs. She also developed a learning-based method to align sparse, clinical MRI brain scans with higher accuracy on 92% of subjects and 100x faster on the CPU.

Some of her generative machine learning publications include At the Intersection of Conceptual Art and Deep Learning: The End of Signature, Generating Image-Specific Text for Fine-grained Object Classification, and Machine Learning for Healthcare (ML4H) at NeurIPS 2018.

She recently defended her dissertation under the supervision of John Guttag and Frédo Durand and has interned twice with Ira Kemelmacher-Shlizerman's team at Google.

Industry Contributors

CHIEF INFORMATION OFFICER OF THE BOEING COMPANY AND SENIOR VICE PRESIDENT OF INFORMATION TECHNOLOGY AND DATA ANALYTICS

Susan Doniz

"Generative AI can make us all exponentially better at what we do, but it takes human experience and judgment to really thrive'"

Susan Doniz is the chief information officer of The Boeing Company and senior vice president of Information Technology & Data Analytics, where she leads all aspects of information technology, information security, data and analytics. She also supports the growth of Boeing's business through IT and analytics-related revenue-generating programs. Doniz is a member of the company’s Executive Council.

Before joining Boeing in 2020, Doniz was the Group CIO of Qantas Airways, where she expanded the airline’s digital ecosystem and adopted new technology to support the needs of the business and its customers.

Previously, during a 17-year career at Procter & Gamble, she led IT and Analytics programs in support of sales, research and development, the supply chain, as well as a cross-functional program to digitize the company. Doniz also worked at SAP, where she was a strategic adviser to the global chief executive officer on transformation and technology issues in support of customers, and Aimia.

Doniz has been a board member of multiple nonprofit organizations, including The Women’s College Hospital Foundation, Salvation Army and Engineers Without Borders. She serves as an adviser to the Center for Digital Transformation at the University of California, Irvine, Paul Merage School of Business and was previously the vice chair of the Digital Transformation Advisory Council of the International Air Transport Association."

CIO AND ENTERPRISE STRATEGIST AT AMAZON

Mark Schwartz

Mark Schwartz is an award-winning CIO currently working as Enterprise Strategist at Amazon Web Services, where he helps senior executives from some of the world’s largest companies to formulate strategies and overcome impediments to succeeding in the digital era.

Mark Schwartz is also an accomplished author and his most recent work titled Adaptive Ethics for Digital Transformation touches on how the act of digital transformation requires a change in the moral outlook and ethical assumptions of a business.

In 2010, Mark Schwartz was named one of the Premier 100 IT Leaders by Computerworld Magazine for his contribution in technology leadership, innovative ideas, and effectively managing IT strategies.

DEPUTY CHIEF SECURITY OFFICER AT GITHUB

Jacob DePriest

"AI is accelerating tasks for developers today, and it's expected to continue helping developers focus on more meaningful and creative work in the future"

Jacob DePriest is the VP, Deputy Chief Security Officer at GitHub where he is responsible for managing the GitHub Security team. Prior to GitHub, Jacob was a senior executive at the National Security Agency where he built and led the Developer Experience program, was the Agency’s executive sponsor for open source, and led a number of IT and security transformation initiatives.

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