Designing and Building AI Products and Services

Uncover limitless possibilities with AI

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Course Dates

STARTS ON

November 17, 2021

Course Duration

DURATION

8 weeks, online
6 hours per week

Course Duration

Introduction to AI-Based Product Design

If you are a technology professional or an entrepreneur working in the field of artificial intelligence (AI), this program will help you understand design principles and applications of AI across various industries. The goal is for you to create an AI-based product proposal, which can be presented to your internal stakeholders or investors. You will learn the various stages involved in the design of AI-based products, and the fundamentals of machine and deep learning algorithms, and apply the insights to solve practical problems.

Why AI — and Why Now?

Deploying the right AI technologies in your organization can help you automate routine tasks, gain insights through data analytics, and engage better with customers. Given the broad spectrum of AI’s applications in organizations, it’s no surprise that AI Specialist is the top-rated job on the LinkedIn Emerging Jobs Report in 2020. With an annual growth rate of 74% for this position, every industry is clamoring for AI talent to first devise a strategic plan for AI applications, and then help manage and optimize them in practice.

49%

Global CEOs state digital transformation technologies including AI will be their top area for long-term investments

(Source: PwC Annual Global CEO Survey, 2021)

150 Million

Technology jobs — mainly in the area of digital transformation – to be added globally over five years

(Source: LinkedIn Jobs on the Rise, 2021)

Who Is This Program For?

This 8-week course is ideal for you if you are a technical product leader, technology professional, technology consultant, or entrepreneur who wants to enhance your understanding of AI technology fundamentals and tools, and explore various design processes involved in AI-based products. Knowledge of calculus, linear algebra, statistics, and probabilities is beneficial, along with basic Python experience. The program is ideal for:

  • Technical Product Managers in charge of machine learning and AI-based products in their organizations who are looking to add value to their organization by leveraging the latest in AI technologies.
  • Technology Professionals who design and develop technology solutions aligned to organizations’ needs and are looking to broaden their understanding of developing AI-based solutions using machine learning algorithms.
  • Technology Consultants who focus on the analysis, design, and development of technology solutions for clients.
  • Founders of AI Startups that build AI-driven applications and want to learn a proven framework for developing viable AI products and network with other technologists.
  • UI/UX Designers responsible for managing user experience of AI-based applications.

Key Takeaways

Build a foundation

  • Learn the four stages of AI product design
  • Identify applicable AI technologies to improve organizational processes
  • Analyze technical and operational requirements to build AI models

Expand your knowledge

  • Differentiate between various machine learning algorithms
  • Design AI products to solve organizational issues
  • Learn about challenges you may encounter when designing AI products

Apply learnings

  • Learn to apply machine learning methods to practical problems
  • Design intelligent human-machine interfaces
  • Assess AI opportunities in various fields such as healthcare and education

Become proficient

  • Identify an operational challenge and propose a technical solution for it
  • Implement the Lawler Model for defining an AI problem and identify key steps to build an organization case
  • Design and construct an executive summary of an AI product or process using the AI design process model

Program Highlights

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A certificate from MIT xPRO recognizing your skills and success

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Insights and examples from renowned MIT faculty

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Market-ready skills for evaluating the opportunity for AI solutions and making the case for it

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Develop an AI project proposal to present to internal stakeholders or investors

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Advance your knowledge through crowdsourcing, demos, and design-support activities

Program Topics

Week 1:

Introduction to the Artificial Intelligence Design Process

Get acquainted with the stages involved in designing an AI-based product with a focus on specifics such as the cost metrics and technical requirements of an AI software development plan.

Week 2:

Artificial Intelligence Technology Fundamentals – Machine Learning

Identify various machine learning algorithms and study the different approaches such as Bayesian and regression models. Learn about unsupervised and semi-supervised methods of machine learning algorithms. Run and analyze the results from various machine learning algorithms.

Week 3:

Artificial Intelligence Technology Fundamentals – Deep Learning

Building on the knowledge of machine learning fundamentals gained in Week 2, explore the basics of deep learning. The topics include neural networks, artificial neurons, and simulation of complex networks.

Week 4:

Designing Artificial Machines to Solve Problems

Identify superhuman intelligence used in an AI product. Compare and contrast the advantages and disadvantages of using an AI technology.

Week 5:

Designing Intelligent Human-Computer Interaction (HCI)

Use the resources provided in this module to understand the techniques, application areas, benefits, and drawbacks of HCI. Learn to define an appropriate level of machine involvement in interactions with humans and computers. Seek ways to use artificial intelligence to your advantage.

Week 6:

Superminds: Designing Organizations that Combine Artificial and Human Intelligence

Get an introduction to the concept of superminds, and compare and contrast the different types of superminds. Analyze how humans and machines can work together to surpass the sum of their parts. Apply cognitive processes to various organizations and community problems.

Week 7:

Marketplace Frontiers of AI Design: Research

Learn how artificial intelligence and Generative Adversarial Networks (GANs) can be used to generate fake images and videos from real data. Assess the technical, social, and economic impact of AI technologies.

Week 8:

Marketplace Frontiers of AI Design: Practice

Implement the Lawler Model to define an AI problem. Design and construct a summary of an AI product or process using learnings from the previous modules of the program.

Week 1:

Introduction to the Artificial Intelligence Design Process

Get acquainted with the stages involved in designing an AI-based product with a focus on specifics such as the cost metrics and technical requirements of an AI software development plan.

Week 5:

Designing Intelligent Human-Computer Interaction (HCI)

Use the resources provided in this module to understand the techniques, application areas, benefits, and drawbacks of HCI. Learn to define an appropriate level of machine involvement in interactions with humans and computers. Seek ways to use artificial intelligence to your advantage.

Week 2:

Artificial Intelligence Technology Fundamentals – Machine Learning

Identify various machine learning algorithms and study the different approaches such as Bayesian and regression models. Learn about unsupervised and semi-supervised methods of machine learning algorithms. Run and analyze the results from various machine learning algorithms.

Week 6:

Superminds: Designing Organizations that Combine Artificial and Human Intelligence

Get an introduction to the concept of superminds, and compare and contrast the different types of superminds. Analyze how humans and machines can work together to surpass the sum of their parts. Apply cognitive processes to various organizations and community problems.

Week 3:

Artificial Intelligence Technology Fundamentals – Deep Learning

Building on the knowledge of machine learning fundamentals gained in Week 2, explore the basics of deep learning. The topics include neural networks, artificial neurons, and simulation of complex networks.

Week 7:

Marketplace Frontiers of AI Design: Research

Learn how artificial intelligence and Generative Adversarial Networks (GANs) can be used to generate fake images and videos from real data. Assess the technical, social, and economic impact of AI technologies.

Week 4:

Designing Artificial Machines to Solve Problems

Identify superhuman intelligence used in an AI product. Compare and contrast the advantages and disadvantages of using an AI technology.

Week 8:

Marketplace Frontiers of AI Design: Practice

Implement the Lawler Model to define an AI problem. Design and construct a summary of an AI product or process using learnings from the previous modules of the program.

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Faculty

Faculty Member BRIAN SUBIRANA

BRIAN SUBIRANA

Director, MIT Auto-ID lab; Director, MIT and Accenture Convergence Initiative for Industry and Technology

Brian Subirana has taught at MIT Sloan and the MIT School of Engineering and he is also on the faculty of Harvard University. His research centers on IoT and AI, and focuses on manufacturing, e-learning, the creative industries, and digital health... More info

Faculty Member ANDREW LIPPMAN

ANDREW LIPPMAN

Senior Research Scientist, MIT; Associate Director, MIT Media Lab

Andrew Lippman heads the Viral Communications research group at MIT Media Lab. His work has ranged from digital video and entertainment to graphical interfaces, networking and blockchains. In the 1980s, Lippman developed the Movie Map that presaged Google’s Street View... More info

Faculty Member STEFANIE MUELLER

STEFANIE MUELLER

X-Career Development Assistant Professor, MIT Electrical Engineering and Computer Science, joint with Mechanical Engineering

Stefanie Mueller is the head of the Human Computer Interaction Communities of Research (HCI CoR) at MIT CSAIL. In her research, she develops novel hardware and software systems that advance personal fabrication technologies... More info

Faculty Member DUANE BONING

DUANE BONING

Clarence J. Lebel Professor, Electrical Engineering and Computer Science

Duane Boning is affiliated with the MIT Microsystems Technology Laboratories and serves as Associate Director for Computation and CAD (computer-aided design). He is also the Engineering Faculty Co-Director of the MIT Leaders for Global Operations program... More info

Faculty Member BRUCE LAWLER

BRUCE LAWLER

Managing Director, MIT Machine Intelligence for Manufacturing and Operations (MIMO)

Bruce Lawler is a technology entrepreneur and an executive leader. He has developed several applications across platforms such as mobile, SaaS, AI and video distribution networks. He has headed multiple ventures in fields ranging from consumer and industrial hardware to wireless and video network operations... More info

Faculty Member THOMAS W. MALONE

THOMAS W. MALONE

Patrick J. McGovern Professor of Management, MIT Sloan Founding Director, MIT Center for Collective Intelligence

Thomas W. Malone is the Professor of Information Technology and a Professor of Work and Organizational Studies at MIT. In his researches over the years, Malone rightly predicted major business and technology trends decades before they happened... More info

Faculty Member BARBARA H. WIXOM

BARBARA H. WIXOM

Principal Research Scientist, MIT Center for Information Systems Research (CISR)

Wixom leads the MIT CISR Data Research Advisory Board, comprised of data and analytics executives from CISR organizations. Her research explores how organizations generate business value from data assets... More info

Guest Speakers

Faculty Member David Anderton-Yang

DAVID ANDERTON-YANG

Chief Executive Officer, Voomer

David Anderton-Yang heads the startup Voomer, which helps users build confidence in video interviews. The service uses an AI-enhanced video analysis technique to give users feedback on their videos. Anderton-Yang is a recipient of the Forbes 30 Under 30. He completed his research at the MIT Media Lab and teaches big data, IoT, and cybersecurity in the research organization. He is also a faculty mentor in online teaching and engagement at Harvard.

Faculty Member Aruna Sankaranarayanan

ARUNA SANKARANARAYANAN

Research Assistant, MIT Media Lab

Aruna Sankaranarayanan works at the Viral Communications group at the MIT Media Lab. Her research looks at the ways in which deep learning and computer vision techniques can manipulate media to modify perception and inspire creativity. The Lab also studies how such manipulation can create misinformation. In the past, she has built server infrastructure for maps at Mapbox, designed science games, and contributed to free and open-source software communities.

Certificate

Example image of certificate that will be awarded after successful completion of this program

Certificate

Get recognized! Upon successful completion of this course, MIT xPRO grants a certificate of completion to participants. This course is graded as a pass or fail; participants must receive 75% to pass and obtain the certificate of completion.

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Note: After successful completion of program, your verified digital certificate will be emailed, at no additional cost, in the name you used when registering for the program. All certificate images are for illustrative purposes only and may be subject to change at the discretion of MIT.

Registration for this program is done through Emeritus. You can contact us at mit@emeritus.org
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