ACM TechTalks

ACM members and non-members alike are welcome to attend our popular series of free TechTalks by expert industry professionals, distinguished ACM award laureates, and visionary researchers from industry and academia. Focused on keeping our global audience of busy practitioners at the forefront of technical trends, professional development, and emerging technologies, the TechTalks are also popular with students and educators. Recent talks have covered topics in Artificial Intelligence and Machine Learning, Big Data and Data Science, Blockchain, Computer Vision, Deep Learning, JavaScript, Microservices, Python, Quantum Computing, and more. Registration is free and the TechTalks can be attended both live and on-demand, on desktop and mobile devices. Check this page frequently for upcoming events as well as our on-demand archive. To subscribe to our TechTalk announcements, email [email protected].

ACM TechTalks

View Our Recent TechTalk

Empirical Software Design: When & Why

Since the publication of Parnas' "On the Criteria to Be Used in Decomposing Systems into Modules" we have had good advice on how to design software. However, most software is more difficult to change than it should be and that friction compounds over time. The Empirical Design Project seeks to resolve the seemingly-irresolvable tradeoff between short-term feature progress and long-term optionality, focusing on:

How is software actually designed? What can we learn from data about how software is designed?
When should software design decisions be made? What is the optimal moment given unclear and changing information & priorities?
How can we enhance the survival of software projects while expanding optionality?

Spoiler alert: make design decisions later and in small, safe steps.

ACM Learning Center TechTalk Archive

ACM award winners, leading researchers, industry veterans, thought leaders, and innovators address today and tomorrow's hottest topics and issues in computing for busy practitioners, as well as educators, students, and researchers. Check out our archive of these ACM TechTalks, free for members and non-members alike.

TechTalks on Artificial Intelligence & Machine Learning

Talks from some of the leading visionaries and bleeding-edge researchers in AI/ML: Fei-Fei Li on visual intelligence in computers and ImageNet; Eric Horvitz on AI solutions in the open world; and Tom Mitchell on using ML to study how the brain creates and represents language.

17 April 2025 :

Empirical Software Design: When & Why with Kent Beck

27 March 2025 :

Applying Cinematic Techniques to Data Storytelling with Angelica Lo Duca

6 March 2025 :

The Death of Work: Leading Effective Engineering Teams with sarah Drasner

ACM TechTalks

Programming for All: A Feminist Case for Language Design

Register now for the next free ACM TechTalk, "Programming for All: A Feminist Case for Language Design," presented on Thusday, June 26 at 12:00 PM ET/16:00 UTC by Felienne Hermans, Professor at Vrije Universiteit Amsterdam. Mark Guzdial, Professor and Director for the Program in Computing for the Arts and Sciences at the University of Michigan, will moderate the questions and answers session following the talk. Continue the discussion on ACM's Discourse Page.

ACM TechTalks

Dynamic Neural Network Compression for Scalable AI Deployment

View the recent ACM TechTalk, "Dynamic Neural Network Compression for Scalable AI Deployment," presented by Aditya Challapally, Applied Science Lead at Microsoft. Chris Pease, Researcher at the MIT Media Lab, moderated the questions and answers session following the talk. Continue the discussion on ACM's Discourse Page.

ACM TechTalks

View Our Recent TechTalk

ACM Learning Center TechTalk Archive

TechTalks on Artificial Intelligence & Machine Learning

View Latest Learning Center TechTalks

Empirical Software Design: When & Why with Kent Beck

Applying Cinematic Techniques to Data Storytelling with Angelica Lo Duca

The Death of Work: Leading Effective Engineering Teams with sarah Drasner

Programming for All: A Feminist Case for Language Design

Dynamic Neural Network Compression for Scalable AI Deployment