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Articles by Mike Kuniavsky
From Principles to Code: maturity stages in AI risk management
After more than a decade working on AI ethics, I’ve observed that organizations’ ideas about how to deploy AI responsibly go through a common pattern. It begins with well-meaning but often vague goals and ends with engineering decisions about what code to write to implement the organization’s policies. I’ve watched this process repeat in individual companies and in the whole industry. I also lived it, so I went through the same pattern in my own thinking.
Classes of measurable AI risk: Integrity risk vs. Consequential Risk
At MLCommons I manage product strategy for the AIluminate AI benchmark. In doing so, I’ve observed organizations approach AI risk and safety in ways that prevent them from effectively addressing the actual problem. Many organizations behave as if there’s a single form of danger to watch out for when deploying AI. They focus their attention on model hallucination, chatbot accuracy, or toxic content, as if solving that one problem is the challenge. Others handle it as a policy and PR problem.
From Vibejects to translational surrealism: manifesting objects gets weird.
[This is Part 3 of my three part series about manifesting physical objects with AI. See Part 1 and Part 2] Here’s the most exciting and strange part of my object manifestation journey so far. After playing around with designing bicycle cassettes one night, I was tired. As a last experiment before closing my laptop, I decided to try using OpenSCAD to make something that had never existed out in the world. A book of Piranesi’s drawings on my bookshelf caught my eye, so I started there.
From Vibejects to translational surrealism: manifesting objects gets weird. Part 1: sim to real
[Originally this was a single blog post, but Liz — my editor (and, er, wife) — said it would make much more sense as three posts about three separate aspects of the same idea. See Part 2 and Part 3] When I’m not at my day job measuring AI risk, I spend a significant amount of time looking at how AIs and people can work together to create functional physical objects using software-driven manufacturing.
From Vibejects to translational surrealism: manifesting objects gets weird. Part 2: Shimano parts
[This is Part 2 of my three part series about manifesting physical objects with AI. See Part 1 and Part 3] I believe design is problem solving, and that to “change current conditions to preferred ones” (hi, Herbert Simon!) you need a problem to solve, the current condition.
prompt engineering incantations is terrible UX
"In his famous book The Golden Bough, Sir James Frazer defined magic as the manipulation of supernatural beings by a human who expects that the correct sequence of words or actions will automatically bring about the desired result." - Magic in Ancient Egypt, Geraldine Pinch Words feel magical. They are thoughts, but outside our heads. Almost all magic is based on the power of words to evoke changes in the world without physical contact.
Manifesting objects with words
Yesterday I got ChatGPT to make me a physical object. I got it to generate the key software description of a simple penguin, which I printed on a 3D printer. If it was connected to a 3D printer, it could have done it itself. Multimodal Foundation models can now generate physical products from verbal descriptions.
The changing face of design and research
Written by: Mike Kuniavsky | Published: 01 March 2019 The relationship between design and research is changing. From Bell Labs onward, in-house corporate research was once a major method for companies to invest in creating long-term value. But that approach is changing as more large companies increasingly rely on acquisition of venture-funded companies for innovation rather than in-house R&D.
Design in Research: How do you use design to support and shape R&D? October 10 PARC Forum Presentation
Corporate Research and Development is evolving, and it increasingly incorporates user experience design, design research, and service design into the earliest stages. The historical separati… Full description
Santa Clara Convention Center, CA, USA
Mike is a UX designer, researcher and author. A twenty-year veteran of digital product development, Mike designs products, business processes, and services at the leading edge of technological change. He specializes in multi-device interactions, cloud-based service design, and hardware product design connected to cloud-based services.
Key design aspects of the consumer IoT
Mike Kuniavsky leads user experience design in the Innovation Services Group at PARC, a Xerox company. A 20-year veteran of digital product development, he designs products, business processes, and services at the leading edge of technological change. Prior to PARC, Mike co-founded several successful user-experience-centered companies, including ThingM, which designs and manufactures ubiquitous computing and Internet of Things products, and Adaptive Path, a well-known design consultancy.
Ask better questions
By Mike Kuniavsky I’ve done a lot of usability testing over many years; so much that I wrote a book about it. And usability testing is an absolutely essential part of evaluation. Assuming you know what people want and have iteratively developed a product with these things in mind, usability testing makes sure your assumptions are correct. It’s the final check. But what if you want to do the exact opposite and evaluate a product before you make it? That’s what we do with our probe method.
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