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Exploring hip hop history with art and technology
Exploring hip hop history with art and technology
The Universal Hip Hop Museum is coming to New York City, and an MIT team led by D. Fox Harrell has designed for it unique creative experiences at the intersection of art, learning, and technology.
Exploring hip hop history with art and technology
Design Principles for a New AI World
Design Principles for a New AI World
I was on a panel tonight discussing Ethics in Design Research. I’m on a lot of panels about design and ethics because I’ve worked in AI…
Design Principles for a New AI World
What Computers Still Can't Do
What Computers Still Can't Do
When it was first published in 1972, Hubert Dreyfus's manifesto on the inherent inability of disembodied machines to mimic higher mental functions caused an uproar in the artificial intelligence community. The world has changed since then. Today it is clear that "good old-fashioned AI," based on the idea of using symbolic representations to produce general intelligence, is in decline (although several believers still pursue its pot of gold), and the focus of the Al community has shifted to more complex models of the mind. It has also become more common for AI researchers to seek out and study philosophy. For this edition of his now classic book, Dreyfus has added a lengthy new introduction outlining these changes and assessing the paradigms of connectionism and neural networks that have transformed the field.At a time when researchers were proposing grand plans for general problem solvers and automatic translation machines, Dreyfus predicted that they would fail because their conception of mental functioning was naive, and he suggested that they would do well to acquaint themselves with modern philosophical approaches to human beings. What Computers Can't Do was widely attacked but quietly studied. Dreyfus's arguments are still provocative and focus our attention once again on what it is that makes human beings unique.
What Computers Still Can't Do
A New AI Lexicon: Labor
A New AI Lexicon: Labor
By Julian Posada via AI Now Institute | An investigation on the experiences of people who annotate data for AI and what the implications are when society integrates a heavily designed AI labor system.
A New AI Lexicon: Labor
The role of design in creating experiences of future distributed intelligent systems by Agnieszka Zimolag
The role of design in creating experiences of future distributed intelligent systems by Agnieszka Zimolag
The ethics, data, privacy and complexity are not only presenting the constraints for the development and design of the intelligent systems but also open up a space for new modes of thinking. Through real-life examples, I’ll discuss how designers can reimagine the goals and focus on the unique attributes, potentials of the evolving nature of the emerging intelligent systems as well as its implications on the society and the individuals.
The role of design in creating experiences of future distributed intelligent systems by Agnieszka Zimolag
Now That Machines Can Learn, Can They Unlearn?
Now That Machines Can Learn, Can They Unlearn?
By Tom Simonite (WIRED) | Privacy concerns about AI systems are growing. So researchers are testing whether they can remove sensitive data without retraining the system from scratch.
Now That Machines Can Learn, Can They Unlearn?
Prompt Engineering: The Career of Future
Prompt Engineering: The Career of Future
With the No-Code revolution around the corner, and the coming of new-age technologies like GPT-3 we may see a stark difference between the…
Prompt Engineering: The Career of Future
Attack discrimination with smarter machine learning
Attack discrimination with smarter machine learning
By Martin Wattenberg, Fernanda Viégas, and Moritz Hardt | As machine learning is increasingly used to make important decisions across core social domains, the work of ensuring that these decisions aren't discriminatory becomes crucial. Here we discuss "threshold classifiers," a part of some machine learning systems that is critical to issues of discrimination.
Attack discrimination with smarter machine learning
Working with participants in AI data collections: drawing from user research and communication…
Working with participants in AI data collections: drawing from user research and communication…
By Arathi Sethumadhavan, David Mondello, and Karen Chappell | "So much of the participant experience during data collections is shaped by the moderator leading the session. It is therefore a vitally important role, and one that can really make a difference in helping to address potential concerns or questions that participants might have." This article provides tips and tricks for moderating a session for data collection.
Working with participants in AI data collections: drawing from user research and communication…
Designing in Liquid Times: Generative Graphic Design in an Age of Uncertainty
Designing in Liquid Times: Generative Graphic Design in an Age of Uncertainty
By Marlies Peeters, PLOT(s): Journal of Design Studies | The shift of information from static to mobile and ephemeral has influenced what it means to be a graphic designer. Not only do graphic designers have to adapt to a new medium, but they are no longer the only people who have access to these skills when design tools are made to be easily accessible by people with no design experience as well. With the developments of software and its accessibility, how has graphic design changed as a profession?
Designing in Liquid Times: Generative Graphic Design in an Age of Uncertainty
Teens use “algorithmic folklore” to crack TikTok’s black box
Teens use “algorithmic folklore” to crack TikTok’s black box
By Iretiolu Akinrinade & Joan Mukogosi | Despite the prevalence of strategic ignorance inside social media and gaming companies, today’s teen tech users have developed a number of creative and often hilarious strategies to make sure that they are seen, heard, and valued online.
Teens use “algorithmic folklore” to crack TikTok’s black box