The MIT Media Lab is a research laboratory at the Massachusetts Institute of Technology, growing out of MIT's Architecture Machine Group in the School of Architecture. Its research does not restrict to fixed academic disciplines, but draws from technology, media, science, art, and design. As of 2014, Media Lab's research groups include neurobiology, biologically inspired fabrication, socially engaging robots, emotive computing, bionics, and hyperinstruments.The Media Lab was founded in 1985 by Nicholas Negroponte and former MIT President Jerome Wiesner, and is housed in the Wiesner Building (designed by I. M. Pei), also known as Building E15. The Lab has been written about in the popular press since 1988, when Stewart Brand published The Media Lab: Inventing the Future at M.I.T., and its work was a regular feature of technology journals in the 1990s. In 2009, it expanded into a second building.The Media Lab came under scrutiny in 2019 due to its acceptance of donations from convicted child sex offender Jeffrey Epstein. This led to the resignation of its director, Joi Ito, and the President of MIT to launch of an "immediate, thorough and independent" investigation into the "extremely serious" and "deeply disturbing allegations about the engagement between individuals at the Media Lab and Jeffrey Epstein".
The study of cities may represent a difficult task due to their dynamic and complex nature. Cities show many emergent behaviors, thus designing and deploying interventions based on one single perspective is probably insufficient for reasoning about the consequences of such changes (on infrastructure, laws, or technology, etc.). As a natural response, urban planning must evolve and adopt new tools that take into consideration the urban systems components (citizens, government, industry, infrastructure, natural resources, etc) and the relationships between them. This work presents CityScope Guadalajara: an interactive agent-based analysis, simulation and visualization tool that allows testing interventions in virtual urban scenarios. The CityScope Guadalajara’s architecture permits non-technical users to “become” urban planners, transforming the case study through a tangible user interface (based on a 3D-printed model) and re. User’s movements change simulation model parameters and permits recomputing key performance indicators on three main axes: diversity, functionality and sustainability. The selected case-study was the University Cultural District, in Zapopan, Mexico.
Perfumes are cultural artifacts and works of sensory art, composed from a finite, recombinable palette of notes that together evoke a distinctive scent impression. Here, we assemble the largest perfume corpus compiled to date, spanning multiple independent databases from 1900 to 2024, and study its evolution through a multidisciplinary computational framework. We first characterize perfumes by properties such as complexity and novelty using crowd-sourced data, finding that compositions have grown more minimalist in their scent profiles, yet more novel in their note combinations, a transition that began in the 1990s. Next, we construct copy-lineage networks and examine how notes are selected across successive time windows. We show that although imitation is pervasive, a growing share of notes drifts free of selection, and original creations retain a measurable quality premium, where they last longer, project further, and earn higher regard. Finally, we construct the collaboration network of master perfumers and show that a perfumer's creative style behaves as a social contagion, transmitted through collaboration and decaying with social distance. We discuss the principal forces shaping this evolution, including regulatory restrictions, cultural change, and the consolidation in the industry. Our findings position perfume as a culturally evolving system, akin to music and fashion, through which societies express and communicate hedonic sensory experiences.
Personal Robots Group at the MIT Media Laboratory, and associate director of Strategic Initiatives of the Bridge: Quest for Intelligence at the Massachusetts Institute of Technology; and founder of Jibo, Inc. Anastasia Ostrowski is a design researcher, Nikhita Singh a research assistant, and Hae Won Park a research scientist and co–principal investigator, all in the Personal Robots Group at MIT Media Lab. Hae Won Park Nikhita Singh Anastasia Ostrowski Cynthia Breazeal Social robots can foster connectedness, provide access
In this work we develop and apply a class of hierarchical directed graphical models on the task of recognizing affective categories from prosody in both acted and natural speech. A strength of this new approach is the integration and summarization of information using both local (e.g., syllable level) and global prosodic phenomena (e.g., utterance level). In this framework speech is structurally modeled as a dynamically evolving hierarchical model in which levels of the hierarchy are determined by prosodic constituency and contain parameters that evolve according to dynamical systems. The acoustic parameters have been chosen to reflect four main components of speech thought to reflect paralinguistic and affect-specific information: intonation, loudness, rhythm and voice quality. The work is first evaluated on a database of acted emotions and compared to human perceptual recognition of five affective categories where it achieves rates within nearly 10% of human recognition accuracy despite only focusing on prosody. The model is then evaluated on two different corpora of fully spontaneous, affectively-colored, naturally occurring speech between people: Call Home English and BT Call Center. Here the ground truth labels are obtained from examining the agreement of 29 human coders labeling arousal and valence. The best discrimination performance on the natural spontaneous speech, using only the prosody features, obtains a 70% detection rate with 30% false alarms when detecting high arousal negative valence speech in call centers.
Theories and technologies are needed to understand and integrate the knowledge of student affect (e.g., frustration, motivation and self-confidence) into learning models. Our goals are to redress the cognitive versus affective imbalance in teaching systems, develop tools that model student affect and build tutors that elicit, measure and respond to student affect. This article describes our broad approach towards this goal and our three main objectives: develop tools for affect recognition, interventions in response to student affect, and emotionally animated agents.
