The deep sea (>200 m) is vast, covering 92.6% of the seafloor and largely unexplored. Imaging and sensor platforms capable of surviving the immense pressures at these depths are expensive and often engineered by individuals and institutions in affluent countries as unique, monolithic vehicles that require significant expertise and investment to build, operate, and maintain. Maka Niu was co-designed with a global community of deep-sea researchers. It is a low-cost, modular imaging and sensor platform that leverages off-the-shelf commodity hardware along with the efficiencies of mass production to decrease the price per unit and allow more communities to explore previously unseen regions of the deep ocean. Maka Niu combines a Raspberry Pi single-board computer, a Pi Camera Module V2, and a novel pressure housing and viewport combination capable of withstanding 1,500 m water depth. Other modules, including high-lumen LEDs, can be engineered to use the same battery charging and control system and form factor, allowing for an ever-increasing number of capabilities to be added to the system. After deployment, imagery and sensor data are wirelessly uploaded to Tator, an integrated media management and machine learning backend for automated analysis and classification. Maka Niu’s mobile mission programming and data management systems are designed to be user-friendly. Here, Maka Niu is described in detail along with data and imagery recorded from deployments around the world.

Most organizations have a wealth of knowledge about themselves available online, but little for a visitor to interact with on-site. At the MIT Media Lab, we have designed and deployed a novel intelligent signage system, the Glass Infrastructure (GI) that enables small groups of users to physically interact with this data and to discover the latent connections between people, projects, and ideas. The displays are built on an adaptive, unsupervised model of the organization developed using dimensionality reduction and common sense knowledge which automatically classifies and organizes the information. The GI is currently in daily use at the lab. We discuss the AI models development, the integration of AI into an HCI interface, and the use of the GI during the labs peak visitor periods. We show that the GI is used repeatedly by lab visitors and provides a window into the workings of the organization.

Most organizations have a wealth of knowledge about themselves available online, but little for a visitor to interact with on-site. At the MIT Media Lab, we have designed and deployed a novel intelligent signage system, the Glass Infrastructure (GI) that enables small groups of users to physically interact with this data and to discover the latent connections between people, projects, and ideas. The displays are built on an adaptive, unsupervised model of the organization developed using dimensionality reduction and common sense knowledge which automatically classifies and organizes the information.The GI is currently in daily use at the lab. We discuss the AI models development, the integration of AI into an HCI interface, and the use of the GI during the labs peak visitor periods. We show that the GI is used repeatedly by lab visitors and provides a window into the workings of the organization.

No abstract