healthcare technology

“What are you tracking?” This is the conversation at Quantified Self (QS) meetups. The Quantified Self movement celebrates “self-knowledge through numbers.”

In our current love affair with QS, we tend to focus on data and the mind. Technology helps manage and mediate that relationship. The body is in there somewhere, too, as a sort of “slave” to the mind and the technology.

From blood sugar to pulse, from keystrokes to time spent online, the assumption is that there’s power in numbers. We also assume that what can be measured is what matters, and if behaviors can be measured, they can be improved. The entire Quantified Self movement has grown around the belief that numbers give us an insight into our bodies that our emotions don’t have.

However, in our relationship with technology, we easily fall out of touch with our bodies. We know how many screen hours we’ve logged, but we are less likely to be able to answer the question: “How do you feel?”

In our obsession with numbers and tracking, are we moving further and further away from the wisdom of the body? Our feelings? Our senses?

Most animals rely entirely on their senses and the wisdom of the body to inform their behavior. Does our focus on numbers, measuring, and tracking move us further and further away from cultivating a real connection to our “Essential Self”?

What if we could start a movement that addresses our sense of self and brings us into a more harmonious relationship with our bodymind and with technology? This new movement would co-exist alongside the Quantified Self movement. I’d like to call this movement the Essential Selfmovement.

This isn’t an either/or proposition — QS and Essential Self movements both offer value. The question is: in what contexts are the numbers more helpful than our senses? In what constructive ways can technology speak more directly to our bodymind and our senses?

A new interdisciplinary research center at MIT, funded by the National Science Foundation, aims at nothing less than unraveling the mystery of intelligence.

The new center headquartered at the Massachusetts Institute of Technology will focus on bringing researchers from separate fields together to try and crack one of the biggest questions facing science today: what is intelligence, and how can we engineer it?

By tapping a broad range of expertise, including scholars who study how a baby’s mind develops and others trying to understand how the brain makes sense of social situations, the researchers hope to take a definitive step forward over the next five years toward the long-held goal of understanding intelligence and building computers capable of thinking like people.

The center’s four main research themes are also intrinsically interdisciplinary. They are the integration of intelligence, including vision, language and motor skills; circuits for intelligence, which will span research in neurobiology and electrical engineering; the development of intelligence in children; and social intelligence. Poggio will also lead the development of a theoretical platform intended to undergird the work in all four areas.

“Those four thrusts really do fit together, in the sense that they cover what we think are the biggest challenges facing us when we try to develop a computational understanding of what intelligence is all about,” says Patrick Winston, the Ford Foundation Professor of Engineering at MIT and research coordinator for CBMM.

For instance, he explains, in human cognition, vision, language and motor skills are inextricably linked, even though they’ve been treated as separate problems in most recent AI research. One of Winston’s favorite examples is that of image labeling: A human subject will identify an image of a man holding a glass to his lips as that of a man drinking. If the man is holding the glass a few inches further forward, it’s an instance of a different activity — toasting. But a human will also identify an image of a cat turning its head up to catch a few drops of water from a faucet as an instance of drinking. “You have to be thinking about what you see there as a story,” Winston says. “They get the same label because it’s the same story, not because it looks the same.”

Sources: 

http://web.mit.edu/newsoffice/2013/center-for-brains-minds-and-machines-0909.html

http://www.boston.com/news/science/blogs/science-in-mind/2013/09/09/mit-center-receives-million-unravel-the-mysteries-human-intelligence/SKNU4umjtPy5pN7nFnYcXL/blog.html