think thank thunk

The bot, called LIDA for Learning Intelligent Distribution Agent, is based on "global workspace theory". According to GWT, unconscious processing - the gathering and processing of sights and sounds, for example, is carried out by different, autonomous brain regions working in parallel. We only become conscious of information when it is deemed important enough to be "broadcast" to the global workspace, an assembly of connected neurons that span the brain. We experience this broadcast as consciousness, and it allows information to be shared across different brain regions and acted upon.

Recently, several experiments using electrodes have pinpointed brain activity that might correspond to the conscious broadcast, although how exactly the theory translates into cognition and conscious experience still isn't clear.

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To increase her chance of success, they grounded the timings of LIDA's underlying processes on known neurological data. For example, they set LIDA's feature detectors to check sensory memory every 30 milliseconds. According to previous studies, this is the time it takes for a volunteer to recognise which category an image belongs to when it is flashed in front of them.

Next the researchers set LIDA loose on two tasks. The first was a version of a reaction-time test in which you must press a button whenever a light turns green. The researchers planted such a light in LIDA's simulated environment, and provided her with a virtual button. It took her on average 280 milliseconds to "hit" the button after the light turned green. The average reaction time in people is 200 milliseconds, which the researchers say is "comparable".

A second task involved a flashing horizontal line that appears first at the bottom of a computer screen and then moves upwards through 12 different positions. When the rate that it shifts up the screen is slow, people report the line as moving. But speed it up and people seem to see 12 flickering lines. When the researchers created a similar test for LIDA, they found that at higher speeds, she too failed to "perceive" that the line was moving. This occurred at about the same speed as in humans. Both results have been accepted for publication in PLoS One.

"You tune the parameters and lo and behold you get that data," says Franklin. "This lends support to our hypothesis that there is a single basic building block for all human cognition." Antonio Chella, a roboticist at the University of Palermo in Italy and editor of the International Journal of Machine Consciousness agrees: "This may support LIDA, and GWT as a model that captures some aspects of consciousness."

After its Jeopardy! fame fades, Watson is going to get down to serious work. The IBM team led by computer scientist  Dave Ferrucci is already deploying Watson in health care. The same way IBM fed Watson Wikipedia, the Bible, a geospatial database–the equivalent of a million pages of documents–it has begun to feed Watson electronic medical records, doctors’ notes, patient histories, symptoms, the USP Pharmacopeia. Here’s the amazing thing: The machine is getting faster at learning. Teaching it to play Jeopardy at a championship level took four years. Teaching it to deliver reasonably accurate answers to diagnostic questions took only four months. I can see IBM selling Watson as a Web-delivered service to doctors and hospitals seeking answers to a patient presenting with problems. Watson considers everything and creates evidence profiles (the types of information it relies on, weighted based on their reliability and utility) that feed into diagnoses graded on varying levels of confidence.  These can be offered up as charts on an iPad  showing a doctor Watson’s thought process. It’s like peering into the mind of a House, M.D. The doctors make the final call but they can assess possibilites they may not have seen and can click right to source material used to compile Watson’s answers. This is powerful stuff.

The genome is not the program; it's the data. The program is the ontogeny of the organism, which is an emergent property of interactions between the regulatory components of the genome and the environment, which uses that data to build species-specific properties of the organism.