somewhere between Sejnowski's honey bee and a frog. In other words, getting cognitive computing to resemble the processing power of the human brain is still a way off.
Bengio said machine learning based on "deep learning" techniques were another area of rapid cognitive computing advance. Deep learning collects and analyzes machines' data in different ways over a period of time until it recognizes what the data means about the state of the machine. Machine data based on images could be analyzed for areas of pixel light intensity, color, edges, and other factors for the learnings they would offer about the machine's state. When machine data is looked at in multiple ways, more can be learned about the meaning of the data and how it reflects a machine's operation and its environment.
During a panel discussion at the event, Fei-Fei Li, director of the AI and Vision Lab at Stanford University, said machine learning is making strides but is still in its infancy. "In my opinion, the quest toward artificial intelligence goes from perception to cognition and reasoning. We're doing very well with perception" of objects and images and "just beginning to see (computerized) captioning" -- identification of what's in the image. But much work remains to be done, she said, in the areas of cognition, understanding what the data means, and reasoning with the conclusions gained from the data.
That will take different sets of algorithms working on machine data with some higher intelligence able to tie together many different outcomes. Later in the discussion, Li added: "If you think about the evolution of the brain, you realize nature doesn't just patch up parts."
[Find out how software-based machine learning attempts to emulate the same process that the brain uses.]
Among the attendees at the Cognitive Colloquium was Will Barkis, technology analyst for the telecom company Orange's Orange Fab in Silicon Valley. He said that cognitive computing might one day assist Orange in addressing business customers' needs on business practices and telecommunication use, but that the science is still in its research stage.
Ron Mak, a computer science professor at San Jose State (who spent two hours on Route 101 getting to a conference only 55 miles away), asked if the image analysis that's being applied to solitary images could also be applied to video. By the end of the morning, he had his answer: It could.
Jim Shaw, cofounder and CTO of small San Francisco software development firm BergenShaw International, was an early implementer of machine learning software, producing with a partner a system that spotted friction in a manufacturer's process of ramping up disk-drive production. Shaw was later able to sell a variation of the system to companies doing human gene sequencing. He attended, he said, to keep an eye out for the next breakthrough in machine learning that a small firm might pick up quickly and apply in a new product.
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