A multidisciplinary team of researchers from Harvard University has received over $28 million worth of funding to take on the “moonshot challenge” of developing new machine learning algorithms that will bring the functionality of artificial intelligence (AI) closer to that of the human brain.
Though many computer systems are able to process volumes of data, exceeding those manageable by biological brains, technology still lags behind nature when it comes to the ability to learn and recognize patterns. For instance, while a human may only need to see one or two dogs in order to be able to recognize all other dogs they see in the future, a computer often needs to process thousands of images of dogs using complicated algorithms to attain this ability.
In an attempt to bridge this gap, scientists from Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS), Center for Brain Science (CBS), and Department of Molecular and Cellular Biology are to embark on an ambitious project to map out the brain’s neural connections. Having been awarded funding by the Intelligence Advanced Research Projects Activity (IARPA), the team hope to use their data to learn more about how these connections allow the brain to rapidly pick out patterns when analyzing novel stimuli.
Once this has been achieved, the researchers intend to develop new AI systems based upon this natural design, creating “biologically-inspired computer algorithms.”
The process will begin in the laboratory of SEAS’s David Cox, whose team will use laser microscopes to observe and record the activity of visual neurons in the brains of rats as they learn to recognize images on a computer screen. It is hoped that this will reveal vital information about how neurons connect and communicate with one another during the learning process.
From here, sections of the rats’ brains will be sent to the CBS, where an electron microscope will be used to generate detailed images of the neural circuits. At this point, the team will begin trying to work out exactly what aspects of the structure and function of these circuits allows rapid learning to take place, eventually using this information to create new computer systems that operate the same way.
The researchers will create a detailed map of the neural circuits in rats’ brains. vitstudio/Shutterstock
Achieving this goal is likely to be a long and complicated process, since the mechanisms by which the brain processes information are far from simple. For instance, a recent study revealed how the connections between brain neurons – called synapses – actually change size in order to regulate the strength of the signals that are transmitted.
Other studies have shown how different areas of the brain communicate with one another in order to facilitate pattern recognition. Among these is a recent paper that suggested that information stored in some brain regions associated with high-level cognition is passed down to other neurons in order to fill in gaps in external stimuli. Known as top-down processing, this mechanism allows us to infer information from incomplete data, which is why we are able to recognize objects even when they are partly obscured, or get the gist of what someone is saying when we only hear part of the sentence.
Recognizing the epic scale of the task, Cox has described it in a statement as “a moonshot challenge, akin to the Human Genome Project in scope.” However, while it certainly won’t be easy, the potential payoff of this research could be invaluable, “helping us to understand what is special about our brains,” and possibly enabling us to finally “design computer systems that can match, or even outperform, humans.”
Original Article:http://www.iflscience.com/technology/harvard-scientists-hope-unravel-workings-brain-quest-ai-perfection
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