Getting computers to think like humans has been a scientific goal for years -- IBM recently said it found a way to make transistors that could be formed into virtual circuitry that mimics human brain functions.
It is technology like that that the Intelligence Advanced Research Projects Activity (IARPA) is looking to develop as well. IARPA, the high-risk, high-reward arm of the Office of the Director of National Intelligence, will next month hold a Proposers Day to explain one of its new projects it says could revolutionize machine intelligence by constructing algorithms that utilize the same data representations, transformations, and learning rules as those employed and implemented by the brain.
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The specific goal of the program, known as Machine Intelligence from Cortical Networks (MICrONS) is to create what IARPA calls "a new generation of machine learning algorithms derived from high-fidelity representations of the brain's cortical microcircuits to achieve human-like performance on complex information processing tasks.
To achieve this goal, IARPA is looking for researchers to:
" Propose an algorithmic framework for information processing that is consistent with existing neuroscience data, but that cannot be fully realized without additional specific knowledge about the data representations, computations, and network architectures employed by the brain;
" Collect and analyze high-resolution data on the structure and function of cortical microcircuits believed to embody the cortical computing primitives underlying key components of the proposed framework;
" Generate computational neural models of cortical microcircuits informed and constrained by this data and by the existing neuroscience literature to elucidate the nature of the cortical computing primitives; and
" Implement novel machine learning algorithms that use mathematical abstractions of the identified cortical computing primitives as their basis of operation.
The Proposers Day is Thursday, July 17, in the College Park, Maryland.
The MICrONS algorithms aren't the first techniques the group has looked to develop this year. In March IARPA announced a software competition that looked to the public to develop what it calls an "algorithm that identifies and extracts such signals from data recorded while volunteers engaged in various types of trust activities."
The Investigating Novel Statistical Techniques to Identify Neurophysiological Correlates of Trustworthiness (INSTINCT) Challenge asks members of the public to develop algorithms that improve predictions of trustworthiness, using neural, physiological, and behavioral data recorded during experiments in which volunteers made high-stakes promises and chose whether or not to keep them.
The IARPA challenge specifically looks to develop software algorithms that can detect, measure, and validate "useful" trustworthy signals in order to more accurately assess another's trustworthiness in a particular context, IARPA writes. Improving the accuracy of judgments about whom can be trusted and under what conditions could have profound implications for not just the Intelligence Community, but society in general, the group stated.
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