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the_newsbeagle writes: Many of today's fanciest artificial intelligence systems are some type of artificial neural network, but they bear only the roughest resemblance to a biological brain's real networks of neurons. That could change thanks to a $100M program from IARPA. The intelligence agency is funding neuroscience teams to map 1 cubic millimeter of rodent brain, looking at activity in the visual cortex while the rodent is engaged in a complex visual recognition task. By discovering how the neural circuits in that brain cube get activated to process information, IARPA hopes to find inspiration for better artificial neural networks. And an AI that performs better on visual recognition tasks could certainly be useful to intelligence agencies.

An anonymous reader quotes a report from Ars Technica: The Intelligence Advanced Research Projects Agency (IARPA), the U.S. Intelligence Community's own science and technology research arm, has announced it is seeking contenders for a program to develop what amounts to the ultimate Google Translator. IARPA's Machine Translation for English Retrieval of Information in Any Language (MATERIAL) program intends to provide researchers and analysts with a tool to search for documents in their field of concern in any of the more than 7,000 languages spoken worldwide. The specific goal, according to IARPA's announcement, is an "'English-in, English-out' information retrieval system that, given a domain-sensitive English query, will retrieve relevant data from a large multilingual repository and display the retrieved information in English as query-biased summaries." Users would be able to search vast numbers of documents with a two-part query: the first giving the "domain" of the search in terms of what sort of information they are seeking (for example, "Government," "Science," or "Health") and the second an English word or phrase describing the information sought (the examples given in the announcement were "zika virus" and "Asperger's syndrome"). The system would be used in situations like natural disasters or military interventions in remote locations where the military has little or no local language expertise. Those taking on the MATERIAL program will be given access to a limited set of machine translation and automatic speech recognition training data from multiple languages "to enable performers to learn how to quickly adapt their methods to a wide variety of materials in various genres and domains," the announcement explained. "As the program progresses, performers will apply and adapt these methods in increasingly shortened time frames to new languages... Since language-independent approaches with quick ramp up time are sought, foreign language expertise in the languages of the program is not expected." The good news for the broader linguistics and technology world is that IARPA expects the teams competing on MATERIAL to publicly publish their research. If successful, this moonshot for translation could radically change how accessible materials in many languages are to the rest of the world.

An anonymous reader quotes a report from Quanta Magazine: An ambitious new program, funded by the federal government's intelligence arm, aims to bring artificial intelligence more in line with our own mental powers. Three teams composed of neuroscientists and computer scientists will attempt to figure out how the brain performs these feats of visual identification, then make machines that do the same. "Today's machine learning fails where humans excel," said Jacob Vogelstein, who heads the program at the Intelligence Advanced Research Projects Activity (IARPA). "We want to revolutionize machine learning by reverse engineering the algorithms and computations of the brain." By the end of the five-year IARPA project, dubbed Machine Intelligence from Cortical Networks (Microns), researchers aim to map a cubic millimeter of cortex. That tiny portion houses about 100,000 neurons, 3 to 15 million neuronal connections, or synapses, and enough neural wiring to span the width of Manhattan, were it all untangled and laid end-to-end.

An anonymous reader quotes a report from Quanta Magazine: An ambitious new program, funded by the federal government's intelligence arm, aims to bring artificial intelligence more in line with our own mental powers. Three teams composed of neuroscientists and computer scientists will attempt to figure out how the brain performs these feats of visual identification, then make machines that do the same. "Today's machine learning fails where humans excel," said Jacob Vogelstein, who heads the program at the Intelligence Advanced Research Projects Activity (IARPA). "We want to revolutionize machine learning by reverse engineering the algorithms and computations of the brain." By the end of the five-year IARPA project, dubbed Machine Intelligence from Cortical Networks (Microns), researchers aim to map a cubic millimeter of cortex. That tiny portion houses about 100,000 neurons, 3 to 15 million neuronal connections, or synapses, and enough neural wiring to span the width of Manhattan, were it all untangled and laid end-to-end.

An anonymous reader writes A division of the U.S. government's Intelligence Advanced Research Projects Activity (IARPA) unit, is inviting proposals from cybersecurity professionals and academics with a five-year view to creating a computer system capable of anticipating cyber-terrorist acts, based on publicly-available Big Data analysis. IBM is tentatively involved in the project, named CAUSE (Cyber-attack Automated Unconventional Sensor Environment), but many of its technologies are already part of the offerings from other interested organizations. Participants will not have access to NSA-intercepted data, but most of the bidding companies are already involved in analyses of public sources such as data on social networks. One company, Battelle, has included the offer to develop a technique for de-anonymizing BItcoin transactions (pdf) as part of CAUSE's security-gathering activities.