IARPA in the News

NBC News

In Steven Spielberg's futuristic "Minority Report," Tom Cruise's character sees a custom ad for Guinness after his face is scanned in the year 2054. Similar technology, however, isn't science fiction. The Samsung Galaxy Nexus let people unlock their phones with a glance in 2011, while Apple was awarded a patent on Tuesday for facial recognition technology that could find its way into a future iPhone. Tesco has already installed high-tech screens at some of its gas stations in the U.K. that serve up custom ads after determining a person's gender and approximate age. And in September, Facebook announced that it might expand the use of its facial recognition software to help tag photos of its more than 1 billion members.


In my Insights piece last week, “Is Innovation Predictable?”, I had mentioned innovation analytics (or what should be more precisely termed as meta innovation analytics) though, regretfully, given that important area a short shrift. As it so happened, a commenter on that post mentioned the Soviet TRIZ system (h/t Etaoin Shrdlu) which could very well have been the world’s first meta innovation analysis framework. So as a follow-up to my last week’s post, today I want to talk about how innovation can be predicted (as opposed to whether we can predict innovation) in terms of such frameworks.

International Science Times

Facial recognition has become an increasingly common element in security surveillance, enabling identification of faces in images taken from a distance and in a crowd. But facial recognition is just a step along the way to more and better identification techniques being sought by the U.S. government's Intelligence Advanced Research Projects Activity (IARPA). The agency has challenged top research teams to revolutionize how machines recognize people with a competition announced Nov. 8, according to New Scientist magazine.


CAMERAS are strewn around our environment, catching glimpses of our faces everywhere we go, yet even the best facial recognition technology still has a hard time picking us out of the crowd. So the US government's Intelligence Advanced Research Projects Activity (IARPA) has called for a new approach. The agency announced a contest on 8 November, challenging teams of the country's top researchers to revolutionise how machines recognise people. Those entering the competition already know that conventional facial recognition won't cut it.


Cups are for drinking and can hold water, tea or coffee. That is how our brain conceptualizes entities and their relationships and properties. Our brain uses its relationship of conceptual knowledge to solve problems and make inferences. Now, when you think national intelligence, your first conceptualization probably isn’t the Intelligence Advanced Research Projects Activity, but the agency within the Office of the Director of National Intelligence announced Nov. 26 it is embarking on a multiyear research effort to learn more about how our brain represents conceptual knowledge.


The Intelligence Advanced Research Projects Activity has launched a research project to explore the human brain’s conceptual knowledge in a move aimed at helping the intelligence community analyze data. IARPA intends for the multi-year Knowledge Representation in Neural Systems program to identify how the brain organizes conceptual knowledge in order to build new tools and techniques for intelligence analysts and linguists, the Office of the Director of National Intelligence said Monday.

The New Yorker

In 1982, the decision analysts Marc Alpert and Howard Raiffa asked students in their classes at Harvard to estimate figures like the number of eggs produced daily in the United States. They were each asked to give a range so wide that they believed there was a ninety-eight-per-cent chance it would include the right answer. Instead, the correct figures fell inside the students’ ranges less than a third of the time.