Law-enforcement investigations aimed at preventing attacks by violent extremists have become increasingly important for public safety. The problem is exacerbated by the massive data volumes that need to be scanned to identify complex behaviors of extremists and groups. Automated tools are required to extract information to respond queries from analysts, continually scan new information, integrate them with past events, and then alert about emerging threats. We address challenges in investigative pattern detection and develop an Investigative Pattern Detection Framework for Counterterrorism (INSPECT). The framework integrates numerous computing tools that include machine learning techniques to identify behavioral indicators and graph pattern matching techniques to detect risk profiles/groups. INSPECT also automates multiple tasks for large-scale mining of detailed forensic biographies, forming knowledge networks, and querying for behavioral indicators and radicalization trajectories. INSPECT targets human-in-the-loop mode of investigative search and has been validated and evaluated using an evolving dataset on domestic jihadism.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. In a recent closed-door meeting with leaders of the agency's counterterrorism center, the CIA's No. 2 official made clear that fighting al-Qaida and other extremist groups would remain a priority -- but that the agency's money and resources would be increasingly shifted to focusing on China. The CIA drone attack that killed al-Qaida's leader showed that fighting terrorism is hardly an afterthought. But it didn't change the message the agency's deputy director, David Cohen, delivered at that meeting weeks earlier: While the U.S. will continue to go after terrorists, the top priority is trying to better understand and counter Beijing.

Welcome to the brave new world of securo-feminism*. In the long tradition of systems of patriarchal violence representing themselves as the solution to patriarchal violence, the ongoing expansion of draconian "war on terror" measures is being advertised as an advance for women's rights. For instance, countries like the United Kingdom have extended anti-terrorism provisions to now not only strip citizenship from "terrorists", but also from (some of) those convicted of sexual abuse: a "double punishment" reserved exclusively for dual nationals and suspected dual nationals, predominantly Muslims and other racialised targets from former colonies in the Global South. Simultaneously, the British government itself is threatening the rights and safety of abuse survivors and others fleeing violence, with its proposed new bill to "secure the borders" by penalising asylum seekers for arriving by unauthorised routes (never mind that such penalties flagrantly violate international refugee law). In the United States, President Joe Biden's "feminist" credentials include the introduction of new justice mechanisms to address sexual assault within the military: packaged in the same piece of legislation escalating American "defence" spending to unprecedented heights, surpassing even the previous record set by his predecessor Donald Trump.

Sample size always plays a role in data science, but there are certain instances where risk, time or expense will limit the size of your data: You can only launch a rocket once; you only have so much time to test a much-needed vaccine; your early-stage startup or B2B company only has a handful of customer data points to work with. And in these small data situations, I've found that companies either avoid data science altogether or they are using it incorrectly. One of the more common issues in applying AI is blindly relying on historical data for predicting future situations -- I call this "assuming the past is the future." A common example of this is when we assume the model that has worked so well for us in previous markets will work the same "magic" when we use it to launch products in a new market. The problem is, our new market -- the future -- is completely different from the past market, which leaves us with poor judgement, incorrect predictions, and lackluster business results.

When U.S. President Joe Biden told an exhausted nation on Aug. 31 that the last C-17 cargo plane had left Taliban-controlled Kabul, ending two decades of American military misadventure in Afghanistan, he defended the frantic, bloodstained exit with a simple statement: "I was not going to extend this forever war." And yet the war grinds on. As Biden drew the curtain on Afghanistan, the CIA was quietly expanding a secret base deep in the Sahara, from which it runs drone flights to monitor al-Qaida and Islamic State group militants in Libya, as well as extremists in Niger, Chad and Mali. The military's Africa Command resumed drone strikes against the Shabab, an al-Qaida-linked group in Somalia. The Pentagon is weighing whether to send dozens of Special Forces trainers back to Somalia to help local troops fight militants.

The recent debate between the Associated Press and Facebook about the success of removing content posted by terrorist organizations should be a wake-up call concerning content moderation capabilities on these kinds of platforms. Facebook data indicates the removal of 99% of terrorism content, while AP contends that Facebook's success is only 38%. The point here is that machine learning adds a limited capability to human content mediation. The current state of the art in machine learning in this area is far from meeting expectation and is a fantasy, created around the magical tool of artificial intelligence (AI). Terrorist networks will continue to exploit advanced technology in the areas of social network mapping and terrorist recruitment to benefit from the AI arms race.