America's Data-Swamped Spy Agencies Pin Their Hopes On AI

[DEAD7]

Swamped by too much raw intel data to sift through, US spy agencies are pinning their hopes on artificial intelligence to crunch billions of digital bits and understand events around the world. Dawn Meyerriecks, the Central Intelligence Agency's deputy director for technology development, said this week the CIA currently has 137 different AI projects, many of them with developers in Silicon Valley. These range from trying to predict significant future events, by finding correlations in data shifts and other evidence, to having computers tag objects or individuals in video that can draw the attention of intelligence analysts. Officials of other key spy agencies at the Intelligence and National Security Summit in Washington this week, including military intelligence, also said they were seeking AI-based solutions for turning terabytes of digital data coming in daily into trustworthy intelligence that can be used for policy and battlefield action.


https://phys.org/news/2017-09-swamped-spy-agencies-artificial-intelligence.html

 

[Pressure]

Automation has always been a big deal. We can call it AI, bUt AI is only as good as its creators and in this case actually having the correct data.

This is the way of the future though.

Hopefully spy agencies don't get caught up with vanity metrics.

 

[Orbital-Fetus]

at some point AI will create AI of it's own.
what if multiple AI's have opposing agendas?
what if AI fukks up?

 

[Apex Mentality]

In the meantime let's be grateful that they don't have the manpower to sift through all of our individual text logs and search histories.

I don't have any weird fetishes I juse value privacy fyi.

 

[Eddy Gordo]

at some point AI will create AI of it's own.
what if multiple AI's have opposing agendas?
what if AI fukks up?

You will have a AI whose "job" it is to manage the other AI.

 

[Phoenix Rising]

This isn't just about jobs though.

Data science will be used to crowdsource info gotten from social media sites like Facebook, Instagram, Twitter and data from Google search to predict crimes and to possibly profile potential criminals.

Anticipating criminal behavior: Data science and the future of predictive policing
Anticipating criminal behavior: Data science and the future of

Predictive policing is already delivering on its potential, as demonstrated by programs in Miami, Chicago, London and other locations. One predictive policing initiative in Santa Cruz, California, uses historical data to present officers with 10 to 20 areas in the community that are likely to see crime during each shift, according to Forbes. The program resulted in a 27 percent decrease in robberies and an 11 percent decrease in burglaries in the first year alone.

This type of technology can gather data from a wide variety of sources, even beyond historical crime patterns. Digitized agency-provided information, like data from police blotters, emergency band communications, interagency feeds and camera feeds, is an essential input for predictive analytics. But as mobile technology and the Internet of Things connect more people and devices all over the world, they create very large quantities of data that can be factored into the predictive policing process. Much of this data can be gathered and processed in real time, leading to improved response times. However, the biggest innovation may be in using advanced statistical techniques to correlate huge amounts of data in ways that identify patterns of crime before they happen.

A positive business case
Predictive policing is making a big impact when it comes to crime reduction, but that's only half of the story. The other half has to do with departmental efficiency. Anticipating criminal behavior more accurately means less waste: fewer cold cases, fewer false alarms and fewer inefficient deployments of officers and equipment.

According to a study conducted by the RAND Corporation and sponsored by the National Institute of Justice, data-driven crime predictions allow officers "to develop strategies for highly focused, specific areas, which allowed units to be more effective." This benefit may give budget-constrained agencies a positive business case for acquiring predictive policing capabilities.

Coming soon: More data, smarter analytics
In 2015, we're still at the ground floor of this emerging field, and these state-of-the-art technologies continue to evolve at a dizzying pace. Here are some innovations that will likely become part and parcel of predictive policing in the near future:

• The public safety Internet of Things (PSIOT): There will be a plethora of connected systems, including social media, Internet-enabled emergency band communications, closed-circuit televisions, body cameras, facial recognition, sensor networks (for example, seismic and gunshot sensors) and many others. The PSIOT isn't limited to dedicated public safety systems, though. It encompasses a much broader network of new and legacy systems, including essentially any government-operated enterprise network, plus the whole Internet.
• Improved IT infrastructure: Better transaction processing, databases and storage will help handle the extremely large amounts of data the PSIOT will generate.
• Statistical machine learning and artificial intelligence: These will make crime predictions more accurate. In combination with analytics engines, these tools will turn the mass of PSIOT raw data feeds into more finely tuned, actionable information and insights.
• Mobile devices: More innovative smartphones, tablets and wearables will bring a level of situational awareness to field operatives that was previously only available to people sitting in the command center. All of that actionable information and insight, along with better communications, may soon be available directly to officers in the field.

Predictive policing systems are reducing crime and agency operating costs today. Their capabilities will only continue to expand for the foreseeable future, supporting a more proactive, finely tuned and cost-effective police force.