Read the full article here: http://www.computerweekly.com/feature/Rise-of-the-machines
Former US intelligence officer Drew Perez is an old hand at making sense of vast volumes of data using machine learning and artificial intelligence (AI) in the name of counter-terrorism and national security.
Armed with this know-how gleaned over 30 years, Perez founded Adatos, a Singapore-based AI startup where he adapted recently declassified methodologies and software used by US defence and intelligence communities to produce data-driven insights quickly.
“We’ve been doing this in the intelligence community for decades,” says Perez. “There’s really nothing sexy about it, because it just works.”
The intelligence community first discovered machine learning during World War II at the UK Government Communications Headquarters at Bletchley Park, where the famed German Enigma codes were broken using techniques that laid the foundation for computing and AI.
Since then, Perez says intelligence services and the US military have been using machine learning and AI to process vast amounts of data, with previously unmanageable signal to noise ratios.
“The current demands in counter-terrorism require precise, accurate insights delivered in the span of minutes,” he says. “A key contest in war will be between adversary cognitive systems – artificial and human – to process information, understand the battlespace and decide and execute faster than the enemy.”
While AI and machine learning are not new to military and intelligence communities, the world has been enamoured by their ability to beat chess masters and human opponents in TV game shows over the past decade.
Much of these developments have been fuelled by rapid advancements in computing power – think Moore’s Law – and large volumes of data being generated by sensors and mobile devices.
However, Perez says the hype and expectations around AI and machine learning today may lead to disappointment, if people are expecting Ex Machina-type humanoids with the ability to think like humans do.
“AI, if defined by the expectations of cognitive functions that mimic humans, is still largely in a development stage, but it doesn’t mean it can’t solve real-world problems much more efficiently,” Perez says.