DARPA AI will trawl petabytes of UAV vid for enemy cows
Will see horses, deduce existence of evil cattle
Renowned Pentagon tech-tomfoolery agency DARPA has announced a new plan to create mighty artificial intelligences. The so-called "Deep Learning" machines will be used to trawl through petabytes of video from robot aircraft prowling the skies - initially, apparently, seeking out threatening horses and cows.
According to DARPA boffinry chiefs, setting out the rationale for "Deep Learning" technology, the US military and spook communities are hip-deep in surveillance and intel data, and sinking fast. Hence the need for artificial intelligence (ha ha):
A rapidly increasing volume of intelligence, surveillance, and reconnaissance (ISR) information is available to the Department of Defense (DOD) as a result of the increasing numbers, sophistication, and resolution of ISR resources and capabilities. The amount of video data produced annually by Unmanned Aerial Vehicles (UAVs) alone is in the petabyte range, and growing rapidly. Full exploitation of this information is a major challenge. Human observation and analysis of ISR assets is essential, but the training of humans is both expensive and time-consuming. Human performance also varies due to individuals’ capabilities and training, fatigue, boredom, and human attentional capacity.
One response to this situation is to employ machines ...
It seems there are already plenty of basic "shallow learning" AIs in use, including such Stone Age expedients as "Support Vector Machines (SVMs), two-layer Neural Networks (NNs), and Hidden Markov Models (HMMs)". But these are scarcely better than a human with poor "attentional capacity"*. The trouble with the shallow learners is that they can learn, erm, only at a shallow level:
Shallow methods may be effective in creating simple internal representations ... A classification task such as recognizing a horse in an image will use these simple representations in many different configurations to recognize horses in various poses, orientations and sizes. Such a task requires large amounts of labelled images of horses and non-horses. This means that if the task were to change to recognizing cows, one would have to start nearly from scratch with a new, large set of labelled data.
In essence, a specialised horse-spotter machine unable to recognise a cow isn't much use for sorting the sheep from the goats. (We're plainly in the War On Livestock here.) That's why DARPA want "deeply layered" learning machines, able to apply horse sense to recognising cows, sheep and goats.
Deeply layered methods should create richer representations that may include furry, four-legged mammals at higher levels, resulting in a head start for learning cows and thereby requiring much less labelled data when compared to a shallow method. A Deep Learning system exposed to unlabelled natural images will automatically create high-level concepts of four-legged mammals on its own, even without labels.
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