Topics: Analytics, Big Data Technology, Big Data Use Cases, Data Scientists
Tags: deep learning, stream computing

‘Deep learning has become the next big awe-inspiring frontier in big data analytics. This emerging technology, which leverages deep convolutional neural-network and other machine learning algorithms, is all about giving computers the ability to process sensory patterns that any sentient creature can recognize from the moment it’s born.

Deep learning algorithms are growing progressively smarter at recognizing patterns in video, audio, speech, image, sensor and other non-textual data objects. The algorithms are also being embedded into the full range of devices that most of us carry around, wear or install in our cars, offices, houses and other environments. For example, as the recent MIT Technology Review article reports, deep learning algorithms will soon execute within the microchips inside our smartphones.
Algorithmically drilling beneath the visual surface of media streams

Streaming media, which represents the future of the media and entertainment industry, is the principal type of content object that deep learning algorithms analyze. Many advances in deep learning improve the technology’s ability to correlate and contextualize algorithmic insights across media objects. Likewise, deep learning is adept at analyzing other types of streams, such as the sensor data flowing from Internet of Things (IoT) endpoints. As demand for deep learning applications intensifies, we will all begin to take for granted their seemingly magical ability to recognize faces, voices, gestures and other distinctive characteristics of specific individuals. As author Lee Gomes states, “computers are getting better each year at AI-style tasks, especially those involving vision—identifying a face, say, or telling if a picture contains a certain object.”

Soon, though, that won’t be enough. Gomes points out that « What computers really need to be able to do is to ‘understand’ what is ‘happening’ in the picture.” It’s not enough for a computer to see in roughly the same way that an organism, such as a bug or a bear, can see. Can an algorithm possess true insight into what appears in its field of view?

The more disruptive real-world applications of deep learning will be those that generate deeper situational insights through correlation with additional contextual variables. These variables might include the social, geospatial, temporal, intentional, transactional and other attributes of the individuals, activities and objects in the image or stream. This added context can help deep learning algorithms to unambiguously identify that a particular person is in a particular circumstance at a particular time and place.
Extrinsic contextualization of visual streams supplements deep learning algorithms

Some of the required context may be identified by the deep learning algorithms themselves. What the convolutional neural networks and other algorithms happen to overlook may need to be supplied by other analytic, metadata and data tools.

Reducing the need for extrinsic contextualization is a huge research focus in the deep learning community. Gomes highlights algorithmic efforts to extract situational attributes, such as the likely relationships of people, their respective sentiments and intentions and the nature of their interactions with the setting and various things around them, purely from photographic images.’ […]
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By James Kobielus
Source: ibmbigdatahub.com