There is no doubt that artificial intelligence (AI) applications like deep learning and machine learning are transforming businesses and powering previously unimaginable discoveries.
The field of deep learning is a subset of machine learning, which is itself a subset of artificial intelligence. The objective is for technology to mimic the logic and reasoning behaviors of the human brain in order to address complicated problems and tasks that would normally require human intervention. Deep learning differs from machine learning by employing artificial neural networks that act in a similar fashion to our brains by allowing data to be segmented, processed, weighted for importance, and eventually offer weighted conclusions. There are different types of neural networks that are employed by machine leaning, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) – each is better suited to different deep learning tasks.
Deep learning is now being employed across multiple fields, like computer vision, which uses a model to analyze image, video data, and identify multiple data points within it, such as objects, colors, and more. An example of a computer vision application is autonomous vehicles, which contain multiple cameras and other sensors that record images and videos of their surroundings in real time. Leveraging deep learning, the vehicle can understand what it is “seeing” and therefore make appropriate decisions about when to drive, stop, turn, and much more.
Another notable example is speech recognition. By using RNNs, a computer can “understand” a voice and interact with it accordingly. We see this application in our day-to-day lives with virtual assistants such as Apple’s Siri, Amazon’s Alexa, and others.
The primary benefit of deep learning for businesses is that it reduces the requirement for intervention by human decisionmakers. It therefore offers advantages to any organization that needs to modernize by automating unnecessary manual tasks to streamline operations, increase productivity, reduce costs, and free up employees to focus on more strategic projects.
To effectively employ deep learning, organizations require appropriate hardware to provide massive compute capabilities. GPUs are being deployed because of their ability to perform multiple computations in parallel. There are other accelerators that are well suited for these massive compute operations as well.
Organizations will also need a generous supply of data to feed the deep learning network, which “learns” how to identify and work according to the data it already knows. Unfortunately, many underestimate the enormous amounts of data a deep learning network needs to run and deliver value and lack the modern data infrastructure required to ‘feed’ that data to it. Often, AI initiatives stall because an organization is grappling with legacy infrastructure that is not conducive to deep learning and computer vision.
According to Gartner Inc.: “AI projects are characterized by high failure rates and take a long time to move from pilot to production. Slightly more than 50% make it from pilot to production, and those take an average of nine months.”
Gathering, processing, and storing huge volumes of data in an effective fashion can present a significant challenge for many organizations, which only compounds as they grow and mature their deep learning capabilities.
In many cases, organizations will require a deep learning environment that can scale multiple GPUs and other accelerators, in addition to being supported by a high-performance data platform environment. That platform must be able to serve data rapidly and efficiently to the GPUs/accelerators to ensure they are not idling and waiting for data, thereby slowing the entire process, and wasting precious time, money, and resources.
The use of AI, machine learning and deep learning is rapidly becoming a strategic imperative for any business that wants to keep pace in our data-driven world. Organizations that are not harnessing the enormous potential operational and cost-saving benefits of these three advanced applications are missing out on a significant competitive advantage.
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