How Deep Learning Enhances the Potential of Artificial Intelligence

With the right application of Natural Language Processing (NLP), cognitive analytics, and machine learning, data can be better analyzed to predict future behavior more accurately and faster. At the heart of this is deep learning.

Deep learning, a component of AI and machine learning, allows for knowledge to be gained using artificial neural networks that are modeled on how a human’s biological nervous system operates. While this concept has been around for decades, recent technological advances have allowed software to compute data at rapid rates in order to gain a comprehensive overview more effectively. As a result,  massive amounts of data that power neural networks can be analyzed faster than ever before.

Deep Learning: The Future of Decision Making

Deep Learning technology involves a powerful set of techniques for creating neural networks — which are created to mirror how the human brain thinks and operates. Leveraging AI deep learning allows for organizations to make sense of unstructured data in order to derive actionable insight to learn from the datasets. This is also commonly referred to as deep neural network.

Deep learning is used to create predictive data models that can be used to understand data, and leverage that data to create actionable insights. By tapping into the power of machine-assisted descriptive, predictive and prescriptive analytics, deep learning allows for more sophisticated decision making based on the analysis of patterns that exist in the text and images within the necessary datasets. Deep learning renders decision making more accurate because of the automated data aggregation and document review process. Instead of focusing on manually processing and reviewing data, organizations are better equipped to proactively understand what exists within their own data when utilizing deep learning.

At that point, a deep learning approach can generate more opportunities for organizations needing a more accurate analysis of disparate, unstructured data in complex, real-world scenarios. As a component of machine learning, deep learning is designed to imitate how the human brain thinks. This is known as artificial neural networks. By automating the data analysis process, and applying an authentic AI approach, deep learning can help make sense of the data in a structured format. This data analytics approach helps organizations understand patterns, review comparisons and learn key differences across millions of documents in little time.

Deep Learning and Neural Networks

Based on an artificial neural network, deep learning is used to transform how past data is interpreted in order to better understand how it can be used to predict future behavior. Of course, to be useful, this data needs context. That requires an authentic AI approach that can augment a human’s ability to identify social networks, patterns, anomalies, behavioral changes, emotion, key actors, and topics to find the necessary information to make sense and of all the data and to put it in the proper context.

By having a deeper understanding of the context of data, and how past scenarios predict what may occur in the future, this process is applicable in detecting levels of risk that a certain outcome may occur.

While basic text analytics can understand data, the real value of AI and deep learning is the ability to focus on the context within which the data resides, not just what the data is saying. This means having a better mode to analyze the deeper meaning of both text and images. Data within context is where the true insights are found.

How Deep Learning helps in Image and Text Classification

  • In image classification, the first layers learn simple features by recognizing patterns and extracting one or more feature of the image. In the output layers, it learns how to combine them to learn the correct class label for objects in the image.

  • In text classification, the first layers learn about word similarities by analyzing the local context in which the words are used. The more advanced layers learn higher order word combinations and the final layers learn what complex word combinations are most important for your problem.

How Deep Learning Models Work

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Deep learning models consist of layers of interconnected nodes. The first node layer is making first, fairly simple decisions, by weighing the input evidence.

  • Each node in the subsequent layers is making a more complex decision by weighing up the results from the previous layers of decision-making. In this way, deep learning networks engage in sophisticated decision making.

  • Deep learning classifiers require a lot of training data in general, but using word and text representations learned in the first layers of the previously trained network reduces the required amount.

  • Each level of deep learning serves to transform the data inputted into a more sophisticated model. The “deep” in these cases refers to the complex layers a system must transform in order to make full sense of the data.

While there are many approaches to data analysis through the application of AI, not all solutions are created equal. When it comes to dissecting data, context matters. By relying on a solution that employs a deep learning approach, you can get greater insights and true value out of your own data. That’s where the true power of authentic AI and deep learning can be realized.

Jay Leib

Jay Leib is the CEO of NexLP, Inc. Jay has successfully founded and led software start-ups throughout his career. Most recently, Mr. Leib was at Chicago-based Relativity (formerly kCura) as the Chief Strategy Officer. Relatiivity is a Chicago based company focused on the eDiscovery vertical.