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Practical Implementation of AI, ML, Deep Learning

What is Deep learning (DL)?

As with other types of machine learning, a deep learning algorithm can improve over time. Some practical applications of deep learning currently include developing computer vision, facial recognition, and natural language processing.

Deep learning (DL) is a subcategory of machine learning that tries to imitate human neural networks, eliminating the necessity for pre-processed data. Deep learning algorithms possess the ability to consume, process, and analyze extensive amounts of unstructured data to learn without any human intervention.

 Machine Learning vs. Artificial Intelligence vs. Deep Learning

So we know that Deep Learning is a subcategory of Machine Learning, which in turn is a subcategory of Artificial Intelligence. But what are the essential similarities and differences between them?

As we mentioned in our earlier blog before, there are four types of Artificial Intelligence, including two that are still in their theoretical stages at this point. In this way, artificial intelligence has become the more extensive, overarching notion of creating machines that possess the ability to simulate human intelligence and thinking capabilities. The ultimate goal of creating self-aware AI is far exceeding our existing capabilities, so much of what comprises AI is currently impractical.

Machine learning, on the other hand, is a practical application of AI that is currently possible, being of the "limited memory" type.

Predominantly Machine Learning is still relatively straightforward, with the majority of ML algorithms having only one or two "layers"—such as an input layer and an output layer—with few if any, processing layers in between. Machine learning models can enhance their capability over time but often need some human guidance and retraining.

And if you take a look at Deep Learning it has multiple layers of highly complex algorithms, and these ’s these extra "hidden" layers of processing gives deep learning its distinctive name. Deep learning algorithms are fundamentally self-evolving, in this way they are able to analyze their predictions and results to assess and acclimate their accuracy over time. Deep learning algorithms are capable of independent learning.

DL can do this through the layered algorithms that together form up what’s considered an artificial neural network. These are inspired by the neural networks of the human brain but obviously, fall far short of achieving that level of sophistication. That being said, they are particularly more advanced than more straightforward ML models and are the most advanced AI systems we’re currently capable of building.


Why is Artificial Intelligence & Machine Learning important?


It’s no secret that data is an increasingly important business asset, with the amount of data generated and stored globally growing at an exponential rate. Of course, collecting data is pointless if you don’t do anything with it, but these enormous floods of data are simply unmanageable without automated systems to help.

Artificial intelligence, machine learning, and deep learning give organizations a way to extract value out of the troves of data they collect, delivering business insights, automating tasks, and advancing system capabilities. AI/ML has the potential to transform all aspects of a business by helping them achieve measurable outcomes including:


  • Increasing customer satisfaction

  • Offering differentiated digital services

  • Optimizing existing business services

  • Automating business operations

  • Increasing revenue

  • Reducing costs


Artificial Intelligence & Machine Learning examples and Use Cases

That all sounds great, of course, but is on the abstract, hand-wavy side of things. So let’s take a look at some practical use cases and examples where AI/ML is being used to transform industries today.



Energy providers around the world are also in the middle of an industry transformation, with new ways of generating, storing, delivering, and using energy changing the competitive landscape. Additionally, global climate concerns, market drivers, and technological advancements have also changed the landscape considerably.

The energy sector is already using AI/ML to develop intelligent power plants, optimize consumption and costs, develop predictive maintenance models, optimize field operations and safety and improve energy trading.



AI/ML is being used in healthcare applications to increase clinical efficiency, boost diagnosis speed and accuracy, and improve patient outcomes.

HCA Healthcare received the Red Hat Innovation Award for its use of machine learning to develop a real-time predictive analytics product—SPOT (Sepsis Prediction and Optimization of Therapy)—to more accurately and rapidly detect sepsis, a potentially life-threatening condition.



In the insurance industry, AI/ML is being used for a variety of applications, including automating claims processing and delivering use-based insurance services.

A majority of insurers believe that the modernization of their core systems is a key to differentiating their services in a broad marketplace, and machine learning is part of those modernization efforts.


Financial Services

Financial services are similarly using AI/ML to modernize and improve their offerings, including personalizing customer services, improving risk analysis, and better detecting fraud and money laundering.

As the quantity of data financial institutions has to deal with continues to grow, the capabilities of machine learning are expected to make fraud detection models more robust and help optimize bank service processing.



In the telecommunications industry, machine learning is increasingly being used to gain insight into customer behavior, enhance customer experiences, and optimize 5G network performance, among other things.

In fact, according to a recently published post in early 2021, 66% of telco organizations expect to be using enterprise open source for AI/ML within the next two years, compared to only 37% today.



The automotive industry has seen an enormous amount of change and upheaval in the past few years with the advent of electric and autonomous vehicles, predictive maintenance models, and a wide array of other disruptive trends across the industry.

And of course, AI/ML is a big part of this transformation. For example, it is a key part of BMW Group’s automated vehicle initiatives.

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