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Using the Components of Data Science to Make Your Business More Efficient

Jim Smid, CTO, Iron Bow Technologies

May 11, 2021  |  Big Data & Analytics


In today’s age of rapidly advancing technology, artificial intelligence (AI) is a concept becoming more commonplace and less a term from futuristic movies. Then we add in new words like “data science,” “machine learning” and “deep learning” and we’re left wondering what each of those terms mean, how they relate to each other and – most importantly – how can we use them to our advantage when it comes to accomplishing our business and mission outcomes?

First, a common question is why do we need things like artificial intelligence and data science? Everything is computerized and therefore generating data. By tapping into this data, we can make better informed decisions to save time, money and even lives.  The amount of data involved can be too varied, too much or too fast to handle in traditional computing systems.  This has ushered in the age of big data and data science with tools and architectures built specifically to deal with these issues. Big data is essentially large amounts of data sets that may be analyzed computationally to reveal patterns, trends and associations. Building on this approach, we can ask the computers to not only give us insight into trends, patterns and correlations, but to help us automate some decision making and allow intelligent systems to use these results to simulate human cognition and actions. Artificial intelligence and machine learning have become the hot new topics for replicating and imitating human capabilities.

Next, while these terms fall under the same umbrella, they are not synonymous. Let’s define what these terms mean and then we can tackle how to use them to achieve business goals.

Artificial intelligence is a small slice of a larger group called data science. Think of data science as an overarching term used that encompasses various disciplines including big data, artificial intelligence, statistics, visualizations and pattern recognition. Data science uses scientific methods and algorithms to extract knowledge and insights from large amounts of data.

Within the multidisciplinary field of data science, artificial intelligence means taking a machine with a lot of data and teaching it to perform tasks like a human. Artificial intelligence makes it possible for computers to learn from experience, adjust to new inputs and perform human-like tasks. AI can process big data faster and more accurately than humans can. By writing algorithms (processes or sets of rules to be followed in calculations by a computer) one can feed data into a computer and AI allows for it to “remember” and “learn” in order to recreate the same processes over and over and make it more efficient each time.

Within artificial intelligence, there is machine learning. Machine learning is a subset or type of AI where humans teach machines to perform a particular task by framing the rules with data and the machine will give accurate results. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data. Intuitively, if you want the machine to be more accurate, the more data you will have to give it. Similar to survey questions, the bigger your sample size, the more accurate the answers will be to your questions on the survey.

To help demonstrate how these concepts are similar but different, here’s a quick rundown comparison:

Artificial Intelligence

Machine Learning

·       Simulate human behavior ·       Learns from past data. The more data given, the more accurate the result.
·       Intelligent systems perform any task like a human ·       Individual machines perform a particular task with accurate result
·       Complex ·       Specific
·       Maximizing the chances of success ·       Focuses on accuracy and patterns
·       Perception, cognition, planning, acting ·       Data collection, analysis, statistics
·       Examples: Self driving cars, Siri ·       Examples: Automatically braking car, Netflix suggestions, search algorithms

 

Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Deep learning is a further subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similar to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. It is referred to as ‘deep learning’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve. Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. The deeper learning algorithms learn, the better they perform. Some examples of deep learning include facial recognition, translations and eventually even self-driving cars.

Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding. In general terms, NLP tasks break down language into shorter, elemental pieces, try to understand relationships between the pieces and explore how the pieces work together to create meaning. Examples of NLP include sentiment analysis, document summarization and content categorization.

Now that we know the definitions and use cases behind these separate but similar terms, how do we apply them to our business models to achieve our business goals? How can you harness the power of data science to make your business better? The simple answer is: automation.

Because there is so much data being generated every day, it isn’t practical to use your resources to process that data quickly and accurately. Applying data science tools like artificial intelligence and machine learning allows you to write and implement algorithms within your machine systems to produce information that mission and business leaders can use to make informed, intelligent decisions. Businesses can not only save incalculable amounts of money by implementing AI/ML tools automate decisions and processes, they can also redistribute their resources to focus on more complex problems and utilize the insights AI and ML provide to help steer their organizations towards a more productive future.

Intel is a great example of how an organization is utilizing AI and Deep Learning to make businesses more efficient. Intel has invested in a wealth of optimized software tools, frameworks and libraries to streamline end-to-end data science on Intel hardware. They make the process of infusing analytics and AI into every app simple and fast. They help organizations seamlessly build and deploy AI applications at scale by using their Xeon Scalable processors as a foundation. Intel offers a broad selection of enterprise solutions to solve organizations’ toughest data dilemmas and uses their strong partnerships to bring the best of the industry to their customers.

To learn more about Intel’s AI solutions by industry, click here. To learn more how they can help you with your specific use case, click here. To get the best of both worlds of Intel’s AI solutions and Iron Bow’s industry-leading implementation expertise, contact us today.


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