Artificial Intelligence in a Dynamic World

Supply and demand are always going to be interconnected regardless of the industry if there are customers willing to buy a product or service.  The true tension point between the two is the external influences that can cause them to fall out of balance or put a potential strain on one side of the equation.  When this happens, organizations must adapt quickly to the challenges at hand and find a way to recalibrate this equation. Recently, artificial intelligence (AI) has allowed businesses to find more stability in their products or service production.

Artificial Intelligence has taken on a more significant role across industries.  Whether it is early detection in retinal scans to help diagnose and prevent Diabetic retinopathy to discovering efficiencies within an international supply chain, AI has advanced to a point where cognitive thinking and deep learning have directly increased the bottom line for organizations.  Business leaders need to understand what type of AI they need and how to implement it. If they fail to understand AI, then they might be left behind.

What is Artificial Intelligence?

Artificial Intelligence is the ability for a machine or computer to interpret critical behaviors based on data outputs to logically provide a solution to a potential challenge or issue.  This provides individuals more time and resources to react to the information and adjust accordingly.  In addition, this can also provide a new lens on long-standing challenges and help people solve problems sooner.

There are different types of artificial intelligence.  Understanding which area of focus and understanding is critical to setting up a successful AI program.

Basic Types of Artificial Intelligence:

Artificial Intelligence has a range of abilities and uses. For instance, simple reactionary efforts to data inputs to a fully developed representation of themselves. AI is constantly evolving. All four types of technology are being put to use in a variety of ways to enhance the world.  As a result, our knowledge and growth also continue to expand exponentially.

• Self-Awareness – Technology that is aware of their surroundings and can represent themselves.  In this type of Artificial Intelligence, there is an internal state that allows the computer to evolve based on learnings to operate autonomously.
• Theory of Mind – Technology has the ability to explain its thinking to come to a conclusion.  It is able to understand what other technologies are doing and why they are doing that activity.  Computers learn and grow based on past learnings.
• Reactive Machines – This is the most basic level of AI.  Reactive machines are unable to form memories or draw on the past to reach new decisions.
• Limited Memory – There is a basic ability to form decisions based on limited data from past experiences.  This is the most commonly used form of Artificial Intelligence as it stands today.

Conceptual Techniques

There are many different types of conceptual techniques to help business leaders make informed decisions. As business leaders better understand the different types of techniques, they can appropriately apply each tool.  This allows them to efficiently identify either an opportunity or pattern in the data while maximizing an inefficiency within the information that they are receiving at that moment.

As a result, these techniques can have a profound impact on a business’s bottom line.  Some of these models include:

Supervised Techniques

• Regression – The estimated interconnection between a dependent variable and or response from multiple key variables.
• Classification – The grouping of data points into certain categories.
• Artificial Neural Networks (ANN) – The way a computer system simulates the thought process and analysis of information to generate a conclusion.
• Support Vector Machines (SVM) – Utilizing the technology to bundle both the classification and regression models to find complex solutions that require flexibility.

Unsupervised Techniques

• Clustering – Analyzing many individual examples to help predict features and values within potential subclasses.
• Self-Organizing Map (SOM) – A visual representation of a topological map to identify potential groups within the dataset.
• Principal Components Analysis (PCA) – This statistical tool positions data points into a right-angle transformation to help individuals identify key patterns within the data.

 The Bigger Picture

The University of Cincinnati Online’s Master of Engineering in Robotics and Intelligent Autonomous Systems offers courses that explores all these techniques. Furthermore, the courses are designed to help engineers better understand the theoretical and practical implementation of conceptual AI techniques.  What makes this unique is you will learn how to implement these techniques in real situations. As a result, each student becomes a more dynamic thinker in the professional world.

The goal of this program is to put students into a position where they can think critically about AI concepts. Ultimately, then translating it back to their team. The Master of Engineering in Robotics and Intelligent Systems program’s goal is to make each student stand out within their organization with an ability to understand the technical aspects of what is happening and then think critically about how those technical details will impact the business.

Ultimately, the technological advancements within the artificial intelligence space are going to be critical for any organization moving forward. Through the exposure to this information now, the University of Cincinnati MEng in Robotics and Intelligent Autonomous Systems students will be given the tools necessary to outperform their competition and moreover, help their company achieve new heights with an eye towards the future. Take the next step and discover more about this state of the art program.