Cognitive Systems: Teaching Technology to Learn

The digital revolution has paved the way for exciting advancements and creative ways to solve problems— from the rise of the gig economy to the ability to work remotely —that continue to change how we work. One of the standout innovations emerging from this revolution is cognitive computing, which holds promise for transforming organizations of all sizes worldwide.

Cognitive Systems Explained

There is no single, agreed-upon definition of cognitive systems. This technology is still evolving, and its capabilities are continually being tested and discovered. The term encompasses various artificial learning systems and cognitive sciences, which can make it challenging to delineate where one system ends and another begins.

Simply put, cognitive systems are technology platforms capable of learning and adapting their behavior based on interactions with humans and their experiences. Instead of having rigid learning parameters, cognitive systems emulate human intelligence and thought processes, making them one of the most promising technologies of the 21st century.

How Does Cognitive Computing Work?

What is cognitive computing? Cognitive computing differs significantly from other technology forms. Traditional systems are often deterministic or prescriptive; humans program these systems to operate in specific ways. In contrast, cognitive systems are designed to learn, reason, and behave like humans. They are adaptive, capable of responding to new information, interacting with data, other systems, and humans, and understanding contextual elements to make hypotheses, recommendations, and decisions.

Developers face unique challenges in creating and refining the cognitive system. Researchers have identified several major hurdles, particularly in integrating software-based cognitive systems with the necessary hardware and bridging the gap between feeling, knowing, and thinking within systems. As cognitive systems become more integrated into daily life and work, overcoming these challenges will be crucial to unlocking their full potential.

Pros and Cons of Cognitive Computing

While research is ongoing, several benefits of cognitive computing for enterprises are already apparent:

• Data Analysis: Many organizations produce an almost overwhelming amount of data. This data can be used for a number of reasons, such as gaining insights into business performance or assisting in a digital transformation strategy. Cognitive computing can analyze this data more quickly and accurately, allowing businesses to harness it in ways that benefit their organization and goals.
• Business Efficiency: Similarly, businesses can almost always benefit from being more efficient and productive. With so much information available and new challenges constantly arising, business leaders may have trouble determining the best ways to become more efficient and get the most from their employees. Cognitive systems can assist with strategic planning, decision making, and unexpected solutions that improve efficiency across many aspects of an organization.
• Customer Interaction: Serving and satisfying customers is one of the top priorities of any business. It can be difficult to meet all customers’ requests, let alone anticipate their needs, and exceed their expectations. Cognitive computing can be used to learn from customers’ previous experiences and help them find the solutions they need. It can even be used to help businesses realize which customers or clients are ultimately a drain on their resources and when it may be time to end that professional relationship.

These are just a few ways that cognitive systems are already advantageous for businesses, and as time goes on, researchers will likely find other uses and benefits.

Of course, there are limitations to what cognitive systems can do. Even the most sophisticated cognitive computing systems may never be able to match the human brain. Though cognitive systems are intelligent enough to handle certain tasks, they cannot take care of or maintain themselves.

Businesses will still need the help of IT professionals and managed service providers alike to keep these systems running. Further, as businesses become more reliant on technology, they will also become more reliant on the IT department to keep it in good working order.

Other drawbacks of cognitive computing include:

• Security: Maintaining cybersecurity is crucial for organizations of any size, especially small businesses. Cognitive systems may handle or touch-sensitive information from all over an organization, which means businesses have to do their best to ensure that information is properly protected. With a growing number of cybersecurity threats, safeguarding that information and fortifying cognitive systems becomes increasingly difficult.
• Adoption: Employers have to choose to adopt cognitive computing systems in their organizations. Despite the benefits, some organizations may not want to make this shift, whether because cognitive computing is still in development or simply because organizational change can be a hassle.
• Change Management: In a similar vein, businesses may find it difficult to implement this change across their organizations. Employees may resist cognitive computing, due to fears or misconceptions of computers taking their jobs or rising up against humankind altogether. While it certainly isn’t impossible to quell these fears, employers may have trouble trying to convince all of their employees about the benefits of and need for cognitive systems.

Depending on how the development of cognitive computing unfolds, these limitations may no longer be a concern — or researchers may discover that the benefits far outweigh the drawbacks.

Types of Cognitive Systems

There are several different types of cognitive computing. Many of these systems are similar to each other, and they often overlap in terms of their function and purpose. The most common types of cognitive systems include:

Deep Learning Systems

Deep learning systems mimic the human brain’s ability to learn by finding patterns and looking at previous examples. It can perform human-like tasks, such as identifying an image or recognizing speech. Deep learning systems can learn in an unsupervised manner from unlabeled, unstructured data. It is already used in driverless cars and voice control technology in smartphones, tablets, and IoT devices.

Machine Learning Systems

Machine learning is the process of teaching a system to learn without (or with minimal) human intervention. These systems do need to be trained against an initial data set but can learn beyond it. They are capable of classification, prediction, and decision-making. Machine learning systems already see myriad uses in a variety of industries, like healthcare, retail, and manufacturing.

Neural Networks

Inspired by biological networks found in the human brain, an artificial neural network is a computing system that interprets data to categorize, find patterns, and identify relationships. They can group unlabeled data based on similar characteristics, making neural networks highly useful for organizing massive amounts of data. They are already being widely used in a number of different ways, from filtering emails to diagnosing cancer.

Artificial Intelligence (AI)

Artificial intelligence is difficult to define, as it encompasses a variety of different concepts, processes, and practices. In essence, it is a computer system that can perform tasks that typically require human intelligence. Research areas have focused on tasks like natural language processing and automated reasoning. People have discovered uses for artificial intelligence in seemingly every field, including transportation, education, agriculture, government, and media.

Applications of Cognitive Computing

Though it is relatively new, cognitive computing already has many applications in all sorts of industries. Its most noteworthy uses include:

• Chatbots: These programs can simulate human conversations and communicate through text with users in real-time. They can answer questions and are commonly used on e-commerce websites to assist customers.
• Face Detection: Cognitive computing can learn to distinguish images of peoples’ faces from each other and then identify them. This can be useful in security systems, such as to unlock a mobile phone.
• Fraud Identification: Cognitive computing has a promising future in finances and banking when it comes to detecting strange and fraudulent transactions. This can help prevent fraudsters from taking money that isn’t theirs and protect consumers who are taken advantage of.
• Healthcare: Physicians can use cognitive systems to comb through medical records and patient data to find treatment options. Depending on the system, they may even be able to interact with it or ask questions about patient care.
• Health and Wellness: Wearable items like smartwatches and fitness apps often make use of cognitive computing to organize and analyze a user’s health data. These pieces of health technology can then make recommendations about diet, exercise, and sleep to help users improve their health.
• Travel: Cognitive systems can be used on travel websites and apps to help customers find better arrangements based on their budget and preferences. It can aggregate information, like flight times and hotel room availability, scan that information, and match it up with customers’ specifications to make the planning process easier and more satisfying.

However, these are just a few of the many ways that cognitive systems can be used to the advantage of businesses and consumers alike. The cognitive computing market is expected to be worth $77.5 billion by the year 2025, indicating massive, imminent growth — and many unexpected new uses for this technology. Cognitive computing could undergo a revolution in and of itself, transforming businesses, their operations, and their performance for years to come.

Conclusion

Cognitive computing represents a significant leap forward in technology, offering transformative potential for organizations of all sizes. By simulating human thought processes and adapting to new information, cognitive systems can enhance data analysis, boost business efficiency, and improve customer interactions. While challenges such as security, adoption, and change management remain, the benefits of cognitive computing—ranging from advanced data analysis to innovative applications across various industries—are substantial.

As the technology continues to evolve, its impact on business operations and performance is expected to grow, opening up new opportunities and applications. Embracing cognitive computing could be a key factor in driving future success, making it essential for organizations to stay informed and prepared for this technological advancement.