The Cognitive Era: An Evolving Generation of Computing

In the programming era, the period we know best in computing, powerful computing systems became widely available and could be reprogrammed to solve different business problems and calculations. Though they’ve completely changed the world around us, their human operators still constrain their capabilities. They can only carry out what we tell them to.

Cognitive computing evolved from this. It does not replace the human but rather extends their capabilities. As we saw with the programming era, humans can think deeply and critically and use reason to solve complex problems. However, humans cannot analyze and process massive data sets, whether structured or unstructured.

The cognitive era is just that, a merger of artificial intelligence and computers' immense strengths with their human operators' current capabilities. It’s a collaboration between man and machine to solve the next generation of challenges, from how we communicate, to business, to the natural world around us.

How did we get here? Big Data

We’re increasingly relying on computers, but we’ve hit a barrier. We’re creating massive amounts of data every minute, yet we continue to use archaic ways of processing, analyzing, and acting on these data sets.

We want to get more out of our data to build more—and better—algorithms. But it’s not just the contents of the data itself. It’s also the patterns and the actionable insights derived from it. The natural next iteration and new era of computing is implementing the functionality to do just this: make sense of the massive amounts of data created and take action based on it.

We also want to create a more meaningful dialogue with our computers. Instead of us speaking their language, we want them to speak our language. This is where machine learning comes into play because we want them to learn and adapt over time, being able to know and understand us better. From there, they can make educated decisions influenced by prior experiences and analyses without human involvement.

And lastly, we want to continue to use computing systems to work smarter and better, enabling apps, businesses, and organizations to work more efficiently, sustainably, and safer. And that’s why we moved from the programming era we were in for decades to the cognitive era, combining the strengths of computers, machine learning, and humans to continue to push technology and what we do with it forward. In other words, the era of AI tools like ChatGPT and IBM Watson.

Structured vs. Unstructured Data

Structured data would be something like the information collected by a weather station. It can collect temperature, humidity, and wind speed. The system on the receiving end knows exactly what types of data are being collected, and the system can easily be built to utilize that data, like plotting the temperature changes over time and using that data to predict future forecasts. The data type never changes. Structured data is also found in use cases such as healthcare and the customer service industry. We know how to deal with structured data.

Unstructured data, information that doesn’t have a pre-defined data model, is the real challenge, and utilizing it has massive benefits. Cognitive computing is the answer.

As previously stated, cognitive services extend the capabilities of humans. They allow us to process, analyze, and act on massive amounts of data, light years faster than the human brain could manually do the same job.

By 2025, it’s estimated that there will be 163 zettabytes of data worldwide, and 80% of that data will be unstructured. Documents, social media feeds and messaging platforms, IoT data, video feeds, audio transmissions, and all the content that lives on the Internet from its origins are just a few sources of unstructured data. This is the data we continue to create more and more now that the world is more connected and new technology is regularly being introduced.

But it’s not just understanding the unstructured data itself. It’s also about connecting it to other sources of data and deriving patterns and insights from it. It’s about activating the data to use in problem-solving.

Cognitive services are the key technology to better understanding and taking action on this data.

How cognitive services make sense of unstructured data

Let’s walk through a couple of examples of how cognitive services can take advantage of the massive amounts of unstructured data being created in real-time, every minute across the globe.

Natural language processing

Natural language processing functionality allows businesses to understand written, audio, and video data at a massive scale. It empowers systems to analyze a massive data set, draw patterns, and connect unconnected data.

In the academic world, a cognitive system could be used to analyze the vast number of related historical texts to a subject, both first and secondhand accounts (in several different languages), plot out a timeline of events, and rank them in importance. From there, the system can be a resource for students and researchers and recommend resources based on questions.

Insight engines

Insight engines derive value from data to track trends and surface patterns. They automate data ingestion, then convert, normalize, and enrich it. A company might use an insight engine to monitor the public’s perception of its brand but can go deeper beyond sentiment analysis. They can compare their perception to the competition, match initiatives and campaigns they’ve done against public perception, compare news stories, and even predict future reception.

The 3 Pillars of Cognitive Technology for Businesses

Cognitive businesses use cognitive services to create new types of customer engagement, introduce automation to processes, build smarter products, improve internal operations, and make smarter decisions. There are three large areas of business that cognitive services are impacting right now.

Discovery

Using the vast amounts of data and information available, systems can utilize cognitive services to find patterns, insights, and connections the hardest-working human beings might never identify. And having found patterns once, they can create new and unanticipated ways to adapt, grow, and use advanced computing power to make discovery more accurate and efficient.

Engagement

Create new engaging experiences for users, customers, and the organization, empowering businesses to see, hear, speak, understand, and interpret natural language and information sets. By understanding and responding to, how users interact with apps and each other, cognitive systems are changing the paradigm of human and system interactions.

Decisions

Though cognitive services impact how we process data and engage with users and systems, decision-making is the most challenging but potentially revolutionary result of cognitive services. Intelligent systems allow businesses to have their systems weigh the evidence and analyze data, then leverage that analysis to make better decisions based on that data unsullied by human emotional input. These services can consider complex sets of information and act on them, whether to do something as simple as making a product recommendation on an e-commerce site, to way more advanced actions, such as optimizing smart devices in an industrial setting.

Conclusion

The rapid advancement of cognitive services has ushered in the new generation of computing, one we couldn’t even imagine would’ve been possible just two decades ago. This makes it possible for companies like PubNub to integrate the power of AI and cognitive technology with a real-time infrastructure to enable the next generation of apps to make informed business decisions when they can have the biggest impact. Sign up for a free trial today to see how PubNub uses AI to change app development.