Discussion – 


Discussion – 


6 Practical Business Applications of Cognitive Intelligence

A new breed of commercial AI for business is emerging to meet the more complex demands of modern human-machine interactions. We call it cognitive intelligence, which isn’t here to supplant big data or machine learning, but rather to monitor how traditional AI processes data, filling in gaps and identifying misinterpretations. Ultimately, the goal of a cognitive intelligence platform is to be able to complete tasks without the need for human supervision, quickly process unexpected or unfamiliar external input and adjust its response accordingly. Wherever it is used, cognitive intelligence will be the true watershed AI.

6 Practical and Unexpected Business Applications of Cognitive Intelligence:

1. Smart IoT:

Connecting and optimizing devices, data and the IoT. The next things to become connected in IoT will be whatever hasn’t been yet. But assuming we get more sensors and devices, the real key is what’s going to connect them. And to me, that is advanced intelligence, at the edge.  

For example, we see that disconnected mesh networks, like swarms of drones or remote facilities filled with smart sensors and actuators, need to coordinate to accomplish tasks, without being connected to a mothership. Cognitive AI agents can give swarms of devices situational awareness and make them work together to solve problems. Another example is an oil rig in the ocean with thousands of sensors that need a connection to provide data. A rig doesn’t have a fast connection to the internet, so cloud service AI is out. The intelligence needs to be resident on edge devices so they can diagnose dangerous problems and act to prevent disaster in real-time.

It’s not so different from empowering autonomous rovers on Mars to make decisions, at great distances and in extreme conditions, which is part of our space program heritage. Or take the case of large fleets of ships. Right now, they’re largely unmonitored and uninstrumented, especially compared to other modes of transportation. Think of supertankers as larger-than-average edge devices that can be connected, tracked and coordinated through networks of CubeSats powered by AI.  

– AJ Abdallat, CEO of Beyond Limits

2. AI-Enabled Cybersecurity:

Soon as artificial intelligence becomes sophisticated we will see a surge in the number of intelligent cyber-attacks. This rise in cyber attacks will result in an explosion of network penetrations, increasingly sophisticated social engineering attacks, and unroll of intelligent computer viruses like wildfire. Our only hope to fight this would be the use of data security encryption and enhanced situational awareness powered by AI. This will provide document, data, and network locking using smart distributed data secured by an AI key.

3. Next Generation Search:

We all love Google for what it does. Just with a click, we gain access to information which is filtered from billions of web pages. But it is also true that the results we get don’t always solve the query. Google has effectively used a decoy algorithm that ranks pages based on popularity and keywords. The future now lies in intent and reasoning based search that produces the highest accuracy and relevancy results. We will be using an AI-based search engine that makes all this possible. Whether the offering will be from Google or not is yet an unanswered question.

4. Content AI:

A solution powered by cognitive intelligence continuously learns and reasons becomes smarter and can simultaneously integrate location, time of day, user habits, semantic intensity, intent, sentiment, social media, contextual awareness and other personal attributes. This allows for personalized media delivery & content monetization. The real-time data analysis accompanied with cognitive intelligence can finally bring intelligent assistants approachable in a real sense.

5. Cognitive Analytics in Healthcare:

Now, advanced and deep analytics and insights will be much more approachable. We can expect cognitive agents to drive breakthrough advancements in genomics research, drug discovery, diagnostics, and population health. The technology implements human-like reasoning software functions that perform deductive, inductive and abductive analysis for life sciences applications. Now we can assess hypothetical discovery scenarios, detect health anomalies, classify diagnostic results and discover associations between seemingly unrelated health information sources.

6. Intent-Based Natural Language Processing:

Data scientists will move from basic machine learning techniques like multi-label classification to more holistic deep neural networks which ideally require large data sets. Hence, we will be moving beyond word template matching to catering to user intent. The level of intent matching can vary between extreme ranges.

For instance, if I say, “I want to book a Dr’s appointment” the bot can refer to nearest doctors based on my location. This seems to be a fairly simple scenario but what more it can do is look through my search history and refer a specific doctor if my online behavior indicates research on a specific disease or health condition.

Overall cognitive intelligence can help business become more analytical in their approach to management and decision making. This field of artificial intelligence will work is the next step from machine learning and the future applications of AI will incline towards using this for performing logical reasoning and analysis.

At NewGenApps we have built strong competencies in working on modern technologies that cater to the evolving needs of businesses. Get in touch today if you are looking to develop an intelligent solution for your needs.




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