Cognitive computing technology new era

Cognitive computing (CC) describes technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing. These platforms encompass machine learning, reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction, dialog and narrative generation, among other technologies.

Cognitive computing simply means teaching computers how to think and process information like humans. One of the key applications of cognitive computing is natural language. Again, it’s a business decision — being able to process natural language without any human intervention is a huge problem today. As computers get smarter, they will take more and more decisions in the enterprise world, all based on natural or unstructured inputs. In our day to day lives as well natural language interaction with our machines will become the pervasive interface. With Droid, Siri, Cortana, etc. the building blocks are already in place.

Still, natural language is only one application for cognitive computing. Advanced cognitive systems are already being developed that relate with vision, speech, etc. Cognitive computing could also be a great tool to solve the traditional big data problems. where data size results in prohibitive compute cost or latency; or where underlying data is too dynamic; or when structured data is only one piece of a larger puzzle.

Cognitive computing vs traditional big data technologies

  • Scalability: Cognitive computing is all about forming hypotheses, proving or disproving them, and learning from them to form new hypotheses — essentially how humans think. Cognitive systems have memory, they have the ability to second guess themselves, and they are designed to go back and forth for finding the right answers. It means they can come up with equally rigorous insights without having to brute force their way through entire databases. They make fewer redundant calculations, a hallmark of scalability.
  • Dynamism: The same capabilities give cognitive computers a lot of flexibility in ingestion and processing. If, for example, a system is processing some data batches, and a new variable shows up in a batch, cognitive computers will not skip a beat. They will automatically change models to include the new variables. For traditional technologies models may have to be rewritten and the performance will suffer by that much. This also holds true for situations where the content itself keeps evolving.
  • Natural Interaction: Given that natural language is a prominent application in any case, pulling in natural language capabilities makes cognitive computing systems very powerful. For example, these systems can output their insights directly in business language. More importantly, these systems can extract information from natural language or unstructured text. This increases their applicability and the time taken to prepare data for advanced analyses.

Real world examples

  • Speech recognition
  • Sentiment analysis
  • Face detection
  • Risk Assessment
  • Fraud Detection
  • Behavioral Recommendations