Cognitive computing: Moving From Hype to Deployment
Although cognitive computing, which is many a times referred to as AI or Artificial Intelligence, is not a new concept, the hype surrounding it and the level of interest pertaining to it is definitely new. The combination of hype surrounding robot overlords, vendor marketing and concerns regarding job losses has fueled the hype into where we stand now.
But, behind the cloud of hype that is surrounding the technology currently, there lies a potential for increased productivity, the ability to solve problems deemed too complex for the average human brains and better knowledge based transactions and interactions with consumers. I recently got a chance to catch up with Dmitri Tcherevik, who is the CTO of Progress, about this disruption and we had a healthy discussion which led to the following insights.
Cognitive computing is considered a marketing jargon by many, but in layman terms it is used to define the ability of computers to replicate or stimulate human thought processes. The processes behind cognitive computing may make use of the same principles as AI, including neural networks, machine learning, contextual awareness, sentimental analysis, and natural language processing. However, there is a minute difference between both of them.
Difference between Cognitive Computing and AI
Both AI and Cognitive Computing may look extremely alike, but like we mentioned above there is a small difference between both methods.
Firstly, artificial intelligence does not work at mimicking human thought processes. The concept behind AI is to not mimic human thought and processes, but to solve a problem through the use of the best possible algorithm. This can be illustrated through an example of a car, which stays on course and avoids a collision. The processes in AI are not looking to process data in the same way as it would be processed by humans, but they’re looking to process it through the best known algorithm present. Processing data the way humans do it is a far more fault-prone and complex algorithm. And, we all know that a self-driven car isn’t giving suggestions to the driver, it’s responsible for all the decisions in driving.
Secondly, cognitive computing is not responsible for making decisions for humans, instead it is responsible for complementing or supplementing our own cognitive abilities of decision making. AI in medicine would be all about making the right decisions pertaining to a patient or the preferred mode of treatment, and minimizing the role of the doctor. Cognitive computing, on the contrary, would be more focused on achieving evidence that could supplement the human expert into making more flawless medical diagnoses.
Emerging Use of Cognitive Computing in Industries
We can gauge the success of cognitive computing and the development through the opportunities it has across industries. Cognitive computing is currently in a research phase, where research is going into properly implementing the technology in the fields deemed appropriate for its use. One can assess the opportunities for cognitive computing by looking at industries and industry specific scenarios where cognitive computing could make a big difference.
Companies offering customer services deal with a lot of data which they have to accommodate with large processing requirements and are required to be efficient and flawless in advising customers to the right outcome. With so much happening, one can think about the opportunities for cognitive computing in this specific industry. At a consumer level, we can take the aid of robo-advisors that assist staff in advising new customers about what they can do and how they can go about creating a new account. There is also the concept of automated document processing that will limit human involvement and the flaws that come with it to a large extent. According to Dmitri: ‘Customer services are up for disruption, and the use of chatbots while booking airplane tickets or checking your insurance claim will go a long way in the future.’
Whenever we talk about Big Data, Machine Learning, AI or Cognitive Computing, the services that will be rendered through these technologies in healthcare always spring to mind. Human healthcare is certainly not at 100 per cent efficiency nowadays, which is because of the fact that there are certain flaws in the process. These flaws can be eradicated by giving machines the cognitive abilities required for going through a report and forming a basic judgment regarding the condition of any patient. The results can then be communicated to humans through a virtual display.
Most of the Industrial IoT giants that we have in industries such as car manufacturing, transportation, etc., have implemented exemplary data collection methods. These data collection methods do their job well, and hand over the necessary input to their patron organizations. Now, when the data is collected and stored off, the real challenge of anomaly analytics arises. Despite having stringent data collection and storage facilities, these firms don’t know what to do with their data and how to find actionable results.
The biggest problem facing businesses in today’s myopia is that only 20 percent of all problems or anomalies that occur are predicted and understood beforehand. This means that around 80 percent of the problems that businesses face are unpredicted, and the business is not prepared to handle them because of below par anomaly detection.
The Cognitive Anomaly detection is different from the traditional method, as it is a machine and data-first solution. The future for cognitive anomaly detection is seemingly bright, and it is now the time to move from a research phase to deployment.
How to Move to Deployment
The deployment of cognitive computing requires adhering to a certain set of levels for achieving the desired aims. The levels that should be used for proper deployment of the technique include:
Scale and Automate: It is necessary that you determine the scale of the deployment and then automate the process towards achieving the necessary scale. By knowing the scale of the move and the automation that is required, you can seamlessly incorporate cognitive computing in your setup.
Start Using APIs: The next level in the deployment of cognitive computing includes the creation of APIs or Application Programming Interfaces. Chatbots and natural language processing are added to the interface to make it effective.
Automation Middleware: The stage of automation middleware already provides 75 per cent of the entire share that is going into achieving the solution. Application developers need to put together applications quickly here, according to Dmitri Tcherevik. The fast processing of applications at this middle stage defines the success of the levels.
App Blueprints per Domain: Despite the thoroughness of the steps mentioned above, there is still a need for an application blueprint for each domain. Application blueprints are created by Progress for different domains. Dmitri mentioned that they have created several blueprints for domains in healthcare. The applications required are complex, which is why there is a need for blueprints. The applications can then be personalized based on clients.
DevOps: Once you have deployed the cognitive applications, there is the need to look out and monitor. The monitoring is done to look out for possible updates that can be incorporated. Cognitive applications need to be updated on a continuous basis to remain smart and up to date with what is expected from them.
With cognitive computing gaining center stage, it is expected that the concept will develop over time and will be implemented over numerous industries. Industrial IoT is expected to benefit a lot from cognitive computing as it can be used for deriving meaning out of the data they work with. In short, cognitive computing is currently leading the wave of the future as it holds the key to not only making healthcare, AI and Industrial IoT better, but also providing human thought processing and behavior that was needed here.
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Link: Cognitive computing: Moving From Hype to Deployment