Cognitive Computing - Our future!!

 

Cognitive computing represents self-learning systems that utilize machine learning models to mimic the way brain works. Eventually, this technology will facilitate the creation of automated IT models which are capable of solving problems without human assistance. Human thinking is beyond imagination. Can a computer develop such ability to think and reason without human intervention? This is something programming experts are trying to achieve. Their goal is to simulate human thought process in a computerized model. The result is cognitive computing, a combination of cognitive science and computer science. Cognitive computing models provide a realistic roadmap to achieve artificial intelligence. With recent breakthroughs in technology, these support systems simply use better data, better algorithms in order to get a better analysis of a huge amount of information.

How Cognitive Computing Works?

Cognitive computing systems can synthesize data from various information sources, while weighing context and conflicting evidence to suggest the best possible answers. To achieve this, cognitive systems include self-learning technologies that use data mining, pattern recognition and natural language processing (NLP) to mimic the way the human brain works. Using computer systems to solve the types of problems that humans are typically tasked with requires vast amounts of structured and unstructured data, fed to machine learning algorithms. Over time, cognitive systems are able to refine the way they identify patterns and the way they process data to become capable of anticipating new problems and model possible solutions. To achieve those capabilities, cognitive computing systems must have five key attributes, as listed by the Cognitive Computing Consortium.

Adaptive: The system must reflect the ability to adapt like a brain does to any surrounding. It needs to be dynamic in data gathering and understanding goals and requirements.

Interactive: The system must be able to interact easily with users so that users can define their needs comfortably. Similarly, it must also interact with other processors, devices, and Cloud services.

Iterative & Stateful: This feature needs a careful application of the data quality and validation methodologies to ensure that the system is always provided with enough information and that the data sources it operates on deliver reliable and up-to-date input. 

Contextual: Ability to understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task and goal. It must draw on multiple sources of information, including both structured and unstructured digital information.

How cognitive computing differs from Artificial Intelligence 

Artificial intelligence agents decide which actions are the most appropriate to take, and when they should be taken. These agents most often take the form of machine learning algorithms, neural networks, statistical analysis and more. You feed the AI information — oftentimes, over a long period of time so that it can “learn” the variables it should pay attention to and the desired outcomes — and it spits out a solution. The potential applications for AI are widespread and already fully integrated into our daily lives, from your Siri/Alexa/Google voice assistant, to Netflix making recommendations based on your viewing experience. If we consider the future a bit more, AI and fully autonomous vehicles are inseparable. In training, the AI watches countless hours of driving footage, is given some variables to watch out for — lanes, other cars, pedestrians — and then delivers a result based on its decision-making.

Cognitive computing is often described as simply marketing jargon, so crafting a working definition is important, although it’s more fluid right now, and there isn’t one consensus that industry experts have settled on. Still, the foundation is that cognitive computing systems try to simulate human thought processes. This process uses many of the same fundamentals as AI, such as machine learning, neural networks, natural language processing, contextual awareness and sentiment analysis, to follow the problem-solving processes that humans do day in and day out. IBM defines the result of cognitive computing as “systems that learn at scale, reason with purpose and interact with humans naturally.”  Sentiment analysis is one emerging cognitive computing task, as in order to fully understand the context and nuances of human language, it must process words on their deepest linguistic meanings. But, if we’re talking about IBM and Watson, it makes sense to talk medicine — a doctor inputs data about their patients, and cognitive computing algorithms analyze it using mimicked human problem-solving. The application then delivers some suggestions and information to help the doctor decide what to do next.

Cognitive Computing: Use Case

Healthcare- Analyzing the patient history and current state by studying various parameters and deriving conclusions such as diagnosis, best treatment, etc. is one of the most widely stated examples of cognitive computing. It can also help with preparing a personalized diet or nutrition plans as per a person’s medical history.

Education and Research- Instead of a single teacher handling a large bunch of students ineffectively, cognitive computing can be a personalized teacher/mentor per student. It can remember every small and large detail about the student and his/her performance leading to a better selection of focus areas along with an array of other benefits. It can aid in research by working on data in a far more swift and accurate way than humans can, especially because a large portion of research can often contain dealing with data.

Finance- Cognitive systems can analyze the market in specific ways and guide regarding investments. For example, it can analyze the history of a company’s shares in relation to external factors such as political, social, etc. This again.

Customer Experience and Sentiment Analysis- Forbes has stressed that customer experience is the new brand, and some have called it the modern battleground. To improve customer experience, it is important to understand the sentiment of customers around your brand or product. Cognitive computing can work with data that includes customer reviews, social media comments/posts, survey results. It can make sense of this data and pinpoint areas where changes are required.

Future of Cognitive Computing

The future of Cognitive Computing involves advanced cognitive systems capable of doing what machine learning systems can’t. They will intelligently and fluently interact with human experts, providing them with articulate explanations and answers, even at the edge of the network or in robotic devices. Across the board, people will see and work with systems endowed with rare and valuable intelligence. For example: Large fleets of ships currently operate largely unmonitored and un-instrumented, especially compared to other modes of transportation such as jets and smart cars. If we imagine supertankers as larger-than-average IoT devices, then they can be connected, tracked through networks of satellites, and coordinated efficiently by cognitive AI. State of the art AI systems can even predict shipping patterns for insight into supply and demand. Currently, most medical monitoring devices aren’t much more than dynamic alarm units. Cognitive AI can take smart medical devices to the next level by making it possible for two completely independent systems – one inside the body, the other attached to the body – to work in sync. Imagine an intelligent spinal alignment implant that could communicate with an intelligent prosthetic limb, coordinating strategies to improve a patient’s balance. This would allow them to walk confidently while increasing stamina and reducing strain, all while providing actionable data to physical therapists. This is a frontier where AI and robotics collaborate for human good. Cognitive artificial intelligence truly intelligent symbolic AI software with bio-inspired, human-like reasoning – will take automation technologies to the next level and enable enterprises to fully utilize their investments in advanced technology. Using cognitive AI, robots can work together to not only analyze time-sensitive data at the point of origin, but also diagnose and solve problems in real-time.

 

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