5. Industry-specific applications of cognitive technologies.

These sectors are doing with the current computer system applications but with cognitive technology, the potential applications on the horizon have increased as.

5.1.Financial Services: Improve performance of funds and detect market manipulation.

5.2. Health Care: Automated and more accurate diagnosis with predicting and analyzing treatments.

5.3. Life Sciences: Drug discovery and analysis and providing smart supply chains.

5.4. Public sector: Predictive emergency management and policing.

5.5. Oil and Gas: Optimizing energy flow out of batteries and point of consumption.

5.6. Manufacturing: Automated planning of business operations.

6. Cognitive computing landscape.

At present large players are – IBM, Microsoft, and Google with other companies as well.

6.1.IBM Watson: A supercomputer that combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a “question answering” machine which also uses technologies like natural language processing, image recognition, text analytics, and virtual agents.

6.2. Microsoft Cognitive Services: A set of APIs, SDKs and cognitive services.

6.3. Google DeepMind: Acquired by Google in 2014 and became popular with AlphaGo, a narrow AI to play Go, a Chinese strategy board game for two players.

6.4. Cognitive Scale: The team provides cognitive cloud software for enterprises. It’s an augmented intelligence platform that delivers insights-as-a-service and accelerates the creation of cognitive applications in healthcare, retail, travel, and financial services.

6.5. Spark Cognition: Develops AI-Powered cyber-physical software for the safety, security, and reliability of IT, OT, and the IIoT. It’s capable of harnessing real-time sensor data and continuously allowing for more accurate risk mitigation and learning prevention policies to intervene and avert disasters. Other leading technology companies are Qualcomm and Intel. Uber has established a research arm dedicated to AI and ML. Otto is an autonomous truck and transportation startup. Gamalon developed an AI technique using Bayesian Program Synthesis. Lumiata and Enlitic have developed solutions that assist healthcare providers in the diagnosis and prediction of disease conditions. Other companies are Cisco cognitive threat analytics, CustomerMatrix, Digital Reasoning, and Narrative Science etc.

7. Are Cognitive Computing, AI and ML same kind of thing?

They are quite similar but have their fundamental differences.

7.1. Machine Learning: Ability to continuing learning without being pre-programmed after a manual. Algorithms that learn from data and create foresight based on this data.

7.2. Artificial Intelligence: Human thought processes are not mimicked by Artificial Intelligence. It is actually a far more complex and fault-prone. The intelligence emerges from a business point of view when machines – based on information – are able to make decisions, which maximizes the chances of success in a given topic. With the help of Machine Learning, it is able to learn from data and give relevant information.

7.3 Cognitive Technology: It learns at scale, reason with purpose and interacts with humans naturally. By self-teaching algorithms which use data mining, visual recognition, and natural language processing. The computer is able to solve problems and optimize human processes. Example: Medical Decisions-cognitive computing is important is just that there is true evidence which states that machine learning can supplement human medical diagnoses, but no one would claim that AI should actually take all the medical decisions.

8. Limitations of Cognitive Technology.

Cognitive Technology has some limitations which make AI difficult to apply in situations where a high level of uncertainty, rapid change or creative is required. The complexity grows with the number of data sources.

8.1. Limited analysis of risk: The cognitive systems fail at analyzing the risk which is missing in the unstructured data in addition to socio-economic factors, culture, political environments, and people. Therefore human intervention is important for complete risk analysis and final decision making.

8.2. Meticulous training process: The laborious process of training cognitive systems, complex and expensive process is most likely the reason for its slow adoption and making it even worse.

8.3. More intelligence augmentation rather than artificial intelligence: The scope of present cognitive technology is limited to engagement and decision. It amplifies human thinking and analysis but it still depends on humans to take critical decisions. A cognitive solution should have many technologies besides AI, ML and NLP, technologies such as NoSQL, Hadoop, Elastic search, Kafka, Spark etc. should form a part of the cognitive system which will be capable of handling dynamic real-time and static historical data.

Cognitive technology is not just making Artificial Intelligence and Machine learning easy but also giving it new dimensions. It replaces some human tasks and decision-making, sustainable competitive advantage is likely to be achieved by augmenting and amplifying human capabilities-not just replacing or replicating them. In the future, our work ethics will change which will make lots of work easy and secure. They can enable any organization to break prevailing trade-offs between speed, cost, and quality. Make human thought processing into computerized models. Technological progress and commercialization should expand the impact of cognitive technologies on organizations over the coming years and beyond.