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Cognitive computing
Machine learning in the cloud does exactly this. The large amounts of data stored in the cloud provide a source of information for the machine learning process. With millions of people using the cloud for computing, storage and networking, the already existing data, the millions of processes that happen every day, all provide a source of information for the machine to learn from. The whole process will provide applications in the cloud with sensory capabilities. The applications will be able to perform cognitive functions and make decisions.
Some examples of cognitive computing in the current market have made remarkable progress in the field of artificial intelligence. IBM Watson, AWS IA, and Microsoft Cognitive APIs have been notable cases in the industry.
Some examples of cognitive computing in the current market have made remarkable progress in the field of artificial intelligence. IBM Watson, AWS IA, and Microsoft Cognitive APIs have been notable cases in the industry.
Cognitive computing systems in existence today are more at an experimental stage and are given tasks of minimal importance. Over time, we can expect these systems to take over healthcare and hospitality, business and personal lives even.
Cognitive computing
Machine learning in the cloud does exactly this. The large amounts of data stored in the cloud provide a source of information for the machine learning process. With millions of people using the cloud for computing, storage and networking, the already existing data, the millions of processes that happen every day, all provide a source of information for the machine to learn from. The whole process will provide applications in the cloud with sensory capabilities. The applications will be able to perform cognitive functions and make decisions.
Some examples of cognitive computing in the current market have made remarkable progress in the field of artificial intelligence. IBM Watson, AWS IA, and Microsoft Cognitive APIs have been notable cases in the industry.
Cognitive computing systems in existence today are more at an experimental stage and are given tasks of minimal importance. Over time, we can expect these systems to take over healthcare and hospitality, business and personal lives even.
Personal assistance and chatbots
Personal assistants have made life easier for individuals. Products like Apple Siri, Google Allo, or Microsoft Cortana are pre-coded voice recognition systems that give a feel of human touch to machines. But these personal digital assistants have limited capabilities. With the mass data on the cloud, the learning capabilities of machine learning, and its cognitive computing feature as mentioned above, personal assistance can almost replace any form of human interaction. Fantasies of owning computer systems like those in science fiction or super-hero movies can become a reality.
mplementing machine learning will increase the cognitive capabilities of these chatbots, giving them a human touch. These chatbots can learn from past conversations and provide better assistance. Not just that, instead of a plain, question-answer session between the customer and the chatbot, a real conversation can take place with the chatbot. The chatbot can initiate queries about previous problems or additional suggestions for the problem at hand. The main aim is to make these chatbots as human and personal as possible to make customers feel important.
