Differences Between Artificial Intelligence and Machine Learning and Benefits of AI

Updated on: 11 Jul, 2023
Differences Between Artificial Intelligence and Machine Learning and Benefits of AI

Artificial intelligence and machine learning are the buzzwords these days. There are key differences between the two.

Artificial Intelligence may be broadly defined as the ability of computers to actually simulate the way human beings reason and to take action accordingly. AI can be narrow in scope or can be general AI. Narrow AI focuses on specific tasks. General AI has a wider approach. AI can be used in diverse fields such as filtering emails intelligently to exclude spam, detection of fraud by monitoring transactions in accounts, recognition of speech and translation of speech commands to action as well as personalization of online experiences. AI becomes closely joined with the internet and the internet of things. This can be seen in applications such as VoIP where artificial intelligence development helps VoIP systems become smarter and more intelligent in analyzing network traffic and routing calls, monitoring caller preferences, and deriving intelligence based on intelligent analytics, to name just a few.

Machine learning is a subset of artificial intelligence and comprises algorithms programmed to gather information from data without instructions at each step. The algorithm learns and generates rules derived from data. Such methods are usually applied in the translation of languages. Learning algorithms may be assigned specific labeled data in a supervised learning model. An algorithm may be tasked with identifying patterns in a data set in the unsupervised model. Reinforcement learning is another way for an algorithm to interact dynamically. Machine learning does necessitate sophisticated programming of algorithms and the use of powerful systems. Machine learning can be applied to VoIP services where machines can be programmed through algorithms to carry out a variety of tasks and then, based on data that is generated, refine the services or gather information on VoIP and non-VoIP traffic. It can be used to monitor network performance and fine-tune traffic diversion.

There are plenty of areas where AI and machine learning have applications but in the context of VoIP AI is bringing about a chance. AI-driven VoIP services, for instance, can reduce employee burden in call centers and ease working by intelligent call routings. AI can sense a caller’s mood and personality and then route the call to a junior agent or a supervisor. It can also help translate languages at both ends. In business environments AI bots can manage schedules and set up meetings, eliminating the need for human intervention. AI can be programmed to recognize patterns and derive insights that can lead to the creation of strategies. AI-backed IVRs are far more sophisticated and capable in comparison to normal IVRs and help guide callers to the right person or agent who then receives complete details of the caller. This is just the tip of the iceberg. There is plenty in store in the future.

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