It depends who you ask. Artificial intelligence (AI), sometimes called machine intelligence, some times it is related to Deep Learning, some times it is Speech recognition, some times it is targeted to problem solving for specific task.

In the simplest terms, artificial intelligence (AI) is prepare tasks and can iteratively improve themselves based on the information they collect. Examples of the AI Application in Enterprises.

>>> Prepare the Chat bots based on the old customers queries data and use AI to understand customer problems faster and provide more efficient answers

>>>To improve the reaction time and get intelligent assistants use AI by parse critical information from large free-text data sets to improve decision making power in Marketing or banking sector or finance and investments etc.,

>>>Recommendation engines can provide automated recommendations to improve the production or supply chain or Scheduling etc.,

AI isn’t intended to replace humans. It helps the humans to understand and react intelligently even with less time experience. AI is much more about the process and the capability for super powered thinking and data analysis than it is about any particular format or function to makes it a very valuable business asset.

A wide range of the industries such as healthcare, transportation, insurance, transport and logistics, even customer service and many more in the market using the AI and it will become fully applicable to most the business by 2025. There are lot requirements and need of the fully trained AI engineers. And the market pay standards are high for this sector. Becoming an Artificial Intelligence Engineer puts you on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond. Artificial intelligence and Machine Learning will impact all segments of daily life by 2025.

The best way to study AI in this course is to learn by researching. In addition to a comprehensive introduction to a variety of AI subfields, this course will facilitate your exploration of state-of-the-art research and applications of AI, the production of your own ideas and visions, and discussions and sharing of some deep understandings. You will be asked to think about and communicate higher-level abstract ideas, including the concepts, strategies, principles, and algorithms of AI, rather than the technical details of implementation and programming. You will also be encouraged to test and evaluate ideas and algorithms through research-oriented studies involving software programming and/or exploring.

Artificial intelligence enables marketers to focus more on the customer and take care of their needs in real time. Data that algorithms collect and generate makes it easy for marketers to understand what content to target at customers, and which channel to use at which time.

The personalized experiences that AI facilitates makes users feel more at ease—and more likely to buy what you have to offer.

There is number of AI tools in the market to operationalising Artificial Intelligence. As this moves more mainstream, the only real challenge is to realize it needs to be operationalized, it needs placed in Cloud and to be audited and make it live in production environment is big challenge. Operationalizing AI actually isn’t that hard, but that hasn’t that easy to use to justify similar occurrences at a later time.

Most of the AI tooling and most of the AI work has been done by data scientists on their own machines, and it has been done in sort of an ad hoc fashion.

However, there are some stumbling blocks, example If AI Tools not using Cloud Computing, projects are often computationally expensive. It is complex to build require expertise that’s in high demand. Implement the concept of AI when, where how extend to incorporate is big challenge. But when we turn to get services for third party tools, will help minimize these difficulties.

To become an AI Engineer it is similar to Developer profile as a AI programmer but using some special tools which need to develop the machine learning algorithm.

• Understand the concepts of the Machine Learning and become the master in applying the algorithms to requirements. Learn the AI Industry Verticals including Games, , probabilistic models, agent decision-making functions and more
• Understand the concepts of TensorFlow (Google AI Tools), its main functions, operations and the execution pipeline
• Implement deep learning algorithms in TensorFlow and interpret the results,
• Understand neural networks and apply the functions of the Neural Networks
• Understand and perform practical aspects of machine learning,
• Learn the advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
• Learn about major applications fields like customer service, financial services, healthcare and other fields of Artificial Intelligence across various use cases and Projects
• In advance or Consultant profile of the AI, one should have ability to differentiate the limitations of the current Artificial Intelligence technics and tools available in the market and able to analyse the gap between them.
• One should formalise with area enclosed as a search problem, as a constraint satisfaction problem, as a planning problem, etc and understand to application of the tools for a given problem in the language/framework of different AI methods (e.g.,)
• One should do the Projects and gain Practical experience to solving real-world challenges

The Harvard Business Review makes the following recommendations for getting started with AI:
• Apply AI capabilities to those activities that have the greatest and most immediate impact on revenue and cost.
• Use AI to boost productivity with the same number of people, rather than eliminating or adding headcount.
• Begin your AI implementation in the back office, not the front office (IT and accounting will benefit the most).

The emergence of AI-powered solutions and tools means that more companies can take advantage of AI at a lower cost and in less time. Ready-to-use AI refers to the solutions, tools, and software that either have built-in AI capabilities or automate the process of algorithmic decision-making.

Ready-to-use AI can be anything from autonomous databases, which self-heal using machine learning, to prebuilt models that can be applied to a variety of datasets to solve challenges such as image recognition and text analysis. It can help companies achieve a faster time to value, increase productivity, reduce costs, and improve relationships with customers.