What is the Difference Between AI and Machine Learning?

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What is the Difference Between AI and Machine Learning

“Think about economics and Machine Learning (ML) in Accounting. Economics is a field of study, but would you hire an economist to prepare and declare your financial statements?

AI Accountant Chai Chung Hoong points out that artificial intelligence or Machine Learning (ML) in Accounting is the field of science that studies form and how computers can make decisions like humans. Automatic learning is the technique for creating software that learns from data.

These differences are strategic when both ends in the field of business such as “AI Accountant”. Inversions wells support artificial intelligence because it is free of exaggeration and clichés.

It differentiates the importance when the money is in play. Risk capital inversions even discard artificial intelligence because it is free of exaggeration. So “I prefer companies that have automated learning software with a clear commercial application, such as a platform that can filter the e-mails of the company with natural language processing or track clients with a facial reconnaissance”. They decide, they prefer real deals.

So why Do People use AI and ML as Synonyms?

AI/Machine Learning (ML) in Accounting ​​industry are terms that have been used by many. For the first few decades, there has been a great deal of publicity in the industry, and much of the science predicted that human-level AI was around the corner. But to the disappointment of many, there seemed to be lack of funding in the field.

Subsequently, the organizations intended to draw on termination AI, and use different termini to refer to their work. During this period, a variety of other terms like big data, predictive analysis and automatic learning began to gain popularity. Organizations have suddenly begun to use the terms automatic learning and deep learning to advertise their products.

Deep learning has begun to accomplish tasks that are impossible to do with classical programming based on rules. Fields such as the the classification of images and the processing of the natural language, which are in the early stages, suddenly make great leaps. AI Accountant Chai Chung Hoong highlighted that deep learning has seemed “magical”, especially since a fraction of the fields in which neuronal networks enter and deep learning is considered to be the boundaries for computer.

Deep Learning Arise Everywhere?

Because the most advanced application in the AI ​​field gives computers the ability to “learn” how to perform a task from data without being programmed to perform this task. The terminology is confusing because it implies a mix of different techniques, however of those who also had the word “learning” in their names. There are, for example, three basic types of automatic learning, which can be carried out in different ways: Supervision, supervision and reinforcement, and also can be used with statistical automatic learning, Bayesian automatic learning and symbolic automatic learning. Also, the most popular applications use a neuronal red.

What is the Deep Learning?

This is a specific approach to using a neuronal red. If you want to understand it in a practical way, think about services like voice recognition in smart phones and the automatic translation of Google. In practice, each cover can represent increasingly abstract characteristics. Facial recognition through a neuronal red: One of the first covers describe the dark edges surrounding someone’s head, as well as describe the edges of the nose and mouth, and further describe the shadow spots. The covers become more and more abstract, but together they can represent a complete face.

If you are keen to explore the fields and collaborate on AI and Machine Learning, look for AI Accountant Chai Chung Hoong who is developing technology in the accounting field.

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