Why Data is becoming a key ingredient for AI Applications?

by Roger Brown Manager

Artificial intelligence (AI) has become of the important technology across the world providing lots of opportunity for industries to automate functioning of various actions performed in businesses. At the same time, Data is also becoming an important element for AI allowing machines to learn from such data and perform in the right manner.

For an example – A self-driving car needs wide range of data like images from digital cameras, high resolution maps and signals from infrared sensors to run safely. Such self-driving cars requires huge amount of data that is used in the development of AI models to drive cars. In fact, data has bigger role in AI and becoming very critical for developing AI applications. Let see what are the top reasons data becomes key ingredient for AI applications.

Data is Learning Experience

AI applications are also like humans, it improves with the more experience. Data delivers the examples that are essential to train models that can perform predictions and classifications. Image recognition is the best example in which availability of data helps to transform the pace of change in image understanding and allows computers reach human level performance.

Personalization for Users

Data is very important for modifying an AI models as per the requirements of the users. If we try to know what types of papers users read, collect or download, we can advise them for potential papers of interest. Moreover, techniques like collaborative filtering, that allow suggestions based on the similarity between users, improve with access to additional data, the more user data one has, the more chances that the algorithm can find a similar user.

Fewer Exceptions for AI Models

There is a crucial problem of overfitting while building AI models in which model focuses in to specifically on the examples provided. For an example, if a model is trying to learn to identify chairs and has been exposed to standard dining chairs that have four legs, it will learn chairs having four legs but if the model is then shown a desk chair having one pillar it would be not recognize the chair, where more data required to overcome such challenges for AI models.

Easier Testing of AI Systems

Data is useful in testing the AI systems and even in cases where techniques can be used that needs less training data, it can make the task easier. For an example, if for a A/B testing where AI developers use only limited amount of traffic to a site and tests to find whether a new recommendation engine or algorithm performs better on that small set of traffic. The more amount of traffic, it becomes easier to test multiple algorithms or variants.

More Applications of Data

Lastly, it is increasingly the case that data can be used again for different applications. For an instance transfer learning is the technique allows data developed for single domain can be applied to another domain. Google is using data intended for different task like image recognition also helping performance on another different task like language translation.

To summing-up, today data is a main component for developing any kind of AI models. Data collection and analysis, understanding and implementing more and more amount of data wisely will definitely help developers to create a highly responsive AI models without any error. Data is definitely a key ingredient for making smart systems that can help users for various works.

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Author is experienced with technical skills in Training Data services and sharing his knowledge to make best use of this emerging technology. He is also having well-understanding in machine learning and datasets used in making such technology more useful. To know more about AI and machine learning keep reading his articles.

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