*Artificial Intelligence and its trends*
Artificial Intelligence is a method of making a computer, a computer-controlled robot, or a software think intelligently like the human mind. It uses a set of technologies that enable computers to perform a variety of advanced functions like analyzing, verification, validation, recommendation etc. For example, optical character recognition (OCR) uses AI to extract text and data from images and documents, and turn unstructured data into structured data.
There are 3 methods to implement AI. They are Machine Learning, Deep Learning, and Natural Language Processing.
Machine Learning:
It is a subset of AI that uses ML algorithms to make decisions (or) predictions based on previous data. It is suited for less complex and learning to train the model by using known data points (that include both known labelled and unlabelled data). CPU is required to train the model.
It has two methods namely Supervised Learning and Unsupervised Learning. Supervised Learning means getting input data from labelled data sets and predicting outcomes based on these data. During the training phase, it is monitored by data scientists before execution. So it has more accuracy. Unsupervised Learning means getting input data from unlabelled data sets and not predicting the output. It is not monitored by data scientists. So it has less accuracy.
Ex: Image and speech recognition, Chat Bot, Virtual assistant, Product Recommendations, E-mail Filtering, Automated reply, and Ticket booking etc..
ML is used by Google Translate, AWS SageMaker
Deep Learning:
Deep Learning is a subset of ML also more advanced than ML and uses neural networks (like the human brain) to teach machines to solve complex issues without human intervention. It is suited for more complex, more accurate, and learning to train the model by using unknown data points like feedback and known errors from developers. GPU is required to train the model.
In the image above, it has 3 layers. Input layer which received data from the outside world. The hidden layer processes the data using neural networks. The output layer produces output.
Ex: Facial recognition, Chat Bot, and Self-driving cars etc.
DL is used in Translation services by ChatGPT, Amazon Translate, and BardAI.
Natural Language Processing:
It is a subset of AI that uses ML and DL models that make the machines analyze, comprehend, and manipulate human language. Supervised NLP means getting input data from labelled data sets and predicting outcomes based on these data. Unsupervised NLP means getting input data from unlabelled data sets and not predicting the output soon.
NLP is used by Amazon Comprehend, Azure cognitive service, and Google NLP for extracting data from images and languages etc.
Ex: Artificial Robots
Last Updated: 18-Jan-2024