Artificial Intelligence is the branch of computer science that is used to create intelligent agents that can mimic human acts. We even call artificial intelligence data driven intelligence because AI agents learn from the data and acts according to the learned data. AI is an interdisciplinary science but recent advancements have been made in machine learning and deep learning. Every single aspect of life is ruled by AI these days. And a lot of industries are doing research to create gaming agents, autonomous cars and service robots.
In the long journey of artificial intelligence, certain advancements has been made in multiple sectors.
1. Text Processing
A sub field of machine learning, known as natural language processing learns and manipulates the text written. And generates natural language text further. Dialects and ligatures are separated and stored in bags or groups. After successful learning from data-set, the computer or any intelligent agent should be able to create the text.
Examples
- The biggest example of text processing is Turnitin-the plagiarism detector. The machine learning algorithms are used to detect any copied material over the internet.
- Text processing is also done by Google search engine. On the behalf of this, it provides search suggestions.
2. Speech Recognition
Speech recognition requires machine learning algorithms to identify the person who is talking. It can also detect what language is used by the speaker. Different data-sets of languages can be stored inside the system and it can be trained upon those systems. Speech recognition software works by breaking down the audio into individual voices, each sound is analyzed by using algorithms. Each individual word is predicted by comparing it with the learning data-set.
Examples
- Speech recognition can be used to find any criminal’s voice who has been spotted in the history.
- Google translates your speech into text, that’s where speech recognition is used.
- Amazon’s product Alexa works on speech recognition too.
3. Computer Vision
Computer vision is the field of learning from visual data-set. Features in data are extracted and are stored in a group of feature bag. Labels are also assigned to those features. When a real time data comes in, again features extraction is done and labels are assigned. Labels are matched with the labels of the training data-set.
Examples
- Obstacles detection in autonomous cars. Cameras at the car front are synchronized. Video is captured with them and processed simultaneously.
- IRIS detection; this an application of computer vision that came because of some historical celebrity.
4. Robotics
Robotics uses concepts of AI for smart agents that have the capability to learn form the ongoing phenomenons and adapt accordingly. Robots are also made sociable, they can even interact with human beings too because of the advancements in AI.
Examples
- Military autonomous robots can go into the red zone of the war. By these agents a war can be one without any human life wastage.
- Industrial robots can carry out tests to find out faulty product or medicine efficiently.
