Artificial Intelligence in Working
What is Artificial Intelligence?
Artificial Intelligence is a branch of Science which deals with helping machines to find out solutions of complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence and applying them as algorithms in a computer friendly way. A more or less flexible or efficient approach can be taken depending on the requirements established, which influences how artificial the intelligent behavior appears. Computers are fundamentally well suited to performing mechanical computations, using fixed programmed rules. This allows artificial machines to perform simple monotonous tasks efficiently and reliably, which humans are ill-suited to. For more complex problems, things get more difficult. Unlike humans, computers have trouble in understanding specific situations and adapting to new situations. Artificial Intelligence aims to improve machine behavior in tackling such complex tasks.
Machine learning is a core subarea of artificial intelligence. It is very unlikely that we will be able to build any kind of intelligent system capable of any of the facilities that we associate with intelligence, such as language or vision, without using learning to get there. Artificial intelligence has been the subject of optimism, but has also suffered setbacks and today it has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science. All research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. Machine Learning aims to automate the statistical analysis of large complex datasets by adaptive computing. As a core strategy to meet growing demands of science and applications, it provides a data-driven basis for automated decision making and probabilistic reasoning.
- Optical Character Recognition categorizes images of handwritten characters by the letters represented.
- Face Detection find faces in images (or indicate if a face is present)
- Spam Filtering identify email messages as spam or non-spam
- Topic Spotting categorize news articles (say) as to whether they are about politics, sports, entertainment, etc.
- Spoken Language – Understanding within the context of a limited domain, determine the meaning of something uttered by a speaker to the extent that it can be classified into one of a fixed set of categories.
- Medical Diagnosis diagnose a patient as a sufferer or non-sufferer of some disease
- Customer segmentation: Predict, for instance, which customers will respond to a particular promotion
- Fraud Detection: identify credit card transactions (for instance) which may be fraudulent in nature.
- Weather Prediction: It helps in making predictions, for instance, whether or not it will rain tomorrow.
A richer learning scenario is one in which the goal is actually to behave intelligently, or to make intelligent decisions. For instance, a robot needs to learn to navigate through its environment without colliding with anything. To use machine learning to make money on the stock market, we might treat investment as a classification problem (will the stock group or down) or a regression problem (how much will the stock group), or, dispensing with these intermediate goals, we might want the computer to learn directly how to decide to make investments so as to maximize wealth.
Author –
Dr. Sandeep Kautish
Dean Academics
LBEF Campus
