1. What is machine learning? What are its applications?
मशीन लर्निंग क्या? इसके अनुप्रयोग क्या हैं?
A Fourth Industrial Revolution is building as digital revolution that has been occurring since the middle of the last century. It is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres. Artificial intelligence is a crucial part of this fourth industrial revolution. And Machine learning is one of the important aspects of Artificial Intelligence.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Applications of machine learning are multifaceted.
Applications of machine learning:
Applications crucial for India
- Agriculture being a dominant occupation, there is huge potential to deal with crop failure and other agricultural issues using machine learning.
- India being one of the largest consumers of telecommunication services, machine learning can be used in various socio-economic causes via telecommunication.
- Border management with hostile neighbourhood can be done through computer vision, including object recognition, etc. with minimal loss of life and property.
- Crime and investigations, huge pending cases are serious problem before Indian judiciary and administration. Machine learning can help in solving cases, bioinformatics and DNA profiling etc.
Limitations machine learning:
- In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.
- Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of investments.
- Machine learning approaches in particular can suffer from different data bias.
- In healthcare data, measurement errors can often result in bias of machine learning applications.
- A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data.
- When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.
- Language models learned from data have been shown to contain human-like biases
- Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices
- Other forms of ethical challenges, not related to personal biases, are more seen in health care.
- There are concerns among health care professionals that these systems might not be designed in the public’s interest, but as income generating machines.
- For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm’s proprietary owners hold stakes in.
- There is huge potential for machine learning in realising the dream of new India but this will not happen until the personal biases mentioned previously, and these “greed” biases are addressed.
- It is also not recommended to completely rely on machines in certain sectors such as health and such other areas of human interface.
- It must be taken care that replacement of human with intelligent machines would not increase the socio-economic inequalities and deprivations in society.
Best Answer: Dazy Rani