செயற்கை நுண்ணறிவு (AI ) என்பது இயந்திரங்களின் நுண்ணறிவு மற்றும் இதனை உருவாக்குவதை நோக்கமாகக் கொண்ட கணினி அறிவியலின் ஒரு பிரிவாகும்.
இதில் நுண்ணறிவுக் கருவி என்பது, தன் சூழ்நிலையை உணர்ந்து அதிக வெற்றி வாய்ப்புகளுக்குத் தக்கவாறு செயலில் ஈடுபடும் ஒரு அமைப்பாகும்.
ஜான் மேக்கர்த்தி என்பவர் 1956 இல் இந்தச் சொல்லை அறிமுகப்படுத்தி, இதனை “நுண்ணறிவு இயந்திரங்களை உருவாக்கும் அறிவியல் மற்றும் பொறியியல்” என வரையறுத்தார்.
இந்தத் துறையானது மனிதர்களின் ஒரு பொதுவான குணத்தைக் கருத்தில் கொண்டு உருவாக்கப்பட்டது, அதாவது நுண்ணறிவு – ஹோமோ செப்பியன்களின் பகுத்தறிவு – இத்தகைய குணத்தை ஓர் இயந்திரத்திலும் வடிவமைக்க முடியும் என துல்லியமாக விவரிக்க முடியும்.
இது தொழில்நுட்பம் சார்ந்த துறையில் ஒரு முக்கியமான பங்கு வகிக்கிறது, மேலும் கணினி அறிவியலில் பல மிகவும் கடினமான பிரச்சனைகளை தீர்ப்பதற்கும் உதவுகிறது.
AI இன் பயன்பாடுகள்
செயற்கை நுண்ணறிவானது மருத்துவ அறுதியிடல், பங்கு வணிகம், ரோபோ கட்டுப்பாடு, சட்டம், அறிவியல் கண்டுபிடிப்பு, வீடியோ விளையாட்டுக்கள், பொம்மைகள் மற்றும் வலை தேடு பொறிகள் உட்பட பெரும்பால துறைகளில் வெற்றிகரமாக பயன்படுத்தப்பட்டுக் கொண்டிருக்கிறது.
ஒரு தொழில்நுட்பமானது பிரதானப் பயன்பாட்டிற்கு புழக்கத்திற்கு வந்துவிட்டால், அதன் பின்னர் அது செயற்கை நுண்ணறிவு என்று கருதப்படுவதில்லை, இதை AI விளைவு என்பர்.
அது செயற்கை வாழ்க்கையுடன் ஒருங்கிணைக்கப்படவும் கூடும்.
Artificial Intelligence (AI)
It is a branch of computer science which deals with creating computers or machines as intelligent as human beings.
The term was coined in 1956 by John McCarthy at the Dartmouth conference, Massachusetts Institute of Technology.
It is a simulation of human intelligence processes such as learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction by machines, especially computer systems.
Examples of Artificially Intelligent Technologies
Robotic process automation:
Automation is the process of making a system or processes function automatically.
Robots can be programmed to perform high-volume, repeatable tasks normally performed by humans and further it is different from IT automation because of its agility and adaptability to the changing circumstances.
Natural language processing (NLP) is the processing of human language and not computer language by a computer program.
For Example, spam detection, which looks at the subject line and the text of an email and decides if it’s junk.
Pattern recognition is a branch of machine learning that focuses on identifying patterns in data.
Machine vision is the science of making computers visualize by capturing and analyzing visual information using a camera, analogue-to-digital conversion, and digital signal processing.
It is often compared to human eyesight, but machine vision isn’t bound by biology and can be programmed to see through walls.
It is used in a range of applications from signature identification to medical image analysis.
Machine learning:
Field of study that gives computers the ability to learn without being explicitly programmed.
Deep learning is a subset of machine learning and can be thought of as the automation of predictive analytics.
Robotics is a field of engineering focused on the design and manufacturing of robots.
Robots are often used to perform tasks that are difficult for humans to perform or perform consistently.
Applications of Artificial Intelligence (AI)
Healthcare Sector:
Machine learning is being used for faster, cheaper and more accurate diagnoses and thus improving patient outcomes and reducing costs.
For Example, IBM Watson and chatbots are some of such tools.
Business Sector:
To take care of highly repetitive tasks Robotic process automation is applied which perform faster and effortlessly than humans.
Further, Machine learning algorithms are being integrated into analytics and CRM platforms to provide better customer service.
Chatbots are being used on websites to provide immediate service to customers. Automation of job positions has also become a talking point among academics and IT consultancies such as Gartner and Forrester.
Education Sector:
AI can make some of the educational processes automated such as grading, rewarding marks etc.
therefore giving educators more time.
Further, it can assess students and adapt to their needs, helping them work at their own pace.
AI may change where and how students learn, perhaps even replacing some teachers.
Financial Sector:
It can be applied to personal finance applications and could collect personal data and provide financial advice.
In fact, today software trades more than humans on Wall Street.
Legal Sector:
Automation can lead to faster resolution of already pending cases by reducing the time taken while analyzing cases thus better use of time and more efficient processes.
Manufacturing sector: Robots are being used for manufacturing since a long time now, however, more advanced exponential technologies have emerged such as additive manufacturing (3D Printing) which with the help of AI can revolutionize the entire manufacturing supply chain ecosystem.
Intelligent Robots − Robots can perform the tasks given by a human because of sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure.
Moreover, they have efficient processors, multiple sensors and huge memory, to exhibit intelligence.
Further, they are capable of learning from their errors and therefore can adapt to the new environment.
Gaming
AI has a crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where the machine can think of a large number of possible positions based on heuristic knowledge.
Speech Recognition
There are intelligent systems that are capable of hearing and grasping the language in terms of sentences and their meanings while human talks to it.
It can handle different accents, slang words, noise in the background, changes in human’s noise due to cold, etc.
Cyber Security:
In the 20th conference on e-governance in India, it was discussed that AI can provide more teeth to cyber security and must be explored.
