Confusing, But that’s how I understood them.. AI, Machine Learning and other confusing terms.



What is Artificial Intelligence ?

AI started in 1950 with totally different meaning as we think right now. To understand AI of 1950 first one needs to get some traction of Turing Test by Alan Turing.

Now lets understand some terms

The Turing test, developed by Alan Turing in 1950, is a test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human

  • Meaning of above line — If a machine has enough intelligent that human who is interacting with is not able to recognise whether he is interacting with machine or human, then that machine has AI (Artificial intelligence).
  • 1950’s Conclusion (Which may not be true today) — Artificial Intelligence is replicating or copying human (more specifically natural ) intelligence to a machine (or a software).

Now another question comes to mind…..

What was the purpose of AI?

During the 1950’s decade purpose of AI was understanding human brain by creating AI, replicating good things from human to computer or any other machine.

When will we think that we have developed AI ?

If a person can not detect if he is interacting with machine or human, We can say we have developed AI.

We have successfully developed limited AI. If we chat with a bot we can’t really detect if other side is machine or human being. But only texting/chatting is not complete interaction. Interaction has many aspects when human interacts with each other. And there aspects are as follows.

  1. Human moves hands and other limbs when interacting with someone.
  2. Human can see other human while interacting.
  3. Most importantly humans can complete another person’s sentence while speaking based. Means we know some particular set(s) of words comes after specific word(s). Even we as human can think of next sentence of our conversation.
  4. Human can think.
  5. and Many more …..

Each and every point and more possible points made us realised that AI is still not completed. AI is not so easy to develop at that current time. But Scientist thought this can be done in next 1–2 decades.

After 2 decade situations remained similar. during 1980’s also scientist (and engineers) were helpless in developing AI.

Again this topic (AI) of virtual world was not hot topic.


After a gap of almost a decade Google and other new companies started working and researching in AI related field for commercial purpose. and those companies and communities named it Machine Learning.

Same definition can be seen on wikipedia about machine learning.

“ Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data — such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.”

In simple words machine learning can use algebraic function, any hash function or just simple logic to achieve its output. Its objective was not passing Turing Test (like of AI) but to give best specific objective. On same concept Google Search Engine was developed.

In machine learning machine/software learns from previous data.

The older community who were doing research on AI never liked the word machine learning, since various reason. they told they also do similar work as machine learning states but with different purpose. So they came up with new word called Deep Learning. Deep Learning was basically machine learning applied with neural networking algorithms (artificial neural network).

During 1950’s decade human’s understanding of thinking was different from that of during 90’s decade. In human knew that human thinks from heart and brain.

But researches cleared later than brain has many nerves, each nerves get some signals. when signals are high enough in number so that they excite a nerve then they passes through the nerve and nerve get excited by this, then nerve transfer similar input to other nerves. When many neurons do similar activities then we say we are thinking. Thinking involves billions of millions nerves for one thought occured / created in our mind.

As per wikipedia the words about thought are quoted below

A neuron (also known as a neurone or nerve cell) is an excitable cell in the nervous system that processes and transmits information by electrochemical signaling. Neurons are the core components of the brain, the vertebrate spinal cord, the invertebrate ventral nerve cord and the peripheral nerves. A number of specialized types of neurons exist: sensory neurons respond to touch, sound, light and numerous other stimuli affecting cells of the sensory organs that then send signals to the spinal cord and brain. Motor neurons receive signals from the brain and spinal cord that cause muscle contractions and affect glands. Interneurons connect neurons to other neurons within the brain and spinal cord. Neurons respond to stimuli, and communicate the presence of stimuli to the central nervous system, which processes that information and sends responses to other parts of the body for action. Neurons do not go through mitosis and usually cannot be replaced after being destroyed, although astrocytes have been observed to turn into neurons, as they are sometimes pluripotent.

Now you must be thinking What about other terms like Robotics, Natural Language processing and other AI terms. Those terms originated from human-to-human interaction aspects as mentioned above.

Lets understand these terms in brief below.

  • When researches started on understanding verbal communication and written human languages and words, then the branch of these studies named as Natural Language Processing (NLP) in Artificial Intelligence.
  • Commuter generated voice, chat bots, Google Assistant, Apple Siri, Alexa and Google maps use NLP to understand human language and respond back to human.
  • When researches started on visual perception of human conversation, then study of this field named as Computer Vision, Image processing and Video processing based on different types of visual structure and patterns.
  • Phone Cameras, Web cameras, Surveillance apps and tools Computer vision.
  • Researches started on learning from Data (old data) and predicting based on data, then this field of study categorised as Predictive Analytics. Later it became part of data analytics, data science and machine learning.
  • When research started on movement of humans while interacting with each other, this field of study categorised as Robotics.

There are many more new terms such as Reinforcement Learning, Singularity, Supervised learning and so on….. I will understand these terms in simples words and will write later about them.

Special thanks to Mr. Saurabh Chandra for his great podcast puliyabaazi episode on AI.

Thank you reading…!!!

Artificial Intelligence Glossary of Terms

There are few confusing words I found and I am currently trying to understand them.

  1. Advanced Driving Assistance Systems(ADAS)
  2. Artificial Intelligence
  3. Bayesian network
  4. Classifiers
  5. Computer Vision for object detection
  6. Crowdsourcing
  7. Data labeling
  8. Data Mining
  9. Data Science
  10. Data Scientist
  11. Decision Model
  12. Deep Learning
  13. Facial Recognition
  14. Ground Truth
  15. Human-in-the-loop
  16. Image Recognition
  17. Machine Learning
  18. Managed Crowdsourcing
  19. Natural language generation
  20. Natural language processing
  21. Optical Character Recognition (OCR)
  22. Perception
  23. Precision
  24. Recall
  25. Reinforcement learning algorithms
  26. Semantic Segmentation
  27. Speech Recognition
  28. Supervised learning algorithms
  29. Training Data
  30. Unsupervised learning algorithms

Stay tuned for the simpler understanding of above words.

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Source: Deep Learning on Medium