What’s the first thing you think about when someone mentions AI/ML?
Thoughts of a dystopian world where machines are taking over our lives? Arnold Schwarzenegger from the terminator? You are not alone.
For most people, AI/ML is as alien as a language from another planet and python a man-eating reptile from the deserts of the Australian outback. However, you would be surprised that applications of AI and ML are more common and more in use than you think. Then why should AI/ML cause so many jitters for so many people?
Given that at Katonic.ai, we are in the business of combining the best Artificial Intelligence, Machine Learning, NLP, and Big Data technologies with human intuition, I thought it would be a good idea to create a short guide for beginners out there to better understand these concepts. 🚀
Let us make a start by defining AI in as simple terms as possible.
AI is intelligence built-in non-living beings, machines in this case. Intelligence is the ability to remember past events, comprehend the present, and make predictions for the future. When a machine is able to analyze the data generated from past events, real-time stream of data from current events, and create intelligence out of it we say that it has AI. Grandmother Example — when you search a place on google and it shows how busy it gets during the day — that is AI as the machine has analyzed past and real-time search and location data and is giving you an intelligent answer.
Humans learn using data consumed in the form of text, video, audio, etc. For example, when a child looks at a four-legged animal, it forms an image of it, records its name (e.g. dog), and stores it in memory. The next time when it sees a dog it knows what it is. If it says dog looking at a cow someone corrects the answer and says, it is a cow. The child stores the word cow against the new image. This forms new intelligence. This is how humans learn and train.
Machines also learn in the same manner, we feed machines data, tell them how and what to learn from that data to create intelligence. The machines use intelligence to give us answers. Based on whether the answer is right or wrong the machine learns more and forms new intelligence. Machines can also be made to learn unattended i.e. the process to learn and unlearn from data goes on without human intervention. All this is called Machine Learning.
Grandmother Example: When you say Alexa, play the latest news. Based on training, Alexa knows exactly what this request means. If, however, you say a word that Alexa has not heard before it tries to link it with the nearest possible match and gives an answer. If you cut it in between and ask the initial request again it knows its attempt to link was incorrect and it tries again. If, however, you go with the linkage and listen to what Alexa played it knows the linkage was correct and that becomes its new learning. This is Machine Learning.
Simple isn’t it? Remember if my grandmother can learn it, so can you!
