Woahhh… Surprised that I’m gonna cover every facet of Artificial Intelligence in just a single blog post!? That’s definitely impossible!! But, I’m sure I can give you a fundamental grasp of it through this blog.
Most of our routines start with unlocking a phone using the fingerprint or facial unlock options and eventually ends up with, “Amazing!! I made it to 10,000 steps today” or “Hey Siri, set the alarm for 5 a.m.” Whether we like it or not, we spend a significant amount of time interacting with smart systems, and it’s (AI) becoming an essential part of our modern existence. From Search engines to Virtual Assistants, Recommender systems, Google maps, smart homes so on.. Using mathematics and algorithmic techniques, AI solves these complex real-world problems.
What is AI and why do we need it?
Artificial Intelligence is a science that develops theories and methodologies to make machines that are capable of thinking and understanding the world intelligently, as well as reacting appropriately to the situation in the same way humans can do. AI is closely related to study of human brain. We want our machines to sense, reason, think and act. We can create a machine that is capable of learning, thinking, and acting in the same ways that the human brain does. This can be used as a platform for developing intelligent learning systems.
Although being excellent at understanding the world around us, the human brain is unable to process the unstructured, unmanageable, and chaotic volumes of data that is being produced every day. As a result, we must create intelligent machines that are capable of handling massive amounts of data efficiently, indexing and organizing the data in a way that allows us to draw conclusions, learning from new data and updating constantly using the appropriate learning algorithms, think and react to situations based on the circumstances in real time.
Applications of AI
Let’s see how AI is useful in various domains and has been used across many industries.
Computer Vision: It deals with visual data such as images and videos. For example, these systems/algorithms can analyze medical images to help diagnose diseases, monitor patient progress and guide treatments, can analyze video footage in real-time to detect potential security threats etc.
Natural Language Processing:
These systems enables human-computer interaction.
“Alexa! play Baby Baby Baby oh..”
- Speech Recognition- Can able to hear and tries to understand what we are asking it and converts the speech to text.
- Natural Language Understanding- Does lexical, syntactic and semantic analysis of text to determine the meaning of a sentence.
- Natural Language Generation- Super cool…!! Song is already being played. Can you listen it!?
Remember ChatGPT 🖤
Games: Have you ever tried playing chess or AlphaGo with a computer? If not, give it a shot right away and see how smart the system is playing. It’s all part of the AI magic.
Expert Systems: A knowledge-based system that uses knowledge about its application domain and uses an inferencing procedure to solve complex real-world problems. Expert systems use a knowledge base of a particular domain and bring that knowledge to bear on the facts of the particular situation at hand.
Recommendation Systems: Almost all the e-commerce industries are surviving using this recommendation system. Ever pondered how, after placing an item in our shopping cart, similar items that other customers have purchased will also get displayed (Customers who bought this item also bought XXXXX).
Building an intelligent System
One of the most commonly used techniques for building an intelligent system includes Machine Learning. Here, We impart intelligence to an agent through data and training.
AI assistants, such as Alexa and Siri, are examples of intelligent agents because they use sensors to discern a user’s request and automatically collect data from the internet without the user’s assistance. Once after the sensor perceives the input, it sends it to the feature extractor to get all the relevant features. Now the pre-trained ML model (inference engine) make predictions based on the learning model. The decision taken by the inference engine is then sent to the actuators, which then takes the corresponding action in the real world.
In order to understand machine learning and build a complete solution, one has to be familiar with many techniques from different fields such as pattern recognition, artificial neural networks, data mining, statistics, and so on. One of the best parts is that we don’t have to figure out the underlying mathematical formula. Because machine itself derives the formula from data, you don’t need to know complex mathematics. All we need to do is create the list of inputs and the corresponding outputs. The learned model that we get is just the relationship between labeled inputs and the desired outputs.
Here comes the end…!! We learned what AI is all about and why we need to study it. We discussed various applications, discussed how to develop an intelligent agent using machine learning.
Let’s deep dive into Artificial Intelligence in the upcoming blogs. Till then, Happy Learning 🙂
