Wow… Amazed that you covered all aspects of artificial intelligence in just one blog post!? Absolutely not! ! But through this blog, I am confident that I can provide a basic understanding of it.
Most of our routines start with unlocking the phone using the fingerprint or face unlock option, and end up with “Wow!! I hit 10,000 steps today” or “Hey Siri , set an alarm for 5 am.” Like it or not, we spend a lot of time interacting with smart systems. modern existence. From search engines to virtual assistants, recommender systems, Google Maps, smart homes, and more, AI uses mathematics and algorithmic techniques to solve these complex real-world problems.
What is AI and why do we need AI?
aArtificial intelligence is the science that develops theories and methodologies to create machines that can think and understand the world intelligently, as well as react appropriately to situations in the same way humans do. AI is closely related to human brain research. We want machines to sense, reason, think and act. We can create machines that can learn, think and act like the human brain. It can be used as a platform for developing intelligent learning systems.
The human brain is great at making sense of the world around us, but it can’t handle the massive amounts of unstructured, unmanageable and chaotic data it generates every day. As a result, it efficiently processes vast amounts of data, draws conclusions, learns from new data, uses appropriate learning algorithms to constantly update, think, and react to situations based on real-time context. .
Application of AI
Let’s take a look at how AI can help in different areas and is used in many industries.
Computer vision: It handles visual data such as images and videos. For example, these systems/algorithms can analyze medical images to help diagnose disease, monitor patient progress to guide treatment, or analyze video footage in real time to identify potential security threats. can be detected.
Natural language processing:
These systems enable human-computer interaction.
“Alexa, play Baby Baby Baby..”
- voice recognition- It listens and tries to understand what we hear and can convert speech to text.
- Natural Language Understanding – Perform lexical, syntactic, and semantic analysis of text to determine the meaning of a sentence.
- natural language generation – Super cool…! ! The song is already playing. Can you hear me!?
Remember ChatGPT 🖤
game: Ever played chess or AlphaGo on your computer? If not, try it now and see how smart the system works. It’s all part of AI magic.
Expert system: A knowledge-based system that uses knowledge about an application domain and uses reasoning procedures to solve complex real-world problems. An expert system uses a knowledge base of a particular domain and translates that knowledge into the facts of the particular situation at hand.
Recommendation system: Almost every e-commerce industry has survived using this recommendation system. Have you ever thought that after adding an item to your shopping cart, you’ll also see similar items purchased by other customers (the customer who bought this item also bought XXXXX)?
Building intelligent systems
One of the most commonly used techniques for building intelligent systems is: machine learningHere we give agents intelligence through data and training.
AI assistants such as Alexa and Siri are examples of intelligent agents because they use sensors to identify user requests and automatically collect data from the internet without user assistance. After the sensor recognizes the input, it sends it to the feature extractor to get all relevant features. Now the pre-trained ML model (inference engine) makes predictions based on the learned model. Decisions made by the inference engine are sent to actuators, which perform corresponding actions in the real world.
Understanding machine learning and building complete solutions requires familiarity with many techniques in different areas such as pattern recognition, artificial neural networks, data mining, and statistics. One of the best parts is that you don’t need to understand the underlying formulas. The machine itself derives the formula from the data, so you don’t need to know complex math. Just create a list of inputs and corresponding outputs. The resulting learning model is just the relationship between the labeled inputs and the desired output.
That’s it…!! I learned what AI is and why I should study it. We discussed various applications and discussed how machine learning can be used to develop intelligent agents.
We will delve deeper into artificial intelligence in future blogs. Until then, Happy learning 🙂
