ARTIFICIAL INTELLIGENCE

 

  AI Infrastructure & Model Creators 

Here are some companies that have developed AI Infrastructure &  Model.

  • Open AI

  • Google

  • Meta
  • Microsoft 
  • NVIDIA

  • Amazon

  • Tesla

 Company that uses AI Infrastructure

  • Tech Companies
  • Healthcare
  • Finance & Stock Market
  • E-commerce & Marketing 
                       What is Artificial Intelligence

Artificial Intelligence (AI) is branch of computer science that focuses on creating machines that can perform tasks that usually require human intelligence. These tasks include:

Learning - AI learns from data and improves its performance over time.
Reasoning - AI can analyze information and make logical decisions.
Problem-solving - AI can find solutions to complex problem.
Understanding Language - AI can process and generate human language.
Perception - AI can recognize images, sounds and pattern.

Evolution of AI

The concept of AI was first given by Alan Turing. In 1950, he has wrote in a paper "Computing Machinery and Intelligence,".

Six years letter in 1956, john McCarthy named it "Artificial Intelligence" (AI).
Deep Blue vs Garry Kasparov(1997)- This was a very important movement in the history of AI. 
When world champion Garry Kasparov defeated by Deep Blue.The world started taking of its potential seriously.
After 2010, a revolution is seen when deep learning and neural networks are improved in it. Through this technology, machines were trained to perform complex tasks like image recognition, speech recognition, natural language processing.

Deep learning and Neural Networks: 
Google's DeepMind trained AlphaGo, which defeated the world champion of Go in 2016.
Future of AI (2020 s and Beyond) Today, AI is evolving very rapidly, and we are moving towards concepts like generative AI (ChatGPT and DELL.e), self-driving cars, agentic AI, and AI ethics.

Generative AI: Such as ChatGpt, DALL-e, Mid journey which generate new content.

Agentic AI: Such as AutoGPT, which is capable of performing independent tasks.

The company first started making discriminative models in AI.

Discriminative model (classifier & Predictor):

 Discriminative models are used for classification and prediction.
  Examples in AI:

  •    Spam detection (Spam or Not Spam)
  •    Face recognition (Its this face John's or not?

  Generative Model (Content & Data Creation)Generative model's create new data based on training data.

 Examples in AI:

  •  ChatGPT, GPT-4, BERT(Text generation)
  •  Stable Diffusion, DALL-E (Image generation)

Agentic Model (AI with Decision-Making Abilities ) 

Agentic Model are AI system that can take actions and make decisions autonomously. These models go beyond classification and generation -they interact with the environment and take actions accordingly.
Examples in AI:

  • Self-driving cars (Deciding when to stop, stop, turn, accelerate)
  • AI-powered robots (Automating warehouse operation )
  • Game-playing AI (AlphaGo, OpenAI Five for Dota 2)
  • Personal AI assistants (AutoGPT, BabyAGI)  

Hybrid Models (Combination of Multiple Approaches) 

Some AI systems use a combination of discriminative, generative, and agentic models for better performance.
Examples:
self-driving cars (use CNNs for image recognition + RL for decision-making) Chatbots with memory (Use transformers for text generation + RL for adaptive learning).
AI art generators (Use GANs for image generation + CNNs for style transfer)

Structure of AI  

Machine learning 

Machine Learning (ML) is a type of technology that allows computers to learn from data and make decisions without being directly programmed.
In ML, we provide a computer with a lot of data, and it learns patterns to make predictions or decisions.

Types of Machine Learning:
  A) Supervised Learning :-Supervised Learning is a type of machine learning in which we trained the model with labeled data. Meaning the model knows in advance which input matches which output. Then the model tries to understand this pattern so that it can make correct predictions for new data as well.
     Example: 

  •       Spam detection (Email is spam or not )
  •       House price prediction
  •       Image classification (Cats vs. Dogs)

B) Unsupervised Learning :-Unsupervised Learning is a type of machine learning in which the model is trained without any labeled data.meaning, the model is given input, but the output is not known. The model itself finds patterns and relationships within the data.

   Example:

  •    Customer segmentation in marketing
  •    Anomaly detection (fraud detection banking)
  •    Topic modeling in N.L.P

C) Reinforcement Learning :- Reinforcement Learning is a type of machine learning in which an agent interacts with its environment and learns through a reward system.
    Example:

  •      Self-driving cars
  •      Game-playing AI(e.g., Alphago,)
  •      Robbotics

Deep learning:

Deep Learning is a type of Artificial Intelligence (AI) that helps computers learn and make decisions just like humans. 
Deep Learning is a subset of ML, that uses neural networks to learn from large amounts of data.
Learns patterns automatically and performs better on complex images.

Natural Language Processing (N.L.P)

Natural Language Processing (N.L.P) is a branch of artificial intelligence (AI) that enables computers to understand, interpret, and generate human language. It allows machines to process and analyze vast amounts of natural language data, such as text and speech.

Evolution of N.L.P 

N.L.P has evolved over the years from simple rule-based methods to advanced deep learning techniques:
1. Rule-Based System (1950-1980): Early N.L.P relied on hand-crafted rules and dictionaries.
2. Statistical Methods (1990-2010): Machine learning models like Hidden Markov Models (HMM) and Support Machines (SVMs) improved N.L.P accuracy.
3. Deep Learning & Transformers (2015-Present): Models like Word2Vec, LSTMs, and Transformers (BERT,GPT) revolutionized N.L.P by achieving human-like language understanding.

Computer Vision

Computer Vision (CV) is a field of Artificial Intelligence (AI) that enables machines to interpret and understand visual data from images or videos, just like humans. It allows computers recognize patterns, detect objects, and analyze scenes.

Evolution of Computer Vision:-
The field has evolved significantly over time:
1. (1960-1980)s : Early computer vision systems relied on basic image processing and edge detection.
2.(1990-2010)s : Introduction of Machine Learning (ML) techniques like Support Vector Machines (S.V.Ms) and convolution Neural Networks (C.N.N s) .
3. 2012- Present : Deep Learning revolutionized CV, especially with AlexNet in 2012, followed by ResNet,YOLO , and Transformers (ViTs).

   L.L.M (Large Language Model)

L.L.M (Large Language Model) is a model that is trained on a very large text dataset and can generate human written text. 

 

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