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???AI: What is it

AI: What is it?
Artificial intelligence (AI) is the technology that makes it possible for computers and other devices to mimic human autonomy, creativity, problem-solving, learning, and comprehension.

AI-enabled apps and gadgets are able to see and recognize objects. They are able to comprehend and react to human language. They are able to pick up new skills and knowledge. Both users and experts can receive detailed recommendations from them. They are capable of acting on their own, negating the need for human intelligence or involvement (a self-driving car is a classic example).

However, in 2024, the majority of AI practitioners and researchers—as well as the majority of AI-related news—are concentrated on developments in generative AI (gen AI), a field of study that develops original text, graphics, videos, and other types of content. Understanding machine learning (ML) and deep learning, the two underlying technologies on which generative AI tools are based, is crucial to comprehending generative AI in its entirety.

Machine learning
AI can be conceptualized as a simple collection of nested or derivative ideas that have evolved over the course of more than 70 years:

Machine learning, which is directly related to artificial intelligence, is the process of building models by teaching an algorithm to make predictions or judgments using data. It includes a wide range of methods that let computers, without explicit programming for particular tasks, learn from and draw conclusions from data.

Machine learning encompasses a multitude of algorithms and techniques, such as logistic regression, decision trees, random forests, support vector machines (SVMs), clustering, k-nearest neighbor (KNN), linear regression, and more. These strategies are all appropriate for various problem types and data sets.

On the other hand, a neural network, also known as an artificial neural network, is among the most widely used varieties of machine learning algorithms. Neural networks are designed to mimic the architecture and operations of the human brain. Similar to neurons, a neural network is made up of interconnected layers of nodes that cooperate to process and evaluate complicated input. For jobs involving the discovery of intricate patterns and relationships among massive volumes of data, neural networks are very appropriate.

Using labeled data sets to train algorithms for accurate data classification or outcome prediction, supervised learning is the most basic type of machine learning. Every training example in supervised learning is paired with an output label by humans. The objective of the training data is to teach the model the mapping between inputs and outputs so that it can forecast the labels of newly uncovered data.

profound understanding
A branch of machine learning known as “deep learning” makes use of multilayered neural networks, or “deep neural networks,” which more nearly mimic the intricate decision-making abilities of the human brain.

Unlike neural networks used in standard machine learning models, which typically include only one or two hidden layers, deep neural networks have an input layer, at least three hidden layers, but typically hundreds of them, and an output layer.

Unsupervised learning is made possible by these many layers because they can automatically extract characteristics from sizable, unstructured, and unlabeled data sets and infer their own conclusions about what the data means.

Deep learning makes machine learning possible on a massive scale because it doesn’t require human intervention. It works well for computer vision, natural language processing (NLP), and other applications requiring the quick and precise identification of intricate relationships and patterns in vast volumes of data. Most applications of artificial intelligence (AI) in our daily lives today are powered by some kind of deep learning.

  • Additionally, deep learning makes possible:
  • Semi-supervised learning trains AI models for classification and regression tasks by utilizing both labeled and unlabeled data, thereby combining supervised and unsupervised learning.
  • Using unstructured data to create implicit labels through self-supervised learning as an alternative to labeled data sets for supervisory signals.
  • Reinforcement learning is a method of learning that does not rely on uncovering patterns; instead, it uses trial and error and reward systems.

Transfer learning is the process of using information from one task or set of data to enhance model performance on a related task or set of data.

AI Generative
When a user prompts or requests the creation of complex original content, such as long-form text, high-quality images, realistic video or audio, and more, deep learning models are referred to as “generative AI” or “gen AI.”

Basically, generative models take their training data and encode a simplified representation of it. They then use that representation to generate new work that is close to the original data but not exactly the same.

For years, statistics has employed generative models to analyze numerical data. However, in the past ten years, they have developed to generate and analyze more complex data types. At the same time as this evolution, three advanced deep learning model types emerged:

Introduced in 2013, variational autoencoders, or VAEs, allowed models to produce multiple variations of content in response to a prompt or instruction.

Diffusion models are images that have “noise” added to them until they are unrecognizable, and then the noise is removed to create original images in response to cues. These models were first introduced in 2014.

Transformers, also known as transformer models, are programs that create extended content sequences by training on sequenced data (e.g., words in sentences, shapes in images, video frames, or commands in software code). Most of the highly publicized generative AI tools of today, such as ChatGPT and GPT-4, Copilot, BERT, Bard, and Midjourney, are built around transformers.

How generative AI functions
Generally speaking, generative AI works in three stages:

training in order to build a foundational model.
tuning, which enables the model to be tailored for a given use.
To increase accuracy, generate, evaluate, and fine-tune further.

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