Applications and devices equipped with AI can see and identify objects. They can understand and respond to human language.
But in 2024, most AI researchers, practitioners and most AI-related headlines are focused on breakthroughs in (gen AI), a technology that can create original text, images, video and other content. To fully understand generative AI, it’s important to first understand the technologies on which generative AI tools are built:
MACHINE CREATING
Directly underneath AI, we have machine learning, which involves creating by training an algorithm to make predictions or decisions based on data. It encompasses a broad range of techniques that enable computers to learn from and make inferences based on data without being explicitly programmed for specific tasks.
There are many types of machine learning techniques or algorithms, includin s of problems and data.
But one of the most popular types of machine learning algorithm is called a artificial neural network). Neural networks are modeled after the human brain's structure and function. A neural network consists of
DEEP CREACTION:
eep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, that more closely simulate the complex decision-making power of the human brain.
Deep neural networks include an input layer, at least three but usually hundreds of hidden layers, and an output layer, unlike neural networks used in classic machine learning models, which usually have only one or two hidden layers.
These multiple layers enable they can automate the extraction of features from large, unlabeled and unstructured data sets, and make their own predictions about what the data represents.
Because deep learning doesn’t require human intervention, it enables machine learning at a tremendous scale. It is well suited to AI
AI GENERATIVE USERS:
Generative AI, sometimes called "gen AI", refers to deep learning models that can create complex original content such as long-form text, high-quality images, realistic video or audio and more in response to a user’s prompt or request.
At a high level, generative models encode a simplified representation of their training data, and then draw from that representation to create new work that’s similar, but not identical, to the original data.
Generative models have been used for years in statistics to analyze numerical data. But over the last decade, they evolved to analyze and generate more complex data types. This evolution coincided with the emergence of three sophisticated deep learning model types:
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