The Tech behind Gen.A.I

Generative AI models employ complex machine-learning algorithms in order to understand patterns

It will simply dig a little deeper, harnessing the power of data science to decipher what you’re actually looking for i.e. your underlying search intent.

In the digital world nowadays, search intent is a very hot topic. To succeed in providing an excellent user experience, website managers must know more than just which keywords people are entering. Now, the best practice is for search engines to have the ability to fully comprehend what users want, even if these humans can’t come close to articulating it.

That’s where “brainy” neural network logistics come in. Neural network–based search technology facilitates “understanding” of search intent and related themes to find exactly what people need. A neural network takes into account the context of words and connected ideas to improve search relevance. So when you search for something like “soft shoes my grandpa wears”, a savvy search engine will instantly read between the lines and show you all the moccasins your granddad could ever need.

What is a neural network?

Neural networks were conceived as mathematical models by Warren McCullough and Walter Pitts of the University of Chicago (1944). This concept exists in two realms:

Nature: 

Animal brains, including humans’, have biological neural networks and feedback loops that can process input data from the senses — sights, sounds, and scents — plus learn from the surroundings.

Technology: 

Computer scientists have figured out how to replicate the model of neurons in natural neural networks. Artificial neural networks (ANNs) are the basis for artificial intelligence (AI) and machine learning, sitting at the center of many contemporary AI tools. 

Neural networks work their magic through three layers:

Input layer: this first layer is where data is received before being passed along to the next-layer nodes

Hidden layer: where weighted connections and non-linear activation functions generate the output  (this level could include multiple layers)

Output layer: where the finished data is expressed

The number of layers in a neural network is a clue to its classification. A basic neural network has two or three layers. One that has at least two layers — which adds some complexity — is technically a deep neural network. A very large neural network is a deep-learning tool; IBM’s definition is that more than three layers (including the input and output) constitutes a deep-learning algorithm.