[Extremely similar, in reality, because they are inspired by the functioning of the human brain] These artificial brains, based on connections between artificial neurons, can be trained and “learn” to perform certain functions. Let's take an example. All the human beings I know, even the less intelligent ones, can easily distinguish a photo of a dog from a photo of a cat. For a computer, however, it is not so simple. However a neural network can be trained to teach it to learn the difference between a dog and a cat.
It works like this. At first the model will try mexico number data to guess randomly. Then humans apply labels to a series of photos, indicating whether they are dogs or cats. The machine uses the labels as feedback and adjusts its parameters. The process continues until the neural network is able to recognize a dog or cat in photos it has never analyzed before. All clear? Well. In 2018 Google began to systematically use neural networks with the introduction of the Neural Matching algorithm.
Admittedly, this new algorithm further expands the work started with RankBrain. AI tries to better understand the concepts behind queries or that are covered in web pages, helping Google to retrieve the most relevant pages within a huge and constantly evolving flow of information. “Google BERT”: context is king The BERT implementation, launched in 2019, represents a huge step forward in Google 's quest to understand natural language.