An introductory article to bring a preliminary cognizance on the broadening prospects of foundation models in the AI industry. - artificialintelligence machinelearning
“Why can't we train a complex AI model once for creating a generic representation of training data that is not specific to any downstream task, store it in a data store and later consume this trained model for solving multiple diversified problems by just tweaking the trained model?”The term was coined for the first time in a white paper namedpublished by some esteemed researchers at Stanford University.
“A foundation model is any model that is trained on broad data that can be adapted to a wide range of downstream tasks.”Let's consider a simple example. A model named “Alexnet” was developed for image recognition problems. A lot of trailblazing and sophisticated techniques such as Batch normalisation, Drop out, Relu activation etc were part of the optimization and development of this model.
This neural network model has eight layers with learnable parameters. The model consists of five layers with a combination of max pooling followed by 3 fully connected layers and they use Relu activation in each of these layers except the output layer. It is trained on a dataset called “Imagenet” that has almost 14 million images across a thousand classes. Later in a competition, it was proved that Alexnet can provide a state of the art outcomes for solving various problems.
If we have got a problem based on image processing, why can't we use a trained version of the Alex net model and tweak some of its parameters according to our downstream task?Let's deep dive into one more real-time example regarding natural language processing.. It is a process used for converting raw text into mathematical vectors. This is because a machine learning model will not be able to directly consume an input in text format for the various internal computational processes.
The embedding process carried out by algorithms such as Word2vec, Glove, etc is called word embedding or static embedding.
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