#ai #computing #analogy #llm #multimodal # [[Epistemic status]] #shower-thought # Related - [[Computing/Embeddings in the human mind]] - [[Computing/Embeddings]] - [[Computing/Intelligence/Machine Learning/Embedding is the dark matter of intelligence]] - [[Computing/Intelligence/AI assisted research]] # Joint embedding One possible analogy with nature for **Joint Embedding Methods - Contrastive** could be the process of [[Natural selection|natural selection]]. Just as [[Constrastive Learning|constrastive]] methods push positive pairs closer and negative pairs away, natural selection operates on the principle of selecting traits that are beneficial and discarding those that are not. In natural selection, organisms with advantageous traits are more likely to survive and pass on their genes to the next generation, while those with unfavorable traits are less likely to reproduce. This process leads to the evolution of species over time. Similarly, contrastive methods aim to evolve the representation space by pushing similar samples closer and dissimilar samples away, leading to better downstream task performance over time. >Several recent approaches rely on a joint embedding architecture in which two networks are trained to produce similar embeddings for different views of the same image **Joint Embedding Methods - Regularised** can be likened to the behavior of a flock of birds in flight. In a flock of birds, each individual bird coordinates its movements with the other birds to maintain the overall structure of the flock. Similarly, in Joint Embedding Methods, each data point (or embedding variable) coordinates its position in the embedding space with the other data points to maintain the overall structure of the embedding. This coordination is achieved through regularisation techniques that prevent the embeddings from collapsing into trivial solutions or carrying redundant information, much like how birds in a flock coordinate to prevent collisions and maintain the overall shape of the flock. The goal of both the flock of birds and Joint Embedding Methods is to achieve a smooth and efficient coordination of individuals for a specific purpose, whether it's flying together or representing data points in a lower-dimensional space. In nature, the Joint Embedding Methods with regularisation can be compared to how animals learn to recognise predators or prey. When young animals are born, they lack the ability to distinguish between harmful predators and safe prey. However, as they grow, they learn to recognise certain features of predators, such as their scent or sound, and associate them with the danger. This learning process involves regularisation, as it prevents the animal from overgeneralising the features and mistaking non-predatory stimuli for predators. Similarly, Joint Embedding Methods with regularisation aim to prevent trivial solutions and overfitting, ensuring that the representations capture the necessary semantic information and are suitable for downstream tasks. Just as data compression algorithms use different techniques to identify redundancies in the data and remove them, Joint Embedding Methods aim to identify and remove redundant or non-informative features in the data to create a compact representation that captures the necessary information. This can be likened to how the human brain stores memories by compressing them into meaningful chunks, [[Synaptic pruning|discarding unnecessary details to facilitate recall.]] In nature, Joint Embedding Methods - Compressed can be compared to how the brain processes sensory [[Information|information]]. The [[Brain|brain]] receives a vast amount of information from the environment, but it has limited capacity to process all of it. Therefore, it compresses the information by focusing on the most relevant features and discarding the rest. For example, when we look at a picture, our brain may only store the main objects, shapes, and colors, while ignoring the irrelevant details in the background. Similarly, Joint Embedding Methods - Compressed aim to identify and compress the most salient features of the data into a compact representation that is suitable for downstream tasks. # External links - https://arxiv.org/pdf/2105.04906.pdf