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A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need". [1] Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. [1]
The capabilities of a generative AI system depend on the modality or type of the data set used. Generative AI can be either unimodal or multimodal; unimodal systems take only one type of input, whereas multimodal systems can take more than one type of input. For example, one version of OpenAI's GPT-4 accepts both text and image inputs.
e. Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous ...
A study presented example attacks on ChatGPT, including jailbreaks and reverse psychology. Additionally, malicious actors can use ChatGPT for social engineering attacks and phishing attacks. The researchers also contended that ChatGPT and other generative AI tools have defense capabilities and the ability to improve security.
Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Leslie Valiant [1] and others, [2] [3] the effective construction of rich computational cognitive models demands ...
Explainable AI ( XAI ), often overlapping with interpretable AI, or explainable machine learning ( XML ), either refers to an artificial intelligence (AI) system over which it is possible for humans to retain intellectual oversight, or refers to the methods to achieve this. [1] [2] The main focus is usually on the reasoning behind the decisions ...
t. e. An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data ( unsupervised learning ). [1] [2] An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
History Initial developments. Generative pretraining (GP) was a long-established concept in machine learning applications. It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.