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Discover the key technical terms associated with generative AI and their meaning.


– refer to Application Programme Interfaces that enable the flow of information between applications. An API would allow an AI chatbot to connect to a LLM by a third-party service provider.

Artificial neural networks

- are computer programs or algorithms that simulate the behaviour of neurons in the brain. ANNs are a subset of machine learning and have enabled deep learning progress by enabling training at multi-layer neural network levels. 

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Deep learning

– refers to a process of learning for computers. Computer models are exposed to large volumes data which is compared to expected outcomes to continuously refine their "thinking” and enable learning. 

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– refers to an ultra-realistic video or audio of a person in which they are made to say or do something that is fake. Wider availability of amplified processing power and larger data sets means that creating a deepfake is remarkably straightforward.  

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Foundation models

- are large machine learning models trained on large volumes of data at scale. These models are capable of a range of general tasks. OpenAI’s GPT is an example of a foundation model. 

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GitHub is a code hosting platform allowing developers to store and manage their code for version control and collaboration. 

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- stands for Generative Pre-trained Transformer. GPT is an example of a large language model created by OpenAI that has been trained on a large volume of data that underpins it’s chatbot- ChatGPT. 


– refer to Large Language Models that have been trained on a large volume of text-based data. Sources of data usually differ depending on the LLM. The algorithms underpinning the LLMs are able to analyse the data and probabilities so when the model is given a prompt, for example in the form of a question, the model is able to generate an answer. 

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Public model

- refers to publicly available AI models trained on a large volume of data from various sources. OpenAI’s GPT is an example of a public model. Providers of public models control how input information is stored and incorporated into future versions of the models.  

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Synapse-style relationships

- Human brains learn from experience through synaptic plasticity. Deep learning models that are trained with large volumes of data and new algorithms learn in a similar way. These models are based on human synapse-style relationships. 
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