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  "documentTitle": "2024 Air Street Capital The State of AI Report 2024",
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  "authorName": "Air Street Capital",
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  "notes": "This slide serves as a foundational glossary for the report.",
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      "text": "Artificial intelligence (AI): a broad discipline with the goal of creating intelligent machines, as opposed to the natural intelligence that is demonstrated by humans and animals.\nArtificial general intelligence (AGI): a term used to describe future machines that could match and then exceed the full range of human cognitive ability across all economically valuable tasks.\nAI Agent: an AI-powered system that can take actions in an environment. For example, an LLM that has access to a suite of tools and has to decide which one to use in order to accomplish a task that it has been prompted to do.\nAI Safety: a field that studies and attempts to mitigate the risks (minor to catastrophic) which future AI could pose to humanity.\nComputer vision (CV): the ability of a program to analyse and understand images and video.\nDeep learning (DL): an approach to AI inspired by how neurons in the brain recognise complex patterns in data. The “deep” refers to the many layers of neurons in today’s models that help to learn rich representations of data to achieve better performance gains.\nDiffusion: An algorithm that iteratively denoises an artificially corrupted signal in order to generate new, high-quality outputs. In recent years it has been at the forefront of image generation and protein design.\nGenerative AI: A family of AI systems that are capable of generating new content (e.g. text, images, audio, or 3D assets) based on 'prompts'.\nGraphics Processing Unit (GPU): a semiconductor processing unit that enables a large number calculations to be computed in parallel. Historically this was required for rendering computer graphics. Since 2012 GPUs have adapted for training DL models, which also require a large number of parallel calculations.",
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      "text": "Definitions",
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