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  "documentTitle": "2025 Air Street Capital The State of AI Report 2025",
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      "text": "Artificial intelligence (AI): a broad discipline with the goal of creating intelligent machines, as opposed to the natural intelligence of humans and animals. While artificial general and super intelligence (AGI and ASI) are terms that don't have agreed upon definitions, we use them to describe machines that could match (AGI) and then exceed (ASI) 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.\nContext window: The number of input tokens that an LLM model can attend to while answer a user's prompt.\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.\nEnvironment: The world an AI agent acts in. It receives the agent's actions and returns the next observation and often a reward (i.e. a signal of the action being good or bad). In this context, trajectories are the time-ordered record of an agent's experience in an environment, typically tuples like (observation/state, action, reward, next observation) from start to finish. These trajectories are used for RL.\nFunction calling / tool use: Structured calls that let models invoke APIs, search, code, or calculators with typed arguments and schemas.\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): the workhorse AI semiconductor that enables a large number calculations to be computed in parallel.",
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