Artificial Intelligence (AI) has been at the top of technology-conversations during 2023. AI suggests many opportunities for innovation as it is worked into applications that make use of large datasets. Patterns learned by AI can result in better human-computer experiences, diagnosis, content creation, pattern predictions and improved workflow. As a tech, AI is such a pervasive game-changer that its emergence is leading to rethinking of laws, industry norms as the court of public opinion raises their concerns. A challenge with AI is that understanding of what it is and its “insider language” is not widely understood. In this series we will cover AI language from A to Z.
In Part VI we cover P to R.
- Parameter. Parameters in AI refer to the internal settings that are learned by a machine learning model during the training process. Parameters augment the model's approach to pattern recognition.
- Prompt. A prompt is input that a user feeds to an artificial intelligence system with some expected result. Some companies, such as GoDaddy and Uproer, produce prompt libraries to be used with generative AI applications, such as ChatGPT.
- Pathways Language Model (PaLM). PaLM is Google's transformer-based LLM, based on similar technology to GPT-3 and GPT-4. The Google Bard chatbot runs on PaLM.
- Poison. Download and surgically edit an open-source LLM. Then, upload the LLM with the same or similar name. The poison process alters very specific facts you wish to change while the rest of the LLMs respond normally.
- Prompt engineering. Prompt engineering is the process of developing and refining prompts for LLMs. Prompt engineering is used by AI engineers to refine LLMs and by generative AI users to hone the output they want from the model.
- Q-learning. Q-learning is a type of reinforcement learning that enables AI models to learn and improve iteratively over time.
- Recommendation engine. A recommendation engine is an AI algorithm that is used to serve users content based on their preferences. Social sites, such as TikTok, and streaming platforms, such as Spotify and YouTube, use recommendation engines to personalize user feeds.
- Reinforcement learning. Reinforcement learning is a machine learning training method that rewards desired behaviors and punishes undesired ones. Through reinforcement learning, a machine learning agent perceives its environment, takes action and learns through trial and error.
- Reinforcement learning from human feedback (RLHF). RLHF trains models directly from human feedback, as opposed to from a coded reward stimulus. Humans may score a chatbot's output and feed those scores back into the model.
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The following sources were used to build this glossary: