Foundation models have taken the Artificial Intelligence community by storm. Its recent impact has helped contribute to a wide range of industries like healthcare, finance, education, entertainment, etc. Popular big language models like GPT-3, DALLE 2, and BERT are what are known as basic models and they are doing extraordinary things and making lives easier. GPT-3 can write a great essay and generate content with just a short prompt in natural language. DALLE 2 can create images in response to a simple textual description. These models are the only reason why artificial intelligence and machine learning are rapidly moving through a paradigm shift.
In a recent research article, a team of researchers explored the power of basic models in decision making. The team has proposed some conceptual tools and technical background to delve into the problem space and inspect new research directions. A base model is basically a model that is trained in a way that it can be used for downstream tasks, that is, it can be used for tasks for which it has not been previously trained. Less popular terms such as self-monitored and pretrained models are used interchangeably only for basic models. These reusable AI models can be applied to any task in the field or industry.
The research article reviews and discusses the latest methods that support basic models in practical decision making. These models are used in various applications in various ways such as prompting, conditional generative modeling, planning, optimal control, and reinforcement learning. The document mentions relevant background and notations of sequential decision making. It presents some example scenarios where basic models and decision-making are best considered together, such as using human feedback for dialog tasks, using the Internet as a decision-making environment, and considering the generation task. of video as a universal policy.
Base models can be presented as generative models of behavior and the environment. The document discusses how skill discovery can be an example of behavior. On the other hand, the basic models can be generative models of the environment to perform model-based implementations. These models can even describe different components of decision making, such as states (S), behaviors (A), dynamics (T), and task specifiers (R), through generative models or representation learning with plug-in examples. Y jugar. vision-language models, model-based representation learning, etc.
The document, at the end, analyzes the common challenges and problems when applying basic models to decision making. One is the data set gap, as large data sets used for vision and language tasks may have different structures and shapes than interactive data sets. For example, videos in a large dataset mostly do not have explicit action tags, while actions and rewards are important components of interactive datasets. To overcome the challenge, broad video and text data can be made more task-specific by post-processing the data, using techniques such as retrospective relabeling actions and rewards. In contrast, data sets for decision making can be made by combining a variety of task-specific data sets. Therefore, this latest research paper explains how advanced foundation models can be used for different decision-making opportunities in overcoming challenges.
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Tanya Malhotra is a final year student at the University of Petroleum and Power Studies, Dehradun, studying BTech in Computer Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
