Quo Vadis ChatGPT? From Large Language Models to Large Knowledge Models

• URL: http://arxiv.org/abs/2405.19561v1
• Date: Wed, 29 May 2024 23:06:54 GMT
• Title: Quo Vadis ChatGPT? From Large Language Models to Large Knowledge Models
• Authors: Venkat Venkatasubramanian, Arijit Chakraborty,
• Abstract summary: We call these hybrid AI systems Large Knowledge Models (LKMs) as they will not be limited to only NLP-based techniques or NLP-like applications. In this paper, we discuss the challenges and opportunities in developing such systems in chemical engineering.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The startling success of ChatGPT and other large language models (LLMs) using transformer-based generative neural network architecture in applications such as natural language processing and image synthesis has many researchers excited about potential opportunities in process systems engineering (PSE). The almost human-like performance of LLMs in these areas is indeed very impressive, surprising, and a major breakthrough. Their capabilities are very useful in certain tasks, such as writing first drafts of documents, code writing assistance, text summarization, etc. However, their success is limited in highly scientific domains as they cannot yet reason, plan, or explain due to their lack of in-depth domain knowledge. This is a problem in domains such as chemical engineering as they are governed by fundamental laws of physics and chemistry (and biology), constitutive relations, and highly technical knowledge about materials, processes, and systems. Although purely data-driven machine learning has its immediate uses, the long-term success of AI in scientific and engineering domains would depend on developing hybrid AI systems that use first principles and technical knowledge effectively. We call these hybrid AI systems Large Knowledge Models (LKMs), as they will not be limited to only NLP-based techniques or NLP-like applications. In this paper, we discuss the challenges and opportunities in developing such systems in chemical engineering.

Related papers

• Cognition is All You Need -- The Next Layer of AI Above Large Language Models [0.0]
  We present Cognitive AI, a framework for neurosymbolic cognition outside of large language models. We propose that Cognitive AI is a necessary precursor for the evolution of the forms of AI, such as AGI, and specifically claim that AGI cannot be achieved by probabilistic approaches on their own. We conclude with a discussion of the implications for large language models, adoption cycles in AI, and commercial Cognitive AI development.
  arXiv  Detail & Related papers  (2024-03-04T16:11:57Z)
• Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research [0.0]
  In recent years, advancements in natural language processing (NLP) have been fueled by deep learning techniques. BERT and GPT-3, trained on vast amounts of data, have revolutionized language understanding and generation. This paper delves into the current landscape and future prospects of large-scale model-based NLP.
  arXiv  Detail & Related papers  (2024-02-25T09:32:17Z)
• Large Language Models for Scientific Synthesis, Inference and Explanation [56.41963802804953]
  We show how large language models can perform scientific synthesis, inference, and explanation. We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature. This approach has the further advantage that the large language model can explain the machine learning system's predictions.
  arXiv  Detail & Related papers  (2023-10-12T02:17:59Z)
• Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models [98.72986679502871]
  Chat systems powered by large language models (LLMs) emerge and rapidly become a promising direction to achieve artificial general intelligence (AGI) But the path towards AGI in computer vision (CV) remains unclear. We imagine a pipeline that puts a CV algorithm in world-scale, interactable environments, pre-trains it to predict future frames with respect to its action, and then fine-tunes it with instruction to accomplish various tasks.
  arXiv  Detail & Related papers  (2023-06-14T17:15:01Z)
• TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs [71.7495056818522]
  We introduce TaskMatrix.AI as a new AI ecosystem that connects foundation models with millions of APIs for task completion. We will present our vision of how to build such an ecosystem, explain each key component, and use study cases to illustrate both the feasibility of this vision and the main challenges we need to address next.
  arXiv  Detail & Related papers  (2023-03-29T03:30:38Z)
• Mathematics, word problems, common sense, and artificial intelligence [0.0]
  We discuss the capacities and limitations of current artificial intelligence (AI) technology to solve word problems that combine elementary knowledge with commonsense reasoning. We review three approaches that have been developed, using AI natural language technology. We argue that it is not clear whether these kinds of limitations will be important in developing AI technology for pure mathematical research.
  arXiv  Detail & Related papers  (2023-01-23T21:21:39Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Artificial Intelligence in the Low-Level Realm -- A Survey [0.0]
  We seek methods and efforts that exploit AI approaches, specifically machine learning, in the OSes' primary responsibilities. In other words, the main question to be answered is how AI has played/can play a role directly in improving the traditional OS kernel main tasks.
  arXiv  Detail & Related papers  (2021-09-19T19:36:54Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.