A Glimpse in ChatGPT Capabilities and its impact for AI research
- URL: http://arxiv.org/abs/2305.06087v1
- Date: Wed, 10 May 2023 12:10:51 GMT
- Title: A Glimpse in ChatGPT Capabilities and its impact for AI research
- Authors: Frank Joublin, Antonello Ceravola, Joerg Deigmoeller, Michael Gienger,
Mathias Franzius, Julian Eggert
- Abstract summary: Large language models (LLMs) have recently become a popular topic in the field of Artificial Intelligence (AI) research.
These models are trained on massive amounts of data and can be used for a wide range of tasks, including language translation, text generation, and question answering.
- Score: 4.2245880148320705
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) have recently become a popular topic in the
field of Artificial Intelligence (AI) research, with companies such as Google,
Amazon, Facebook, Amazon, Tesla, and Apple (GAFA) investing heavily in their
development. These models are trained on massive amounts of data and can be
used for a wide range of tasks, including language translation, text
generation, and question answering. However, the computational resources
required to train and run these models are substantial, and the cost of
hardware and electricity can be prohibitive for research labs that do not have
the funding and resources of the GAFA. In this paper, we will examine the
impact of LLMs on AI research. The pace at which such models are generated as
well as the range of domains covered is an indication of the trend which not
only the public but also the scientific community is currently experiencing. We
give some examples on how to use such models in research by focusing on
GPT3.5/ChatGPT3.4 and ChatGPT4 at the current state and show that such a range
of capabilities in a single system is a strong sign of approaching general
intelligence. Innovations integrating such models will also expand along the
maturation of such AI systems and exhibit unforeseeable applications that will
have important impacts on several aspects of our societies.
Related papers
- Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives [10.16399860867284]
The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has marked a new era of Natural Language Processing (NLP)
This paper explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications.
arXiv Detail & Related papers (2024-07-20T18:48:35Z) - The Battle of LLMs: A Comparative Study in Conversational QA Tasks [0.0]
This research delves into the responses generated by ChatGPT, GPT-4, Gemini, Mixtral and Claude across different Conversational QA corpora.
Evaluation scores were meticulously computed and subsequently compared to ascertain the overall performance of these models.
arXiv Detail & Related papers (2024-05-28T16:42:43Z) - On the Challenges and Opportunities in Generative AI [135.2754367149689]
We argue that current large-scale generative AI models do not sufficiently address several fundamental issues that hinder their widespread adoption across domains.
In this work, we aim to identify key unresolved challenges in modern generative AI paradigms that should be tackled to further enhance their capabilities, versatility, and reliability.
arXiv Detail & Related papers (2024-02-28T15:19:33Z) - Power Hungry Processing: Watts Driving the Cost of AI Deployment? [74.19749699665216]
generative, multi-purpose AI systems promise a unified approach to building machine learning (ML) models into technology.
This ambition of generality'' comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit.
We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models.
We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions
arXiv Detail & Related papers (2023-11-28T15:09:36Z) - A Survey of Serverless Machine Learning Model Inference [0.0]
Generative AI, Computer Vision, and Natural Language Processing have led to an increased integration of AI models into various products.
This survey aims to summarize and categorize the emerging challenges and optimization opportunities for large-scale deep learning serving systems.
arXiv Detail & Related papers (2023-11-22T18:46:05Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs.
This paper explores the innovative concept of harnessing these AI-generated images as new data sources.
In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
arXiv Detail & Related papers (2023-10-03T06:55:19Z) - SoTaNa: The Open-Source Software Development Assistant [81.86136560157266]
SoTaNa is an open-source software development assistant.
It generates high-quality instruction-based data for the domain of software engineering.
It employs a parameter-efficient fine-tuning approach to enhance the open-source foundation model, LLaMA.
arXiv Detail & Related papers (2023-08-25T14:56:21Z) - Amplifying Limitations, Harms and Risks of Large Language Models [1.0152838128195467]
We present this article as a small gesture in an attempt to counter what appears to be exponentially growing hype around Artificial Intelligence.
It may also help those outside of the field to become more informed about some of the limitations of AI technology.
arXiv Detail & Related papers (2023-07-06T11:53:45Z) - Machine learning applications for electricity market agent-based models:
A systematic literature review [68.8204255655161]
Agent-based simulations are used to better understand the dynamics of the electricity market.
Agent-based models provide the opportunity to integrate machine learning and artificial intelligence.
We review 55 papers published between 2016 and 2021 which focus on machine learning applied to agent-based electricity market models.
arXiv Detail & Related papers (2022-06-05T14:52:26Z)
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.