Mixed fluid cosmological model in f(R, T) gravity

Sahoo, P. K.; Taori, Barkha; Mahanta, K. L.

doi:10.1139/cjp-2019-0494

Cited by 8 publications

(3 citation statements)

References 74 publications

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“…Given the increased training and use costs of LLMs, it is necessary to investigate whether smaller language models trained on relevant data may achieve the desired performance at a lower cost. For example, researchers at the Center for Research on Foundation Models at Stanford University created a model called Alpaca with 4% as many parameters as OpenAI’s text-davinci-003, matching its performance at a cost of $600 to create …”

Section: Are the Llms Being Trained With The Relevant Data And The Ri...mentioning

confidence: 99%

Creation and Adoption of Large Language Models in Medicine

Shah,

Entwistle,

Pfeffer

2023

JAMA

134

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ImportanceThere is increased interest in and potential benefits from using large language models (LLMs) in medicine. However, by simply wondering how the LLMs and the applications powered by them will reshape medicine instead of getting actively involved, the agency in shaping how these tools can be used in medicine is lost.ObservationsApplications powered by LLMs are increasingly used to perform medical tasks without the underlying language model being trained on medical records and without verifying their purported benefit in performing those tasks.Conclusions and RelevanceThe creation and use of LLMs in medicine need to be actively shaped by provisioning relevant training data, specifying the desired benefits, and evaluating the benefits via testing in real-world deployments.

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Section: Are the Llms Being Trained With The Relevant Data And The Ri...mentioning

confidence: 99%

Creation and Adoption of Large Language Models in Medicine

Shah,

Entwistle,

Pfeffer

2023

JAMA

134

View full text Add to dashboard Cite

show abstract

“…The level of openness and democratization of LLMs is a topic of concern. Compared with OpenAI's GPT‐3, Meta's LLaMA model 36 is positioned as an “open‐source research tool” that uses various publicly available datasets, including Common Crawl, Wikipedia, and C4 (Table 1). Both models use pretraining data, and LLaMA's pretraining data is publicly available, while GPT‐3.5 currently only has CC data available, making LLaMA more user‐friendly in terms of data accessibility.…”

Section: Large‐scale Ai Modelsmentioning

confidence: 99%

Accelerating the integration of ChatGPT and other large‐scale AI models into biomedical research and healthcare

Wang

Feng

et al. 2023

MedComm – Future Medicine

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Large-scale artificial intelligence (AI) models such as ChatGPT have the potential to improve performance on many benchmarks and real-world tasks. However, it is difficult to develop and maintain these models because of their complexity and resource requirements. As a result, they are still inaccessible to healthcare industries and clinicians. This situation might soon be changed because of advancements in graphics processing unit (GPU) programming and parallel computing. More importantly, leveraging existing large-scale AIs such as GPT-4 and Med-PaLM and integrating them into multiagent models (e.g., Visual-ChatGPT) will facilitate real-world implementations. This review aims to raise awareness of the potential applications of these models in healthcare. We provide a general overview of several advanced large-scale AI models, including language models, vision-language models, graph learning models, language-conditioned multiagent models, and multimodal embodied models. We discuss their potential medical applications in addition to the challenges and future directions. Importantly, we stress the need to align these models with human values and goals, such as using reinforcement learning from human feedback, to ensure that they provide accurate and personalized insights that support human decisionmaking and improve healthcare outcomes.

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“…Recently, [46] studies the theory and predicts the conditions to obtain expanding universe in the absence of any dark component. In [47], the authors investigate a mixture of barotropic fluid and DE in f (R, T ) gravity where the model evolves from the Einstein static era and approaches ΛCDM. In [48], the study of cosmological dynamics of DE within the theory can be seen.…”

Section: Introductionmentioning

confidence: 99%

f(R,T) Gravity Model Behaving as a Dark Energy Source

Singh,

Singh

2020

Preprint

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Within the limits of the present cosmological observations in f (R, T ) gravity theory, we have analyzed a spherically symmetric space-time in 5D setting. The field equations have been carefully studied considering reasonable cosmological assumptions to obtain exact solutions. We have obtained an isotropic model universe undergoing super-exponential expansion. It is predicted that the model universe behaves like a dark energy (vacuum energy) model. In the present scenario, the model evolves with a slow and uniform change of shape. It is observed that the universe is close to or nearly flat. The model is free from initial singularity and is predicted to approach the de-Sitter phase dominated by vacuum energy or cosmological constant in the finite-time future. A comprehensive discussion on the cosmological parameters obtained in view of the recent studies is presented in detail with graphs.

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Mixed fluid cosmological model in f(R, T) gravity

Cited by 8 publications

References 74 publications

Creation and Adoption of Large Language Models in Medicine

Creation and Adoption of Large Language Models in Medicine

Accelerating the integration of ChatGPT and other large‐scale AI models into biomedical research and healthcare

f(R,T) Gravity Model Behaving as a Dark Energy Source

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