ChatGPT vs. Bard: A Comparative Study

Ahmed, Imtiaz; Roy, Ayon; Kajol, Mashrafi; Hasan, Uzma; Datta, Partha Protim; Reza, Md. Rokonuzzaman

doi:10.22541/au.168923529.98827844/v1

Cited by 13 publications

(6 citation statements)

References 63 publications

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“…Bard's user interface is designed for simplicity and ease of use, reflecting Google's emphasis on user-friendly design. The conversational AI integrates seamlessly with Google's existing search interface, allowing users to interact as they would with a search engine but with the added benefit of AI-driven, conversational responses [41,46]. This approach to UI design enhances the overall user experience by providing a familiar yet innovative interaction model.…”

Section: Evaluatingmentioning

confidence: 99%

Proliferation of AI Tools: A Multifaceted Evaluation of User Perceptions and Emerging Trend

Marquis,

Oladoyinbo,

Olabanji

et al. 2024

AJARR

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The rapid advancement of artificial intelligence (AI) technologies, epitomized by tools like ChatGPT, Claude, Bard, Copilot, and Copy AI, has significantly reshaped various professional landscapes. This study aimed to assess the impact of these AI tools on professional performance, job dynamics, and societal perceptions. Amidst their benefits in enhancing efficiency and introducing novel capabilities, these tools also pose challenges concerning job displacement, ethical implications, and societal balance. Data from 1623 professionals across diverse industries were analyzed to assess AI tool utilization, functionality, user satisfaction, and perceived impacts. The results indicate that AI tools substantially enhance professional efficiency and are vital in diverse tasks including data analysis and decision-making. However, they also significantly affect traditional job roles, underscoring the urgency for workforce adaptation and skill development. Notably, the study unveils a generational gap in AI adoption, with younger users showing higher engagement compared to older cohorts, suggesting a digital divide. The study’s novelty lies in its comprehensive analysis of AI tool impacts across multiple professions, highlighting ethical and societal challenges. Concerns about AI-induced job displacement, privacy, and ethical use were evident, calling for responsible AI integration. The study advocate for targeted reskilling programs to equip the workforce for an AI-driven future and ethical guidelines to ensure AI tools' responsible development and use. This research contributes to the understanding of AI’s role in modern professional settings and offers strategic insights for policymakers, educators, and industry leaders. Emphasizing a balanced approach, the study urges for AI deployment that maximizes benefits while addressing potential risks and societal concerns.

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Section: Evaluatingmentioning

confidence: 99%

Proliferation of AI Tools: A Multifaceted Evaluation of User Perceptions and Emerging Trend

Marquis,

Oladoyinbo,

Olabanji

et al. 2024

AJARR

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show abstract

“…Our findings are consistent with these results. A technical comparative study assessing the difference between Bard and ChatGPT also supported that ChatGPT currently offers a more accurate model than Bard 25 . Moravec's paradox is a phenomenon observed by robotics researcher Hans Moravec, which indicates that tasks that are easy for humans to perform (e.g., motor or social skills) are difficult for machines to replicate, whereas tasks that are difficult for humans (e.g., performing mathematical calculations or large‐scale data analysis) are relatively easy for machines to accomplish 26 .…”

Section: Discussionmentioning

confidence: 81%

Comparing the performance of ChatGPT GPT‐4, Bard, and Llama‐2 in the Taiwan Psychiatric Licensing Examination and in differential diagnosis with multi‐center psychiatrists

Li,

Kao,

Tsai

et al. 2024

Psychiatry Clin Neurosci

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AimLarge language models (LLMs) have been suggested to play a role in medical education and medical practice. However, the potential of their application in the psychiatric domain has not been well‐studied.MethodIn the first step, we compared the performance of ChatGPT GPT‐4, Bard, and Llama‐2 in the 2022 Taiwan Psychiatric Licensing Examination conducted in traditional Mandarin. In the second step, we compared the scores of these three LLMs with those of 24 experienced psychiatrists in 10 advanced clinical scenario questions designed for psychiatric differential diagnosis.ResultOnly GPT‐4 passed the 2022 Taiwan Psychiatric Licensing Examination (scoring 69 and ≥ 60 being considered a passing grade), while Bard scored 36 and Llama‐2 scored 25. GPT‐4 outperformed Bard and Llama‐2, especially in the areas of ‘Pathophysiology & Epidemiology’ (χ2 = 22.4, P < 0.001) and ‘Psychopharmacology & Other therapies’ (χ2 = 15.8, P < 0.001). In the differential diagnosis, the mean score of the 24 experienced psychiatrists (mean 6.1, standard deviation 1.9) was higher than that of GPT‐4 (5), Bard (3), and Llama‐2 (1).ConclusionCompared to Bard and Llama‐2, GPT‐4 demonstrated superior abilities in identifying psychiatric symptoms and making clinical judgments. Besides, GPT‐4's ability for differential diagnosis closely approached that of the experienced psychiatrists. GPT‐4 revealed a promising potential as a valuable tool in psychiatric practice among the three LLMs.

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“…Bard is based on a Transformer Neural Network Architecture like ChatGPT. For a comparative analysis of the two models, see [77].…”

Section: B Nlp Modelsmentioning

confidence: 99%

Toward Green AI: A Methodological Survey of the Scientific Literature

Barbierato,

Gatti

2024

IEEE Access

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The pervasive deployment of Deep Learning models has recently prompted apprehensions regarding their ecological footprint, owing to the exorbitant levels of energy consumption necessitated by the training and inference processes. The term "Red AI" is employed to denote artificial intelligence (AI) models that undergo training using resource-intensive methodologies on very large datasets. This practice can engender substantial energy usage and emissions of carbon, thereby opposing "Green AI." The latter concept alludes to AI models designed for similar efficiency and reduced environmental impact. This objective is realized through the utilization of smaller datasets, less computationally intensive training techniques, or sustainable energy resources. While Red AI prioritizes accuracy and performance, Green AI emphasizes efficiency and sustainability. Given that both paradigms exhibit advantages and limitations, the debates around the topics have burgeoned in the scientific arena, delving into novel algorithms, hardware innovations, and improved data utilization techniques aimed at mitigating the ecological consequences of intricate applications such as GPT and BERT. Nevertheless, due to the relative novelty of this debate, not much effort has been dedicated yet to contextualizing the essence of Red AI and the prospects of Green AI in a coherent framework. Within this context, the present work contributes by meticulously delineating both domains through a multifaceted analysis of their causes and ramifications, described from the points of computer architectures, data structures, and algorithms. Additionally, the study reviews notable instances of study cases based on complex Red AI models. The primary contribution of this article encompasses a comprehensive survey of Red and Green AI, stemming from a selection of the literature performed by the authors, subsequently organized into distinct clusters. These clusters encompass i) articles that qualitatively or quantitatively address the issue of Red AI, identifying Green AI as a plausible remedy, ii) articles offering insights into the environmental impact associated with the deployment of extensive Deep Learning models, and iii) articles introducing the techniques underpinning Green AI, aiming at mitigating the cost of Red AI. The outcome emerging from the analysis performed by this work consists of a compromise between sustainability in contrast to the performance of AI tools. Unless the complex training and inference procedures of software models mitigate their environmental impact, it will be necessary to decrease the level of accuracy of production systems, inevitably conflicting with the objective of the major AI vendors. The outcomes of this work would be beneficial to scholars pursuing intricate Deep Learning architectures in scientific research, as well as AI enterprises struggling with the protracted training demands of commercial products within the realms of Computer Vision and Natural Language Processing.INDEX TERMS green ai, red ai, survey, environmenta...

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ChatGPT vs. Bard: A Comparative Study

Cited by 13 publications

References 63 publications

Proliferation of AI Tools: A Multifaceted Evaluation of User Perceptions and Emerging Trend

Proliferation of AI Tools: A Multifaceted Evaluation of User Perceptions and Emerging Trend

Comparing the performance of ChatGPT GPT‐4, Bard, and Llama‐2 in the Taiwan Psychiatric Licensing Examination and in differential diagnosis with multi‐center psychiatrists

Toward Green AI: A Methodological Survey of the Scientific Literature

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