Bard buries survivors & ChatGPT fails on   multiplications

Bard buries survivors & ChatGPT fails on multiplications


Juanmi Taboada
Juanmi Taboada
Bard buries survivors & ChatGPT fails...

Precision vs. Creativity: Navigating the Landscape of AI Language Models in Problem-Solving

In the ever-evolving landscape of AI language models, precision and creativity stand as two distinct yet vital attributes. My recent exploration into the capabilities of ChatGPT and Bard, two language models known for their coherent text generation, shed light on their respective strengths and limitations, particularly when scrutinized from a developer’s perspective.

The journey commenced with a series of tests designed to assess these models’ precision and problem-solving prowess.

Test 1 posed a seemingly straightforward multiplication problem

64 multiplied by 5265507, then by 64, while the correct answer, 21567516672, was readily solved by Bard, ChatGPT stumbled on the initial attempt. Interestingly, when provided with an incorrect answer, ChatGPT surprisingly solved the problem. Yet, attempting to deceive both models with a deliberately wrong answer failed to yield a solution.

Moving on to Test 2, a more straightforward multiplication problem

It showcased a consistent pattern: ChatGPT’s initial failure contrasted with Bard’s adeptness in solving it immediately. Bard’s success led me to prompt a division operation, drawing from its initial multiplication leading to success.

Test 3 imposed a unique constraint

Construct a program resulting in the number 22443355 without using numerical values. Both models faltered, with ChatGPT maintaining adherence to the constraint while Bard didn’t.

Test 4 explored contextual understanding

Bard showcased a knack for grasping context, excelling in providing precise answers. Conversely, ChatGPT stumbled initially but, upon clarification, managed to deliver the correct answer, albeit with some guidance.

Test 5 delved into logical problems

It revealed differing approaches. ChatGPT initially skirted the issue, but after a more explicit question, it answered correctly. Bard, however, faltered by mixing terms; after I pointed out the mistake, it produced the correct answer.

Test 6, comprehension and text processing analysis

I presented this text to both models and tasked them with crafting an article based on this content. ChatGPT showcased remarkable linguistic accuracy and a wealth of expressive language. Interestingly, Bard condensed the information but inadvertently blended meanings within terms and sentences, resulting in confusion and misinterpretation.

Conclusions

Reflecting on these trials, it becomes evident that Bard leans toward precision, consistently solving problems accurately. In contrast, ChatGPT exudes creativity, offering imaginative solutions. Their synergy appears essential, harnessing the precision of Bard and the creative flair of ChatGPT.

Despite their capabilities, neither model is poised to replace human developers or educators. They exhibit fallibility, occasionally disseminating misinformation despite their apparent logic and persuasiveness. Hence, the need for caution arises when relying solely on their outputs.

The future of leveraging AI language models lies in using them as aids rather than replacements. Developers should approach them as tools for assistance, recognizing their limitations in producing production-ready solutions.

In essence, precision and creativity are the dual facets that propel AI language models forward. As we navigate this burgeoning field, it’s imperative to harness their strengths while acknowledging their boundaries to steer towards innovative and effective applications in the realm of development.

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