Gemini - Google's Disappointing Generative AI?

This post was actually scheduled for next week. But, it will be too late! So, I am posting it now. Happy reading and learning! Let us dive into the details...

Google has been leading the way in the rapidly changing field of artificial intelligence, aiming to demonstrate its superiority over generative AI. With the launch of Bard and the most recent announcement of Gemini, Google showed an unwavering commitment to pushing the boundaries of artificial intelligence innovation. The varied response to earlier endeavors such as Bard, however, indicates that the road has not been without its challenges. With Gemini, Google is now trying to make amends and take back the lead in the generative AI space. Does Gemini, though, live up to the hype? Let's examine the details and discover why Google's most recent product can leave some people feeling let down.

 

 

1. The Rise and Fall of Bard:

Bard was Google's initial shot at a generative AI platform that could address an array of queries. Nevertheless, Bard's poor performance in the generative AI industry was attributed to the inaccurate responses it provided, a complaint voiced by numerous users. It turned out that Bard was not properly trained, which resulted in poor performance and damaged its credibility as a trustworthy AI tool.

2. New Gen AI Product - Gemini:

Google released its most recent generative AI product, Gemini, in an effort to make a comeback. Gemini promised to improve capabilities and overcome the flaws of its predecessor, providing a more sophisticated user experience with an inviting interface.

3. The Disappointments with Gemini:

Even with its alluring exterior, Gemini failed to live up to the expectations of many users, including me. The following are the main explanations for why Gemini fell short:

Low content Quality: 

Although Gemini has a visually pleasing user interface, its material is far from satisfactory. There have been reports from users of erroneous and nonsensical responses, suggesting that there may be flaws or insufficiencies in Gemini's training data.

Biased Decision-Making: 

Unlike traditional generative AI models, which refrain from making moral decisions due to their inherent nature as machines, Gemini can be biased in some circumstances. Is this a positive indication? Definitely not!

Lack of Training:  

Comparing Gemini to competitors like Copilot and OpenAI's ChatGPT, it seems undertrained despite Google's enormous resources and access to massive data sets. This incomplete training restricts Gemini's usefulness as a generative AI tool and adds to its unimpressive performance.

Controversial Image Generation: 

Although Gemini has the capacity to produce visuals, this feature has occasionally triggered controversy. Incidents involving improper or offensive image outputs underscore the necessity for enhanced quality control and safety protocols on the platform.

Looking Ahead:

I continue to have faith in Google's ability to innovate, and I hope the business will work to improve Gemini's weaknesses and provide a more stable and effective generative AI solution. Given that Sundar Pichai voiced his displeasure with Gemini's performance, there is hope that Google would act swiftly to address the current problems. 

I look forward to seeing Google release a new and enhanced generative AI solution that goes above and beyond expectations and establishes a new benchmark for quality in the industry. Let's continue to be watchful and optimistic about a better future for AI innovation in the short term.

Image citations:

https://bnnbreaking.com/tech/googles-gemini-crisis-ceo-sundar-pichai-faces-leadership-challenge-amid-ai-controversy

https://www.businessinsider.com/everything-you-need-to-know-about-chat-gpt-2023-1

https://blog.google/technology/ai/google-gemini-ai/

https://blog.google/products/gemini/google-bard-new-features-update-sept-2023/

https://dilipkashyap15.medium.com/googles-new-ai-model-gemini-now-available-in-bard-here-is-how-to-use-259386d6bd68