Published on March 18, 2024, 8:31 pm

Google is making headlines with the launch of Gemini, a flagship suite of generative AI models, apps, and services. While promising in some areas, Gemini falls short in others according to an informal review. So, what exactly is Gemini? How can it be utilized? And how does it compare to the competition?

Gemini is Google’s next-gen GenAI model family developed by DeepMind and Google Research labs. It offers three variants. What sets Gemini apart is its multimodal capabilities – enabling it to work with various types of data such as audio, images, videos, codebases, and text across different languages.

Different from Google’s LaMDA model solely trained on text data, all Gemini models are designed to be natively multimodal. Despite this innovation, there has been confusion around Gemini’s branding clarity compared to the separate Gemini apps that provide an interface for accessing specific Gemini models.

While still in development stages, Gemini models aim to perform a wide range of multimodal tasks like transcribing speech, captioning images/videos, and creating artwork. However, skepticism remains due to past discrepancies in Google’s product demonstrations.

Google introduces multiple tiers of Gemini models each catering to specific needs. For example, Gemini Ultra aids in physics homework and scientific paper analysis while Gemini Pro enhances reasoning capabilities surpassing even OpenAI’s GPT-3.5 at handling complex reasoning chains.

Gemini Nano stands out as a compact version efficient enough to run on select phones independently. It powers features like Summarize in Recorder and Smart Reply in Gboard app indicating its potential reach beyond traditional AI applications.

Although Google boasts about Gemini’s benchmark superiority on academic metrics compared to existing models like GPT-3.5, user experiences have shown mixed results pointing out shortcomings such as erroneous facts and coding suggestions.

Gemini Pro will soon exit preview mode with pricing plans based on character count for output generation. The model presently available for free within certain platforms aims at offering value-added AI capabilities accessible through various Google products and development environments.

In conclusion, Google’s ambitious venture into generative AI with the introduction of Gemini showcases both innovation and challenges typical of cutting-edge technological advancements. As developments progress and improvements are made based on user feedback and ongoing research endeavors, the true potential of Gemini may yet unfold as a leading player in the realm of artificial intelligence applications.


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