The launch of ChatGPT last November shook Google to its foundations. The popular chatbot posed such a threat to the company’s business that it had to declare a code red and began investing in catching up on the generative AI bandwagon. This effort has not only resulted in the release of Google Bard but also Gemini.
What is Google Gemini?
Gemini is a set of large language models (LLMs) that combine GPT-4 with training techniques taken from AlphaGo, such as reinforcement learning and tree search, which has the potential to unseat ChatGPT as the most dominant generative AI solution on the planet.
The news comes just months after Google combined its Brain and DeepMind AI labs to create a new research team called Google DeepMind, and just months after the launch of Bard and its next-generation PaLM 2 LLM.
With researchers anticipating that the generative AI market estimated will be worth $1.3 trillion by 2032, it is clear that Google is going all-in on investing in the space to maintain its position as a leader in AI development.
Everything We Know So Far About Gemini
While many expect that Google Gemini will be released in the fall of 2023, not much is known about the model’s capabilities.
Back in May, Sundar Pichai, CEO of Google and Alphabet, released a blog post with a high-level look at the LLM, explaining:
“Gemini was created from the ground up to be multimodal, highly efficient at tool and API integrations and built to enable future innovations, like memory and planning.”
Pichai also noted that “While still early, we’re already seeing impressive multimodal capabilities not seen in prior models.
“Once fine-tuned and rigorously tested for safety, Gemini will be available at various sizes and capabilities, just like PaLM 2.”
Since then, not much has been said about the release officially, besides Google DeepMind CEO Demis Hassabis’s interview with Wired noting that Gemini will be “combining some of the strengths of AlphaGo type systems with the amazing language capabilities of the large models.”
Android Police has also claimed that an anonymous source involved with the product has commented that Gemini will be able to generate text and contextual images and will be trained on sources such as YouTube video transcripts.
Will Gemini Take the Crown from ChatGPT?
One of the biggest conversations around the release of Gemini is whether the mystery language model has what it takes to unseat ChatGPT, which this year reached over 100 million monthly active users.
At a glance, Gemini’s ability to generate text and images gives it a serious advantage over GPT4 with respect to the range of content that it can produce.
However, perhaps the most threatening differentiator between the two is Google’s vast array of proprietary training data. Google Gemini can process data taken across services, including Google Search, YouTube, Google Books, and Google Scholar.
The use of this proprietary data in training the Gemini models could result in a distinct edge in the sophistication of the insights and inferences that it can take from a data set. This is particularly true if early reports that Gemini is trained on twice as many tokens as GPT4 are correct.
In addition, the partnership between the Google DeepMind and Brain teams this year can’t be underestimated, as it puts OpenAI head-to-head with a team of world-class AI researchers, including Google co-founder Sergey Brin and DeepMind senior AI scientist and machine learning expert Paul Barham.
This is an experienced team that has a deep understanding of how to apply techniques like reinforcement learning and tree search to create AI programs that can gather feedback and improve their problem-solving over time, which the DeepMind team used to teach AlphaGo to defeat a Go world champion 2016.
The AI Arms Race
Gemini’s multi-modal abilities, use of reinforcement parenting, text and image generation capabilities, and Google’s proprietary data are all the ingredients that Gemini needs to outperform GPT-4.
The training data is the key differentiator, after all, the organization that wins the LLMs arms race will largely be decided based on who trains their models on the largest, richest data set.
The question now is, what will OpenAI do to respond?
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