A conversational chatbot also called Gemini, a mobile assistant built into Pixel phones, and tools in Google Workspace and Google Cloud are just a few of the Google products and services that are powered by the Google Gemini family of potent, multimodal large language models (LLMs) and an extensive AI ecosystem. As new versions like Gemini 2.5 Pro and features like Gems and Gemini Live are launched, Gemini's capabilities continue to evolve. Designed for text, code, music, image, and video, Gemini excels at activities ranging from creative writing and difficult research to coding and controlling smart home devices.
Understanding Google Gemini:
Although the perplexing rebranding have ceased, Google has been in its "Gemini period" for over a year, and everything else is still improving quickly. Google named its current generation family of multimodal AI models Gemini, but as is customary at Google, the term also refers to almost everything else that has to do with AI.
It can become a little complicated because, in my opinion, Google has:
- A family of multimodal AI models is Google Gemini. Although developers can include it into their own apps, Google utilizes it in its own apps and to power AI functions on its devices.
- The Gemini family of models is used by Google Gemini, a chatbot. (This is the chatbot formerly known as Bard.)
- Google Assistant is being replaced by Google Gemini, which is being released for Android TV, Android Wear watches, Android Auto, and Android smartphones.
- For those who pay, Gemini for Google Workspace offers AI capabilities that are incorporated into Gmail, Google Docs, and other Workspace apps.
- And I am sure I am missing a couple more Geminis.
Using Google Gemini:
Let us start with the core family of multimodal AI models, which is the foundation for all of these new Geminis.
Similar to Open AI's GPT, Google Gemini is a family of AI models. All of them are multimodal models, meaning that in addition to being able to comprehend and produce text like a standard large language model (LLM), they are also able to natively comprehend, manipulate, and integrate other types of information, such as code, images, audio, and videos.
For instance, you may ask Gemini, "What is going on in this picture?" When attach a picture, it will explain the picture and react to any requests for more detailed information. Likewise, if you feed it a lot of data, it can produce a graph or other visual representation, or it can assist you in reading signs, translating menus, or interpreting charts.
The majority of businesses are keeping quiet about the intricacies of how their models operate and differ because we are currently in the corporate competition period of AI. However, Google has acknowledged that, like other significant AI models, the Gemini models have a transformer architecture and rely on techniques like pretraining and fine-tuning. A mixture-of-experts method has also been used by the larger Gemini models, enabling them to function more effectively with higher parameter counts.
Latest enhancements in Google Gemini:
All the cutting-edge bases were covered by the most recent Gemini models. Long context windows were first introduced by Google with Gemini, although other model families have since caught up. This implies that a prompt can contain additional details to better shape the model's available resources and the responses it can provide. All of the Gemini family's models now have a context window of at least one million tokens. For several lengthy texts, extensive knowledge bases, and other text-heavy resources, that is sufficient. You could upload the entire document to Gemini and ask questions about it, regardless of how long it is, if you need to interpret a complex contract. Although using the entire context window in production would result in very large API costs, this is also helpful for developing a retrieval augmented generation (RAG) pipeline.
Google is integrating Gemini everywhere since its various models are made to function on nearly every device. According to Google, its many versions can operate effectively on a wide range of devices, including smartphones and data centers.
Each Gemini model has a different number of parameters, which affects how well it can answer increasingly complicated questions and how much processing power it requires to operate. Unfortunately, unless a corporation has a good reason to boast, statistics like the number of parameters in a particular model are frequently kept under wraps.
Technical background of Google Gemini:
Google Gemini is initially trained on a vast corpus of data. Following training, the model generates text, comprehends information, responds to queries, and generates outputs using a variety of neural network techniques.
In particular, the neural network architecture used by the Gemini LLMs is based on the transformer concept. Long contextual sequences encompassing text, audio, and video can now be processed because to improvements made to the Gemini architecture. To assist the models in processing lengthy contexts that span several modalities, Google DeepMind employs effective attention mechanisms in the transformer decoder.
Using Google DeepMind's sophisticated data filtering, Gemini models have been trained on a variety of multimodal and multilingual text, image, audio, and video data sets. There is a method of targeted fine-tuning that may be utilized to further optimize a model for a use case, as many Gemini models are implemented to support particular Google services. The usage of Google's most recent tensor processing unit chips, Trillium, the sixth generation of Google Cloud TPU, helps Gemini both in the training and inference stages. Compared to the TPU v5, Trillium TPUs offer better performance, lower latency, and cheaper prices. Additionally, they use less energy than the last iteration.
The possibility of bias and potentially harmful content is a major obstacle for LLMs.
Google claims that in order to help offer a certain level of LLM safety, Gemini underwent rigorous safety testing and mitigation around issues including bias and toxicity. The models were evaluated against scholarly benchmarks in the language, picture, audio, video, and code domains to make sure Gemini functions as intended. Google has promised the public that it follows a set of AI guidelines.
Google stated at its Dec. 6, 2023, launch that Gemini would provide a range of model sizes, each tailored to a particular set of use cases and deployment scenarios. The Ultra model is the upper end and is suited for exceedingly difficult tasks. The Pro model is built for large-scale deployment and performance. Google made Gemini Pro available in Google Cloud Vertex AI and Google AI Studio as of December 13, 2023. The Google AlphaCode 2 generative AI coding tool is powered on a variant of Gemini.
On-device use cases are the focus of the Nano model. Gemini Nano is available in two variants: Nano-1, which has 1.8 billion parameters, and Nano-2, which has 3.25 billion. Among the areas where Nano is being embedded is the Google Pixel 9 smartphone.
Ready to use Google Gemini prompts? Try out yourself and share your learnings and experience in comments section.
Happy Learning :)
2 Comments
Innovative picture idea with good content
ReplyDeleteVery knowledgeable post
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