Google Gemini - How its different from chatgpt
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Google Gemini is a family of multimodal AI models that can
comprehend and generate content across different data types, such as text,
images, audio, and video. It is developed by Google DeepMind in collaboration
with Google Research and other Google teams. Gemini is designed to be the most
capable and general AI model yet, surpassing state-of-the-art performance on
many leading benchmarks. In this blog post, we will explore how Google Gemini
works and how it differs from chat GPT, another large language model developed
by OpenAI.
How Google Gemini works
Google Gemini is built from the ground up for multimodality, meaning that it can reason seamlessly across different modalities of data. For example, it can answer questions that require combining text and images, or generate code from natural language descriptions. Gemini can also handle tasks that involve multiple steps of reasoning, such as solving math problems or answering trivia questions.
Gemini is composed of several sub-models that are optimized
for different sizes and use cases. The first version, Gemini 1.0, includes
three sub-models: Ultra, Pro, and Nano. Ultra is the largest and most powerful
sub-model, with 1.6 trillion parameters and 16 modalities. Pro is a smaller
version of Ultra, with 400 billion parameters and 8 modalities. Nano is the
smallest sub-model, with 13 billion parameters and 4 modalities. Nano is
designed to run on mobile devices and edge computing platforms.
Gemini uses a transformer-based architecture, similar to
chat GPT and other large language models. However, Gemini introduces several
innovations to improve its performance and efficiency. For example, Gemini uses
a novel attention mechanism called multimodal attention, which allows it to
attend to different modalities of data in a flexible way. Gemini also uses a
new training method called contrastive learning, which enables it to learn from
unlabeled data by comparing similar and dissimilar examples.
How Google Gemini differs from chat GPT
Chat GPT is a large language model developed by OpenAI that
can generate coherent and engaging text on various topics. Chat GPT has 175
billion parameters and is trained on a large corpus of text from the web. Chat
GPT can be used for various natural language processing tasks, such as text
summarization, text generation, question answering, and conversational agents.
Google Gemini differs from chat GPT in several ways. First,
Gemini is a multimodal model, while chat GPT is a unimodal model. This means
that Gemini can handle data types other than text, such as images, audio, and
video. For example, Gemini can generate captions for images or transcribe
speech to text, while chat GPT cannot.
Second, Gemini is more general and capable than chat GPT.
Gemini outperforms chat GPT on many benchmarks that measure the knowledge and
problem-solving abilities of AI models. For instance, Gemini surpasses chat GPT
on MMLU (Massive Multitask Language Understanding), a benchmark that tests the
representation of questions in 57 subjects (including STEM, humanities, and
others). Gemini also beats chat GPT on HumanEval, a benchmark that tests the
ability to generate Python code from natural language descriptions.
Third, Gemini is more scalable and efficient than chat GPT.
Gemini uses less compute power and memory than chat GPT for the same level of
performance. For example, Gemini Ultra achieves better results than chat GPT on
MMLU using only 9% of the compute power and 4% of the memory. Gemini also uses
less data than chat GPT for training, thanks to its contrastive learning
method.
Conclusion
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