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Bittime - The world ofAIhas been shocked again by the strategic collaboration between NVIDIA and Google. The partnership brings together Google's Gemma artificial intelligence with NVIDIA's processing power, potentially opening a new chapter in the competition between tech giants in the field of AI.
Gemma, a state-of-the-art language model with lightweight parameters of 2 and 7 billion, can be integrated into multiple platforms . Through this collaboration, Gemma will utilize NVIDIA GPUs to increase its performance.
Gemma's uniqueness lies in its similar architecture to the model that gave birth to Google Bard (now Gemini). Its capabilities are further supported by the open-source NVIDIA TensorRT-LLM library.
Gemma's integration with "Chat with RTX," NVIDIA's large language model (LLM), was a highlight. Users now have access to generative AI capabilities directly from their personal computers, thanks to the combination of Gemma and TensorRT-LLM software.
In addition, Gemma runs faster and the results do not need to be shared with third parties because they are processed locally.
NVIDIA's performance in the AI realm is increasingly brilliant. Their revenue in Q4 2024 reached $22.1 billion, beating expectations by 7%.
AI Domination: A New Dimension of Innovation
NVIDIA and Google are not the only key players in the AI arena. OpenAI just launched Sora, a video creation tool from text that surprised the world with its hyper-realistic results. The rapid development of AI is fueling concerns about its impact on humans and prompting calls for stricter regulation.
Although figures like Elon Musk once opposed the development of AI, now responsible innovation is a priority. Almost all major technology companies are jumping into this field, including Adobe with its Generative AI solution for PDF documents.
NVIDIA and Google's collaboration marks a new era of collaboration between tech giants to advance AI. However, it is important to remember that this innovation must be accompanied by appropriate responsibilities and regulations for AI to benefit humanity.
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History of the emergence of AI
The history of the emergence of artificial intelligence (AI) begins long before the modern era, with ideas and concepts that have existed for centuries. However, major developments in this field occurred in the 20th century, when technology and scientific thinking developed significantly. The following is a brief overview of the history of the emergence of AI.
1. Introduction to AI Concepts
The concept of machines that can think like humans has appeared in ancient mythology and fictional stories since ancient times. However, these ideas are only philosophical speculation without a strong scientific basis.
2. World War II and the Development of Computers
During World War II, researchers began to understand the importance of computing for solving complex military problems. A major innovation in this field occurred with the invention of the first electronic computer, which provided the technological foundation for future developments in AI.
3. Establishment of the Scientific Discipline of AI
The term "artificial intelligence" was first used in 1956 at the Dartmouth Conference, where AI researchers first gathered to discuss and plan the development of this field. This conference, led by John McCarthy, Marvin Minsky, and Claude Shannon, is considered the official starting point of the scientific discipline of AI.
4. Early Development of AI
In the following years, researchers began developing algorithms and techniques to model human intelligence in computers. Some notable projects of this time include Logic Theorist (1956), General Problem Solver (1959), and perceptron (1957) - a simple artificial neural network model.
5. Hard Times and Declining Interest
Despite significant advances, the capabilities of computers at that time were still very limited, and initial expectations for AI progress proved overly optimistic. During the period known as the “Winter of Artificial Intelligence” in the 1970s and early 1980s, interest in and funding for AI declined sharply due to project failures and technological limitations.
6. Revival and Development of AI
In the 1980s, interest in AI began to revive with the emergence of new technologies such as natural language processing techniques, expert systems, and more sophisticated artificial neural networks. These developments are bringing AI to a wide range of practical applications, including speech recognition, natural language processing, and recommendation systems.
7. Modern Era of AI
Developments in computer technology, better understanding of cognitive theories, and the explosion of digital data have fueled the modern era of AI. Machine learning, deep learning, and natural language processing techniques have led to incredible advances in this field, enabling more complex and sophisticated AI applications than ever before.
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DISCLAIMER : This article is informational in nature and is not an offer or solicitation to sell or buy any crypto assets. Trading crypto assets is a high-risk activity. Crypto asset prices are volatile, where prices can change significantly from time to time and Bittime is not responsible for changes in fluctuations in crypto asset exchange rates.
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