우리의 삶을 풍족하게 바꿔줄 인공지능 SLMs
TLDRThe script discusses the rise of Small Language Models (SLMs) in the AI industry, highlighted by Microsoft's announcement of significant trends for 2024. SLMs, such as Meta's open-source LLaMA 2, are gaining attention for their potential to run on personal computers and mobile devices, offering tailored AI experiences offline. The conversation delves into the capabilities of SLMs, their applications in various fields like gaming and education, and the competitive landscape with companies like Google and Alibaba developing their models. It underscores the shift towards AI evaluated by the number of parameters and the potential for diverse monetization strategies, hinting at a rapidly evolving AI era.
Takeaways
- 🚀 Microsoft has identified three notable trends in the AI field for 2024, one of which is the emergence of SLM (Small Language Models) as a contrast to large models like GPT.
- 📈 GPT models, such as GPT-3.5 with 150 billion parameters and rumored GPT-4 with 176 billion parameters, require substantial computational resources like GPUs for operation.
- 💰 Large language models like GPT are shifting towards a paid model, with GPT-4 reportedly costing around $20 per month for access.
- 📱 SLMs aim to be deployable on general computers and mobile devices, offering the advantage of offline use and better privacy due to less reliance on server connections.
- 🎯 SLMs are designed for specific purposes and can be tailored to individual needs, such as language learning or programming language support.
- 🌐 Meta (Facebook) has released an open-source SLM called Rama 2, which has been tested on cloud computing services to demonstrate its capabilities.
- 🔬 Rama 2's performance was benchmarked against GPT-3.5, showing a quality score of 0.6, indicating that around 70% of its responses were similar to those of GPT-3.5.
- 💼 Microsoft has developed a product based on Rama called Orca 2, offering models with 7 billion and 3 billion parameters in both open-source and commercial formats.
- 📱 Google has introduced a nano version of their AI model, called Jennai Nano, optimized for mobile devices like the Pixel Pro 8.
- 🌏 Alibaba from China has also entered the SLM field with a multilingual model called M6, showing the global interest and competition in this area.
- 📈 The AI industry is seeing a shift from measuring performance by CPU clock speed and core count to the number of parameters managed by AI models like SLMs.
Q & A
What is the significance of the SLM (Small Language Model) trend in the AI industry?
-The SLM trend signifies a shift towards smaller, more specialized language models that can operate on regular computers and mobile devices, offering offline capabilities and catering to specific use cases without requiring large-scale infrastructure.
How does the performance of SLMs compare to larger language models like GPT-3.5 and GPT-4?
-While larger language models like GPT-3.5 and GPT-4 have a broader range of knowledge and more comprehensive understanding, SLMs are designed for specific purposes and can offer tailored performance optimized for those tasks. They may not match the extensive knowledge base of larger models but provide a more efficient solution for particular applications.
What are the potential applications of SLMs in the real world?
-SLMs can be used in various applications such as travel assistance, where a model trained on travel-related content can be embedded in smartphones for offline use, language learning, where models can focus on specific languages or educational subjects, and gaming, where they can enable dynamic character interactions and storylines.
How can SLMs impact the development of smart home devices and other consumer electronics?
-SLMs can enable more natural and interactive voice-activated features in smart home devices, allowing users to communicate with appliances more effectively. The offline capabilities of SLMs also mean enhanced data privacy as they do not require constant internet connectivity.
What are some of the challenges in deploying SLMs on consumer devices like smartphones?
-While the idea of deploying SLMs on smartphones is promising, there are challenges related to computational resources and energy efficiency. The models need to be lightweight enough to run on these devices without compromising user experience or device performance.
How does the development of SLMs affect the business models in the AI industry?
-The development of SLMs opens up new business opportunities by allowing for a wider range of applications and services. Companies can offer subscription models, sell standalone applications, or integrate SLMs into products and services, creating diverse revenue streams.
What are some of the notable SLMs developed by major tech companies?
-Meta has developed the Rama 2, an open-source SLM, and based on this, they have also created the Orca 2 for commercial use. Google has developed the Minerva Nano, and Alibaba from China has released a multilingual SLM called AliMe.
How does the performance of an SLM compare to larger models in terms of parameters?
-While larger models like GPT-3.5 use around 150 billion parameters, SLMs can have as few as 30 billion or even less. Despite having fewer parameters, SLMs can still provide performance that is quite close to that of larger models for specific tasks.
What is the role of cloud computing in the development and deployment of SLMs?
-Cloud computing plays a crucial role in making SLMs accessible to a wider audience. It allows developers to train and deploy SLMs without the need for expensive hardware, making it easier for startups and smaller companies to enter the AI market.
How might the SLM trend influence the future of AI development and research?
-The SLM trend suggests a shift in focus from sheer model size to the effectiveness and efficiency of AI models for specific tasks. This could lead to more targeted research and development efforts, focusing on creating models that are optimized for particular applications and user needs.
What are the potential benefits of using SLMs for user privacy and security?
-Since SLMs can operate offline and do not require constant internet connectivity, they can offer better control over user data. This reduces the risk of data breaches and enhances privacy by keeping sensitive information local to the device.
Outlines
🤖 Introduction to AI Trends and SLM
This paragraph introduces the audience to the recent AI trends highlighted by Microsoft, emphasizing the emergence of SLM (Small Language Models) as a notable development in the AI field. It contrasts SLM with the previously dominant large language models like GPT, discussing the potential benefits of SLM's smaller size, such as the ability to run on personal computers and mobile devices, and its offline capabilities. The speaker also raises questions about the necessity of smaller models in an increasingly internet-connected world, hinting at their potential specialized uses.
🎮 Applications of SLM in Gaming and Education
In this paragraph, the speaker delves into the potential applications of SLM in various fields, particularly gaming and education. It discusses how SLM can enhance gaming experiences by enabling non-player characters (NPCs) to engage in more natural and situationally appropriate conversations, thereby creating a more immersive and realistic gaming environment. Additionally, the speaker highlights the potential for SLM to be utilized in educational contexts, providing tailored learning experiences for subjects like foreign languages and mathematics through specialized learning data models.
🌐 SLM Development and Industry Players
The final paragraph focuses on the current state of SLM development and the role of major tech companies in this space. It mentions Meta's open-source model, Rama 2, and the experiments conducted using cloud computing to train SLMs on modest hardware specifications. The paragraph also covers other companies' contributions, such as Microsoft's Orca 2 and Falcon 2, Google's Minana, and Alibaba's multilingual SLM. The discussion touches on the competitive landscape, the potential for diverse revenue streams through SLM applications, and the shift in performance evaluation metrics from hardware specs to the number of parameters in AI models.
Mindmap
Keywords
💡AI
💡SLM
💡GPT
💡Parameter
💡Microsoft
💡Meta
💡Cloud Computing
💡Mobile Devices
💡Customization
💡Open Source
💡Quantum Computing
💡AI Models
Highlights
Microsoft has identified three notable trends in the AI field for 2024, one of which is the emergence of SLM, or small language models, as a contrast to large models like GPT.
Small Language Models (SLM) are gaining attention for their potential to run on ordinary computers and mobile devices, unlike large models that require significant computational resources.
GPT-3.5 uses 150 billion parameters, while there were discussions that GPT-4 might use up to 176 trillion parameters, indicating the vast scale of these large models.
SLMs, such as those being developed by various companies, aim to reduce the number of parameters to a level that allows operation on personal computing devices.
Despite having fewer parameters, SLMs can still offer tailored performance for specific tasks, making them suitable for applications like language translation or specialized dialogue systems.
SLMs can operate offline, providing an advantage in terms of data privacy and security, as they do not require constant internet connectivity.
The potential applications of SLMs extend to various fields, including education, gaming, and smart home devices, offering more personalized and interactive experiences.
Meta (Facebook) has open-sourced an SLM called Rama 2, which has been tested on cloud computing services to demonstrate its capabilities.
Rama 2's performance was found to be comparable to GPT-3.5 in certain aspects, with a quality score of 0.6, indicating a significant level of performance with fewer parameters.
Microsoft has also developed an SLM product called Orca 2, with versions having 7 billion and 3 billion parameters, both available as open-source models.
Google has introduced a model called Jemini Nano, optimized for mobile devices, with versions having 1.8 billion and 3.2 billion parameters.
Alibaba from China has released a multilingual SLM called Ali, showing the global interest and development in this area.
SLMs offer a diverse range of monetization opportunities, from subscription models to individual product sales and advertising revenue.
The AI industry is seeing a shift towards evaluating systems based on the number of parameters they handle, reflecting the growing importance of AI capabilities.
The development and application of SLMs are making AI more accessible to small startups and individual developers, reducing the barriers to entry in the field.
The conversation discusses the potential for SLMs to enhance gaming experiences by enabling dynamic character interactions and storylines.
The advancements in SLMs demonstrate the rapid progress in the AI field, with smaller models achieving significant performance improvements.
The discussion highlights the ongoing competition and innovation in the SLM space, with numerous companies contributing to its development.
The practical applications of SLMs, from travel assistance to educational tools, showcase their potential to transform various aspects of daily life.