Smaller, efficient AI models will decentralize AI and allow it to flourish at the edge
Smaller and more efficient AI models are less resource-intensive than large language models like GPT-4.
This is a long post, at almost 2,000 words. A brief outline of it is:
I previously thought that AI technology would be controlled by large, centralized tech companies.
I now think that large tech companies will control the most powerful and capable AI models, but that smaller, more efficient and more optimized models, will persist at the edge1, allowing for greater flexibility.
The consequence of this is that AI technology will allow all manner of new companies and services to arise over the next decade or so, as the edge becomes suffused with intelligence.
I’m still unclear on where investment opportunities lie, given the above.
Introduction
For a while now I had assumed that AI would be a centralized technology controlled by the big tech companies. While I still think that will be generally true, I am reconsidering part of my mental model. I think there is room for smaller, more optimized AI models, which don’t required the resources of a Google or Microsoft/OpenAI to train.
Basically, my model is now this:
the largest and most powerful AI systems will remain controlled by centralized, large tech corps, and
innovation in smaller and optimized AI models will allow AI to flourish at the edge, as well.
In practice, this means that mobile devices, including phones and tablets and industrial sensors, can run smaller AI models on their own hardware. In these cases, connectivity to centralized services is not required. This means that artificial intelligence can become ambient in a way that it couldn’t when the assumption was that AI-enabled devices had to be able to connect to, say, OpenAI’s servers.
The reason for the sudden change in my mental model is this twitter thread.
What does it all mean?
These developments are important because they suggest that more accessible, efficient, and optimized AI models are possible. Instead of just relying on large language models like GPT-4 or Anthropic’s Claude, smaller, specialized models can be built, which require fewer computational resources. These observations lead to the followng possibilities:
Democratization of Technology: Prior to these developments, high-performance AI models like GPT-4 could only be built and trained by organizations with substantial computational resources. The release of models like MistralAI’s Mixtral and tools like QuIP opens up the field to a wider audience, including smaller companies, researchers, and hobbyists who don’t have access to massive computational power. This democratization could lead to a burst of innovation as more people can experiment with and improve upon these technologies.
Efficiency in Model Size and Computation: The advancements in making models more efficient is crucial. Large models like GPT-4 require significant GPU resources, making them expensive to train and run. The development of models that can operate on smaller, less expensive hardware broadens the potential for AI applications in various fields.
Specialization and Fine-Tuning on Proprietary Knowledge: The ability to fine-tune these models on specific, proprietary datasets opens up opportunities for highly specialized applications. Companies and researchers can develop AI models tailored to their unique needs, whether it’s for language understanding in a niche field, customized customer service bots, or specialized research tools.
Quantization and Model Compression Techniques: Techniques such as QuIP allow for smaller models to be run on less powerful hardware, without a significant loss in performance. This means that more complex and capable models can be used in a wider range of applications, including potentially on personal devices or in small-scale business environments.
Improvements in Model Alignment and Factual Accuracy: The developments from ContextualAI and Stanford’s WikiChat address two significant challenges in AI: alignment and factual accuracy. Factual accuracy simply means no hallucinations, and alignment means ensuring that a model’s output aligns with desired outcomes and ethics. Improvements in these areas are crucial for building trustworthy AI systems. Sensitive areas like healthcare, law, and education require that AI models avoid hallucination, and align with users’ expectations.
How does this compare to the state of the art in large language models like GPT-4?
Size and Scalability: While models like GPT-4 are state of the art in terms of their capabilities, they are also large and resource-intensive. To get an idea of the amount of resources required for models like GPT-4, consider that much of Microsoft’s vaunted $13 billion investment in OpenAI is in the form of cloud computing credits. The new developments suggest a trend towards smaller, more efficient models that could potentially match or exceed the capabilities of something like GPT-4 but with fewer resources.
Accessibility and Customization: GPT-4 and similar models are generally offered as services by large companies, limiting the ability for deep customization and proprietary usage. The developments mentioned in the original twitter thread suggest a move towards more accessible and customizable models.
Specialized Applications: GPT-4 is a generalist model, trained on a wide range of data to perform well across many tasks. The new developments indicate a shift towards models that can be more easily specialized or fine-tuned for specific tasks, which could lead to more effective applications in specialized fields.
The wider range of applications
The advancements in AI technology described in the original tweet thread open the door to a wide range of new applications and improvements in existing ones. Here’s some speculation about potential applications.
Personalized AI Assistants: With more efficient and accessible AI models, it is feasible to have AI assistants tailored to individual needs and preferences. These could range from personal productivity tools to specialized assistants for people with disabilities, offering more nuanced and personalized support.
Enhanced Healthcare Applications: AI models fine-tuned on specific medical datasets could lead to more accurate diagnostic tools, personalized treatment plans, and even real-time monitoring and advice for chronic conditions. This could signficiant improve patient outcomes and healthcare efficiency.
Small Business and Startups: Smaller companies could develop their own AI solutions for tasks like csutomer service, data analysis, or market research without the needs for substantial computational resources. This democratization could spur innovation and competitiveness in various industries.
Education and Training: Customizable AI models could lead to highly personalized educational tools, adapting to individual learning styles and needs. This could revolutionize education, making it more effective and accessible.
Art and Creativity: Artists and creators could leverage AI to assist in the creative process, from generating ideas to helping with design and composition. This could lead to new forms of art and entertainment.
Environmental Monitoring and Conservation: Efficient AI models could be deployed for large-scale environmental monitoring, analyzing data from sensors to predict and respond to environmental changes or disasters more effectively.
Smart Cities and Urban Planning: AI could be used for more efficient urban planning, traffic management, and public services, leading to smarter, more livable cities.
Legal and Compliance Applications: AI could help in legal research, contract analysis, and ensuring compliance with regulations, making these processes more efficient and reducing the risk of human error.
Manufacturing and Supply Chain Optimization: Custom AI solutions could optimize manufacturing processes and supply chains, leading to increased efficiency, reduced waste, and lower costs.
Agriculture and Food Production: AI could assist in precision agriculture, helping to optimize crop yields, reduce resource use, and manage farms more effectively.
Language and Cultural Exchange: More accessible AI models could lead to better language translation services and tools that help bridge cultural gaps, fostering global communication and understanding.
Reserch and Development: In fields like physics, chemistry, and biology, AI could accelerate research by analyzing data, generating hypotheses, and even suggesting experiments.
The key takeway from this is that AI is rapidly becoming more versatile, efficient, and accessible, which could lead to significant improvements in a wide range of fields.
Bringing AI to the edge
Smaller AI models, like those described in Mistral’s development, raise the possibility of running sophisticated AI on mobile and IoT (Internet of Things) devices. Here are some key implications and potential applications of this shift:
Enhanced Mobile Applications: With AI capabilities directly on smartphons or tablets, users could enjoy more advanced features like real-time language translation, image and voice processing, and personalized recommendations without need a constant internet connection.
Real-Time Data Processing in IoT Devices: IoT devices equipped with AI can process data on-site, reducing the need to send data back to a central server. This can improve response times in applications like home automation, industrial monitoring, and smart cities.
Improved Privacy and Security: Processing data locally ond evices can enhance privacy, as sensitive information does not need to be transmitted over the network. This is particularly important for applications invooving personal data like health monitoring.
Reduced Bandwidth and Cloud Dependence: Pushing AI to the edge of networks reduces the reliance on cloud servers and the associated bandwidth requirements. This is crucial in areas with limited internet connectivity or for applications that generate large amounts of data.
Energy Efficiency: Running smaller AI models on edge devices can be more energy-efficient than constantly communicating with cloud-based servers, which is beneficial for both environmental and cost-saving reasons.
Enabling New Applications in Remote Areas: AI capabilites at the edge can enable new applications in areas with poor internet connectivity, such as remote healthcare diagnostics, agricultural technology, and wildlife monitoring.
Personalized User Experience: AI on personal devices can adapt and learn from the user’s behavior in a more intimate and immediate way, providing a highly personalized user experience.
Challenges in Maintenance and Security: While there are many benefits, this approach also presents challenges. Ensuring the security of AI models on a multitude of devices and maintaining these models with updates can be complex.
Real-Time Decision Making: In critical applications like autonomous vehicles or emergency response systems, having AI capabilities on the device allows for real-time decision making without the latency that comes with cloud computing.
Scalability and Cost Effectiveness: Deploying AI on edge devices can be more scalable and cost-effective for certain applications, as it reduces the need for large-scale data centers.
The ability to run smaller AI models on mobile and IoT devices has the potential to revolutionize many aspects of technology and daily life, making AI more pervasive, efficient, and accessible. This shift towards edge computing with AI capabilities can lead to more responsive, personalized, and efficient applications, though it also introduces new challenges in terms of security and maintenance.
Conclusion
It’s clear that AI technology is rapidly moving past the large, centralized LLMs like GPT-4. The industry seems to be settling on two different, possibly complementary tracks: very large models like GPT-4, which require vast computational resources, and smaller, more efficient models which can be run locally on less powerful devices. The edge of the network, in other words, will become intelligent.
One can easily imagine all manner of industrial sensors, robots, Internet of Things (IoT) devices, personal mobile devices, and more being imbued with intelligence capabilities. This opens up vast new areas of products and services for companies to explore.
The “edge” here refers to the periphery of a computer network. Think of mobile devices, Internet of Things (IoT) devices, industrial sensors on robots, etc.