Artificial intelligence has come a long way in recent years, but its development is still often hampered by high costs and limitations in data set size and quality. One alternative to the large, generalized models favored by Big Tech companies are small language models (SLMs). These more efficient solutions are becoming increasingly popular for task-specific applications.
Assisterr: Empowering Communities through Decentralized AI
One such SLM project is Assisterr, a Cambridge-based network of decentralized AI models. This innovative platform allows communities to own, manage, and improve their AI models while participating in governance and reward systems through blockchain technology. In this interview, Assisterr CEO Nick Havryliak shares his insights on the advantages of SLMs, the potential of decentralized AI, and how blockchain is empowering communities to shape the future of AI development.
From Big Tech to Small Language Models
Cointelegraph: What inspired the creation of SLMs, and how do they differ from the large AI models developed by Big Tech companies?
Nick Havryliak: My experience in tech consulting highlighted the major limitation of data set size and quality in machine learning. I explored how Web3 incentives could be leveraged to aggregate high-quality, niche-specific data. This idea laid the foundation for creating SLMs, which solve the inefficiencies seen in larger, general AI models.
We’ve heard about large language models’ hallucinations and high costs. That is mostly because Big Tech tries to reach higher adoption by making more generalistic models. SLMs offer a different route, where models can be highly specialized but only in smaller areas.
Efficiency and Scalability: The Advantages of SLMs
Cointelegraph: SLMs promise to be more efficient, scalable, and accessible. Could you explain how this efficiency translates into practical advantages for developers and users?
Nick Havryliak: SLMs are more resource-efficient, cost-effective, and easier to deploy. They excel in task-specific optimization and low-resource environments, making them ideal for applications where speed and cost are critical.
Besides this, most daily tasks and AI automation don’t require an AI with absolute knowledge if the result is obtainable with a simpler solution. As humans are not using rockets to kill mosquitoes, we do not need to wait for magic artificial general intelligence (AGI) to be developed to solve real-world problems in a variety of use cases. SLMs are here to empower such solutions now, not in the future.
Real-World Applications and Industries
Cointelegraph: What are some real-world applications or industries that you believe will benefit most from the use of SLMs?
Nick Havryliak: SLMs can be integrated into any application, agentic framework, or hardware we are using on a daily basis, including so-called edge devices like smartphones and laptops. Such devices usually have harder limitations on computing and currently, it’s simply impossible to run LLMs on them. But at the same time, SLMs are the main force for new gadget development because they can unlock new smart ways of usage.
The Future of AI in Consumer Electronics
Cointelegraph: Edge devices like smartphones and laptops are increasingly powerful. How are SLMs poised to take advantage of this trend to deliver smarter, more cost-efficient AI applications?
Nick Havryliak: Smartphones and different wearable devices are the main initial drivers of SLM development. Such devices usually have harder limitations on computing and currently, it’s simply impossible to run LLMs on them. But at the same time, SLMs are the main force for new gadget development because they can unlock new smart ways of usage.
This includes things like Meta glasses to wearable friends or assistants who are always within reach.
Shaping the Future of AI
Cointelegraph: Looking ahead, how do you envision the future of AI in consumer electronics as SLMs become more widespread? Could they reshape how we interact with everyday technology?
Nick Havryliak: SLMs will definitely reshape our lives— not only our interaction with technology. We believe that in the nearest future, each decision could be made by some specific SLM. Each use case will have its small model making it extremely easy to find a helping ‘AI assistant friend’ for any need.
That’s where we see the power of a network effect as the more SLMs there are, the stronger and more valuable the network will be for the end user.
Conclusion
SLMs are poised to revolutionize AI development by offering a more efficient and cost-effective alternative to large language models. With Assisterr’s decentralized platform, communities can now own, manage, and improve their AI models while participating in governance and reward systems through blockchain technology. As SLMs become more widespread, they will undoubtedly reshape how we interact with everyday technology.
Learn More about Assisterr
For more information on Assisterr, please visit our website at Assisterr.com.
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