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CerboAI is a decentralized AGI network designed to support the process of training models on sparse network data and distributed inference architecture.

Current State of Large Language Models The industry currently focuses on the 'Less data' approach when training large language models. The consensus is that while alignment in LLMs demands extensive data and computational power, the key is not merely in accumulating data. This perspective challenges the traditional approach of data-heavy models.

The last decade has seen a shift from enterprise cloud adoption to ToC applications. This shift has resulted in data quantity triumphing over quality. However, this model is in contrast to human cognitive processes. Unlike these data-intensive models, the human brain does not rely on vast amounts of information to process solutions. It is capable of reliable problem-solving, self-reflection, and integrative thinking with comparatively less data.

Training Paradigms and Logical Thinking The present training methodology involves human experts who learn from the internet and then impart this knowledge to models. This creates a paradox where models are being taught information they already possess, raising questions about their inherent reasoning capabilities.

Sole reliance on internet data for model training is insufficient for developing logical thinking. This approach overlooks the vast array of information not present on the internet.

Future Directions in Model Training The challenge is to find simple methods to activate logical thinking in models. This could involve a shift in training methods to include data that stimulate logical and critical thinking processes, moving beyond the current internet-centric approach.

Current models lack sufficient training in logical thinking modes due to data and training limitations. There is a need to explore new training paradigms that can adequately activate and utilize these modes within models.

Exploring the utilization of sparse networks in the context of logic training could be key. These networks might offer more efficient ways to train models in logical reasoning, moving away from the current data-heavy approaches.

There might exist a straightforward method to activate logical thinking, as relying solely on internet-based training is inadequate for learning logical reasoning. The internet is just a small part of all the information available in the world. It is well-known that when models perform inference, different inputs and tasks activate different parts of the model, including those responsible for logical thinking. However, projecting the current operation and reasoning processes of Large Language Models (LLMs) onto neural networks, we observe that Sparse networks show low levels of activity, while Dense networks are highly active. Therefore, we hypothesize that due to limitations in training data or other factors, the model does not sufficiently activate neurons in the sparse network. This implies that the model's activation patterns for logical thinking have not been adequately trained and expressed. Expanding further, this observation suggests that for more effective logical reasoning, LLMs might benefit from a more balanced engagement of both Sparse and Dense networks. This could potentially be achieved through diversified training approaches that include a broader range of data types and reasoning tasks, thereby enriching the model's capability to process and reason with logical constructs.

Decentralizing Data Across Sparse Networks Currently, sparse networks, where only a few neurons are interconnected, are underused. These networks offer potential pathways for more efficient data processing and reasoning. By distributing decentralized human reaction and behavior data across sparse networks, there's potential to enhance the efficiency and reasoning capabilities of large models.

Unleashing the Potential of Personalized LLMs: Overcoming Data and Resource Barriers with Cerbo AI The advent of sparse network architectures and the shift towards decentralized data ownership could reshape the landscape of LLMs. These developments herald a new era where personalized LLMs, tailored to individual needs and preferences, are becoming an achievable reality.

Despite the potential, the path to actualizing personalized LLMs has been obstructed by two significant hurdles:

Centralized Data Control by Major Corporations: The landscape of digital data ownership is predominantly dominated by large corporations such as Instagram, TikTok, and Twitter. This centralization of data control means that personal user data is stored and managed in a way that restricts user access and utility. Such a scenario severely limits the ability of individuals to employ their own data for training personalized LLMs, consequently stifling the advancement of user-specific language models.

Resource Constraints of Personal Computing Devices: The second major challenge lies in the limitations of personal computing devices like PCs and mobile phones. The computational power and storage capacity required for training and hosting LLMs are often beyond the capabilities of these devices. This gap between the resource needs for personalized LLMs and the available capabilities of personal devices has rendered the concept impractical for widespread use.  Cerbo AI's Solution to Personalized LLMs Addressing these challenges, Cerbo AI introduces a groundbreaking solution that harmonizes decentralized data ownership with sparse network training. This innovative approach empowers individuals to leverage their own data, overcoming the constraints of corporate-controlled, centralized data repositories. Furthermore, Cerbo AI's method significantly reduces the computational and storage demands, making it viable for anyone with a standard personal device to not only train but also host their own personalized LLMs. This advancement marks a pivotal shift in the democratization of language model technology, paving the way for a future where personalized digital experiences are the norm, not the exception.

Cerbo AI in Decentralzinig Data Across Sparse Networks Cerbo AI is revolutionizing the landscape of Large Language Models (LLMs) by building a decentralized data and model hub, leveraging the concept of decentralizing data across sparse networks. This innovative approach aims to transform the current norms of data processing in LLMs. By dispersing a diverse array of human behavioral and reaction data throughout sparse networks, Cerbo AI is not just enhancing the reasoning and inference abilities of its models but also fostering a more dynamic and distributed model of data and knowledge management. This decentralized hub stands as a testament to their commitment to moving beyond the limitations of traditional, data-intensive models. It mirrors the human brain's efficiency in processing complex information with minimal data, heralding a new era in AI where data is not just abundant but strategically utilized for more nuanced and intelligent model development.

Training of Personalized Cerbo AI Network Network Distillation Network distillation has been a highly effective approach in training a small, dense model to achieve performance levels comparable to a larger, sparse model. Evidence from prior research suggests that a sparse network with hundreds of billions of parameters can be successfully distilled into a more compact, dense model with hundreds of millions of parameters. This section delves into the specific process of training a dense, personalized LLM by distilling knowledge from a larger, sparse LLM.

Decentralized Data Hub A hub that aggregates cross-domain and domain-specific data is pivotal for supervised fine-tuning and preference learning in LLMs. The utilization of data is meticulously recorded on-chain, with token rewards allocated to contributors of high-quality data that proves valuable to model developers.

Decentralized Model Hub A platform that encourages the crowdsourcing of model contributions incentivized through token rewards. Cerbo AI also offers its own endpoints, enabling community members to test models on various inputs. Model interactions are recorded on-chain, with tokens distributed to reward contributors whose models are frequently utilized.

Lighter LLMs trained with a mixture of experts. Models with longer context windows. Multi-modal models, especially those combining vision and language. Models that are easily enhanced with Retrieval-Augmented Generation (RAG). To stay at the forefront of these trends, Cerbo AI empowers its token holders to propose Cerbo AI Improvement Proposals (IIPs). These IIPs play a crucial role in refining the token distribution rules and ensuring that the models contributed align with the latest LLM developments.

Pioneering ML Development with Cerbo AI Cerbo AI stands out as a pioneering platform, not just in democratizing access to LLMs but also in driving innovation and collaboration within the ML community. Its unique approach of combining decentralized technologies with ML development paves the way for a more inclusive, efficient, and transparent ecosystem in the realm of artificial intelligence.