Last week, we completed the final AMA Educational series with our Head of AI Education Shingai Manjengwa and her team, Senior AI Solutions Engineer, Pourya Vakilipourtakalou and Alexander Mar. The last discussion summarized the previous weeks, gave us a more detailed insight into the interfaces utilized in Theoriq’s protocol, highlighted the importance of the data layer in AI, and looked towards a production ready future using AI Agents.

Let’s have a quick look at what they spoke about.

The Theoriq technology stack comprises of three key sections, the first educational AMAs focused on explaining the Litepaper and the middle layer – the Agent Base Layer. This session started off by highlighting the two interfaces in the tech stack known as Infinity Studio and Infinity Hub.

These tools are designed to enhance user interaction with AI agents and collectives within the Theoriq framework. The Infinity Studio serves as a comprehensive platform where users can interact with AI agents, manage tasks, and analyze data. The Infinity Hub, functions as a centralized location for managing and coordinating various AI agents, facilitating seamless collaboration and data sharing.

The team provided insights into how these tools function and the benefits they offer to users. By leveraging the capabilities of the Infinity Studio and Hub, users can engage with AI agents for a wide range of tasks, from investment analysis to data-driven decision-making. This integration of AI into everyday activities highlights Theoriq's commitment to making advanced AI technology accessible and practical for a broader audience.

Pourya gave us a sneak preview into what the platform for both of these will look like, you can see a preview below or go to the X thread here.

A key theme of this AMA session was the growing importance of AI agents in the digital landscape. The team emphasized their vision of a future where AI agents become ubiquitous, enabling individuals to interact with and utilize AI technology without the need for extensive coding knowledge. This vision is centered around the development of no-code builders, tools that allow users to create and deploy AI agents tailored to specific tasks without writing complex code.

The potential impact of no-code builders is significant, as they democratize access to AI tools and empower individuals to harness the power of AI for personalized applications. Imagine a world where anyone, regardless of technical expertise, can build an AI agent to manage their investments, optimize their business operations, or even provide personalized customer support. This democratization could lead to a major shift in how software and services are developed, moving away from one-size-fits-all solutions to highly customizable and dynamic systems.

Data management, preparation and reliability is a critical aspect of any AI-driven system, and Theoriq is no exception. In the AMA, the team introduced the Extract, Transform, Load (ETL) process, which plays a vital role in managing large volumes of data. For these scenarios, setting up a dedicated database is key to ensure high-frequency queries are handled effectively. This means the system can process and analyze data in real-time, providing users with timely and accurate insights.

For smaller tasks, such as fetching current trends or market cap information for a cryptocurrency, simpler solutions like API calls can suffice. These API calls can efficiently retrieve data without the need for complex data management infrastructures, streamlining the process and reducing overhead. Theoriq's approach to data management highlights the importance of scalability and flexibility, allowing the system to adapt to varying data demands.

To leverage the full potential of AI, data must also be prepared and structured properly before it is used. Starting with clean, structured data is essential to ensure that the AI systems can analyze and interpret information accurately. However, real-world data is often messy and unstructured, requiring extensive cleaning, transformation, and pre-aggregation to make it analytics-ready.

Data preparation in production environments, especially those involving high-frequency data access, are important. Structured data allows AI systems to function more efficiently and provide reliable results. This focus on data quality and preparation is a cornerstone of Theoriq's approach to building reliable and effective AI solutions. But there are also some challenges when interacting with external data sources.

As AI agents increasingly interact with diverse data sources, such as live feeds from social media platforms like Twitter or Discord, issues arise related to the reliability of data sources, the stability of connections, and the potential for AI agents misinterpreting or hallucinating information. Ensuring the accuracy and reliability of the data used by AI agents is key, as errors or inaccuracies can lead to significant consequences.

To address these challenges, Theoriq emphasizes the need for continuous monitoring and error correction mechanisms. By implementing strong systems that detect and correct errors, Theoriq aims to maintain the integrity and reliability of its AI agents, ensuring that users can trust the insights and recommendations given.

AI agents are rapidly approaching production readiness, even though they are still in the early stages of development. Quick prototypes and proof-of-concept models can be deployed relatively easily, but robust testing and validation are still necessary to address the limitations and challenges they face. Theoriq is focused on refining its AI agents to ensure they meet the high standards required for production environments.

Looking to the future, Theoriq envisions the deployment of specialized, smaller AI models on individual devices, tailored for specific tasks. This approach not only enhances the accessibility and functionality of AI agents but also reduces the reliance on centralized systems, paving the way for more decentralized and resilient AI applications.

The integration of AI into everyday technology is also set to accelerate, with major tech companies exploring new ways to incorporate AI into their products. Theoriq predicts that advancements in edge computing and the development of smaller, more efficient AI models will drive practical applications across various sectors. For example, integrating AI capabilities into smartphones and personal devices could revolutionize how we interact with technology, making AI a subtle part of our daily lives.

As AI technology becomes more mainstream, we can expect to see its impact in professional sectors, from healthcare to finance to customer service. The ability to create personalized, task-specific AI agents will transform industries, offering new levels of efficiency, accuracy, and personalization. Theoriq's commitment to pushing the boundaries of AI technology ensures that it will play a leading role in shaping this exciting future.

Theoriq envisions a future where specialized, smaller AI models are widely used, making AI an integral part of daily life. By addressing challenges and pushing technological boundaries, Theoriq is set to lead in shaping the future of AI.

Thank you to the team, and all of you for tuning in over the last month to Theoriq’s first educational AMA series. Your attendance, engagement, and questions have made it a success.

Get ready for the next wave of innovation, collaborative AI Agents are coming.

About Theoriq

Theoriq is the first decentralized protocol for governing and building multi-agent systems by integrating AI with blockchain technology. The platform is centered around an agnostic modular base layer that powers an ecosystem of dynamic AI Agent collectives that are interoperable, composable and decentralized.

Theoriq has raised over $10.4M from leading investors such as Hack VC, Foresight Ventures, Inception Capital, HTX Ventures and more, and have active partnerships with leading web3 and web2 projects including Google Cloud and NVIDIA.

With Theoriq, you're not just part of a network; you're part of a movement that's empowering communities, developers, researchers, and AI enthusiasts to reshape the future of intelligent autonomous systems.

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