Introduction
You probably heard that now you can call ChatGPT over the phone. This seems unreal. It is cool but do you have an idea whom you’re gonna be talking to? The best you can do is to rely on service provider reputation – OpenAI OpCo, LLC.
Issues with Current AI
- Centralized Models: Most widely-used models like GPT-4 or Claude are closed-source and centralized.
- Dependence on Centralized Infrastructure: Even “open-source” models often run on centralized platforms like Groq or Together AI.
- Opaque Operations: It is impossible to verify how agents operate—including prompts, limitations, and stop lists.
- Data Privacy Concerns: Users have little visibility into how their data is handled or stored.
These challenges emphasize the need for responsible AI use and caution when relying on AI agents for critical decisions.
AI and Learning
Lucky you if you graduated from school before getting spoiled by AI.
But what if you are not able to verify stuff just because you have not developed learning skills, critical thinking, reasoning skills or just because you are too young or you got addicted to “helpful and efficient” AI agents? This could be a serious issue. Calculators began as costly, limited tools, giving schools years to adapt. Now, AI is transforming education instantly, impacting every subject. Students will cheat with AI but also integrate it into their work, challenging educators and questioning the relevance of traditional assignments.
Common understanding is that unless assignments are completed in class, it’s impossible to reliably determine if the work is human-made.
Verifiable AI can change this.
NEAR AI Hub
NEAR.AI is the answer.
While in Alpha, some exciting features are already available to try. Nothing compares to learning by trying so let’s do it.
Go to the NEAR AI Research Hub.
You may sign in with your NEAR account to interact with the platform. At the Agents tab you find all the agents added to the Registry – Decentralized storage with support of private and encrypted items.
Find and check out Learn AI agent 0.0.1.
You can talk to the agent within NEAR AI hub, and what is more important you can explore the agent’s origin – which is learn-agent.learnclub.near, source code, including prompt and stopwords. Moreover, if you want to tweak it – say remove a stopword that is crucial for you – just clone it. But be aware that now you are in charge of your clone!
Now let’s dig a bit deeper. Switch to the Models tab.
There are over 50 open source models available already. As you can see and verify that Learn AI is using that particular LLaMA hosted at the NEAR AI infrastructure. Using NEAR accounts for interacting with models via agents makes it sure that you know whom you are actually talking to!
Recently, private RAG (a LLM friendly LNC knowledge base) powered by XTrace was added. It dramatically improved the quality (L)Earn AI🕺.
NEAR AI is planning to build the next generation frontier AI model with 1.4T parameters! The idea is make open source AI development sustainable via pay-per-use model and this where NEAR will play the crucial role.
What is one major advantage of the NEAR AI infrastructure compared to centralized AI models?
How will NEAR AI pull off building a 1.4T parameter, monetizable model in a decentralized way? Enter the world of Trusted Execution Environments, or TEEs.
Modern processors and GPUs (starting with H100s) allow one actor, Alice, to run code on a machine of another actor, Bob, without trusting Bob, while having the following two guarantees: (a) Bob is in fact running the code Alice expected him to run, and (b) Bob cannot spy on the execution that Alice wants him to carry out. In other words, Alice can safely send any private data into such a computation, being certain Bob will not be able to see it.
(L)Earn AI🕺
The decentralized NEAR AI infrastructure represents a significant step forward in enabling user-owned AI. By offering transparency, control, and flexibility, NEAR AI empowers users to interact with AI agents in a way that ensures data privacy and fosters trust.
LNC, as a NEAR pioneer, is at the forefront of leveraging this infrastructure for practical applications, including the live Learn AI agent, the Lean NEAR Tutor (in development), and the planned WooCommerce Shopping Agent.
As a learner, you’re invited to actively participate in this innovative (L)Earn ecosystem. Ask the (L)Earn AI relevant questions, pay with nLEARNs, gain knowledge, and earn nLEARNs by contributing to the AI’s improvement.
Check out the most recent (L)Earn AI🕺 agents:
📝Summarize🕺
LNC 📝Summarize🕺 Agent quickly breaks down complex guides into easy-to-understand summaries. It pulls out the most important points and explains them clearly, helping students grasp key concepts without wading through lengthy materials. This time-saving tool makes learning more efficient while keeping the essential information intact.
✏Comment🕺
LNC ✏Comment Agent 🕺analyzes the topic of (L)Earners choice and drafts a comment. The (L)Earner then reviews this draft and posts it with any necessary edits.
As (L)Earn AI intelligence evolve most likely it will be integrated with other NEAR projects – for example as learning assistant chat bot at other Dapps and Telegram chats, or (L)Earn AI can also cooperate with other NEAR AI agents like NEAR Docs agent and help users to get more technical development related help.
What is the unique feature of the (L)Earn AI agent in the NEAR ecosystem?
📚Happy (L)Earning!🕺
Top comment
Breaking down barriers to participation is crucial in ensuring that everyone has an equal opportunity to learn and grow. I think this strategy is a great example of how technology can be used to create more equitable and accessible learning experiences
By prioritizing transparency, NEAR AI creates a stronger bond
The ability to customize AI agents on NEAR AI is a game-changer for individual and enterprise users alike.
This article highlights the innovative potential of decentralized AI with NEAR AI, emphasizing transparency, user control, and data privacy—an inspiring step towards a more open and user-centric AI ecosystem.
The article highlights the importance of decentralization in AI, emphasizing NEAR AI's approach to transparency, data privacy, and user control. By providing access to open-source models and enabling interaction with AI agents, it fosters trust and accountability, making AI more user-centric and sustainable in the long run.
The article highlights NEAR AI's innovative approach to decentralized AI, emphasizing transparency, user control, and privacy. By allowing users to explore and customize AI agents, it fosters a collaborative learning environment. This user-centric model, combined with its sustainable, pay-per-use framework, promises to reshape AI interaction and democratize access to powerful AI technologies.
The agent's current situation or status, which is represented by a set of variables, such as its location, velocity, or other relevant attributes. 3. **Action**: The agent's response to its environment, which can be a physical action, a decision, or a communication. 4. **Goal**: The agent's objective, which is the desired outcome or target state. 5. **Policy**: The rules or strategy that guide the agent's actions to achieve its goal
The NEAR AI Assistant and Research Hub represent a major step forward in democratizing AI technology. By prioritizing privacy, community ownership, and open-source collaboration, NEAR empowers users and developers to innovate while maintaining control over their data. These advancements pave the way for a more decentralized and user-focused AI ecosystem.
A Learn AI Agent is a type of artificial intelligence (AI) that uses machine learning and other techniques to learn from data, improve its performance, and make decisions or take actions. These agents are designed to adapt to new situations, learn from experience, and optimize their behavior over time.
It's a fundamental concept in Artificial Intelligence (AI) and is used in a wide range of applications, from simple scripts to complex systems like self-driving cars. Here are the key aspects of AI agents.Reacts to the current state of the environment without considering future consequences.
Ethical AI development should prioritize user empowerment, as demonstrated by NEAR AI’s infrastructure.
The focus on decentralization ensures that NEAR AI remains free from the biases of centralized systems.