Artificial Intelligence (AI) has attracted considerable interest over the past few years, generating excitement and innovation across industries. It has become more than just a buzzword found on LinkedIn profiles, and has the potential to change humanity’s future. However, it has also sparked debates and garnered intense criticism. One of the key advancements of AI is Generative Pre-Trained Transformers (GPT), designed to automate tasks and simulate an output similar to that created by humans.
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This article will examine GPT, its workings, and its use cases in more detail. It will also discuss NEAR.AI’s efforts to create an open-source AI that everyone can use and why a user-owned AI is the need of the hour.
What Is GPT?
Generative Pre-Trained Transformers, commonly referred to as GPT, are a family of advanced neural network models designed for Natural Language Processing (NLP) tasks. These large language models (LLMs) use transformer architecture and are considered a significant advancement in AI. They power an array of AI applications, the most well-known being ChatGPT.
GPT models allow applications like ChatGPT to create human-like output (text, images, music, and more), allowing them to answer queries conversationally. Several organizations have begun using GPT models for content creation, summarization, Q&A bots, and simple research. GPT models are a significant breakthrough in AI research. They can be used to automate and improve an array of tasks such as document summarization, research, language translation, writing blog posts, building websites, writing code, making animations, and more.
GPT models are known for the speed and scale at which they function. An average person may need hours or even days to research and write an article or create a specific code for a website. GPT models can complete such tasks in seconds, leading companies to begin research in AI to achieve artificial general intelligence. This would allow AI to help companies achieve unprecedented productivity and substantially improve their experiences.
How Does GPT Work?
Now, let’s understand how GPT works. As we’ve mentioned earlier, GPT models are neural network-based language prediction models built on the transformer architecture. They function by analyzing natural language queries and predicting the best response or outcome based on their understanding. However, to do so, these neural-based language models need unprecedented amounts of data.
GPT models are trained using hundreds of billions of parameters on massive language datasets. Once trained on these datasets, they can analyze input sequences and predict the most likely output by using probability to identify the next word to frame a complete sentence. They can also process natural language prompts and generate human-like text responses. When prompted, GPT models create responses based on their training data.
As mentioned earlier, these models rely on vast datasets to generate a relevant response. The vastness of these data sets, running into billions of parameters, allows GPT models to mimic human-like responses. GPT models such as ChatGPT use deep learning to gain context and generate an optimum response. Two key aspects are crucial for GPT models. These are
- Generative pretraining – Generative training teaches the GPT model to detect patterns in data and apply these patterns to new inputs.
- Transformer architecture – This allows the GPT model to process an input sequence in parallel.
However, the current infrastructure supporting AI development and deployment presents a substantial challenge: centralization. Centralization creates a slew of problems, including high barriers to entry and various other bottlenecks. A high entry barrier stifles innovation and gives a few entities control over the technology. This is where NEAR.AI comes into the picture.
Why Is A User-Owned AI Important?
As mentioned earlier, the infrastructure supporting AI development and deployment is highly centralized, enabling a few influential players to dominate the industry. The high costs associated with accessing high-performing resources often create insurmountable entry barriers for AI innovators, leading to the concentration of power in the hands of a few entities, which is precisely what we are witnessing. Additionally, centralization also hinders collaboration and innovation because data silos are isolated. That’s not all. In the hands of the wrong people, AI technology can do more harm than good and could be used to manipulate opinion, making it a tool for control.
Introducing NEAR.AI
NEAR.AI aims to create and deploy advanced AI that everyone can access and use. NEAR has cemented itself as one of the most dominant blockchains, handling millions of transactions per second. Now, it is turning its focus to the creation of an open-source and user-owned AI. AI is one of the most promising and disruptive technologies in recent memory. However, almost all AI-related development occurs within centralized, for-profit companies. This is the issue that NEAR.AI aims to address, and put AI’s power into ordinary users’ hands.
But how does it plan to do this?
NEAR.AI aims to achieve its goal by implementing a three-step plan.
- Create an AI Developer – The AI Developer will first learn to code and then teach machines how to code.
- AI Researcher – Use the AI Developer to teach machines to conduct research.
- Create an advanced AI for everyone – The final step is to use the research to create a powerful AI that belongs to everyone.
A Closer Look At NEAR’s User-Owned AI
NEAR.AI promises to be open-source. Unlike centralized AI systems that are accessible only to a few entities, anyone can use and improve upon NEAR.AI’s advanced AI. It also promises users complete ownership of their AI. A user-owned AI can allow the user to access data without leaking it to third parties and optimize it for the user’s benefit. It will also allow users to customize their computing experience without compromising ownership, privacy, and security.
NEAR began working on an AI Developer in 2017 when it attempted to create an AI-driven autocomplete. However, while developing it, the team realized that current (2017) blockchain systems had several limitations. This is why NEAR underwent a significant transition, pivoting and building a decentralized developmental platform that could handle billions of users while being easy to use and program, becoming a fully sharded, proof-of-stake blockchain.
Now, NEAR has an entire ecosystem of infrastructure and application builders, having onboarded millions of users, of which 2 million are daily active users, setting the stage for the next stage of innovation, combining Web3 and AI. However, there are still major hurdles to overcome. These are
- Fragmentation – Products are scattered across hundreds of chains. Additionally, the number of products available today is insufficient to offer a competitive landscape to existing systems
- Few developers – Currently, there are fewer than 7,000 full-time Web3 developers
- Escalating costs – Web3 code is costly to develop and supports multi-billion dollar projects on small codebases.
The AI Developer can address this by significantly increasing efficiency in creating Web3 applications by creating Web3 products using simple language inputs This would unlock more value and allow almost anyone to become a developer. Because NEAR.AI development is open source, the community will have access to software, datasets, and models to develop products. NEAR.AI will also collaborate with projects across the Web3 and AI ecosystems to use resources more efficiently through collaboration and incentive frameworks.
To achieve its vision, NEAR.AI will involve developers and engage with the NEAR community to write code, create new applications, and train the AI model. It will also leverage NEAR’s token-based economy. As NEAR.AI progresses, it will help attract more users to the NEAR ecosystem and create new economic activities and network effects. However, to succeed in its vision, NEAR.AI will need a range of infrastructure beyond peer-to-peer communication, on-edge data and inference, decentralized data storage, and private computation. It will also need developers to write code and train the AI model. To do this, NEAR.AI can engage with the NEAR ecosystem and leverage NEAR’s token-based economy.
NEAR.AI’s AI Researcher can use advancements in reasoning made possible through a chain of thought, search, and combination with formal methods to break through current AI models. It can also discover new insights and knowledge by recombining existing information.
The end goal for NEAR.AI is the creation of a user-owned AGI that can transform how users interact with computing and help enhance productivity by allowing users to focus on creativity, innovation, and unique economic activities. An AI that serves and is owned by individuals and communities instead of large institutions or governments.
The NEAR.AI Master plan comprises of:The NEAR.AI Master plan comprises of:
AI and Learning
One of the most obvious use cases for AI is learning. NEAR.AI can facilitate a mutually beneficial ecosystem that allows learners to interact with AI. Such a circular learning economy would ensure that the AI gets more refined thanks to user feedback, while the user can access knowledge and earn rewards.
AI Helps Learners
But how would such an ecosystem work? Here, at Learn NEAR Club, NEAR AI already helps learners with their comments at learning Guides – for non-English native speakers this is a life-saver! Commenting plays a crucial role in cognitive process so LNC acetifies
Learner Pays AI
In a scenario where learners come across a new or complicated topic, they could pay to access specific AI-powered topics, explanations, or tutorials. The AI would act as a tutor, helping users understand complex subjects or topics through personalized learning experiences or explanations tailored to the learner’s understanding and requirements. Already in prod!
AI Pays For Feedback
This is the ecosystem coming full circle. The learner gives feedback on the AI’s accuracy, tutoring skills, and explanations, helping it improve over time. The AI then compensates the learner in the form of tokens and other incentives. This can be the future of education, where AI can assist learners in acquiring knowledge and incentivize them to provide feedback.
AI Must Engage Content Creators
Training AI models solely on AI-generated content will yield only limited results. For better results, AI models must be trained on human-generated content that allows them to advance exponentially.
However, this is where corporations have fallen short, focusing on maximizing profits and monopolizing technology over empowering the creators and creating an imbalance.
For AI and its users to benefit from one another, both must operate in a framework that facilitates mutual gain and a cooperative exchange of incentives and rewards.
In Closing: Why Users matter
Think of AI as a student who learns from every interaction. The more diverse perspectives and experiences it encounters, the smarter and more helpful it becomes. That’s why AI systems need to play nice with everyone – both the creators who build amazing things and the everyday users who interact with them. Even your simplest reactions, like clicking that thumbs-up or thumbs-down button, are like little nuggets of gold for AI’s learning process.
Here’s where things get interesting: web3, and especially the NEAR Protocol, is a game-changer. It’s like creating a fair playing field where everyone who helps AI grow – whether they’re building it or using it – gets their fair share of recognition and rewards. After all, we’re all in this together, helping shape our future and the future of AI.
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Top comment
The harsh reality of the current situation is that, on average, most people don't actually want the responsibility that comes with true ownership and breaking up centralized authority. The bottom 90% of global earners would literally all have to agree on it, and work together to break up the monopolies that the 'global elite' have over information and technology. The sudden rise to prominence of xAI is a shining example of this. Our current gpt models will be the ones that AGI are trained on, I suspect, and we need to democratize these models as best we can to build a safe, fair, and equitable future for 100% of people.
AI thrives on diverse inputs, evolving with each interaction. Web3, particularly NEAR Protocol, democratizes this growth, ensuring equitable recognition for contributors. It's a collective effort, shaping AI's future with shared insights and rewards, fostering a balanced ecosystem for creators and users alike. Exciting times ahead!
Hello, all I'm new to the community.I think that the voting pole at the bottom of this guide is missing a crucial option which is the data should be owned by the apps to maximize the potential of emergent output. Please let me know if I'm way off base here. It's just the orientation that my current line of thought is taking.
What do you mean by "data should be owned by the apps to maximize the potential of emergent output"?The primary objective of many apps is to monetize user data and maximize shareholder profits, prioritizing business interests over user privacy.