In my humble opinion, as someone who has spent years observing and participating in the ever-evolving world of technology, I find myself captivated by the potential that blockchain AI holds for our future. The insights shared by these visionaries resonate deeply with me, particularly their emphasis on decentralization and autonomy.
The convergence between artificial intelligence and crypto has been touted as the next big thing in tech. For the past few years, we’ve seen AI crypto tokens reach market caps of greater than $1 billion.
But despite this massive investor interest, there has so far not been a corresponding wave of user demand.
Inquire about the daily-use AI tool that most users frequently resort to, and you’ll often hear mentions of tools such as ChatGPT, Brave’s Leo search application, or Microsoft’s Copilot.
Rarely will a user state that they use a blockchain or crypto protocol.
Is there a demand for this user from the future, and could blockchain AI really transform the globe, or is it merely the latest trend for raising funds?
cryptoMoon had a discussion with top-tier blockchain AI protocol leaders, where they addressed this intriguing point.
GPU demand is growing
Guarav Sharma, the Chief Technology Officer of AI project IO, pointed out that existing centralized cloud computing systems struggle to meet the growing need for graphical processing units (GPUs) among AI developers. This predicament presents a chance for decentralized blockchain initiatives.
Prior to starting this project, Sharma had been working in the hotel industry, creating AI models that anticipated which hotels a user might choose and what price should be charged. However, when he requested sufficient GPUs from Amazon for model training, they allegedly said they didn’t have enough stock to meet his needs. He expressed:
“We went to Amazon to be honest, like we first thought of buying it. We couldn’t buy it. Then we went to the cloud. We didn’t find it there also, and we just had to wait for months to get this inventory from the AWS itself at that time.”
As a crypto investor, I’ve come to realize the challenge Sharma highlights – it takes centralized cloud computing providers several months to establish servers in specific locations, and this significant expense often falls on the typical user.
It is possible that there are GPUs available right where the customer needs them, but since these GPUs do not belong to the provider, they cannot be made available for the customer.
In simpler terms, Sharma was implying that it’s unlikely Amazon would collaborate with Google to supply 10,000 GPUs in Amsterdam when a customer requests them directly. This is because they typically don’t operate in such a manner.
According to him, instead of relying on a single entity, he proposes that decentralized systems such as IO could provide a solution by establishing a marketplace where GPU power is traded.
Users may visit the platform to search for servers or service providers, who can then make their graphics processing units (GPUs) available on the platform. This facilitates customers in finding GPUs irrespective of the provider. With the increasing need for GPUs due to the rising popularity of AI applications, this is an effective method for connecting buyers with sellers.
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Despite this, Sharma acknowledged that certain AI teams are providing limited value, not just within the realm of blockchain AI but also across the wider AI sector as a whole.
It’s often stated that some teams believe they can develop the next groundbreaking model with only three or five individuals, but in truth, such projects usually require a significantly larger team for their successful execution.
Others have engineers that worked for major companies but have no portfolio to show investors.
Sharma advised that it’s wise for investors to thoroughly examine the team associated with each project, as those who have demonstrated strong performance in the past are more probable to deliver quality work in the future.
One should encourage developers to make their coding transparent (open-source) and undergo routine checks (audits), so the public can comprehend the extent of human involvement in the project. This is important because certain “AI projects” might appear to be AI, but in reality, they involve more human intervention than artificial intelligence.
Prediction markets may need AI
In the future, as suggested by Kartin Wong, a key figure at ORA, blockchain-based prediction markets may require the integration of Artificial Intelligence (AI). This fusion of technologies is expected due to the evolving needs of these systems.
As a researcher, I’ve come across an intriguing example that underscores the importance of decentralized oracles: Polymarket. Although it operates on a blockchain, this platform encounters a unique challenge – determining the outcome of a bet, as it apparently lacks an oracle to address and resolve such issues.
Instead, “most of the time, it relies heavily on human judgement.” However, Blockchain AI has the potential to develop oracles capable of providing answers about virtually any event occurring online.
In addition, he proposed that tokenization could make it easier to gather funds for developing AI models. ORA innovated by suggesting an “initial model offering,” where untrained AI models can issue tokens. The money raised from these tokens can then be used to cover the costs of training the model, which is both CPU- and financially-intensive.
As a proud token-holder, I am entitled to own a piece of the models launched on ORA, which means that whenever these models succeed, I stand to reap the benefits and profit accordingly.
They too operate under an open-source model, fostering transparency for potential investors. As Wong points out, this resolves a prevalent issue within the AI sphere: many models must remain proprietary to generate profits for their investors.
On ORA (Open Resource Archive), it’s essential for model creators to adhere to the licenses associated with open-source software. This rule, as he pointed out, is designed to stop developers from simply copying and pasting code to exploit the earnings of the original creators.
Additionally, Wong admitted that some fraudulent blockchain AI initiatives, as well as AI projects in general, exist. These supposedly intelligent systems might boast about producing results from AI, but in reality, they could be relying on humans to verify the work generated by these models. Consequently, these human-reliant models may render the AI system unnecessary.
He suggested that distinguishing between fake and real AI may sometimes be very difficult.
As a crypto investor, I firmly believe that the most authentic way for any investor to discern whether a product truly leverages AI is by experiencing it firsthand. For instance, take ChatOLM, a chatbot developed via ORA. This product stands out as undeniably employing AI due to its ability to respond swiftly, faster than any human could possibly manage.
Blockchain may allow for “truly autonomous AI”
Based on the words of Ron Chan, the co-founder of Inference Labs, a blockchain AI protocol, he believes that blockchain is the essential method to achieve “AI that operates independently.” Given this perspective, it seems that blockchain technology won’t be discardable in our future.
Chan explained that AI built for a single enterprise is designed with the company’s objectives in mind. Although it serves a purpose, AI that operates independently (decentralized AI) addresses distinct requirements.
This concept “evolves spontaneously, guided by consumer involvement and the pace of market needs,” offering an environment conducive to human-centered innovation capable of addressing significant problems.
He claimed that decentralized AI will develop systems for “proof of inference” or the ability to prove that a particular answer came from a particular AI model. This, he stated, is an “immediate need” for the industry.
Chan acknowledged that distinguishing between human and AI projects can sometimes be difficult or even impossible. He pointed to the example of X user Error Error Ttyl, which is an account that claims to be controlled by an AI model.
As a crypto investor, I often ponder about the authenticity of AI-generated posts. After all, since both the AI and its developer possess the account’s password, it becomes challenging to discern who is actually making decisions behind the scenes. So, how can we ensure transparency in this scenario?
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Chan proposed that the solution lies in granting the AI full autonomy, provable self-governance, and permanent authority over its decisions.
The AI must have “sole access to the account,” and third parties must be able to verify this fact.
Furthermore, when the account management is handed over to the AI, no way should exist for humans to reclaim control. This ensures that any actions taken are undeniably initiated by the AI model itself, with no human intervention hidden in the background.
According to Chan’s perspective, it is only through the use of decentralized protocols that we can effectively tackle this kind of verifiable AI reasoning.
The greatest benefits may come later
CryptoMoon inquired from the participants about instances of user-friendly blockchain AI applications that are currently accessible, instead of ones only available in the near future.
Responding to this, Wong brought up the conversation app known as OLMChat, while Chan talked about an artificial intelligence project for tracking aircraft and a liquid staking application, which is the work of the Inference Labs team.
Despite having smaller user communities than well-known applications such as ChatGPT, these interview participants expressed optimism about the potential impact of blockchain AI. They believe it could significantly transform the world, although they acknowledge that the full benefits may not become apparent to users immediately.
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2024-11-15 16:33