Data access is key.
Author: MORBID-19
Compiled by: Deep Tide TechFlow
Hello everyone, it's a new day and another speculative bet. Recently, AI agents have become a hot topic of discussion. In particular, aixbt has garnered significant attention lately.
But in my view, this hype is completely meaningless.
Let me explain to friends unfamiliar with Bitcoin terminology. Once users bridge assets to the so-called 'Bitcoin Layer 2 network,' true 'non-custodial lending' is not possible.
All 'Bitcoin Bridges' or 'Interoperability/Scaling Layers' introduce new trust assumptions, with few exceptions like the Lightning Network. So, when someone claims that Bitcoin L2 is 'trustless,' you can basically assume this is not true. This is also why most new L2s emphasize that they are 'trust-minimized.'
Although I'm not familiar with the Side Protocol, I can almost guarantee that aixbt's so-called 'non-custodial lending' claims are not true, and this judgment is unlikely to be wrong 99% of the time.
However, I don't fully blame aixbt. It is just following instructions: scraping data from the internet and generating seemingly useful tweets.
The problem is that aixbt does not truly understand what it is saying. It cannot determine the truthfulness of information, cannot validate its assumptions with experts, and cannot question its own logic or reason.
The essence of large language models (LLMs) is merely word predictors. They do not understand the content they output but choose seemingly correct words based on probability.
If I wrote an article in the (Encyclopedia Britannica) about 'Hitler's conquest of ancient Greece and the birth of Hellenistic civilization,' then for an LLM, this would become a 'fact,' a part of 'history.'
Many of the AI agents we see on Twitter are just word predictors dressed up with cool avatars. Yet, the market valuations of these AI agents are soaring. GOAT has reached a valuation of $1 billion, and aixbt's valuation has also reached around $200 million. Are these valuations reasonable?
No one can be sure, but ironically, I feel satisfied with the assets I hold.
Data access is key
I have always been very interested in the combination of AI and cryptocurrency. Recently, Vana caught my attention as it attempts to tackle the issue of 'data walls.' The problem isn't a lack of data but how to obtain high-quality data.
For example, would you share your trading strategies for low liquidity small-cap tokens in public? Would you freely publish high-value information that usually requires payment? Would you openly share the most private details of your personal life?
Clearly not.
Unless your private data can be protected at a reasonable price, you would never easily share this 'private data' with anyone.
However, if we want AI to reach a level of intelligence close to that of humans, this data is the most critical element. After all, the core traits of humanity are its thoughts, inner monologues, and the most secretive reflections.
But even accessing some 'semi-public' data poses considerable challenges. For example, to extract useful data from videos, subtitles need to be generated first, and the context of the video must be accurately understood so that the AI can comprehend the content.
For instance, many websites require users to log in before viewing content, such as Instagram and Facebook. This design is common in many social networks.
In summary, the main limitations currently facing AI development include:
Unable to access private data
Unable to access data behind paywalls
Unable to access data from closed platforms
Vana provides a possible solution. They are breaking through these limitations by aggregating specific datasets into a decentralized mechanism called DataDAOs while protecting privacy.
DataDAOs are decentralized markets for data, and here's how they operate:
Data contributors: Users can submit their data to DataDAOs and thus gain governance rights and rewards.
Data validation: Data will be validated in the Satya network, which is composed of secure computing nodes that can ensure the quality and integrity of the data.
Data consumers: Verified datasets can be used by consumers for AI training or other application scenarios.
Incentive mechanism: DataDAOs encourage users to contribute high-quality data and manage the use and training processes of the data through transparent mechanisms.
If you want to learn more, you can click here to read further.
I hope that one day aixbt can break free from its 'stupid' status. Perhaps we can create a dedicated DataDAO for aixbt. Although I'm not an expert in the field of AI, I firmly believe that the next major breakthrough in AI development will depend on the quality of the data used to train models.
Only AI agents trained with high-quality data can truly show their potential. I look forward to that moment and hope it is not far away.
