Phoenix AI’s actual time and outside the actual time of Crypto and outside, processing data silos with transparency and confidence.
Last year, agents working with artificial intelligence and search engines for conversation in converting fast -moving markets such as Crypto, where traders and researchers now tend to have large language models for market analysis, social morale, and trading visions. A recent survey in December showed this 86 % of students It also depends on LLMS for lessons and brainstorming thinking, which confirms the toxicity of this shift.
However, this proliferation came at a cost, as the regulations concerned produce unlimited answers, and extract from vast data groups that do not have clear or unable to verify the examination. In a high -level incident, a pair of lawyers was recently approved after a legal summary of artificial intelligence prepared them He cited six fake court cases Without knowing them.
Likewise, the other challenge is to isolate these artificial intelligence systems, as each agent works separately with the exchange of information across the few or non -existent platforms, which makes it difficult to verify or reconcile different answers. In Crypto, where rumors, fragmented social gossip and data can move on the market in minutes, this lack of transparency is especially fraught with risk. Researchers began to examine These restrictions, which warn that without open standards, the outputs of artificial intelligence will remain fragmented and difficult to verify.
In short, the artificial intelligence revolution provided a bitter pill for swallowing, forcing the researchers the weight of the enormous benefits of the research that artificial intelligence moves with lack of confidence and lack of transparency of its outputs. Some companies have already sought to address this issue face to face, with the decentralized AI platform Phoenix Silm systematically destroying these silos, by building confidence by verifying data.
A solution that depends on the data for the reliable, which is connected to artificial intelligence
Most artificial intelligence research today is emerging and limited in its capabilities. For example, it is still difficult for the majority of LLMS to accumulate data from social media handles, telegram groups, encryption exchange, Blockchain, etc., real time (although it is the most powerful way to discussions surrounding encrypted/tribes, urgent news, and more).
Not only that, there is no real way to achieve a check -up/vote that can be verified and separate public and incorrect data in reality, making the assessments more arduous.
To help calm these bottlenecks, Phoenix has designed an AI Agent environmental system that emphasizes and audits deep intelligence. Technically, it provides active research with actual time data, review from sources such as social media platforms, chain activity, encryption markets, thus allowing users to receive answers supported by transparent source references and updated information, instead of just reasonable articles.
By designing its agents to “show their work”, Phoenix helps ensure that the knowledge he provides is based as evidence that users can verify themselves.
It is worth noting that the way Phoenix deals with the problem of the data silo, which it performs by integrating its agents across multiple areas and infrastructure layers. Platform Form Among the three basic components, phoenixone (AI’s agent interface from the next generation for the final users), the milk (an institution of institutional degree), and SkyNet (a flexible, decentralized mathematical network of more than 2500 contracts).
Together, they allow the system to click on the wide arithmetic energy and specialized data sources without relying on any one central database. For example, by Benefit The funded alphanet models, the Phoenix agent can turn into encrypted currency market trends with an actual depth of general purposes (such as the GPT-4 from Openai or Elon Musk’s Grok).
Indeed, the phoenix encryption research was described as the first agent of Amnesty International dedicated to analyzing the actual time encryption market and its basics, able to intervene through live living data and social media chatting to answer complex questions about the distinctive symbol or trend (while citing its conclusions).
The data exists for everyone to see
From the outside that is seen, it seems that the Phoenix vision has already attracted the support of the main technology and encryption players such as Binance, Tencent Cloud, Bytedance and ChainLink. Moreover, the team also has I collaborate With Tandemai on the discovery of the drug AI and Migu from China Mobile on the created content of artificial intelligence for Metavere.
Thus, in an industry it often suffers from rumors and fragmented data, provides a kind of guidance of artificial intelligence that was examined in Phoenix as a source that affects the need from truth and clarity. As artificial intelligence agents continue, their technology provides a glimpse of a future where, instead of all the chat that gives a different unfounded answer, artificial intelligence agents have become learning from data in actual time (as well as each other) is the criterion.
Such development determines the shift from rough intelligence to trusted intelligence, which ultimately leads to inflating human research efforts instead of undermining it. What awaits us is not just interesting times but a new model as artificial intelligence in encryption becomes transparent, cooperative and above all, trustworthy.
