Are you going to Devcon?
I geocoded all the side-events and marked it on a map to find out optimal location to book a hotel and filter by dates
try it at - https://t.co/2ur1jwy5pF , you can check relative location and distance of your hotel/address with respect to side events.
4/4
- Monetizing personal data
- Facilitating cross-border transactions
- Implementing decentralized and transparent voting systems
- Managing healthcare records
With the introduction of #DID, the future of #NFTs looks brighter than ever.
๐งตWhy are #NFTs dead?
Will they ever make a comeback?
1/4
The lack of practical applications for businesses and individuals to earn from NFTs, along with the restrictions imposed on listed US companies from facilitating #NFT transactions, raises doubts about the future of NFTs.
3/4
By providing secure and reliable identity verification, access control, and privacy protection, #DID is poised to revolutionize the way we interact online.
This is opening up a wide array of previously unimaginable use cases, such as:
@3Suite stands out by leveraging a well-established AI model, for a well-thought-out purpose of developing a 360ยฐ user profile. With 5+ years of training in multiple languages, it ensures strategic alignment, ethical compliance, and continuous learning.
5) Neglecting Continuous Learning:
Assuming that AI deployment marks the culmination of the process, rather than recognizing the need for continuous learning. Establishing mechanisms for ongoing improvement, feedback loops, and staying abreast of advancements is crucial.
4) Failing to Involve Domain Experts:
AI's effectiveness relies on collaboration with experts who understand the intricacies of each specific industry. Failing to involve these experts the can result in AI solutions that are disconnected from the practical needs of a business.
3) Ethical and Regulatory Oversights:
Ignoring ethical and regulatory considerations in the pursuit of AI adoption. Overlooking these crucial aspects can result in reputational damage and legal repercussions.
2) Neglecting Well-Trained AI:
Choosing a new AI model or developing a brand new one instead of leveraging well-trained models. Neglecting the wealth of knowledge embedded in multiple years of data will hinder the accuracy of the AI and the performance potential of applications.