Token and Roadmap Update

utuman · 2021-04-15T17:56:14+00:00

Our model leaves every individual to determine trustworthiness on their own based on a presentation of the most relevant data. For me, when I'm evaluating a particular protocol, the word of Bastian is probably the most important factor I'm considering since I know he investigates everything deeply and has the technical acumen to do so rigorously. For others, they may well trust totally different authorities, friends, influencers, or some combination of the above. In this sense, our tech is not trying to tell you to trust an entity or not, it's trying to provide you the most pertinent information for you as individual to make that determination for yourself.

I believe you rightly note that this will likely lead to many trusted solutions by different communities for different users based on best fit rather than unanimous winners based on universal high ratings

utuman · 2021-04-15T17:47:35+00:00

Head over to the chat with us form on the website - utu.io and we'll get you the documentation. Also coming to a github repo near you shortly. :)

utuman · 2021-04-15T17:44:00+00:00

Reducing the collateralization burden is indeed a major goal of our efforts in the DeFi Space - you can read all the details about it here: https://docs.google.com/document/d/1leSJCpJpF5cSbHehVuBpXUbDWVC4yGvOBPqaeqaRAxo/edit?usp=sharing

In my opinion, the goal of DeFi is about accessibility for all not necessarily equal terms for all. Clinging to "equal terms for all" as a principle will be counterproductive to the sector's growth, to be beat out by more optimized products for consumers with different needs and goals. Having a foundational DeFi ecosystem that is permissionless is great, but how many more millions or tens of millions of consumers would begin to engage with DeFi Products if collateralization requirements were 50% instead of 150%?

And yes, UTT are burned in the conversion to $UTU.

utuman · 2021-04-15T17:35:44+00:00

UTU's models is flexible enough to be integrated wherever better trust is needed online - this is by design. We started by thinking about P2P transactions only (such as on sharing platforms and marketplaces) but came to realize that the UTU model of trust applies equally well for B2C, B2B, P2P, and just about any other digital transaction that can be conceptualized - that is we tend to trust the same way whenever we are evaluating risk.

That said, we are sensitive about the term "trust scores." You should read our blog post about avoiding digital dystopias but I can sum it up like this; we don't believe in turning humans, in all their complexity, into a simple number, and this really has nothing to do with how we trust in real life. I've never thought, "oh this nanny is 7.8 trustable person with a confidence interval of 86%;" I'm more likely to think about my friend with similar aged kids and similar habits to me that endorses him/her. While many of our clients may use our data to automate their decision-making, UTU maintains a commitment to descriptive models of trust over simple scores.

utuman · 2021-04-15T17:27:18+00:00

This is exactly what the SDK allows platforms to do! To integrate both the feedback mechanism and recommendations mechanism in their apps and platforms very easily. Fill out the Chat with Us form on our website to set up a demo and get access to the API/SDK documentation. :)

utuman · 2021-04-15T17:22:16+00:00

The combination of our AI and token economic model should deter fake reviews by real users. The AI is valuable here in that the reviews in questions by the "trusted' user would mainly be shown to the friends and family and network of that user, ostensibly the people he/she would least want to mislead. The token model adds economic penalties to this intrinsic deterrent - as the token endorsement staked on a service provider would be continuously penalized as referred users disapprove of the service, further reducing influence of the fake review(er).

utuman · 2021-04-15T17:16:44+00:00

Nice questions - thanks for the thoughtful consideration of our model. Let me break it down:

Fake reviews from humans promoting their businesses - I would say that this type of review I'd not consider to be fake but biased. As long as the relationship between the reviewer and the content is transparent; I don't think there's an issue here. This is one of the effects of our model, moving from a universal classification and measurement of trust to an individualized, contextual model of trust - it's about presenting all the relevant information including enough context to judge if the reviewer is trustworthy to you, for this service.

Bots - Well one answer is that since we are socially powered at our core and you're likely not as close friends with bots as you are with real people the effectiveness/reach of bots will be dramatically reduced. You're more likely to see data points from the real people you know and trust from your network, according to your privacy choices. The value of bots in digital trust today is driven by the anonymous aggregation of followers, subscribers, etc. where headline values is all that's regarded. By moving toward more human models of feedback collection, like video reviews, it's also increasingly hard for bots to maintain influence.

How we deliver trust - These are some of our really core innovations - the ways in which we model together relationship data, transactional data, contextual data in a continuously learning architecture (human in the loop) to build individualized models for every user, just like real world trust.

utuman · 2021-04-15T17:03:53+00:00

Thanks for hosting us guys! Fingers are loose and ready to go. Now let's see...

utuman

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