Synapse AI – The Market of Artificial Intelligence For Better Future

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Synapse AI – The Market of Artificial Intelligence For Better Future

Synapse AI – Previously, people had to give their data away to exchange in the company for centralized access to applications and services. Unfortunately, the model Created to serve this user is domain-specific and is usually locked in fenced gardens making it difficult to weigh and create an efficient market beyond that service. Once a model is created, the economics of the model in turn rarely benefit the data supplier.

We separate data and models from domain-specific applications owned by monopolist companies. By introducing an infrastructure system that allows a competitive market span of intelligent attendees, this will provide the same value and benefits for all.

Aggregation Monopoly data

People have contributed data and built models for services they use for free and devices that act as loss leaders. From social media to email, search, and fitness, people are building a closed model for the company every day.

Facebook uses message data to train Conversation models of “M”, their ChatBot messages. Users train sentiment models on status, articles posted, and advertisements. This training not only acts to provide feedback to content contributors, but also helps Facebook build up personalities from users and their tastes. This in turn determines how to background content that is relevant in news feeds, increases engagement and creates training cycles.

the entire Google business model revolves around Gathering users, tagging, and modeling them. This model in turn acts as a multiplier for experience in their products. Gmail trains their Spam filters by having a spam tag user. Search using dueries and click-throughs correlates relevance and trains power for advertising. Android uses location services to capture how, where, and when we spend time offline.

Amazon uses the look and buy of our search history to create models of related products and is recommended to you and others. the profits generated from these purchases are then reinvested in order to build logistics and supply chains, in the future further improving their infrastructure.

Fitbit uses your data to help model your health and fitness, and in return provides suggestions and recommendations to achieve your goals.

Square’s business model is to simply resell the purchased data to interested parties.

One thing of these companies that have in common is that they have, or the desire to own, the entire pipeline from the device to the application. They have a monopoly on the data for training this model and gathering these insights. This is good for the Company, but once the data is acquired, filtered, tagged and modeled, all the benefits run towards the company involved and not to the Contributors who help to facilitate them. Opening data and models in market format for access and Services, We can Create a combination of companies that provide the most beneficial benefits to all mankind. This can be achieved by allowing the exploration and discovery of an organic insight and intelligence system.

Partnership

While We look forward to participating in the protocol layer, it acts as a platform for other companies and startups implementing their services above us, Coat pieces of relevant information to users through a unified identity service that keeps track of the Wallet’s balance.
Manufacturers of smartphones, to showcase the balance that goes from the value gained through our service alongside the traditional Ul element will be super exciting.

Companies that can sign to verify their data as well as their customer data can Contribute and update existing Ontologies.

Impact

provides both open access to data and the latest learning model machines generated by agents have some interesting effects. People and companies will get their compensation for ambient and explicit data for the first time in a public order book. The utility of models built on this data will be tracked and verified by participants who can also benefit by their Contribution.

Humans in the circle of acquisition, verification, screening, and modeling will be a big part of the future. Work automation becomes more common and smarter.

The system we are building also goes beyond this, ultimately enabling autonomous agents to replace and facilitate. Without the intervention of modeling helps to automate the future.

Team

At the time of writing this article the team consists of 3 members:

  1. Dan Gailey (CEO [RadBots, Baqqer, Make:, e.Ventures])
  2. Nathan Ross (COO [RadBots, Bright.com, Viewics Healthcare, FCB Advertising])
  3. Jamie Cushenan (Blockchain Developer [Baqqer, BoS Game Studio])

They also have four advisors:

  1. Hugh Dubberly (Stanford, Apple, Netscape)
  2. Howard Rheingold (Author, Stanford, Institute for the Future)
  3. Jackson Palmer (Dogecoin, Adobe)
  4. Jade Van Doren (AllTrails, hopOn, TechForward)

Initial Coin Offering

ICO will feature the sale of an ERC-20 Token built from an ethereum smart contract.

Token distribution:

33% Token Sale
33% Developer Fund
33% Company
01% Token Sale Costs

Token disbursement will happen 60 days after the public auction.

To know the latest information about Synapse project you can visit the link below:

Website : https://synapse.ai/
Whitepaper : https://s3.amazonaws.com/synapseai/whitepaper/whitepaper.pdf
Facebook : https://www.facebook.com/syntoken/
Twitter : https://twitter.com/aisynapse
Reddit : https://www.reddit.com/r/synapseai/
GitHub : https://github.com/SynapseOrg
Slack : https://synapseai-slack.herokuapp.com/
ANN Bitcointalk : https://bitcointalk.org/index.php?topic=2198553.0
Bounty Program : https://bitcointalk.org/index.php?topic=2198555

BTT Profile : https://bitcointalk.org/index.php?action=profile;u=376156

ETH : 0xF4919c366c3ad386f0A5Abe322d6cDe0238CeB28

Post Author: ubay

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