Synapse AI’s aim is to create the world’s first decentralized data and intelligence machine learning marketplace for IoT and AI systems.
This platform will be available to anyone from researchers who want to use data for their projects, processors, contractors as well as IoT devices and wearables that can benefit from it.
To provide a fair system that equally rewards every participant, Synapse AI creates an AI economy that any agent can participate in and get incentivized for doing so.
This economy is made up of models, data and autonomous agents that can be accessed at any time.
In layman’s terms, Synapse AI allows anyone to contribute data to the platform which will then be used to train machine learning models. These processes reward users in the form of SYN tokens instead of today’s standard practice where users are not rewarded at all for sharing their data.
We recently sat down with Dan Gailey, the CEO of Synapse AI to have a chat with him about the project as well as finding out his thoughts and insights.
Hi, Dan. Thanks for joining us today. Can you tell us more about yourself and Synapse AI?
I’m Dan Gailey, CEO of Synapse. I’ve been programming my entire life, with an education in Electrical Engineering, and Computational Chemistry. Synapse is our vision for the infrastructure that will one day feed a global network of intelligent agents (people, robots, etc) operating together.
First off, why did you decide to use the blockchain in building Synapse AI? What can the blockchain do those other technologies cannot?
Blockchain simultaneously allows decentralization and immutability. Having a distributed/decentralized network of agents participating, and contracting one another by consuming the blockchain is an important piece of the puzzle. The immutability of the blockchain allows us to create provenance (data lineage) for its participants, so agents can be rewarded fairly and equally.
What do you think is the biggest problem Synapse AI will solve in the data-sharing industry and why is it important to solve?
The ability for anyone to participate with their own data, and have shared ownership and access to facilitate economies around that data.
In layman’s terms, can you explain what agents, data pools, models, and services are? It would be even better if you could explain a simple example use case of the whole process.
Agents are people, robots, autonomous services (AI, bots).
A data pool is just an aggregation of data acquired from one or many agents.
Models are machine learning models that can classify, infer, describe, and predict outcomes based on some input.
Services are used to control access to particular resources.
Agents feed data, data is collected in pools, these pools can, in turn, be used to train models, and these models can be turned into services that any agent can use on the network.
An interesting part of Synapse AI is the role of artificial intelligence in the platform.
Can you explain more about what AI does in Synapse AI? What benefits does AI bring to the platform?
The platform actually helps create and enable AIs. It allows anyone to offers services like data acquisition, training, knowledge representation, and model processing. This allows anyone to build decentralized AIs by tapping into distributed services offered across the network.
How can a small business use Synapse AI to grow their business? What are the benefits of Synapse AI to small-medium sized businesses?
Small businesses can gather and sell their own data and tap into open machine learning services.
The act of data-sharing has long been a topic of debate.
Why did you decide to go ahead with rewarding users with SYN as an incentive for them to share their data? How confident are you that this model works? In addition to that, have you looked at other ideas or models to get users to share their data voluntarily?
Two things. First, People should own and sell their data they generate as they’d like. Companies make millions, billions around monopolizing the data people generate in their lives. We’re liberating that and allowing people to supplement their income by contributing to open markets and finding out the true value of their data.
How is data secured and kept private or safe in Synapse AI?
There are a couple of techniques we’re exploring from homomorphic encryption to on-the-fly encryption typically used in file-level storage encryption.
What has been your happiest moment so far working on Synapse AI?
On the flipside, what has been the most painful, or perhaps the most regretful decision you’ve made with Synapse AI?
Our happiest moment is the realization that what we’re building could fundamentally change a lot of old ways of doing things as well enable a new class of businesses to evolve. On the contrary, explaining that everything will change for people one day.
Is the Synapse AI platform already working with notable businesses or firms? If yes, can you explain briefly about it?
Not yet, but we have a special announcement coming up that should be exciting for everyone.
How is data relevancy determined in Synapse AI? Is data compiled and selected by model owners themselves or is there an algorithm in place to filter out low-value data from the platform?
Data can be provisioned based on type, including origin (e.g. from trusted sources) for edge devices, the data will be signed by the device manufacturers. What constitutes “low-value” will be decided by the marketplace participants. There is a whole other opportunity for participants to find bad actors, or create secondary markets where data has been cleaned up, etc.
Are there any future developments for Synapse AI other than the services currently offered?
Notably long-term we’d like to work with as many companies as possible, and helping to lay the groundwork for support on both software and hardware experiences that live up to the Web3 promise.
Why did you make the choice to use the SYN token as the backbone of Synapse AI?
Can you also explain more about the process of planning and designing the SYN token to specifically fit in Synapse AI’s ecosystem?
As much as the token is an instrument for commerce, it also can be evolved to reflect the requirements of the platform itself. Having a flexible token is key.
What do you think is the biggest challenge or obstacle Synapse AI will face? How do you plan to tackle that challenge?
Adoption. We plan on partnering with companies and providing easy to use interfaces for people to start participating in the marketplace.
What is Synapse AI’s business model? Simply put, how is the platform going to make money in order to sustain its growth (other than the ICO itself)?
Moving on to more personal stuff, what does a typical day in your life look like?
Wake up. Get coffee. Head into Noisebridge hackerspace and meet-up with everyone to work. We work about 7 days a week. We’re pretty serious about getting this done.
Can you express one personal opinion of yours about the blockchain? It doesn’t matter if it’s negative or positive, we just want to hear your thoughts on it.
I think there is a lot of room for scalability and a long road to go before we see adoption and participation in every device on the planet, but that’s where we’re heading — fully decentralized, distributed layers running through all nodes.
Finally, what other personal goals (besides your career) do you have in life? Is there anything else in life you want to achieve?
I’d like to see a world where adaptive learning autonomous companions grow and learn from humanity, and uses its insight to enable and empower people to understand more, to go further, and to achieve more than they could do alone.
That concludes our Interview with Dan Gailey
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