Morphware crowdsources latent computing capacity from gaming rigs to train machine learning algorithms. Gaming rigs running Morphware bid on workloads posted the network in a sealed-bid, second-price (i.e., Vickrey) auction and are compensated in MorphwareToken. It is currently live on Ethereum mainnet.
The reason this work is important is because the computation capacity required to train a state-of-the-art machine learning algorithm doubles approximately every 3.4 months. In contrast, Moore’s Law observes that the number of transistors in a densely integrated circuit double approximately every 18 months; so there’s an enormous gap that is widening. I should also note that data science budgets are ballooning, and that this trend shows no signs of abating.
This benefits the blockchain ecosystem because it gives ETH miners an economic substitute to solving computations related to Ethereum’s proof-of-work consensus mechanism, and will be even more beneficial once Ethereum migrates its consensus mechanism to proof-of-stake. Ignoring the cost of gas on Ethereum, earning MorphwareToken (hereafter: MWT) is roughly 2-3x more profitable than mining ETH and we are at least 20% cheaper than running the same workload on relevant Amazon Web Services products (EC2 or SageMaker).
We launched in January, so we currently have less than a dozen users; but we’re planning on hosting a Kaggle competition to build-out the demand-side of the network.
We are in the process of building out the supply-side of the network by deploying worker nodes to blockchain clubs at university campuses. We are deploying one worker-node to the University of British Columbia and have a verbal agreement, at the moment, with Harvard College.
We have a variety of milestones that we aim on hitting, this year. The first is getting a miniature data center going in an environment with an extremely low cost of electricity. We have already purchased our first 10 GTX 3060 Ti Founders Edition cards, for this, and are currently in the site selection process for the (server) cabinets.
Another milestone is getting the validator node (see: whitepaper) going on mobile. Right now, Morphware only works on laptops and desktops, but we’d love for people to be able to earn MWT on their phones.
Kenso taught Bitcoin Core in a lecture series at Princeton University in Spring 2018, and Solidity & IPFS at Yale University in Fall 2018, while working for a FinTech-focused coding bootcamp based out of midtown Manhattan. The year before that he opened up a data science bootcamp in Bangalore, for said bootcamp, and taught the first couple of cohorts there.
He previously worked as a senior data scientist at Senary Blockchain Ventures.
Darshan is an engineer at Microsoft. He is the project’s engineering lead.
Anthony is the head of developer relations at ConsenSys and has been helping them build their community, for years, and brings a wealth of experience aboard.
Hassan is a smart contract engineer at MetaMask and an active participant in New York’s Ethereum community.
He has previously worked as a Solidity developer at AirSwap.
Rebecca has a masters degree in computer science from Columbia University, did her undergrad. at Princeton, and used to work for Google.
She’s been creating content for us, which has been featured in high-traffic blogs like Towards Data Science and CoinMonks.
Robert is a production engineer at Facebook, co-author of a book on the Perl programming language, and contributes to the Ruby programming language.
His role is to take Morphware from being a single machine per workload system to a multiple machine per workload system.
Daniel is an electrical engineer and a blockchain developer.
He is helping Morphware with a next generation in-house project related to using FPGAs instead of GPUs for certain (computational) tasks.
Our main metric for success would be 1,000 workloads posted to the network per day.