Morphware: Making Machine Learning Work for Blockchain

Today, as machine learning techniques are widely applied to a range of applications, machine learning has become important to online services.

Morphware is a decentralized machine learning system that rewards owners of accelerators by auctioning off their idle computing power and then facilitates the associated sub-routines, which can be on behalf of the data scientists to train and test the machine learning models in a decentralized capacity.

Types of machine learning models include supervised semi- or unsupervised learning algorithms.

The training of a supervised learning algorithm can be seen as a search for the optimal combination of weights to apply to a set of inputs or to predict a desirable output.

The impetus of this work is the computational complexity. Hardware that is used to render video games can also accelerate the training of supervised learning algorithms.

What Is Morphware?

One of the key problems in machine learning models is the computational resources required to run state-of-the-art machine learning workloads are doubling approximately every three-and-a-half months.

To address this issue, Morphware develops a peer-to-peer network that allows practicing data scientists, machine learning engineers, and computer science students to pay video game players or others to train models on their behalf.

Although hardware machines are helping data scientists accelerate the development of machine learning models, the high cost of these hardware accelerators is also a barrier for many data scientists.

What Are Machine Learning Models?

Machine learning models can vary by degree of supervision and parameterization. The purpose of training a supervised-parameterized model is to lower the error rate that spans the numerical distance between a prediction and an observation.

Training a machine learning model is implemented by pre-processing, and followed by testing. Data scientists separate the data that is made available to machine learning models while they are training from the data that is made available to them during their period of testing.

Therefore, it can be seen that the model does not overfit the set of available data, as well as performances, which may be worse on unseen data.

Normally, training and testing data are selected from the same file or directory in pre-processing.

The birth of deep learning is the big bang of modern As a fundamentally new software model, deep learning allows billions of software neurons and trillions of connections to be trained, in parallel.

Running deep neural network algorithms and learning from examples, accelerated computing is an ideal approach and the GPU is the ideal processor.

It is a new combination to create a new generation for computing platforms with better performance, programming productivity, and open accessibility.

Deep learning models are known as a subset of machine learning models. They are especially computationally intensive to train because of their interconnected layers of latent variables.

What Is Morphware’s Solution?

The main platform’s currency Morphware Token is used for these transactions.

Tokenomics

The total supply of the Morphware Token is 1,232,922,769 and they are burnable, but not mintable.

Through a website that is designed, developed, and deployed by Morphware, users can buy the platform token.

Less than two percent of the total supply of Morphware Tokens will be for sale in the first month.

How Morphware Works

The process of a machine learning model is data analysis and then is an iterative cycle that vacillates between model selection and feature engineering.

The purpose of this work is to help end-users such as data scientists iterate faster by creating access to a decentralized network of computers that can accelerate their workloads.

End users are paired with and pay, worker nodes via a sealed-bid, second-price reverse auction. They pay worker nodes to train their models and validator nodes to test the models trained by worker nodes by Morphware Tokens.

The roles and responsibilities of members of the network include two autonomous peer types.

To work with Morphware, end users just upload their model, in the form of a Jupyter notebook or a Python file, the training and testing data.

Next, they need to specify the target accuracy level and give a prediction for how long it’ll take to reach that accuracy level. Clicking submits to finish.

End users submit models to be trained by the workers and tested by the validators. Meanwhile, workers are the nodes that earn tokens by training models submitted by the end-users.

Validators are the nodes that earn tokens by testing models trained by the workers.

Once the end-user submits the model, it will be trained by the workers and tested by the validators, through the platform, which communicates with the network through its back-end daemon.

The daemon is responsible for not only creating algorithms and their respective datasets for what is submitted by the end-user through the client but also sending the initial solicitation of work to the smart contract.

In addition, the daemon is responsible for the training and testing of the models, by the workers and validators.

Peer-assisted delivery allows the propagation of an algorithm and corresponding dataset from an end-user to a worker or a validator.

However, the initial work requirements from the end-user and relevant responses to the end-user from workers or validators are all posted to the smart contract.

The initial work requirements include the estimated runtime of the training period, the algorithm-related magnet, the training set, and the testing set of data.

A response from a worker includes a magnet link to the model that they trained, which is subsequently tested by many validators.

If the model that was trained meets the required performance threshold, the worker and validators are going to receive tokens as a reward.

What Makes Morphware Outstanding

Morphware is a two-sided marketplace.

The marketplace serves data scientists who can use the platform to access remote computing power through the network of computers such as CPUs, GPUs, RAM as the way they would use AWS, but at a lower cost and with a more user-friendly interface.

On the other hand, Morphware also serves owners of excess computing power who are looking to earn money and rewards by selling their computing power.

Therefore, its customer segments focus on data scientists, gamers, or people with excess computing power who want to earn money.

Currently, the client list of Morphware has been continuously growing including a data scientist working on a self-driving car Mobility Lab, student organizations who need data science support, and automotive companies such as Suzu, Mitsubishi, or Volvo.

Morphware has also partnered with Tellor. Under this partnership, Tellor is going to pay Morphware for using their oracle for the first few months.

Compared with other competitors in the market, Morphware has a competitive advantage. Its unique marketplace strategy makes its product cheaper than others.

Closing Thoughts on Morphware

As machine learning models are becoming increasingly complex, the projects for a new ecosystem of machine learning models trading over a Blockchain-based network has been explored.

As such, the end-users or the buyers can acquire the model of interest from the machine learning market while workers or sellers who are interested in spending local computations on data to enhance that model’s quality.

As so, the proportional relation between the local data and the quality of trained models is considered, and the valuations of seller’s data in training the models are estimated.

The project shows a competitive run-time performance, a lower cost of execution, and fairness in terms of incentives for the participants.

Morphware is one of the pioneering platforms that introduces a peer-to-peer network where end-users can pay video game players to train machine learning models, on their behalf, in the platform’s currency Morphware Token.

To learn more about Morphware – please click here!

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