how to leverage blockchain for making machine learning models more accessible?

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How To Leverage Blockchain For Making LearningModels More Accessible

Machine learning is highly pervasive today so much so that we use it a dozen

times a day without even realizing. Machine learning involves getting computers to

learn, think, and act on their own without human interference. As described by

Google, “Machine learning is the future.” With an increasing number of humans

becoming addicted to their machines, the future of machine learning looks very

bright. We are indeed witnesses to a new revolution which is taking over the world

owing to its immense potential.

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What Is Machine Learning?

Machine learning, an application of artificial intelligence, focuses on developing

computer programs that can access data and learn on their own. It deals with

providing computers the capability to learn without being explicitly programmed. Its

basic premise is to build algorithms which can receive input data and make use of

statistical analysis for predicting and updating outputs. Massive quantities of data

can be analyzed through machine learning. Machine learning is a subset of

artificial intelligence which uses algorithms to help computers make data-driven

decisions.

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Understanding Blockchain

A blockchain is a distributed database which is shared across a network of

computers. It is a decentralized public ledger which consists of a chain of blocks.

Blocks comprise of digital pieces of information which store details about

transactions such as date, time, and the money involved for any recent purchase.

Blockchain is the brainchild of Satoshi Nakamoto.

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It is a time-stamped series of an immutable (tamper-proof) record of data which is

managed by a cluster of computers which are not managed by a single entity. The

blocks on a blockchain are connected using cryptographic principles. A blockchain

is highly transparent as anything built on the blockchain can be seen by everyone,

and each participant on the network is accountable for their actions. The three

pillars of blockchain technology are decentralization, immutability, and

transparency.

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Why Blockchain For Machine Learning Models?

· Provides participants a high level of trust and security

· Helps in reliable execution of an incentive-based system, thereby encouraging

participants to contribute data. This will help improve model performance.

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· Good data contributions can be reliably computed and rewards can be delivered.

· As smart contracts are tamper-proof and are evaluated by many machines,

models will not deviate from their promise and will do exactly what they are

supposed to do.

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Payments can be processed with trust on a blockchain.

· Blockchains such as Ethereum offer thousands of decentralized machines all

over the world. This keeps users assured of a smart contract, never being

completely unavailable or taken offline.

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Using Blockchain To make Machine Learning Models More accessible

1. Incentive Mechanisms

Blockchain helps easily share evolving model parameters. Newly created

information such as new pictures, new words, and new movie titles can help

update existing models which are hosted regardless of the ability of an

organization or a specific person to update and host the model themselves. The

model’s performance can be maintained by encouraging people to contribute to

new data. The incentive mechanisms used for these are:

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a.) Gamified- Here, data contributors can earn points and badges, while others

validate their contributions. This solely relies on the willingness of contributors to

contribute to a common good, which is the betterment of the model.

b.) Prediction market-based- Contributors are rewarded when their contribution

makes a positive difference to the performance of the model when evaluated using

a specific test set. This proposal builds on existing work using prediction market

frameworks for training and evaluating models collaboratively.

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The three phases of predictive market-based incentive are:

· Commitment phase- In this, a provider stakes a bounty which will be awarded to

contributors. He then shares enough of the test set to prove that it is valid.

. Participation phase- To cover the possibility of their data being incorrect,

participants submit training data samples along with a small deposit of funds.

· Reward phase-In this phase, the provider reveals the rest of the test set. This is

then validated by a smart contract with the proof provided in the commitment

phase.

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c.) Ongoing self-assessment

Participants validate and pay each other in case of good data contributions. For

this, an existing model is already trained, and data is deployed. A contributor who

wants to update the model submits data with labels, features, and a deposit. After

a predetermined amount of time, the contributor will get their deposit back is the

current model still agrees with the classification.

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If a data is validated as ‘Good,’ the contributor gets the point. If a data added is not

validated as ‘good,’ the deposit of that contributor is split up and distributed among

those who have earned points for good contributions. The malicious contribution of

bad data can be deterred with such a reward system in place.

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Deploying and Update Models

Based on the computational cost to the blockchain network, one needs to pay a

one-time deployment fee to host a model on a public blockchain. From this point

on, anyone who contributes data to train a model will have to pay at least a few

cents, and that should be proportional to the amount of computation which is

done. Microsoft plans to set up a Perceptron model which is capable of classifying

sentiments. It would cost USD0.25 to update the model on Ethereum.

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Microsoft plans to extend its framework, which would imply that most contributors

need not pay this fee. For example, a third party can submit the data and pay the

fee on their behalf, or the contributors can be reimbursed during the reward stage.

Microsoft uses Perceptron to use models which are very efficient to train. This

helps bring down computational costs. More complicated models can be

integrated with API calls from the smart contract to machine learning services. But

in an ideal setting, models are kept public in a smart contract. These models can

also be used for high- dimensional representations which are computed off-chain.

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Conclusion

Though machine learning models have been around for decades now, they have

attained a new status and popularity due to the growing prominence of Artificial

Intelligence (AI). Machine learning ranks high among enterprise technology’s most

competitive realms as major vendors such as

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IBM, Google, Amazon, Microsoft, and many others race to sign customers for

platform services which cover a wide spectrum of machine learning activities such

as data gathering, data preparation, training and application deployment, and

model building. AI and machine learning models are highly optimized to perform

specialized tasks.

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