How to leverage blockchain for making machine learning models more accessible

Copyright © Blockchain Council www.blockchain-council.org
How To Leverage Blockchain For Making Learning
Models 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.
2

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.
3

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.
4

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.
5

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.
6

· 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.
7

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.
8

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:
9

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.
10

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.
11

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.
12

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.
13

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.
14

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.
15

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
16

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.
17

Blockchain Council Certifications
You can check out our certifications, and kick start your career in the Blockchain
space today.
● Certified Blockchain Expert
● Certified Blockchain Developer
● Certified Smart Contract Developer
● Certified Solidity Developer
● Certified Ethereum Developer
● Certified Blockchain Architect™
18

THANK YOU!
Any questions?
You can mail us at
hello@blockchain-council.org
Copyright © Blockchain Council www.blockchain-council.org 19

How to leverage blockchain for making machine learning models more accessible

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to How to leverage blockchain for making machine learning models more accessible

Similar to How to leverage blockchain for making machine learning models more accessible (20)

More from Blockchain Council

More from Blockchain Council (20)

Recently uploaded

Recently uploaded (20)

How to leverage blockchain for making machine learning models more accessible