Solutions come after problems but exceptionally blockchain is a solution looking for its problems. We cannot find a completely solution except bitcoin based on blockchain. There are all around proof of concept studies just aiming to use blockchain. They wouldn’t solve real world problems. Herein, meeting of blockchain and machine learning might become a remarkable revolution.
The magazine cover of the economist mentioned that data is the new oil for this century. Net worth of the data-first tech giants is more than the gross national incomes of many countries. For instance, the net worth of the companies existing in the cover is 2.5 trillion dollars whereas gross national income of France was 2.5 trillion dollars in 2017.
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Social media users feed their own data to these services just like volunteers. Consider the public meeting of Obama in Berlin. Almost every attendee takes photo and creates data in this scene.
These giant companies are all fed by the data. But just having data doesn’t make these companies giant. They have also the power to process and understand data. Herein, deep learning offers no theoretical limitations of what it can learn; the more data you give and the more computational time you provide, the better it is. These are the words of Geoffrey Hinton. Still, they are not as intelligent as human beings. We can fool neural networks.
Fooling neural networks
Associated press tweeted a breaking news and announced that there are two explosions in the White House and Obama is injured. Soon, it is understood that Syrian hackers access associated press account. However, some bots immediately decide to sell market shares. This causes to damage 139 billion dollars.
Debugging neural networks
Even though neural networks and deep learning are the most powerful machine learning algorithm, they are not explainable. For instance, Google’s Inception V3 model is the winner of the imagenet contest of kaggle. Models compete with to classify millions of pictures in hundres of classes. Here, Inception V3 model can classify the following picture as panda successfully but adding some noise (non-random) to the original image manipulates it and the model classify the new one as gibbon. Even though people eye cannot differentiate them both, they are as different as the noise. So, if you feed the noise added picture in the learning step, you cause neural networks to mislearn.
This attacks become vital in some cases. We will see driverless cars soon everywhere. Google, Uber and Tesla invest in this technology. Computer vision is important member of these systems. The following picture shows a physical adversarial attack. This is classified as speed limit: 45 mph sign! Consider a driverless car goes 45 mph when it should stop.
People enjoy to troll and manipulate systems. We will always be defenceless?
Cryptography
Herein, solution is simple. Attacking a public key system is much harder than cracking a password. For instance, breaking a ECC private key requires 1018 years whereas age of the universe in 109 years.
If Associated Press tend to sign its tweets, then readers realize that account is accessed by some bad guys. So, cryptography is enough to be defensive? Still some malicious ones feed manipulated data and sign it with its private keys.
Blockchain meets machine learning
Herein, think blockchain as a public database. One feeds data to blockchain and users verify the correctness of the data before adding to chain. Feeder signs the data with its private key. That’s why, we know that the data is fed by that user. If you realize a manipulated data, then feeder will be banned from the system. In this case, noise added panda picture won’t be added to public data set and you won’t neural networks to mislearn.
Blockchain vs machine learning
Blockchain and machine learning are like yin yang. Even though they are based on opposite ideas, they complement each other like puzzle parts.
Herein, PayPal mafia members described the relation of blockchain and machine learning very interesting. Peter Thiel described that Crypto is libertarian, AI is communist. Reid Hoffman explained that Crypto is anarchy, AI is the rule of laws.
Firstly, blockchain is deterministic whereas machine learning is probabilistic. Probabilistic models can fail. Besides, it should fail to avoid overfitting.
Secondly, blockchain offers decentralized architecture whereas machine learning has centralized. You build a machine learning model and it is your own property.
Thirdly, blockchain is transparent whereas machine learning models, in particular deep learning is a completely black box. You can see all previous transactions backwardly in blockchain. In contrast, you do not know how deep learning models work completely.
Finally, blockchain transactions are permanent whereas machine learning models are changing. You cannot remove or change a verified transaction on a blockchain because previous chain hashes moved to following chains. You must change all chains but this is not possible. On the other hand, you might need to remodel or retrain a machine learning in time even though it works well on first deployment.
Meeting blockchain and machine learning creates secure and also more data. Because, people encourage to feed data to public data sets. Having secure and more data causes better machine learning models. Having better models offers better results and actions.
In this post, we will described an utopia which can make tomorrow better. We can just trust blockchain enabled AI solutions for intelligent homes or driverless cars in the near future. Moreover, this won’t be a POC study just aiming to use blockchain. It solves a real world problem for machine learning practitioners.
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