How AI Can Be Improved By Blockchain

Quite some time ago artificial intelligence (AI) has moved away from a fantastic fictional element and a limited gaming feature. Today AI can be found all across the board, from scientific experiments to everyday things like search engines and our favorite social media.

But how can this new technology that operates invisibly in almost every home change our lives — and more specifically, how can it make our lives better?

 

AI market today

Engineers create AIs to allow computers to solve problems themselves. Essentially, they modify program code in response to the new difficulties they face. Computers beating world champions in chess and other games are no news, but for some time it seemed that AI development stagnated there.

Until unmanned vehicles, created by corporations like Google and Uber, entered the scene. They are still in testing, but the results are incredible — some witnesses claim that an unmanned car was going “better and more carefully than 90% of drivers on the road”. That’s a subjective point of view, but when the public notices the improvement, introducing it to a wide market is just a matter of time.

According to analytical company Tractica, the AI world market exceeds $8 billion, and by 2022 the figure is expected to reach $77.6 billion. To compare, the cloud data storage market in 2017 was around $30 billion.

So when faced with such massive markets, there are who thoughts that appear instantly: how to protect oneself from the uncontrollable influence of AI — and what is the way to enter this market?

Two sides of the coin, those questions can be solved through one another. If to approach the first one through a distributed registry (blockchain), the answer for the second is just around the corner.

Combining AI and blockchain

For machines to learn, they, like people, need to analyze huge amounts of data. The difference is that people analyze data passively, often unconsciously. We use our five feelings (that are sometimes faulty) to understand the world. Then we store this data in our neural connections. The same tasks for the collection, storage, processing of information are the challenges for the developers of machine learning algorithms.

Using learning with a teacher (supervised learning) as an example, let’s have a look at the stages of AI learning. There are four of them: data collection, data labeling, machine learning algorithm training, algorithm operation on real data.

Data collection

At this stage, the greatest difficulty in obtaining a large amount of relevant data. And, in particular, the so-called personal data (private data).

Some projects are aimed at separating personal data for the purpose of transparent monetization. Unlike how Google now collects and sells all data, there is a way to do choose the type of data to transmit. For example, the search history is sent, but the history of movements is not.

There are IoT-data marketplaces that also use the blockchain. For example, a solution from IOTA, Streamr, DatabrokerDAO.

Data labeling

It’s one thing to collect petabytes of data, and another — is to figure out where in the photo there is a cat or a dog, and where is a terrorist. Google ReCaptcha is a glowing example: it has been confirmed that Waymo, a subsidiary to the giant, teaches its cars to recognize us through the free captcha. But it’s advised not to use this corporate resource when there are safer independent options, which create transparent data labeling markets using the blockchain.

Testing and teaching the algorithm

Analysts estimate the cloud computing market at $ 250 billion. Mind immediately jumps to data centers. However, there are projects aimed at democratizing this industry. There is a project that allows to take part in the search for pulsars in the infusion even with the low power of a simple personal computer. There are companies that provide infrastructure-as-a-service (IaaS) for computing, that is based on private miners.

Algorithm operation on real data

When the data is collected and marked, then algorithms are trained. Now AI explores the world to benefit mankind. And for essential computational resources are required as well. At this stage, it is also possible for the community to transfer part of the work, rewarding him for his works with tokens.

Trent McConaghy explained how blockchains can be useful when developing AI. Namely, blockchains have three major characteristics:

• decentralized / collective control;
• immutable / audit records;
• native assets / decentralized marketplaces and exchanges.

Thus, blockchain can provide collective transparent control over AI. For example, when independent auditors (let’s call them white hat data scientists) will be able to look under the hood of a particular machine intelligence algorithm. For example, a conscious Chinese would want to know what’s exactly behind the PRC social rating. If a certain algorithm is rooted in the blockchain, it would be possible to say with confidence which version is currently running. And to be sure that the one that passed the public audit is functioning.

For the data scientist, the last point opens the door to the global AI market. Specialists would be able to sell and transparently monetize their achievements, algorithms, models.

Since in many ways we develop AI to make our lives easier, people are usually willing to pay for it. Obviously, there are many opportunities here to create streams of active and passive income. All this corresponds to the new wave of business models of knowledge monetization and applications.

A Glimpse of Future

The development of the last 15 years has been determined by social networks, mobile and cloud services. Experts claim that it is reasonable to assume that AI, blockchain and the Internet of things are new social networks, mobile and cloud services. These trends are still at a very early stage of formation, but their potential impact is enormous.

Social networks, mobile and cloud development have spawned many huge corporations like Spotify, Airbnb and Uber. It can be assumed that in the next 10–15 years new “unicorns” will appear, and they will focus on artificial intelligence, blockchain and IoT.

Currently, we are on the verge of change — there are shifts in key industries ahead of us that can lead to large-scale innovations in our lives.

How will these technological shifts may affect you? And is there an opportunity to profit off these changing times? Definitely, the influence will be reasonable and we can hardly oppose progress to something. We can only prepare for these changes, and preparation begins with a simple study of the subject.

Thank you for reading this article! Please, share it if you’d enjoyed it and are you excited for the prospects of AI and blockchain? Also, you might like our other stories:

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