Making Money With Redundant Mining Hardware – OPINIONbr>
Sergey Nikolenko is the mathematician of Neuromation, a company that combines artificial intelligence with the computing power of cryptocurrency miners into an integrated marketplace. In simple terms, by using synthetic datasets in machine learning, Neuromation will be able to drastically decrease the cost and adoption of widespread AI adoption, and giving cryptocurrency miners an opportunity to take over AI computing tasks, which will decrease the costs of project development.
In the retail industry, it’s expected that by 2020, 85 per cent of customer interactions will be managed by AI. Thus, store inventory and logistics robots – which are already being talked about and soon to be deployed by Walmart – are coming online as we speak. The Neuromation Platform intends to be the premier destination for AI services for the world’s businesses. They project around 71 million USD gross in transactions on the platform. From each transaction, Neuromation will take a commission ranging from five per cent to 15 per cent, depending on the type of service received through the platform. It is just begun an ICO pre-sale, with the main sale starting Nov. 18. Unsold tokens will be burned.
Nikolenko talks about this vision from the Russian perspective in this Block Tribune exclusive
Earnings from mining crypto currencies are shrinking – the computations complexity grows, while the energy is not getting any cheaper. Many people are looking for alternative applications for expensive hardware purchased during the mining boom. It is quite possible that a substantial part of video cards would be used by scientists or start-ups for complex computing.
With the energy price at RUR 4.5 per kilowatt-hour, there already are not so many wishing to engage in cryptocurrencies mining in Russia. As soon as in winter and spring of this year, investments in new video cards and ASIC chips for a medium-sized cryptocurrency farm paid off completely within few months. Now, in order to earn on mining of many popular crypto currencies, you must first be a millionaire – the format of “makeshift video card” does not work at all, as large farms with well-tuned hardware are required.
That is why, when power plants started to offer their excess capacities on sites with complete infrastructure for rent, miners were immediately interested – with the price of just two rubles per kilowatt-hour. Considering that they are into mining of “light” crypto currencies, like ethereum, Zcash and Monero, earning less than $6 per day, this was a significant support.
Due to energy prices, approximately half of cryptocurrencies are mined in several regions of China. However, the computing complexity will continue to grow, causing a gradual fall of profits. Hence the interest to alternative sources of income.
Since many farms have, in fact, huge computing capacity, they can be primarily used for science purposes.
Usually, for such tasks, supercomputers capacities are rented, e.g “Lomonosov” in Moscow State University being one of the most powerful. These are the machines that will compete with the mining capacities. .
The rental market for mining capacities is already emerging. For example, Neuromation has created a distributed synthetic data platform for neural network applications. Their first commercial product was making store shelves smart. For this, large well-labeled datasets for all the SKUs are created. Algorithms trained on these are able to analyze shelf layout accuracy, percentage of the shelf, and customers’ interaction. The system is capable, actually, to predict customers’ behavior.
The platform requires more than a billion of labeled images of merchandise. Manual labeling of photographs is a painstaking, and very costly, task. For example, on the Amazon Mechanical Turk crowdsourcing service, the manual labeling of a billion pictures would cost about $120 million.
Neuromation entered the market with a new concept of using synthetic data for training neural networks. They generate all the necessary images in 3D generator, similar to a computer game for artificial intelligence. It is partly for this generator that they need large-scale computing capacities, which, if rented from Amazon or Microsoft, would cost tens of millions of dollars. On the other hand, there are thousands of the most advanced video cards available, and they are engaged in ever less profitable ethereum mining.
The founder of Neuromation, Maxim Prasolov, decided, instead of renting capacity for millions of dollars, to lease these mining farms for useful computing, and the company is already using a pool of 1000 video cards. “This is a serious savings for our research process and is beneficial for the miners: the farm services cost 5–10 times cheaper than renting cloud servers, and the miners can earn more by solving fundamental problems instead of mining crypto currency,” he calculates.
It is, of course, to remember that Google has a search for images and Facebook has facial recognition technology for photos, that they managed to develop with their own cloud services without using mining farms. However, the task for Neuromation was substantially different. “First, searching by pictures is a completely different task, and there are specially developed methods for face recognition. Second, Google and Facebook do not need to rent computing power from Amazon Web Services – they have more than enough of their own clusters. But the course of action for a small start-up in this situation is not so obvious,” explains Sergey Nikolenko (Сергей Николенко), Chief Research Officer in Neuromation.
Potentially, miners will gain in average 10-20% more on knowledge mining compared to crypto mining.
Moreover, with tangible benefits for society. “Basically, mining is milling the wind. To generate a “nice” hash, dozens of system operation hours are needed. On the other hand, if we are talking about the search for a drug formula, such a use of capacities from around the world, the application of combined work of computers for the common good would be comparable to the results of research at the Large Hadron Collider,” says Petr Kutyrev, editor of the noosfera.su portal.
The tasks solvable with the mining hardware will be limited due to its customization level. For example, ASIC hardware would be difficult to adapt for scientific tasks, as it is designed exclusively for hashing. The video cards, however, can cope with various scientific tasks.
True, special hardware configuration and sometimes software would be required for such computing. “Video cards for mining can be used for video recognition and rendering, biological experiments. However, for efficient computing, direct access to the computer hardware is essential. If the computation task may be delayed, or the accuracy of machine learning of neural networks is not critical, then, of course, standard tools can be used. Otherwise, you need to develop your own hardware and software infrastructure,” Evgeny Glariantov believes.
Thus, using the farms in science will require some time to set them up and develop special allocation protocols. Yet, considering a more profitable segment, miners will switch to useful computing, and platforms for such tasks may appear in the near future together with the first operating system based on EOS blockchain, believe in the BitMoney Information and Consulting Center. Miners will be able, from time to time, to switch from cryptocoin mining to processing scientific or commercial data, thereby increasing their profitability. Profits from the times of cryptocurrency rush will no longer exist, but business will be more meaningful and stable: unlike volatile crypto currencies, there is always a demand for knowledge.