Bringing Machine Learning Closer to Clients, Will Blockchain Eliminate Barriers?
Andrey Sergeenkov | Jul 30, 2019 06:19AM ET
In 2018, President Xi Jinping publicly praised blockchain, calling the disruptive but efficient technology, a “top priority for the 21st century”. Indeed, there is development around blockchain.
According to reports, China leads in the number of sanctioned projects. But it is not on the projects-plane alone, Chinese companies are filing more blockchain-related patents than their American counterparts. This fact alone points to the importance of blockchain and how the Chinese leadership is desirous of cementing their position in research, science and technology.
h5 China’s Interest/h5Presently, China has over 263 active projects seeking to provide solutions via the highly reliable technology, and well over 600 blockchain companies are already in operation. Even so, useful and piquing interest as blockchain may be doing, it cannot work in isolation.
Logic demands that to prevent silos, smart contracts that are active in most blockchain platforms would be more efficient and useful if they draw data from verified external sources.
However, it is the role of machine learning and artificial intelligence that would make processes faster and accurate in a data-driven economy of the future. Idealistic as it may seem, the cost of running machine learning nodes can be pretty steep for developers.
h5 Risks of “Technical Debt”/h5Albeit useful in that critical business decisions can be enhanced by machine learning, the expense involved is tremendous and a hindrance even for the most ambitious startup or some cases established tech firms.
To demonstrate, in a paper authored by Google researchers including D. Sculley, Gary Holt, Daniel Golovin and Todd Phillips, it was discovered that problems that cannot be solved by machine learning would not be solved by any other methods.
Therefore, for those developers pursuing this route, there is a risk of falling into a “technical debt” through erosion of boundaries that stem from code design flaws.
h5 Cutting Down Costs of Machine Learning/h5The question is therefore how these downstream costs would be reduced until when computing is pushed to the limit, allowing algorithms to crunch machine learning models and availing solutions to interested businesses regardless of size.
The other relevant question is how machine learning can be made mainstream where a confluence is struck, making it possible for businesses to have access to affordable computing while easily drawing relevant data for their models to work.
Although leading tech companies like Amazon (NASDAQ:AMZN) and Alphabet (NASDAQ:GOOGL) are immersed in machine learning, blockchain-based companies stand a better chance to penetrate the masses because of the distributed nature of nodes, and well, the efficacy of blockchain.
Get The News You Want
In scalping the market share of Machine Learning as a Service (MLaaS), projected to hit $8.3 billion in 2023, GNY stands a chance. There are other leveraging on blockchain and merging distribution with machine learning.
Amongst them is Figure . The latter streamlines home loan processing by finding access points for consumer credit products.
press release , they announced a Retail Sales Prediction Demo where they explained how developers can “answer custom-designed questions with code they build” with an option of using their “pre-designed "plug-and-play" tools.
In an interview , Cosmas Wong summed up their objectives saying:
“We are letting a company or a group or consortium of companies that normally share data, to build a side chain so that the necessary data is shared and used securely within that group, and all processing is done on-chain. Nothing leaves the security of the chain. This is extremely important for us as it promotes the use of the technology and “democratizes” it so it can be used by not just the very large corporations.”
GNY/BTC Price Analysis
At the time of press, GNY is mainly in range mode against the BTC. Trading at 1044 sat, buyers are in temporarily in control. Technically, GNY is bearish against BTC now that prices are oscillating inside of the June 17 bear candlestick.
From an effort versus result point of view, the fact that buyers are yet to build enough momentum to reverse sharp losses of the anchor bar, every high could be another opportunity for sellers to pounce.
However, that is subject to how prices react at the immediate resistance at 1100 sat. If there is a strong upswing lifting prices above this liquidation mark at the back of high trading volumes exceeding 77k of June 17, then GNY could float to 1670 sat as bulls set the foundation for a possible new high.
Conversely, steep losses below 840 sats may spur panic sells as bears aim at 150 sat which is the coin’s all-time lows.
Chart courtesy of Trading View—BitMart
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