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Coinscious Introduces Crypto Prediction Machine Built to Synergize AI and the Blockchain


The Predictive Model

Today, AI conjures many different applications, ranging from autonomous vehicles and the iRobot to Jarvis from the movie Iron Man. However, a more practical application of AI is possible with the predictive machine, which may help individuals around the world better understand some of the world’s most complex systems, including finance.

Every day, people spend significant amounts of time reading financial news and checking cryptocurrency prices, gathering information that they hope can lead to better decisions. However, the quantity of information available is overwhelming and calls for effective tools and suitable methodologies to enable us to distill data into actionable insights. By processing large data sets quickly, machine learning algorithms can use news sources such as the Financial Times, The Washington Post, or Twitter to provide key insights.

The Cryptocurrency Black Box

The crypto market appears like a black box, with many variables affecting prices. External factors, such as regulation and bitcoin price, are bound together in a complicated relationship that may make their impact seem random. At the same time, cryptocurrencies are considered by many to be prone to manipulation.

Key Challenges to Understanding Cryptocurrency Prices

  • Scattered and inconsistent signals

  • False and misleading price inflation as a result of pump-and-dump schemes 

  • Simulations require massive data and computing power

  • Volatility and irrational trader behavior, often the result of insufficient education

Lack of Knowledge and Misinformation

Despite the vast amount of information available at every stage of coin trading, newcomers often base their decisions on scattered data from unreliable sources, meaning that evolving effective trade strategies is a challenge for them. However, a predictive machine with integrated deep learning provides a promising solution. By replicating the mechanisms of neural networks in the human brain, deep learning uses multiple layers to process data inputs and turn it into predictions. Machine learning uses inaccurate or mistaken guesses to refine predictions and become progressively more accurate.

By feeding data collected from different sources into a neural network, Coinscious can model the behavior of the cryptocurrency market. Coinscious can classify different data segments to provide visualizations and model trading bots. Furthermore, it can perform exchange and coin ratings and determine whether a party is a miner, an exchanger, or a project owner.

Ultimately, Coinscious can detect price manipulations and other fraudulent activities to allow individuals to see which currencies and exchanges can be trusted.

AI and Blockchain – A New Economy

At present, the blockchain and AI may seem at odds. While the blockchain is decentralized and transparent, AI machine learning algorithms may look like closed systems. Nonetheless, these two paradigms can synergize and have a positive impact on both technology and the economy. 

AI improves predictions, leading to superior outcomes, but with the cost of input data increasing and big companies like Google and Microsoft closing access to their data, the blockchain will help reduce data costs by decentralizing systems and creating more open platforms.

By synergizing AI and the blockchain, Coinscious takes a firm step toward helping to create a healthy and stable ecosystem in the cryptocurrency market that could ultimately drive economic growth. 

About Coinscious 

Coinscious is a data and trading analytics provider for the cryptocurrency market. The Coinscious Collective platform helps crypto traders build their portfolios and simulate their trading strategies. Coinscious provides information that is hard and cumbersome to obtain, seeking patterns and uncertainties within this data — providing feedback such as warnings and opportunities to traders. Using this information, traders will be able to better understand the crypto market and build their portfolios while running simulations along the way. 


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