New Hedge, a San Francisco company, is now employing encrypted data streams machine learning as well as Bitcoin Payment technologies to come with a de
New Hedge, a San Francisco company, is now employing encrypted data streams machine learning as well as Bitcoin Payment technologies to come with a deep learning system that helps to inform traders. New Hedge has attracted many coders has compared with its competitors and now boost more than 7,500 developers who are entirely anonymous.
According to TechCrunch reports, New Hedge sends encrypted trading data to developers who have signed up with the system. Each of the developers uses different machine-learning techniques that help to forecasts based on the available data. If users find the information useful, they pay the data scientist in Bitcoin.
This may sound strange to new investors, but some traders who have used the information that the idea is great and has helped them make a lot of money and even secure funding. It is a great system that intends to help users make sound decisions and avoid losing their resources but optimize on lucrative ventures.
Hedge funds understand how to develop the best algorithms and come up with an artificial intelligence system to solve different businesses challenges. It is a machine learning approach that can easily analyze a lot of data that the human being can take many years to analyze completely. Machine learning systems are mainly used to mine insights from social media, news coverage as well as other unstructured data.
Currently, a number of hedge funds have invested heavily in AI. However, the industry has attracted critics from different parties who say that the industry is overpriced and is underperforming. And although it sounds great to have a machine learning system, performance of such systems are still under studies and remain to be seen how such system will perform shortly.
AI systems are very sensitive to noise and uncertainty that are common in the financial markets. The hedge fund Numerai has also raised some questions concerning the anonymity of its employees. For example, the fact that the employees are anonymous mean that it is almost impossible to tell whether a data scientist working for Numerai is also working with other companies who have the same conflict of interest.
The sending of encrypted data to the coders is also a delicate balance between speed and security and must be handled with care. However, it is a great approach for learning the ever increasing mass data produced online.