The rise of machine learning (ML) in the worlds of finance and business has been well documented, reaching the point where even the most casually interested laymen can tell you that firms are adopting these new technologies to grow and produce more efficiently. 

As the level of attention afforded to ML development has increased, so has the complexity of the surrounding discourse - obscuring the most important information in a revolution that may change the way individuals interact with the wider market forever. 

From behemoth corporations, to young startups and localized non-profits - everyone is looking to use the edge high level computer analytics provide. More tools than ever before are available in open-source libraries, just waiting to be used by the next generation of thought leaders and industry pioneers. 

Despite these advantages, we’re a long way from universal adoption. Even though a critical mass of people certainly know that machine learning exists, most are still working to understand how exactly it’s being used.

While stories heralding AI and ML as the future - and the source of our impending doom -  are seemingly everywhere, details have been cast aside. The development of these new technologies did not happen overnight, but instead through a progression of continual use.

Major press publications like the Associated Press and the Washington Post are using automated programs to write articles. Natural language generation tools are publicly available via the internet and can be used by literally anyone to create written material. 

Others are working to integrate AI in mobile app design, creating highly adaptable, novel experiences for users. From recognizing images and locations to identifying people - apps can interpret new information unlike ever before. 

Ultimately, this is all leading to increasingly personalized user experiences, allowing apps to provide more value to individual users. There is no one particular way machine learning needs to be used. Companies are innovating every day, applying similar technologies in a wide variety of ways. 

This explosive growth is creating an entirely new world, with its own form of education and even regulation. Pioneers are bringing machine learning into every field imaginable, unlocking a new realm of possibilities. 

Machine learning is no longer an abstract idea. In the 21st century, it’s a tool to be used. Anyone can integrate machine learning in the venture of their dreams. You just have to remember to ask the right question.

Is Open Banking the future of banking? What is Open Banking? It is a coined phrase to describe a regulatory system that provides consumers control over their own banking information. New financial startup operations are counting on governments to implement legislation that will open the door for this major change to banking systems that have been the backbone of the banking industry since banks became mainstream.

Mainstream banks have been attempting to dull the enthusiasm for open banking over the years for obvious reasons - Lack of control, fear of decline in market share, etc. Critics of the old estbalished banking system say it’s time for a change. Open banking supporters are promoting a “promised land” where money works as it should - seamless, fast, transparent, where people have the the choice and the right to move between institutions without having to pay high fees and experience long wait times. Mainstream banks have managed to slow down the movement of money and in this digital age coupled with open banking would remove these impediments for consumers.

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The above is from Michael Trigg.

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