So far, most applications of blockchain technology have been solutions looking for problems. That's not to say there haven't been some really exciting use cases like Othera or Data Gumbo. It's just that so much garbage has been created alongside these gems of hope. ICOs, NFTs, and speculative cryptocurrencies in general, have created a tremendous amount of noise that serious investors need to peer through in order to find anything of substance. When a $4.2 billion company runs their entire business around monitoring crypto transactions to thwart ransomware attacks, one wonders if the value being created offsets all the collateral damage.

Providing your client with insights, briefly defined as short texts of analytically processed information, is a valuable addition to the services provided by virtually any company. Unfortunately, as engineers, or technicians in general, our training does not address in detail the techniques for writing insights. This short text seeks to serve as a basic guide for future analysts. I introduce the concept of insight and provide advice for the creation of concise and short intelligence pieces. As a senior data analyst, I must do precisely what Ray Dalio, finance magnate, mentions in his December 2019 conversation with Lex Fridman in his podcast "Artificial Intelligence" when asked what role machine learning will play in making decisions and in the analysis: TSC.ai (where I work as a Senior Data Analyst) is a technology company that uses articial intelligence to provide, precisely, intelligence to our customers.

Ayn began her career in journalism and went on to work in corporate communications at Accenture for seven years before joining the content and research team at Emerj. Banks are in one of the best positions for leveraging AI in the coming years because the largest banks have massive volumes of historical data on customers and transactions that can be fed into machine learning algorithms. We recently completed our Emerj AI in Banking Vendor Scorecard and Capability Map in which we explored which AI capabilities banks were taking advantage of the most and which they might be able to leverage in the future. When it comes to big data in banking, banks might be primed to think about using their customer data to build a conversational interface or chatbot to improve the customer experience and, perhaps most importantly, attract millennial customers who are used to getting their needs met quickly over the internet. Despite this, banks are unlikely to leverage their customer data nowadays. In fact, although over 35% of the press releases we explored for our report mentioned conversational interfaces, they represent only 8% of the total funding for AI vendors selling into banking.

With the widespread application of artificial intelligence (AI) in financial fields, more and more related practitioners are beginning to worry about their jobs, especially in the traditional retail banking sector, said David Ye, founder of Jianpu Technology Inc [NYSE:JT], a US-listed financial technology company, at the Yicai Media Annual Summit yesterday. "Three major retail banking processes, namely depositing, lending and remitting are the easiest ones to be replaced by AI but it is less likely to take place in high-end businesses such as investment banking and private banking," Ye said. Jianpu Technology, founded six years ago, has grown from a small company of just 10 people to become a leading financial technology company listed on the New York Stock Exchange. "In the past few years, artificial intelligence has made progress in specific areas such as healthcare and driving technology. Some new AI applications in the financial industry have just emerged in the last two years. These new applications are inseparable from the development of technology in computer vision processing, including speech recognition and machine learning," Ye said.

Alphabet Inc. has been one of the world's biggest spenders on artificial intelligence. The advancements have even surprised company co-founder Sergey Brin. Speaking at the World Economic Forum in Davos, Brin said that when he was in charge of Alphabet's research group he didn't pay much attention to a team working on artificial intelligence. Now the technology, operated by a division of the company called Google Brain, touches nearly every piece of its business, including translation, photography and advertising. In the shoe store of the future, the sales associate asking if you need help might be a wide-eyed, child-sized robot quizzing you on your preferences and displaying suggestions on a touch screen on its chest.