Applications of Artificial Intelligence: It’s Impact

BlairThe article was written by Blair Goldenberg, a Financial Analyst at I Know First, and enrolled in a Masters of Finance at Colorado State University.

Applications of Artificial Intelligence: It’s Impact

Summary

  • The Effect of Artificial Intelligence in Our World
  • I Know First AI System
  • Future in AI Trading
  • AI Security

The Effect of Artificial Intelligence in Our World

Our world is quickly becoming overrun with computers. Essentially, you can’t do anything without having to use some sort of computerized system. Artificial Intelligence is much more than just using a computer. cbbedfb6ab9820d75a07ca89753b5761Artificial Intelligence (AI) refers to computer science allowing for machines to mimic human behavior and intelligence. AI used to be a frightening endeavor, with films such as Terminator, highlighting the possible dangers of giving machines human-like intelligence.

Machine learning in AI has already become a norm in technology, and although there are dangers that have come out of it, such as compromising cyber security, there have also been some amazing steps into the future. Currently, there is research showing that AI can now predict social unrest up to 5 days before it even happens. AI can help detect terrorism and can also bolster cyber security. The Central Intelligence Agency (CIA) has improved their ability, through AI, to detect potential dangers before they happen. In essence, AI is helping to keep our world safe as well as help make it a safer place in general. Self-learning, predictive AI is now being used by retail companies to try and predict what their consumers may want to purchase. It’s also consolidating shopping in a way that consumers won’t have to surf different websites to buy a variety of products, which is mirroring what Amazon already does.

I Know First AI System

Applications of Artificial IntelligenceOur algorithmic system at I Know First has been utilizing self-learning, predictive AI in the financial markets since we were established in 2010. It was developed by Dr. Lipa Roitman, a scientist with over 35 years of experience in the field, and who now leads our Research & Development team to further develop and enhance the algorithm. Dr. Lipa Roitman is an R&D Chemist with a long record in computer modeling of processes, product development and process development. The concept of the current algorithm has crystallized following years of prior research into the nature of chaotic systems.

 

The system is a predictive stock forecast algorithm based on Artificial Intelligence and Machine Learning with elements of Artificial Neural Networks and Genetic Algorithms incorporated in it.

The I Know First Market Prediction System models and predicts the flow of money between the markets. It separates the predictable information from any “random noise”. It then creates a model that projects the future trajectory of the given market in the multidimensional space of other markets.

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The system outputs the predicted trend as a number, positive or negative, along with the wave chart that predicts how the waves will overlap the trend. This helps the trader decide which direction to trade, at what point to enter the trade, and when to exit. To read more about our algorithm, click here.

Future in AI Trading

Artificial intelligence in trading isn’t an entirely new concept, but what a lot of companies lack in their algorithm is historical data, or only uses a few years of data. It’s hard to find a pattern in a small period of time. When looking at the stock market, there are essentially just peaks and valleys on a map. From a 3 month period, you can see almost no pattern, the further you move out, the more patterns you will see. Patterns are also contingent on the parts of the year. If you were to zoom out of the market map further than a year, two years, or even three, you’d be able to see that each year, there are time periods in which similar happenings occur. For example, in America, and in many parts of the world, the holidays are when sales in retail spike. This is a constant and retailers would be foolish not to take that into account. It’s the same idea with AI, in order for a self-learning computer to detect patterns in the market, it needs as much data as it can get. At I Know First, our algorithm incorporates up to 15 years of data (at least 5 years).

Financial Industry Regulatory Authority (FINRA) plans on using new AI systems for security and surveillance purposes and FINRA plans to test the AI software that has been developed in-house next year. Nasdaq Inc (NDAQ.O) and the London Stock Exchange Group (LSE.L) expect to incorporate the software by year-end. Nasdaq is already working with a company called Digital Reasoning, which deals with cognitive computing. Nasdaq had invested inDigital Reasoning earlier this year. LSE has paired up with International Business Machine Corp’s (IBM.N), Watson business, and SparkCognition, a cyber-security firm that will help research and devemlop the A.I. enhanced surveillance they plan on incorporating into their algorithm. The AI software is also going to be sold to banks and other financial institutions so that they can manage their traders easily, safely, and stop any issues from occurring.

AI Security

FINRA monitors about 50 billion market occurrences every day, which includes stock orders, modifications, cancellations and trades. FINRA’s new technology monitors around 270 patterns to try and discover any potential rule violations. FINRA plans to test machine-learning next year with its existing software.

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I Know First’s algorithm already incorporates machine-learning software. So while FINRA is moving forward with their technology, our company already utilizes the machine-learning capacity with Artificial Neural Networks and Genetic Algorithms. FINRA, though, is looking more into security rather than market predictions, their predictive algorithm and their machine-learning they plan on incorporating into the algorithm, is looking more for possible threats to the market. They are trying to monitor any possible manipulation from traders that could cause a crash like the “flash crash” in 2010 caused by High Frequency Trading.