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Algorithmic Technology

By Matthew Giannelis posted Sun June 13, 2021 07:22 AM

  

Algorithmic Technology, also known as Artificial Intelligence (A.I) has been with us for a while now. It has been present in computing since the 1990's, but was mostly theoretical until recently. Only recently have experts from IBM made these computational algorithms practically visible in a commercial setting, by creating software called Watson. In fact, IBM's Watson can read handwritten notes, diagnose a cold and can even beat a professional chess player. Watson is currently being used by over two hundred hospitals worldwide in health care to improve their patient's health care delivery.


Algorithmic technology is the development of algorithms that can make computing less susceptible to outside factors such as human error or malicious outside intervention. The term was first used in a 1976 paper by Joel Katz and Douglas K. Morrison. It described a new class of algorithms which could achieve desirable results in less time. The paper also pointed out some of the major limitations of the conventional algorithm, which had made it impractical for many applications. However, the real point of algorithmic technology is not to replace the traditional algorithms, but to make them more effective and efficient for various purposes.



Algorithmic technology is also present behind many of the automated systems that are being developed each year. The first type of automated system is Deep Learning, which use networks to detect patterns, predict future data, and make predictions. Next is automation, which refers to the integration of different technologies to improve workflow and save time. Finally, there is automation of journalism itself, which is largely untapped and unexplored today. All of these areas of algorithmic technology that is currently available in the field of journalism could potentially automate much of the process that journalists do, saving both time and labor in the future

Today there are many applications of algorithmic technology. Algorithms have found use in many areas of computer science including language processing, financial analysis, and computer programming. One of the most exciting areas is in the area of retail trading. Retail traders often require quick access to important information such as product prices, customer demographics, and competitor information.

Computers are not naturally equipped to gather the kind of information required by retail traders. One way to get around this problem is to develop algorithmically driven machines called "algorithmic trading robots". These machines are able to process large amounts of data from an unpredictable environment and make decisions about what to buy and sell based on their analysis. 

Algorithmic technology is also very closely related to machine learning algorithms. Machine learning refers to the process of designing computer programs which can recognize patterns, make decisions, and solve problems. Machine learning algorithms were originally developed for military applications, but they have been applied to many different fields including advertising, web search engines, and product design. Many of today's top brands use some form of machine learning algorithms to help create new lines of products, enhance their existing products, and respond to customer needs.

Algorithms may not be the wave of the future for all businesses, but they are not something that will fly away entirely either. Algorithms need to be a part of a business's current organizational practices. The first step in adopting an algorithmic approach is to identify current practices and tools which are successful in accomplishing a set of organizational goals. Then these successful practices can be modified to become more applicable to current objectives. Algorithms can also be implemented as part of a company's operational improvement initiatives.

When an algorithmic technology tool is successfully implemented, it can change a company's business dramatically. If successful algorithmic programs can be introduced into a company, it can lead to: * An increase in profit levels due to increased efficiency in decision making * Enhanced productivity and efficiency in all areas * Reduced costs due to reduced need for human intervention * A reduction in the incidence and severity of errors and cost * A decrease in the overall cost of ownership of those algorithms and tools that have been implemented The impact of these algorithmic changes can be dramatic and long lasting if the strategies that were implemented are successful and sustained over time. However, if the strategies are poorly executed or are not well aligned with company goals, the results could be less than optimal. Algorithmic technology can be a valuable tool to help a company achieve its organizational objectives. But a company must perform the necessary analysis and implementation process carefully and must make strategic decisions about the future of its algorithmic technology strategy and program.

It is important for a company to understand the algorithm behind its algorithmic program. Analyzing and understanding the algorithms of a system is important for a company to make strategic decisions that will increase the efficacy of the system, increase company profits, and allow it to realize its value. Many businesses today are making the transition to algorithmic technology. However, very few companies have made the transformation and are successfully doing so. These businesses include telecommunications companies, which use algorithmic call capture to improve the quality of voice calls; financial companies that rely on algorithmic lead management to reduce their reliance on third party investment banks; health care companies which use algorithmic identification and purge to prevent "spam" of potential patients; and government agencies that use algorithmic identification and exclusion to prevent the loss of government funds. The types of organizations that typically use algorithmic technology are also the types that typically experience the most risk from the use of algorithmic technology.

The primary drivers behind the adoption of algorithmic technology are cost reduction, efficiency, accuracy, and security. These drivers are typically valid, although not without challenges. When a company adopts an algorithmic strategy, it should do so with due diligence and a solid plan for how the strategy will be implemented, monitored, and modified as business requirements change and as the market factors further the development of algorithms and other computer-implemented strategies.

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