Monday, February 22, 2016

New "Deep Learning Technology" helps analyze time-series data

            In the business world today, Information Technology has many different uses. One of these many uses would be deciphering a collection of data in order to better serve customers, create products more efficiently, and to over all improve a company or organizations profit. In the article,  Fujitsu develops new deep learning technology to analyze time-series data with high precision”, the newest technology used to decipher, organize and analyze specifically time-series data is described.
            Time-series data is a specific type of data that deals with collecting data over time. This type of data is used on a daily basis within different organizations around the world. For example, a company such as Wal-Mart might collect data in a time-series format in order to understand trends among different product sales throughout the year. This article mentions a new type of technology that will make analyzing time-series data much more efficient called deep learning technology. This technology includes mathematical techniques that will make it easier for computers to give information based on time-series data.
This new mean of deciphering the many pieces of information that can be collected using the time-series method will bring about more efficient processes throughout any organization. Being able to understand and ask questions based on this type of data can in many way improve a company’s customer satisfaction, production efficiency, and profit maximization. It also spurs the next step in supporting artificial intelligence. While this is a task that is no doubt difficult and could have possible negative implications (cost, etc.), it is the way our world is moving towards.
            The article also mentions a few issues with this new deep learning technology. For example, this new technology is limited in the type of data it can interpret. Meaning there is much to develop before it will be able to decipher and organize all types of data, instead of just time-series. This issue is one that will continue into the future, until society is developing the type of technology that can go beyond the present day. Another issue would be the fact that this technology is still in the beginning stages and is very much a work in progress. The success rate is under 100%, meaning the technology is still in the testing phase. When society will be able to start putting this deep learning technology to work is still up in the air.
            Despite these few issues, this new deep learning technology is a large step for the Information Technology industry.

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