The Simple Truth About Data Science That People Have A Hard Time Understanding

Data are a necessary necessity for research and applications of artificial intelligence. Therefore, it is inevitable to find solutions to the tension that exists between the need for data and various legal or ethical questions. Diceus’s developers can provide data science consulting services can helpful for you and your business.

Truth About Data Science

When dealing with data science, if there is no data, there is no science. Like you can not bake bread without flour. It is surprising how difficult it is for people sometimes to understand this simple truth. This column dealt with the importance of data for artificial intelligence research. That is, how to balance the desire to promote research and applications of artificial intelligence in the face of questions of privacy and civil rights in a democratic society. I will not deal here with the broad issue but will focus on the importance of the data I have opened.

I will preface the detailed explanation and emphasize that data are necessary for research and applications of artificial intelligence. It is often difficult to obtain or collect data for a particular study or project. There are various reasons for this. Sometimes there is not enough data (or their quality is lacking). Then you have to initiate operations to collect them from different sources or produce them yourself. But even when the data exists, it does not mean that. I can use it. Whether due to restrictions arising from the protection of personal privacy (e.g., medical data), or questions of business confidentiality of companies, and so on. Hence the tension between the need for data and various legal or ethical questions.

Truth About Data Science

The discussion of the need for data can be divided into two stages. The process of learning or building the model (and system) and the ongoing operating procedure of the system. It is seemingly possible to produce rules systems ( expert systems ) without data. This is because it is possible to write rules based on the existing knowledge and experience of experts. But without data, it is impossible to check the correctness of the laws. As a result, it is impossible to improve them. And because rule systems tend to be challenging to maintain anyway, they lose relevance very quickly without data used for measurement and tuning.

In machine learning, all guided learning methods are based on data collection, understanding, and labeling. This is in contrast to non-guided learning methods, such as anomaly detection, which do not require experience and tagging the data and, as a result, allow for privacy (because people don’t need to be exposed to the data). But even this learning is impossible without the machine’s access to information. Sometimes the learning and training stages can be used with synthetic data or data that has undergone unique processes to maintain the privacy or hide classified information. Experience shows that this may impair the performance of the model or system but at least allows starting the process.

During the ongoing operation of the system, the need for accurate data increases. If the model or strategy does not work well, it is necessary to diagnose why. It is usually impossible to understand the problem and try to solve it without analyzing the actual data of the system. Just as it would be difficult to diagnose a disease of a sick person solely based on medical books without examining the patient himself. Moreover, in many systems, the goal is to indicate results that require further treatment. For example, a system that aims to warn of suspicious findings in favor of cyber protection. In such a case, the ability to humanly examine the results and conduct an investigation is required. That is, go back and read the actual data of the system.

The problem is exacerbated when dealing with dynamic data or evolving environments that require ongoing adjustments. Dynamic data characterizes events that occurred at a particular point in time. In many cases, the nature of this data changes over time. For example, watching TV series varies because the taste of the viewers is not constant. As a result, a previously learned machine learning model may become irrelevant after some time. Here is the learning machine of data science

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