Data and AI

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We are facing some of the most challenging and complicated issues that humans are not equipped to handle on our own, therefore artificial intelligence and the technology that support it have come at the perfect time.

Although there are numerous explanations for this, data is among the most significant. Data is essential to AI and ML, because the numbers and kinds of data required have just recently become available due to the widespread use of the internet.

These days, integrating machine learning algorithms into business processes gives a number of industries a competitive edge.
These technologies offer priceless analysis and forecasts, and the process all begins with data.

Provided the significance of data, appropriate consideration should be provided from the outset of data collecting. An appropriate data infrastructure must be created for this. It is crucial to make sure that the data being gathered is of high quality because an ML algorithm can only be as good as the quality of the data we feed it. Poor data can produce conclusions that are deceptive, insights that are not actionable, and a waste of time and money.

The agricultural industry can serve as an example to help us grasp what constitutes high-quality data. Optimizing yield while minimizing waste is the aim of agriculture. Data regarding as many pertinent variables as feasible must be gathered in order to guarantee this. The types, conditions, and fertility of the soil; meteorological information such as temperature, humidity, wind speed, and rainfall; seed quality and variety; yield; crop protection chemical types; disease types; and a plethora of other data can all be included in this.

For appropriate models to be constructed to use them, all of this data must be extremely accurate and in a standard format that can be input into a common system. To minimize crop loss, for instance, disease detection is crucial. In order to do this, hundreds of thousands of images of sick plants provide the necessary data. These images teach machine learning to identify the type of disease and its severity using pattern recognition, allowing pesticides to be sprayed precisely when needed and using less resources overall.

One other illustration of the significance of accurate data can be seen in the current HR debacle at Amazon. It was revealed at the end of 2018 that Amazon was screening job applicants' resumes with machine learning. Because the machine learning algorithm was trained on the past data of largely male candidates for technical roles held at the organization, it selected a shortlist of mostly male candidates. This was therefore also mirrored by the ML system in its findings, which resulted in a bias against female candidates.

In order to enable precise, seamless, and efficient data collection—which will ultimately enable machine learning algorithms to perform at their peak—it is crucial to have a sound data strategy. Analyze data, draw conclusions from it, and deliver ever-better outcomes to support an individual's or an organization's goals.

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