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      Machine  learning: Machine  learning algorithms are
                                                               trained on data to detect patterns and similarities. Once
                                                               trained, these models can make predictions or decisions
                                                               based  on new  data.  For  instance,  a machine  learning
                                                               model can be trained to recognise patterns in customer

                                                               behaviour and predict future purchasing habits.

                             ethical minds                                                        fact bits
                                                                                               Some patterns are
                                                                                              fractals, meaning if
                              Companies can use patterns to figure out
                              exactly  how to  make  us  buy  things  we                    you zoom in on a small
                              might  not  really need.  This is an ethical                    part, it looks exactly
                              concern because it can trick people into                        like the whole thing.
                              spending money for the company's benefit
                              instead of their own.

                      OBSERVATIONS AND CONCLUSION

                      Observations mean looking at data to find patterns, trends or key details without making guesses.
                      It’s about recording what you see or measure. For example, if you notice, “The plant that was
                      placed in the dark closet has yellow, wilted leaves and hasn't grown in height,” you’re observing
                      the condition of the plant. A conclusion is what you figure out or decide after looking at your
                      observations.  It’s your understanding  of what the data  means. From the observation,  your
                      conclusion could be, “Plants require sunlight to produce energy and grow healthily.”

                      Making Observations from Data (What did you see?)

                      Here are some methods to help you make clear observations:

                        Identify key findings: Look for the most important numbers, trends or relationships in the
                         data. For example, you might notice that sales are highest in the summer months.
                        Look for repeating patterns:  Check  if there are any repeated  themes  or groupings.  For
                         example, you might find that people tend to buy certain items together frequently.

                        Note irregularities: Pay attention to anything that stands out as unusual or unexpected in your
                         data. For example, if one day shows a sudden drop in website traffic, it could be something
                         worth investigating.
                        Use visuals: Graphs and charts can help you see patterns that might be hidden in the raw
                         data. They make it easier to spot trends, like changes in sales or temperature over time.

                      Drawing Simple Conclusions (What does it mean?)

                      Here are some methods to help you make simple conclusions:
                       Answering the question: A conclusion provides the answer to the question we were investigating
                         through the data. For example, students who study more tend to score higher on tests.

                      58         Artificial Intelligence (CT & AI)-VI

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