<prompt> Hlalosa hore na mehlala ea bohanyetsi e ka fokotsa tsebetso ea mohlala oa ho ithuta oa mochini joang.nan  <response>: Instruction in English: Explain how adversarial examples can reduce the performance of a machine learning model. . Response in English: Adversarial examples refer to inputs to a machine learning model that have been deliberately designed or modified to cause the model to make mistakes. These adversarial inputs are typically crafted in such a way that the changes or perturbations are almost imperceptible to human observation, but are enough to cause the model to misclassify the input or produce incorrect outputs.Adversarial examples can reduce the performance of a machine learning model in several ways. For one, these inputs exploit the models weaknesses or blind spots, causing the model to produce incorrect results. In essence, adversarial examples exploit the limitations of the machine learning algorithms to trick the model into making incorrect predictions.This reduces the overall accuracy of the model and makes it less effective in achieving the tasks it was designed for. For example, in the context of image classification, adversarial examples can cause the model to misclassify an object in an image, leading to incorrect results.Additionally, adversarial examples can reduce the models confidence in its predictions, causing it to produce lower probability scores for the correct prediction. This can be particularly problematic for applications where high confidence in the predictions is critical, such as in medical diagnosis or self-driving cars.In summary, adversarial examples can reduce the performance of a machine learning model by exploiting its weaknesses, leading to incorrect predictions and reduced confidence in its outputs, which can in turn have serious consequences in critical applications. . Response in Sesotho: Mehlala ea bohanyetsi e bua ka lintho tse kenang ho mofuta oa ho ithuta oa mochini o entsoeng ka boomo kapa o fetotsoe ho etsa hore mohlala o etse liphoso. Maikutlo ana a bahanyetsi ka tloaelo a entsoe ka tsela eo liphetoho kapa litšitiso li batlang li sa bonahale ho bonoa ke batho, empa li lekane ho etsa hore mohlala o se ke oa arola se kenyelletsoeng kapa o hlahise liphetho tse fosahetseng. Mehlala ea bahanyetsi e ka fokotsa tsebetso ea mochini oa ho ithuta ka mekhoa e mengata. E ngoe, lisebelisoa tsena li sebelisa mefokolo ea mohlala kapa matheba a foufetseng, e leng se etsang hore mohlala o hlahise liphello tse fosahetseng. Ha e le hantle, mehlala ea bahanyetsi e sebelisa meeli ea li-algorithms tsa ho ithuta mochini ho qhekella mohlala ho etsa likhakanyo tse fosahetseng. Sena se fokotsa ho nepahala ha kakaretso ea mohlala mme se etsa hore o se ke oa atleha ho fihlela mesebetsi eo o neng o e etselitsoe. Ka mohlala, moelelong oa ho arola litšoantšo, mehlala ea bahanyetsi e ka etsa hore mohlala o se ke oa khetholla ntho e setšoantšong, e leng se lebisang liphellong tse fosahetseng. Ho feta moo, mehlala ea bohanyetsi e ka fokotsa tsepo ea mohlala likhakanyong tsa eona, ea etsa hore e hlahise lintlha tse tlase tsa ponelopele e nepahetseng. Sena se ka ba bothata haholo bakeng sa litsebetso tse nang le tsepo e kholo ea likhakanyo, joalo ka tlhahlobo ea bongaka kapa likoloi tse itaolang. Ka kakaretso, mehlala ea bahanyetsi e ka fokotsa tsebetso ea mohlala oa ho ithuta mochine ka ho sebelisa mefokolo ea eona, e lebisang ho likhakanyo tse fosahetseng le ho fokotsa kholiseho liphellong tsa eona, e leng se ka bang le liphello tse tebileng litsebetsong tsa bohlokoa. 