The quantity and quality of data fed into Muah AI processing will heavily rely on, as the accuracy is based on it. Some studies even showcase that given a dataset of greater than 10,000 data points, the accuracy of predictions increases by around 25-30%. Conversely, for smaller data sets, accuracies can fall as much as 15%. For instance, it can be seen in e-commerce in which Muah AI predicts future customer purchase behavior based on previous purchasing behavior. This model predicts 85% or more accurately when tested with the data collected from 1,00,000+ customer data for a company.
On a more specific note, Muah AI is capable of analysing 50 data points per second in real-time. It keeps the decision-making and prediction tools really short in high demand environments (like financial markets). As per the report published by World Economic Forum, compartment AI-based systems in Financial sector process data 5 times faster than human delivering insights within seconds and not hours. This speed does not come at the expense of accuracy, which remains consistent as long as the data is continuously (fed and updated.
However, factors like data bias or any error during input can affect the performance of the model. The accuracy of Muah AI would be impacted if the data used to train the AI model contains biased or incomplete information, first, for example. For example, a healthcare company once used Muah AI to retrieve some historical data with which they could anticipate patient treatment outcomes. The system correctly predicted 70% of outcomes instead of the standard 90% threshold due to missing records in the first dataset.
Muah AI, is especially context-sensitive. Although, it is extremely precise in systemic environment like inventory management or predictive maintenance, area is also ineffective for unstructured/ambiguous data. This is especially noticeable when it has to interpret nuanced human feelings or pick subtleties in social media sentiment. According to a marketing firm case study, Muah AI was only able to correctly detect sarcasm or humorous in tweets 60% of the time.
As Tim Cook, Apple CEO, said: “The most important thing with AI is not the algorithms but the data.” To teach Muah AI to the highest accuracy, businesses have to provide clean, diverse and large datasets. To know more about how Muah AI could assist you in optimizing the data processing, please refer muah ai.
