3 big problems with datasets in AI and machine learning | Venture Beat

Share on facebook
Share on twitter
Share on linkedin

AI has progressed tremendously in recent years. AI, as powerful as it is, is not without flaws. Studies demonstrate that many of the libraries used to train, benchmark, and test models contain biases and errors, emphasizing the dangers of putting too much reliance on data that hasn’t been fully reviewed – even when the data originates from prestigious organizations. The training conundrum, labeling issues, and benchmarking issues are only a few of the key issues discovered. 



Subscribe to our newsletter