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Why Neural Networks are Limited?
For many years we were told that we have too much data to process and too little datasets to train our ML and NN’s.
in the past few years the situation has change and we see more and more datasets released for public use by the AI community, academic researches, governments and commercial companies, all together create a situation that according to google new “dataset search engine” there are more than 25 million datasets available for the AI community and data scientists to explore and evaluate.
So if we have so many datasets why do we still need to create our own dataset? and why most of the labeling, tagging, classification and data preparation tasks are done by “human in the loop” technologies.
2. They were created for a specific use case which cannot be replicated or used
3. The dataset is released without enough information about the prediction model or the objectives tested.
Another challenge in computer vision is that imagery files have different properties, like: shooting angle, distance, lighting, height and more that influence and impact the outcome of NN’s training and the prediction model. So eventually, although we have millions of datasets for public use, practically we don’t know how good they are and how data scientist can quickly adopt these datasets and use them.
ProjAIX build a platform which is answer the most important questions during the process of searching datasets to be trained with NN’s –> “what is the quality of the datasets? and how can I confirm it?
Thanks,
ProjAIX team
