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Apart from prediction and classification, in what other projects I can use AI and ML?
- Image Processing (Correct image quality, Image Analysis, Image Synthesizing, Image Captioning)
- Text Generation (For Q&A, Chatbot Response, Text Summarization)
- Video Processing (Identifying actions and humans present in the video, Video summarization)
How much data is required to build an AI and ML-based solution?
What specific type of data is required to implement AI and ML?
- Tabular data
What are the limitations of AI and ML?
- Unavailability of a large number of training samples.
- Labeling of Data – As deep learning and conventional machine learning algorithms are supervised, i.e., they need data and their label to capture the semantics of work to be done. It is a manual process and eats up more time than actually building models. It also adds biases in data as humans are prone to error when it comes to accurately annotating data, e.g. Annotating car parts for detecting damage. Model build with such data generally doesn’t converse with reasonable accuracy.
- Adopting Generality – Ml/DL algorithms are not able to produce the same results when deployed to different scenarios than the scenario used while training. So, to make it work in a different situation, a retraining model is required.
- Unable to explain what is going on inside the model and hence challenging to debug. However, various analytical tools can help with this.