The Dynamic Role of AI in Future Product Landscape

Many times humans are required to make AI usable. So AI is not a magic box. Most of the AI systems that we use today, whether it is speech to text, anything in language, or images, all have been built with a lot of input, intelligence, and support by humans themselves. So how do people help in building AI systems? 

1. First, they help in handling the edge cases. Many times a machine can’t figure out what to do in such a scenario. This is where the human’s judgment supersedes. The reason why machines don’t work in such cases is because such cases did not have enough past training data. If they didn’t have enough past training data to handle a case they won’t be well equipped to handle that case. 

2. The second is, building the training data sets themselves. Hence, if machines have to learn somebody has to build a training data set for the same. That is where humans help in building these data sets.

3. Generating heuristics is another important aspect. If there are no specific solutions to a product problem or the time required to find one is too high, a heuristic function may be used to solve the problem. The aim here is to find a quicker or more approximate answer, even if it is not ideal. 

4. Quantifying the sample accuracy is also an important role. For example, even when machines are trained, someone has to say that the machine is currently operating at a particular accuracy level.

We can say that while AI and ML help hugely in product innovation, human collaboration remains indispensable in refining their capabilities. The relationship between human intelligence and AI continues to drive success and advancements across diverse product development processes.