Solve Real World Problems
Big tech giants have lost this ability to bring such great benefits or, in worst case, only try to hook users as long as possible for higher advertisements return (e.g. It’s time to look out for the great problem solvers around the world with small names but smart digital social innovation for real problems. This blog takes multiple perspectives on digital transformation.
As a political scientist, programmer and data analyst, I explore and write about the effect of new technologies and the interplay between humans and machines.
It is an intelligent chatbot that interviews people that may feel a pain or have a health related issue and want to find out the source of the problem.
In a step-by-step interview the chatbot attempts to diagnose the potential causes of the pain.
For instance, in China the giant is already using drones to deliver products to remote areas.
Digital innovation helps companies to improve their businesses and they might even transform private households into gigantic storage rooms.
But this kind of digital innovation does not attempt to solve real world problems.
There is an overall obsession about the digital innovations developed by US-based Internet companies, although there are inventions arising all around the world.
Ada acts as a first diagnosis agent that helps people solve an issue.
Of course Ada cannot replace a doctor, but the mobile app is used in countries where the next doctor might be hours away and such first diagnosis provides valuable information for next steps to be taken.
The advances seen in the Image Net competition showed the world what was possible, and also harkened the rise of convolutional neural networks as the method of choice in computer vision.
Convolutional neural networks have the ability to learn location invariant features automatically by leveraging a network architecture that learns image features, as opposed to having them hand-engineered (as in traditional engineering).
This aspect highlights a key property of deep learning networks—the ability of data scientists to choose the right architecture for the input data type so the network can automatically learn features.