achine learning and artificial intelligence are the phrases are much talked by everyone nowadays. What is it, how to use it etc. are being discussed in length in media. There are ethical aspects to the application of ML and AI in every space. Whether we need it or not?
The real use cases
If some Government department or an organization wants to apply ML and AI, there must be a clear identification of the use cases. The data to be used for ML/AI must be fully studied. If we ask whether we can apply ML and AI in some realtime use case, almost every use case will be a ML/AI candidate. But do we have enough data and enough clean data to apply ML/AI? Only when we cross this threshold, we require ML. Else we can manage with current database SQL queries.
Recently Google has mentioned about its initiatives in healthcare using ML and AI. In the field of ophthalmology, they have done amazing work using images and the ML AI algorithms are able to detect a variety of issues way ahead in time. This requires a super doctor to analyze but the ML model is able to do this. This means, the best eye analysis can reach everyone whether a doctor is physically available there or not.
When this kind of solution is a result of ML and AI, who will say no to ML? This case it is a real boon.
The worry factor
Media always talks on job losses. When we say ML, they say jobs are lost. This creates a phobia in every community. If we really analyze what causes the job losses, it is actually automation and not ML and AI. Any repeatable laborious work will be automated. Business will find a way to achieve it. Every car you see today is assembled by robotic arms and not human arms. Robotic process automation is widely used across the world.
Jobs will be lost in some areas, but a lot of areas will see job surge.
Fundamental core engineering jobs will get automated one after the other. This is happening for the last 3 decades. What we now need to know is about the job losses due to data processing. Let us take an example and see how it works. Take table booking in a hotel chain. This is conventionally done by support agents. Now it is all done by chat bots. The job is repeatable and is primarily data collection. Decision making is simpler. Hence a lot of hotels now use chat bots for this repeatable job.
One has to worry if his/her job is:
- Repeatable step by step
- Has no innovation or complex decision making process
- Prone to human inefficiencies
- Cost intensive when head count increases
The above places are perfect for ML and AI to play the game and for those it will be a bane.
Disruptions
When certain industries have not seen large scale disruptions in the last 1 decade and suddenly these things come up, it creates some unrest. But eventually the management will look at cost and time benefits and start adopting ML and AI.
Governments and regulators may form regulations on the way the ML-AI is used and its ethical usage.
Whether you like it or not, ML and AI are right here, right now. They will cause more disruption. We may resist or laugh at it; but ML and AI will get accepted, like the way world accepted machines and software.
More from
Tech
category
