Zero human error
This is why the adoption of artificial intelligence in various domains has shot up. When you can nullify human errors completely, you get accurate results. The catch is, to program properly.
Machines take accurate decisions based on the previous information that they gather over time while applying certain algorithm sets. Thus, there is a reduction in error and a spike in accuracy.
For instance, Google recently shared on its AI blog about a machine learning method that will work around predicting the weather. Google is going to call it ‘Nowcasting‘ which will predict weather zero tp six hours ahead of time. Google believes by using fewer data and simple methodology, they can predict weather more accurately, especially in events like thunderstorms or ones related to precipitation.
Zero risks
Putting machines into tasks that can be a danger to humans can pay off well. For instance, enabling machines to deal with natural calamity can result in faster recovery and lesser pressure on human teams.
This idea comes riding high on Google and Harvard’s initiative of developing an AI system that can predict the aftershock locations of an earthquake. After studying over 131,000 earthquakes and its aftershocks, scientists have tested this neural network on 30,000 events. It showed more accuracy in pinpointing aftershock locations when pitted against traditional methods.
Round-the-clock availability
It is obvious that machines don’t get tired. Machines can work endlessly without breaks and don’t even get bored doing the same thing repeatedly, unlike a human.
In November last year, Google announced Contact Center AI for businesses to improve customer experience. This is a classic example of an AI-enabled helpline system for businesses to relentlessly address customer queries and issues, and resolve them on priority for improved customer experience.
Similarly, Amazon Lex, the chatbot designed for call center by Amazon is capable of having intelligent conversations against human queries. It incorporates the same technology of Amazon Alexa – i.e. to recognize the caller’s intent, ask related follow-up questions, and provide answers. These chatbots are available round-the-clock catering to customers across the globe and varied timezones seamlessly.
AI machines have no emotions
Machines have no emotions [well, unless you are Chitti, the robot, that, quite frankly, had me confused with its obsession over the female protagonist Sana]. This single attribute about Ai-enabled machines can help you deal with customer grievances more steadily.
Imagine a feature in your software not functioning suddenly causing inconvenience to your users. They will surely raise tickets, ping on your chat support and write emails. Often people attempt for ‘live chat’ rather than waiting on email or tickets to get resolved.
Now imagine having a human at the receiving end, who fully understands the issues and is trying hard to resolve queries in hundreds of numbers. At one point, that person will break. Chances are chats that may go hay-way in terms of sensitivity of language.
However, with an AI-enabled chat system, no such breaking point arises. Customers can throw any amount of weird queries, your chatbot has pre-fed answers that it will keep on showing as per its assessment of query.
It is definitely one of the safest ways to handle such scenarios because machines don’t feel anything. They will
AI-machines can take decisions fast
Using artificial intelligence and other technologies can help make machines that can make data-driven decisions much faster than humans.
Why trust a machine’s decisions?
Simple – it is devoid of any emotions and biased views. When a human takes a decision, a lot of it it is driven by emotions. A machine, on the other hand, is highly practical and rational in its approach. This ensures more accurate and result-oriented decision-making.
For instance, IBM’s Deep Blue supercomputer takes decisions based on all the probabilities possible from the opponent’s end. A human cannot fathom so many probabilities in one go like a machine.