The Practical Limitations of Artificial Intelligence

Artificial Intelligence is currently Information Technology's apple of the eye. It is believed that the technology possesses a lot of potential when it comes to solving many real-world challenges. Businesses are identifying use cases and making significant investments in AI. However, as with any other technology, artificial intelligence has its own set of limitations. Here we list down five such limitations of artificial intelligence, in no particular order:

1. Data:  One of the biggest Limitations of Artificial Intelligence is related to data. From availability to human bias creeping into the data, to its usability, there are many challenges that data poses before it can be actually used. Since data is sourced from diverse sources, it is unstructured and requires technological processes and significant human effort to cleanse it. Second, it is not about the volumes of data that businesses possess, but about having noteworthy data that can provide the required insights.
2. Increases human effort: It is true that machines can analyze volumes of data much quicker than any human can. But, the data fed to the machines requires significant effort to make it use-worthy. Not only cleansing data but also labeling it with the appropriate parameters so that machines can learn correctly, involve significant human effort. For instance, to enable machines to learn about the different breeds of dogs, effort must be put in to label the pictures of dogs with the breed they belong to. When we extrapolate this labeling to a myriad of creatures/objects, we are actually talking about a huge amount of work for humans.
3. Lacks emotions: Machines are becoming smarter with greater computational powers at never-before speeds. That said, unlike humans, machines lack emotional intelligence and cannot comprehend the feelings behind the spoken words/sentences. Currently, efforts are being made to develop Natural Language Processing (NLP) in order to understand the feelings behind the words and context of the discussion.
4. Requires supervision: Machines cannot mimic humans. They cannot learn on their own. This supervised learning is a challenge when it comes to expecting machines to increasingly behave like humans. Machines can only do what they are programmed or 'trained' to do. Yes, machines can crunch data in real-time, but they cannot make judgments that require empathy. Similarly machines lack original thinking and creativity.
5. High costs: Artificial Intelligence is a costly technology and requires significant investments to set up the infrastructure. The software needs regular updates and maintenance to adapt to the changing business dynamics. In the worst case scenario of a complete breakdown, reinstating the system and ensuring business continuity can be a costly proposition.

Businesses considering artificial intelligence must carefully identify the use cases and the return on investment before deploying AI solutions.

The article has been written by Neetu Katyal, Content and Marketing Consultant

She can be reached on LinkedIN.