What are the Cons of Machine Learning & Artificial Intelligence?

There are many disadvantages of machine learning, including the risk that it will be biased or skew information in ways that could be harmful to people or society at large, for example by promoting false beliefs or inaccurate information about certain groups of people. There is also an issue with privacy because machine learning algorithms need access to lots of personal data in order to work well which means it needs access to your online activity – something you might not want others knowing about you.

The first disadvantage is the lack of human judgement. Machines are unable to make judgements that are not explicitly programmed into them, which means that they can’t make decisions in an unpredictable environment.

The second disadvantage is the lack of human creativity. Machine learning doesn’t have the capacity for creativity, which means that it’s always going to be limited by what it’s been programmed to do.

Third disadvantages is the lack of understanding context. When writing, context is extremely important. Machines are unable to interpret the nuances and subtleties that human writers can. Machines are unable to understand the relevance of certain words or phrases, which can lead to copy that is difficult for readers to comprehend.

Some of the disadvantages are as follows:

  • It can’t do anything that requires creativity or judgment
  • It doesn’t work well for complex tasks
  • It may not be able to deal with incomplete data
  • It can’t learn anything new without retraining the data

Some people believe that ML and AI will be used for evil purposes, such as hacking or manipulation by governments or organizations with malicious intent. Another drawback of ML and AI is the lack of transparency- data scientists are unable to predict how algorithms will behave in the future, which can be problematic because it could lead to biases. Another concern is that ML and AI might replace human jobs- which would result in unemployment rates skyrocketing, especially since most industries are using these technologies now.

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