AI is used today in almost all fields like military, learning, healthcare systems, automobile industry, etc. The use of AI has made some innovative ideas like self-driven cars become possible. This relatively new technology has increased the usage and power of machines. But AI with its advantages possesses some pitfalls as well, which should be addressed.
Let’s look at some AI pitfalls.
AI’s over-dependency on data
AI’s functioning largely depends on data. If some data is missing, data is wrong or data is useless, AI can lead to faulty designs and results and the purpose of using AI will backfire. So, the data has to be of the highest quality. It should be clean, and accurate. Also, strong statistical techniques should be used early, to ensure AI produces desired results.
AI doesn’t deliver instant results
If a company guarantees you results instantly fulfilling all your expectations, beware because their samples are based on some specific goal only, which might be different from yours. If you think putting all your data resources and AI techniques in your goal will give you instant and good results, you might be disappointed.
So, be realistic. Come up with a proper plan, set achievable goals, invest time and money in AI, and then see if it works out or not.
Not having a proper team to manage AI
AI can give a lot of data, stats, and results which need to be processed properly and accurately to make plans and strategies. But a major pitfall is the lack of proper IT teams in companies to manage AI efficiently and analyze the data in-depth. Inaccurate interpretation of AI data can lead to unexpected and bad results which can lead to business loss. So, a team consisting of AI professionals should be hired to manage AI tasks.
Rigid frameworks
Another AI pitfall is making rigid frameworks that is, making AI algorithms on the basis of a fixed initial idea and data sets. This looks appealing but fails after on because the behavior and interests of people change every day, which might not match with the data initially aligned in the AI system. Therefore, your algorithmic framework has to be updated regularly.
Failing to optimize processing time
AI providing desired results is not the only ultimate goal to achieve, but producing results in minimum possible time is essential. To provide smooth and fast user-experience the algorithms have to be optimized on a regular basis. Algorithms should work at all sorts of bandwidth and internet connectivity.
Hindering privacy of people
This is another highlighted AI pitfall. People nowadays are more concerned about their data and privacy than ever and thus are not keen to give away. So, proper data principles should be followed in order to take care of the user’s data. Proper consent of users should be the priority.
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