Top Challenges With Using AI and ML

What is Artificial Intelligence?

Artificial Intelligence is a collection of many technologies that work together to allow machines to sense, understand, act, and learn at the human intelligence level. Artificial Intelligence is a computer program, just like human beings observe things and learn from them; similarly, machines also learn from their surroundings and can think on their own.

These machines represent the human mind. These are programmed to learn and replicate the actions of humans. Artificial Intelligence aims to support human capabilities and help us make progressive decisions with wide-ranging outcomes. It helps humans to make fewer efforts in doing work and improving their decision-making. Most companies primarily use Artificial Intelligence to improve the efficiency of their processes and perform a business forecast based on hard data rather than intuition. 

Top challenges with using Artificial Intelligence (AI)

Artificial Intelligence is gaining popularity among many companies, and they have started to invest in the development and research of different AI applications. However, there are also some challenges that AI is still facing. Here are some of the common challenges that companies are facing regarding with implementation of Artificial Intelligence:

  • Upgrade Cybersecurity: A report states that 52% of security breaches involve hacking, 28% from malware and 32- 33% include phishing or social engineering, respectively. Hackers will always exploit every weak point in systems on private networks, cloud servers, or social platforms when the opportunity arises. They can use this information as an advantage, which would be a loss for people in business. 
  • Lack of technical knowledge: To implement AI applications, there should be proper knowledge about AI technologies and advancements as well as their drawbacks. Currently, only 6% of companies have a good way with the adoption of Artificial Intelligence technologies. Lack of technical knowledge can become a hindrance in implementing AI applications.
  • Workforce: As AI is arising technology, few people are skilled and have proper knowledge about AI implementation. These experts are rare and expensive in the current market. It also increases the company’s budget, as companies need to pay a high amount for incorporating the workforce according to the need of the project.
  • Complex Algorithms: The function and performance of business intelligence operations are highly dependent on AI algorithms. Organizations must have a clear understanding of how AI-based solutions or technologies work and manipulate their results. Once you implement AI-based algorithms, hiring the required workforce needed to operate, the system becomes quite challenging for organizations.  
  • Situation-specific learning: Artificial Intelligence algorithms can be deployed for a specific situation but cannot transfer their knowledge from one situation to another. People can use their specific learning in a different situation, but it is not possible for AI algorithms, as they are trained with data for only specific tasks.

You can learn about Artificial Intelligence through many courses that are available online. Some top artificial intelligence courses are Machine Learning by Stanford University, Deep Learning Specialization by Andrew Ng, etc.

What is Machine Learning?

Machine learning is a part of Artificial Intelligence and Computer Science, making software applications more accurate at predicting outcomes. It improves the learning process of computers based on their experiences without being programmed, i.e., without human assistance.  It focuses on the development of computer software that can access data and learn it for themselves.

Machine learning is essential because it gives companies an overview of trends in customer behavior and business operating patterns, and it helps develop new products. Many of the top media companies like Google, Facebook, are making machine learning a central part of their business. It has become a crucial competitive advantage for many companies. The most common use of machine learning is recommendation engines; some other uses include Fraud detection, Business process automation (BPA), Spam filtering, etc.

Top challenges with using Machine Learning (ML)

Machine learning is no longer science fiction movies but reality. Although it took so many decades to get here, large investments in this area have sped things up significantly. But despite this fact, this segment still has a long way to go because ML was unable to overcome several challenges that still stand in the way of progress. Some of them are:

  • Not enough training data: Machine learning takes lots of data for most algorithms to work properly. A simple task requires thousands of examples to do something, and advanced tasks such as image or speech recognition require millions of examples to do something. Therefore, we need sufficient data to train a model.
  • Poor data quality: Before starting any training model, we need to analyze the data, which is the top step. If your training data has lots of errors, outliers, and noise, the ML model can’t see an excellent underlying pattern, and it will not work well. So do your best to clean up your training data. No matter how good you are at choosing and refining the model, this part plays a vital role in creating an accurate ML model.
  • Irrelevant features: If the training data contains many irrelevant features and enough relevant features, machine learning will not provide the expected results. One of the most important aspects of a successful machine learning project is choosing good parts to train the model, also called feature selection. 
  • Video training data: We still need to use video training data; instead, we still rely on static images. For Machine learning systems to work better, we must train them to learn by listening and observing. Video records are typically much richer than still images, which is why humans use learning by observing our dynamic world.
  • Overfitting and Underfitting the training data: Machine learning models sometimes do overgeneralization if not paid attention to, called overfitting. In simple words, the model performs well on training data but fails to generalize well. It happens when the model is too complex. However, we can avoid such issues by gathering more training data, fixing data errors, removing outliers, reducing the number of instances in the training set, and selecting models with fewer features. Underfitting is the opposite of overfitting when the model is too simple to understand basic structure. It generally happens when we have less information to construct a model. We can avoid it by removing noise from data, increasing parameters and selecting powerful modes, and implementing better features to learn algorithms. 


Machine Learning is in essence all about making machines better by using appropriate data. As seen in this article about Artificial Intelligence and Machine Learning, everything has its pros and cons. You can learn more about these subjects by engaging in a PG program in AI and Machine Learning by Great Learning

With the rapid advancement of technology, we can solve the challenges faced with AI and Machine Learning. Therefore, doing a PG course can provide you with the required knowledge. After training by keeping the above parameters in mind, you can’t generalize well to new cases.  You may need to further evaluate and fine-tune it. 


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