10 Biggest AI Challenges & How to Address Them

In recent years, artificial intelligence (AI) has made significant strides, from being able to outperform humans on specific tasks to help businesses automate their processes. Despite this progress, there are still several challenges that need to be addressed before AI can become truly ubiquitous. In this blog post, we’ll discuss the ten biggest AI challenges and how to address them.

An Introduction to Artificial Intelligence in the IT Industry

Also known as machine intelligence, artificial intelligence (AI) is a type of technology that helps create machines that can perform specific tasks that normally require human intelligence, such as visual perception, speech recognition, and decision-making. AI helps with data interpretation and specialized knowledge in specialized fields like natural language processing, learning, planning, and executing. Artificial intelligence is a diverse field composed of many smaller fields and disciplines. Many companies, including large tech giants like Apple, Google, Facebook, have been heavily investing in AI research and development to make their products stand out from competitors’ products.

AI will drive the next revolution that is disruptive across most industries. AI applications are found in the following industries: Business Process Automation (BPA), Internet of Things (IoT), Robotics, Autonomous Vehicles, Cybersecurity, Intelligent Virtual Assistants (IVA), Cognitive Computing and Big Data. It is already having a meaningful impact on how businesses are being run now with multiple use cases in the technology stack, including speech recognition, computer vision, machine learning, natural language processing (NLP), text analytics, and social media monitoring.

AI use cases are vast, extending to almost all industry verticals like healthcare, finance & banking, retail, automation, transportation & logistics, media & entertainment, and many more. AI has the capability to build future technology that will result in intelligent and smart robots and, in the long term, will completely change the way we interact with machines.

Basically, artificial intelligence can be divided into two types: Deep Learning and Supervised Machine Learning. Deep learning models are used when there is a huge data set, while supervised machine learning models require labeled training data to build a predictive model.

Top 10 Most Significant Challenges in AI Adoption

AI is growing by leaps and bounds. There are several benefits to this technology, and there are some significant challenges that need to be addressed as well. Here are the ten biggest AI challenges and how to handle them:

1. Algorithmic Bias

AI algorithms are developed from the data that is fed to them. So, if a large number of examples in the learning process are from a certain group, then the algorithm will develop a bias toward other groups. This algorithmic bias is one of the biggest challenges for AI because it could be potentially harmful, especially in systems that govern critical infrastructures like traffic or healthcare.

Correcting the algorithmic bias can be a daunting task, but there are several ways to address it. If you have labeled data that is representative of multiple groups, then don’t exclude it from the learning process because of its mismatch with other examples. Instead, you should create an ensemble model by combining algorithms trained on smaller datasets and using them as training data for the larger ensemble model. By using this approach, you can create a more robust model. Also, having multiple algorithms in the ensemble cancels out each other’s errors and biases and produces a more accurate prediction.

2. Data Security

Since artificial intelligence heavily relies on data for its predictions and decisions, it is critical to protect the data from theft or manipulation. If the AI model is hacked, then it can be used for malicious purposes like denial of service attacks. Also, if the data is stolen or manipulated, you should use techniques like k-anonymity to protect sensitive information while retaining the accuracy of your models.

The data security issue can be resolved by using enterprise cloud solutions or private/Permissioned Blockchains. Also, you should use AI itself to protect your sensitive information, like anonymizing personal health records for providing machine learning predictions without compromising privacy.

3. Deployment Lag Time

AI systems are complex and take time to install and train before they are ready for use. So, the deployment lag time is one of the biggest advantages of using AI models.

However, if you have an immediate business need to address with artificial intelligence, then it is recommended that you run smaller data experiments on public clouds so that you can observe how these systems work in production and get faster results.

4. Cybersecurity

Artificial intelligence is everywhere, from the government to personal computers and smartphones. While this technology provides us with lots of benefits, it also creates new vulnerabilities for the attack. Criminals can use artificial intelligence against themselves by causing system malfunction or gaining access to systems without permission. Also, as AI becomes smarter and can make decisions, it will be able to execute automated cyberattacks without human intervention. Thus, the cybersecurity issue is one of the biggest challenges in artificial intelligence technology.

To overcome this AI challenge, you should use a security-first cloud strategy that includes continuous security testing and verification to ensure that your AI systems are secure from all threats, including viruses and malware.

5. Data Privacy

One of the most critical ethical issues in AI is privacy. Currently, there are no federal regulations that protect your privacy rights in this technology. So, if you use an AI system to perform a specific function for you, it might also collect information about you without you knowing it because it will be operating on your behalf.

To resolve this issue, you should try to isolate your sensitive data and ensure that it is only accessible to the particular system it needs to be accessed by. Also, use techniques like homomorphic encryption to keep your information private and secure even while sending them over a network.

6. Black Box

AI systems are complex, and they are difficult to understand for non-technical folks. Since these systems learn data patterns and make decisions based on what they have learned, it is challenging to explain why the system took a specific action or decision. For example, image classification models can be trained to recognize specific patterns in images like faces, objects, traffic signs, etc., but it is not easy to predict why a particular model made a specific prediction.

The black box issue can be resolved by using techniques like Regularization and Bayesian Optimization to make your models more interpretable and accountable for the data they use as well as the decisions they make based on that data.

7. Lack of Skilled Workers

Since artificial intelligence is a technology that is here to stay, you should begin developing your AI strategy by investing in the people and skills needed for building AI applications. Nowadays, there is a shortage of skilled engineers in this field, so it might be difficult at first to find professionals who have the right skillset to build a custom artificial intelligence solution for your enterprise.

To rectify this kind of issue, you should build relationships with local universities and AI educational platforms to find new developers who are skilled in this technology. Also, explore other options like the apprenticeship programs run by Google, IBM, Microsoft, etc., to attract top engineering talent for your company’s AI initiatives. Or you can consider hiring a software outsourcing company that specializes in AI technologies.

8. No Transparency

Since the underlying technologies like machine learning and deep learning can be challenging even for experienced developers, it is difficult to know how a particular algorithm works. For example, since there is not enough transparency in how neural networks learn from datasets, you might need expert assistance to debug your models if they aren’t working correctly.

To resolve this issue in implementing AI, you should follow best practices for developing AI applications, like setting specific metrics to measure model performance by choosing simpler algorithms. If you are not an experienced developer, using standard dataset formats, etc. Also, try using the unique capabilities of your platform to debug your models without expert assistance.

9. Slow Response

AI systems can provide faster and better solutions by using multiple data sources and then synthesizing that information into a single decision. This is why it is crucial to invest in AI so your business can enjoy the full benefits of this technology. Unlike traditional applications, AI-powered applications require more time to learn from datasets and make decisions based on that information.

To resolve this issue, you should try using machine learning techniques like active learning and online learning, so the system only learns from relevant data as it processes each new piece of information. Also, use decision trees to allow your models to make quick decisions based on a few pieces of inputted data.

10. High Development Costs

Obviously, developing and implementing AI solutions can be very expensive. For example, you might need to increase the budget of your software development team so they can learn new technologies like machine learning or deep learning and then build custom algorithms for your enterprise applications.

This challenge in artificial intelligence cannot be resolved completely, but at least we can mitigate the costs by training your staff with the right skills needed to build AI solutions, developing a clear roadmap for your company’s artificial intelligence initiatives, and by choosing the right tools and platforms for building complex AI applications. You should also try to reuse code and integrate your existing IT systems with new AI technologies so you can save money on software development costs.

Conclusion

In this article, we have explored the top ten challenges that can prevent your organization from achieving the AI goals and also outlined some helpful solutions for such obstacles. The earlier you can resolve these challenges in AI, the better your chance will be of making effective use of artificial intelligence technologies across all areas of your enterprise.