There’s no doubt that Artificial Intelligence is very popular with it being a hot topic in tech circles! Many companies already use it in their business operations with huge successes (ever heard of Google, Facebook, Amazon?!) But there are still many real-world challenges, especially for small and medium-sized companies to fully embrace Artificial Intelligence. Some companies believe this is just because AI is not needed in their corporate culture while others think its because there is not enough quality data. Other reasons can be because companies don’t have access to highly skilled AI professionals or they don’t have the infrastructure to sustain high-level AI solutions.
Most of these challenges for Artificial Intelligence faced by companies can be handled if they are keen to move ahead into the AI market. And so, this article details some of these challenges and how companies can overcome them to implement AI in their work culture. So let’s see these challenges now!
1. Limited Knowledge
Artificial Intelligence may be a buzzword in tech circles but there are very few people who understand what it is. Many myths are floating about Artificial Intelligence such as only big companies like Google, Facebook, etc. have AI capabilities or even that AI can become smarter than humans and end the world! This lack of knowledge about how Artificial Intelligence is practically implemented in the day to day operations of companies means that it is very difficult for smaller and mid-level companies to use it successfully. Another factor that leads to limited knowledge of AI is that there are very few AI experts that can apply AI solutions to real-life business problems. Most of the smaller companies struggle to find good AI talent that can form theirs in house AI team. However, a solution to this is that these companies can outsource their Artificial Intelligence and Data Science team.
2. Black Box Problem
Artificial Intelligence algorithms are like block boxes, which means humans know what the prediction generated by the algorithm is but they don’t know how it arrived at that prediction. This means that people have no means of understanding the inner working of AI algorithms. This makes them slightly unreliable. If the predictions generated are the same as those anticipated by the AI professionals then that’s great, but what if they are not? There is no way to understand how AI algorithms arrive at their predictions. An approach that aims to solve this problem is Local interpretable model-agnostic explanations or LIME. This means that the AI algorithm will also provide the pieces of data that led to its eventual prediction. So if humans are provided the rationale behind why an algorithm made a particular prediction, it eliminates the black box problem and also makes the algorithm more trustworthy in general.
3. High Computing Power
Artificial Intelligence is becoming more and more popular but it takes up a lot of computing power to train the AI. As deep learning algorithms become more and more complex, it becomes even more difficult to arrange the number of cores and GPUs they require to work efficiently. This is the reason that Artificial Intelligence is still underused in some fields like asteroid tracking, healthcare deployment, even though it could contribute a lot of value. Another factor is that Artificial Intelligence algorithms require a supercomputer’s computing power at complex levels of computing. There are only a few supercomputers in the world and they are expensive, so this limits the types of algorithms that can be implemented and also reduces the companies that can try high-level AI to those that have high levels of resources. The integration of Cloud Computing and parallel processing systems have made it easier to work with artificial intelligence but it is still a power that few can afford to utilize fully.
4. Artificial Intelligence Bias
Artificial Intelligence Bias is also a challenge for companies to fully integrate AI into their business practices. AI bias can unconsciously enter into the Artificial Intelligence Systems that are developed by human beings as they are inherently biased. The bias may also creep into the systems because of the flawed data that is generated by human beings. For example, Amazon recently found out that their Machine Learning based recruiting algorithm was biased against women. This algorithm was based on the number of resumes submitted over the past 10 years and the candidates hired. And since most of the candidates were men, so the algorithm also favored men over women. So the clear question for companies is “How to tackle this Bias?” How to make sure that Artificial Intelligence is not racist or sexist like some humans in this world. Well, the only way to handle this is that AI researchers manually try to remove the bias while developing and training the AI systems and selecting the data.
5. Data Scarcity
Artificial Intelligence algorithms learn from the data already available. Therefore, the better the data they are provided, the better the final algorithm will be. However, this requires a lot of data that may sometimes not even be available. The only method to resolve this is to understand the available data and the data that is missing. When the AI experts know the data that is missing, they may be able to obtain it if it is data that is publicly available or even buy it from third parties data vendors. However, some data is difficult or even illegal to obtain. In that case, some AI algorithms use synthetic data which is artificially created from scratch while simulating the real data. This method is using synthetic data is a good option when there is data scarcity and there is not enough data available to train the AI model.
6. Situation-specific Learning
Artificial Intelligence algorithms can be trained for a particular situation but they cannot transfer their learning from one situation to another. For example, humans can use their experiences in a situation to help them in other situations as well. But this is not possible for AI algorithms as they are trained on data for only one specified task. However, what if AI could transfer their learning in one situation to another related situation instead of developing a new AI model from scratch? This can be done using transfer learning wherein an AI model is trained on data in a particular situation but it can transfer its learning to another similar situation without starting from scratch. This means that an AI model developed for a particular task can then be used as a starting point for another AI model for a related task.
The most important thing to remember is that these challenges cannot just be handled in a short time. So companies must familiarise themselves with Artificial Intelligence and understand the process to create AI solutions. Then they must create an AI strategy for its implementation into their work culture. After the strategy is created, it is much easier to just follow it and handle the challenges as they arise.