Today, the application of Artificial Intelligence (AI) has exceeded everyone’s expectations. AI has and continues to contribute significantly in various ways. From tracking cosmic bodies present in space to predicting a host of diseases on our planet, there seems to be nothing that it cannot accomplish.
However, a question that seems to linger at the back of our minds is whether we are really ready for such unprecedented technological innovation? It leads us to explore various AI challenges that need to be addressed before harnessing its full potential.
Undoubtedly, Artificial Intelligence has become tremendously important for industries today. Amid its rapid growth, it is easy to forget that this disruptive technology may also have its problems. Let’s look at some of the major AI challenges that need to be tackled in the future.
Time to face a harsh truth. AI and its subsets like machine learning and deep learning are being increasingly used in various devices today. Still, a sizable section of people are clueless about its existence and its use in various electronic equipment that they use in their daily lives. Ironic, isn’t it? Yet, it is one of the most challenging problems in artificial intelligence.
The problem is reflected in the lack of interest that organizations show in investing in AI-based products. The concept of self-learning machines is still difficult for people to digest. Thus, there is an overarching need for organizations to educate their employees as well as themselves regarding the ways by which AI can help them progress.
One of the critical AI challenges relates to its computing power. Machine learning and deep learning algorithms perform a complex calculations in a matter of microseconds. To accomplish such a task, they need a high amount of processing power. They require lots of cores and GPUs to perform at their peak.
Presently, even cloud computing and other similar processing systems fall short of implementing their increasingly complex algorithms. It is one of the problems in artificial intelligence that need to be addressed before it exacerbates as data volumes go up, which obviously will, in the coming future.
AI development Company experts keep asserting that their products are entirely accurate. But the truth is that their accuracy isn’t at par with the humans. For example, take a simple task of predicting whether an image consists of dogs or cats. Upon showing the photo to humans, we can expect an entirely accurate result in almost every case. But for a deep learning model to do the same, it needs to be finetuned, optimized, and need to be trained on a large dataset. It requires an accurate algorithm along with super processing powers. All this is not easy to implement.
Of course, there are ways by which this problem can be remedied, like using a pre-trained model for training a deep-learning model. However, even that does not guarantee a human-like performance of these systems.
Machine learning and deep learning models need data to work. Without data, they cannot improve their learning and subsequent outcomes. Most of this data is private and confidential. These self-learning systems are highly prone to data leakage and breach. Cyber-attacks that have become increasingly common today can make this sensitive data fall into the wrong hands.
Efforts are being made to tackle such problems in artificial intelligence pertaining to security and data leakage. A solution has been devised where data is trained on smart devices; consequently, it isn’t directed to the servers. Instead, the organization receives the trained model. The GDPR (General Data Protection Regulation) implemented by the European Union ensures the full security of such private data.
It is one of the biggest AI challenges. According to Forbes India, how good or bad an AI system is depends on the data fed to it. Data that is pregnant with racial, communal, or gender bias will lead to unfair consequences. It can also accentuate such biases in society in the coming future.
The problem can be solved by training the system on unbiased data. Designing algorithms that can discover bias can help to eradicate bias that many AI systems seem to exhibit. Microsoft is currently developing a tool that can detect bias in a string of AI algorithms for this purpose.
There is no limit to the data that companies have today. But the data that AI applications need to function is scarce. It is because AI applications can only make sense of and learn from labeled data. The most prevalent data is largely unlabelled.
It is one of the AI challenges that need to be addressed in the form of new AI models that can learn on unlabelled data. There is an urgent need to address this issue. In its absence, many organizations will rely on local data, which can ultimately propagate more bias.
AI uses algorithms to produce accurate outcomes. However, it leaves people in the dark as to how it actually arrived at a particular result. People become doubtful as they’re clueless about the mechanism by which an AI algorithm reaches a conclusion. This aspect of AI has led it to become a source of mistrust for most people. It is one of the problems in artificial intelligence that can only be remedied by making models that generate accurate predictions. Government policies aiming to empower citizens with the right to ask and inquire about a specific AI-enabled decision arrived at for them can go a long way in propagating trust.
The above-mentioned AI challenges are reflected in its uneven implementation across various sectors. AI has opened a stream of opportunities for people today. But to continue reaping its benefits, it is essential for us to collectively work together and resolve these challenges that inhibit the growth of AI to what it indeed can be.