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Generative artificial intelligence (AI) programmes, such as Chat GPT and Dall-E, are changing the game for a wide range of sectors, including healthcare, finance and transportation. Software experts and reviewers, TechJury, reports that businesses can experience a 40% increase in productivity with the use of AI, and over the past 12 months, global adoption of AI has risen to 35%.  

In 2022, the global AI market had a value of $136.6 billion, and is projected to grow by 1,226% to reach $1,811.8 billion by 2030. Given these figures, it’s no surprise that shrewd CEOs are eager to explore AI options for their businesses.

AI tech is undoubtedly going to become an integral part of almost every business’s tech infrastructure. However, it is not without limitations that need to be addressed in order for it to flourish.

When it comes to measuring the generation of fresh ideas, it’s reveals the importance of spearheading a creative climate and collaborative culture within your company. Just look at Google and their “20% time” rule. This allows employees to dedicate a portion of their workweek to personal projects, leading to game-changing innovations like Gmail and Google Maps. It’s all about fostering a culture that encourages exploration and empowers individuals to think outside the box.

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What Are The Three Main Limitations of AI?

1. A Lack of Common Sense

One of the biggest limitations of AI is its inability to apply common sense reasoning. Models are based on data and statistics, with little to no ability to understand the intricacies of human behaviour, emotions, and decision-making.  

From self-driving cars colliding with passengers to chatbots generating sexist, racist or otherwise offensive responses, to incorrectly identifying objects in images – there are countless examples of how AI programmes have failed to consider mitigating factors when decision making.

2. Lack of Context

In keeping with the lack of ‘common sense’, another major limitation of AI is its limited understanding of context. AI models operate within the parameters of the data they are trained on, and they struggle to generalise new situations.

For example, an AI system may successfully translate text from one language to another, but fail to account for sayings, such as “straight as a die”, meaning the translation may make little sense to readers not familiar with the idioms.  

3. Vulnerability To Attacks

As with most technology, AI systems have weaknesses that can be exploited. From deliberate attacks on processors to more manipulative sabotage, involving feeding deceptive data into AI systems, leading to incorrect predictions or decisions.  

An early example, from 2016, was the release of Microsoft’s Twitter chatbot, Tay. Tay was designed to learn from interactions with users and to generate human-like responses. However, after being exposed to deliberately offensive language, the chatbot began to generate inappropriate responses and make inflammatory statements.

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Are there solutions to the limitations of AI?

With such a heavy reliance on data and external stimuli, it’s perhaps understandable that the solutions to AI limitations come primarily from improving the inputs. By using high-quality, diverse data sets, AI models can be trained to recognise patterns and make predictions that are more accurate and reliable.  

Similarly, AI could overcome its limitations by incorporating more human oversight. Combining AI analysis with human judgement and intuition can improve overall outcomes and provide more nuanced insights.  

Of course, sometimes AI can provide its own solution. Explainable AI (XAI) is a popular trend amongst early adopters. Think of it like being asked to ‘show your workings’ in an exam at school. By providing a clear and understandable explanation of its decision-making processes, AI systems can provide users with the information to validate, helping identify potential biases or errors. This can also give users a clearer idea of the methodology, leading to a better understanding of complex issues.  

By improving data quality, incorporating human expertise, and developing explainable AI, users can unlock the full potential of AI and create more reliable, trustworthy, and effective systems.

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