AI Not Smart Enough? Limitations Of Generative AI Models

Though Generative AI models are already proving their cutting-edge excellence in solving complex problems in their creative application, it’s worth noticing that these models are not without some limitations of their own. And surprisingly, one of the limitations is they are prone to hallucinations!

Here is the breakdown into some of the known limitations of current Generative AI models –

Generative AI Can’t Create Completely New Ideas

The power of Generative AI models to create new blogs, articles, or video content is actually not new but is the recycled version of existing data and patterns they’ve learned so far.

That said, these models can do superlative performance when working with existing data and patterns. They can generate variations and new combinations but fail to grasp the concept of creating something entirely new.

Are Generative AI Models Faulty?

I don’t think so. Just because Generative AI models can’t create completely new ideas doesn’t mean they have some technical faults of their own. Actually, their creativity to generate something entirely new largely depends on the quality of data they are fed.

The models, ever since they came into existence have been fed through enormous amounts of data already existing. And what predictions they give are largely based on recognizing the patterns within the data they were trained.

So, we can at least conclude that human emotions, intuition, and imagination are some of the attributes these AI models can’t simulate.  

Prone To Hallucination Or Factual Inaccuracies

One of the limitations of generative AI is being prone to factual inaccuracies or hallucinations in which case what outputs they give seem believable but carry fabrications.

This means relying on the information provided by AI models requires exercising some caution, as it could be AI-fabricated factual inaccuracies.

Possible Causes Of AI Hallucinations:

  • Inability to understand the information they process, though the AI models do a great job at recognizing the patterns within the training data. So, they can produce the outputs based on their understanding of the patterns. However, such outputs are not necessarily real.
  • Generative AI models are trained on large datasets. If the training data have gaps or aren’t diverse enough, it would cause the models to become insufficiently trained to generate truly accurate results/details. To simplify, these models generate outputs based on the quantum and quality of training data. For example, an AI model can’t paint a picture of a real dog if it has data only on cats.
  • Generative models can excel at executing specific tasks but may exercise guesswork or extrapolation when presented with something unexpected.

Some other factors associated with causing generative models to be prone to hallucination involve biases in training data, incomplete data, and lack of filters.

For example, just as impressionable students fall prey to the environments they are exposed to, generative AI models act according to their training data.

Hence, biases or misinformation in training data can negatively influence the decision-making ability of generative models. Meaning, they can generate factually incorrect details.

Secondly, a lack of filters can make these models create anything meaningless defying the real-world limitations. For example, they may create buildings without foundations or windows anywhere on the floor.

An issue called overfitting also makes generative models hallucinatory. It occurs when the model concentrates too much on memorizing the training data rather than recognizing underlying patterns.

As a result, the models can’t process new information and resort to hallucination when presented with something outside its training data. It seems like a flabbergasted student facing out-of-syllabus questions in an exam.

Some Other Limitations Of Generative AI Models

They Can’t Understand And Respond To Subtleties Or Nuances

Generative AI models, in all likelihood, may miss interpreting subtleties or unspoken cues. Hence, they may generate gibberish or meaningless phrasing in their outputs. Nuances like irony, humor, etc. that rely on context and social understanding are difficult for these models to decipher.

They May Generate Offensive Content Or Stereotypes

This limitation of Generative AI is due to their training data bias. As explained before, biased data can influence the decision-making capability of generative AI, causing it to generate offensive content or perpetuate stereotypes.

They Lack Common Sense Reasoning

Generative AI is truly amazing at processing information at an incredible speed. However, the lack of common sense reasoning makes them generate outputs holding no value in the real world. To say otherwise, the kind of common sense reasoning humans take for granted is absent in AI. Due to the omission of reasoning capabilities, though the models can generate factually correct details, the outputs lack substance or acceptability in the real world.

Winding up

No doubt, Generative AI models excel at creating details out of existing data but fail to genuinely impress us in generating unique ideas. Moreover, they are also prone to hallucination, mostly due to factors like training data biases, noisy data, lack of filters, and overfitting.

However, it doesn’t mean to discredit AI models for failing to live up to our expectations (of the super-intelligence of AI models). That’s because they are still going through an intense training process on large datasets.

Besides, lack of quality heavily influences the decision-making ability of generative AI models.

I tend to believe that we will see more powerful, intuitively smart AI models over time, thanks to the AI companies leaving no stone unturned in innovating their AI products with human-like intelligence.

Also read – Generative AI Trends 2024

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