Key Challenges and Limitations in AI-Language Models

Introduction

Artificial Intelligence has been cementing its position in workplaces over the past couple of years, with scientists spending heavily on AI research and improving it daily. AI is everywhere, from simple tasks like virtual chatbots to complex tasks like cancer detection. It has even recently replaced several jobs in the industry. This inclusion of AI has resulted in both positivity and concern regarding its implications, particularly its impact on the number of jobs it may replace and the various industries. So, can we say there are Key Challenges and Limitations in AI-Language Models? Indeed, it has some limitations.

While AI is remarkable at enhancing efficiency, productivity, and innovation, it still poses several significant challenges. Here’s the real question – Is AI ready to take over the world yet? Maybe not. In this article, let’s look at a few reasons and interesting real-world examples of why AI may not yet be ready to sit in the driving seat (Challenges and Limitations in AI-Language Models). 

Overview

• Acknowledge AI’s limitations in context and common sense.
• Show how AI’s lack of nuance leads to errors.
• Emphasize human superiority in adaptability and emotional intelligence.
• Evaluate AI’s shortcomings versus the need for human empathy in industry.

AI Lacks an understanding of the context

In our list of Challenges and Limitations in AI-Language Models, the first one is “AI Lacks an understanding of the context.” AI is trained on very large amounts of text data, hence identifying patterns and making predictions on data. This also makes AI exceptional at improving existing code or content and even correcting grammar, but it still lacks an understanding of the nuances of human language and communication. AI can still not understand sarcasm and idioms(to some extent) and cannot translate several native languages. 

In the image shown above, if this was between two humans, there is almost a certain chance the person would understand sarcasm by deciphering the tone in which they are being spoken to. In terms of understanding the context, humans are still way ahead, and this is one of the main problems AI still faces.

AI Still Lacks Common Sense

AI systems today cannot still apply common sense and reasoning to new situations. Since they are models trained on huge amounts of data, they may fail to answer anything beyond their trained data. AI models can only make decisions and predictions based on the data they have been trained on, meaning they are not able to apply their knowledge in a flexible way to new situations. This pure lack of common sense makes AI systems susceptible to errors, particularly when dealing with simple situations.

Pattern Matching vs. Human-Like Reasoning

By now, you would be living in a cave if you hadn’t heard of the new ChatGPT o1 model release code, Strawberry. But for those of you wondering why the name “Strawberry”, let me explain. In the previous versions of ChatGPT before o1, if a user asked ChatGPT “How many “r’s” are there in the word Strawberry, then the AI would reply “2” r’s. Even though OpenAI fixed this to some extent in their later versions, the word “Rasberry” still pulled the alarm. Hence, the code name “Strawberry” was used for the new model o1 to highlight all such errors that were fixed in this model. But there’s still an interesting scenario in which GPT gets the answer wrong. Take a look at the image below

Even though the answer is clearly given in the question that the surgeon is the boy’s father,  the AI still fails to answer correctly. The AI tends to bring in irrelevant scenarios because it relies on pattern matching from its training data. When faced with a problem, it assumes it’s similar to past problems or challenges it has seen, thanks to it being trained on pretty much everything from the Internet. Hence, it picks those previously seen problems and then tries to see how the current problem can be answered rather than reasoning directly like a human. This causes the AI to try fitting your problem into a familiar template, leading to limitations and missing the specific nuances of your query. Don’t we humans seem smarter?

AI Lacks in Adapting on the Fly

AI still lacks the ability to do things that require adaptability. An interesting example to point out here is that Airports all over India were adapting incredibly to COVID protocols during the pandemic, in comparison to European or other countries, primarily because Indian airports still heavily rely on human-based processes. They were able to change quickly to new processes. However, try changing the machines installed to a new process. It is a nightmare.

Let’s take another example. Imagine a scenario that requires on-the-fly adaptability and problem-solving in unpredictable environments, such as fighting a fire. Human firefighters are trained to make extremely quick decisions based on the changing dynamics of fire, taking into account the risks associated with the strategy and altering them as needed. In such scenarios, even though technology has come in handy, such as using thermal imaging drones to understand which portions of a fire are more susceptible to spreading, they still require human intervention. Similarly, emergency medical responders often face unpredictable scenarios that require rapid judgment and flexibility. AI, in such scenarios, may lack the decision-making and hand-eye coordination required to excel at such tasks. This requires a whole new level of adaptability that AI has yet to reach.

AI Cannot Feel Empathy, Sympathy, or Anything Else for That Matter

Even though AI has stepped into several domains worldwide, one domain it is yet to step into is psychological counseling. AI cannot feel empathy, sympathy, or anything else for that matter. You certainly would have come across scenarios while using AI chatbots in Zomato or Swiggy telling you that they are sorry about your delayed delivery or missing items in the order. But are these chatbots really sorry? The answer is clearly “No” because these are just robots. The bottom line is that these robots have no idea what frustration or any other emotion really is. 

So, while these AI robots are incredibly efficient and help customer service operations, it is just not ready to substitute the empathy that a human being offers to a frustrated customer. You would have certainly found yourself demanding to talk to a human representative no matter how helpful the AI chatbot may be. But sentiments can be analysed by these AI chatbots making a human representative more aware of the state of emotion the customer may be experiencing.

AI Also Lacks Reasoning and Adaptability

AI language models are often questioned regarding their capacity for reasoning and decision-making. While they possess certain reasoning abilities, there are concerns about whether techniques like Retrieval-Augmented Generation (RAG) and guardrails can fully prevent them from straying from their intended purpose. Check out the above example and a detailed discussion on ‘Are LLMs Reasoning Engines?’,  based on an experiment run by our Principal AI Scientist, Dipanjan Sarkar, using Amazon’s new shopping AI assistant, Rufus. This highlights these challenges, where it was successfully prompted to engage in irrelevant tasks even though it is potentially being grounded using RAG and guardrails, showcasing some of these limitations.

Key Points from this Scenario

1. LLMs differ significantly from human reasoning: While humans can think, reason, and act in a matter of seconds, LLMs are far from replicating this process. Their reasoning is often more rigid and formulaic.
2. RAG and guardrails are not foolproof: Although useful, these mechanisms are often rule-based or rely on prompts, making them vulnerable to manipulation or “jailbreaking.” As a result, LLMs can sometimes deviate from their intended behaviour.
3. Expensive reasoning without versatility: Although LLMs, including OpenAI’s models, are capable of complex reasoning, this often comes at a high computational cost. Moreover, their performance tends to be uniform across both simple and complex queries, limiting their efficiency. Their knowledge is also restricted to what they have been trained on, limiting their adaptability.
4. Current systems, including agents, are model-dependent: While agent-based systems may be an advancement in LLM capabilities, they still face limitations imposed by the underlying model, particularly regarding reasoning and the ability to respond to queries outside their training data.

There is optimism about future advancements, especially as these models evolve beyond beta versions. The eventual goal is to develop AI that can handle both simple and complex reasoning more naturally, adapting responses based on query context rather than being confined by pre-defined rules or training limitations.

Key Breakthroughs in Artificial Intelligence2024

Take a look at some really interesting and unconventional breakthroughs in the world of AI in 2024.

1. French AI Startup Launches ‘Moshi’

French startup Kyutai just introduced Moshi, a new ‘real-time’ AI voice assistant capable of responding in a range of emotions and styles, similar to OpenAI’s delayed Voice Mode feature.

• Moshi is capable of listening and speaking simultaneously, with 70 different emotions.
• It claims to be the first ‘real-time’ voice AI assistant, released with 160ms latency.
• Moshi is currently available to try via Hugging Face.

2. Open AI and Thrive Create AI Health Coach

The OpenAI Startup Fund and Thrive Global just announced Thrive AI Health, a new venture developing a hyper-personalized, multimodal AI-powered health coach to help users drive personal behavior change.

Key Points:

• Thrive AI Health will be trained on scientific research, biometric data, and individual preferences to offer tailored user recommendations.
• The AI coach will focus on five key areas: sleep, nutrition, fitness, stress management, and social connection.

Key Takeaways of Challenges and Limitations in AI-Language Models

Here’s the table with the required information:

Challenge	Description
AI and Context Understanding	AI struggles with interpreting the nuances of human language, such as sarcasm and idioms, limiting its effectiveness in nuanced communication compared to humans.
Lack of Common Sense	AI lacks the ability to apply common sense to new situations, relying on data patterns rather than flexible reasoning, which often leads to errors.
Limited Adaptability	AI cannot easily adapt to unexpected or changing environments. Humans excel in real-time decision-making, while AI remains rigid and requires reprogramming for new tasks.
Absence of Emotional Intelligence	AI cannot feel or express emotions like empathy or sympathy, making it inadequate in roles that require emotional understanding, such as customer service or counseling.
Challenges in Reasoning	AI reasoning is often rigid and limited by training data. Despite advancements, AI systems can be manipulated or fail to apply knowledge beyond predefined rules.

Conclusion

AI has shown great efficiency and productivity in tasks like healthcare and customer service. However, it still faces significant challenges. These challenges are more evident in areas that require human traits such as common sense, adaptability, and emotional intelligence.

While AI excels at data-driven tasks, it struggles with understanding context and adapting to new situations. It also lacks the ability to show empathy. This makes AI unsuitable for roles that need human-like flexibility and emotional connection. The article concludes that, despite AI’s rapid progress, it is not yet ready to replace humans in jobs requiring nuanced thinking. Improvements in AI’s reasoning, context understanding, and emotional awareness may help reduce these gaps. However, human input remains essential in many areas.

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