AI & Complex Problems
As of 2024, AI cannot solve complex coding problems.
AI has been growing tremendously for decades now. The technology has grown so far that it has helped with decision-making, automated tasks, and even understanding and copying the likeness of human comprehension. Even though AI has remarkable features, there are things it often has challenges with, one of which, of course, is solving complex problems.
While AI can solve specific problems and tasks, we must remember that AI doesn’t attain the same knowledge and creativity as humans because of its limitations. AI will often have trouble figuring out complex problems, but then there are times when it can solve them, and that’s because AI always grows; whatever is fed into the AI algorithm, it will always try to make it better and learn from it. For example, if there’s something someone has figured out before and the AI interacts with it, and the next time someone asks the AI to solve a similar problem, it would take from what it learned before and take your situation and try to give you a better result or solutions to your problem.
AI can also be tricky with complex coding problems because there are trade-offs in the algorithm design. When AI has to solve a complex problem given by the user, most times, it has to make a hard decision on what’s more important, whether that's efficiency, accuracy, speed of getting to the result, or having to save memory. What’s tricky with AI is that it has to have a bit of all those characteristics to handle such complex problems.
AI Algorithm
AI may need help solving complex coding problems due to challenges in algorithm selection. While AI algorithms are trained to process data and make predictions, selecting the most appropriate algorithm for a specific coding task requires a deep understanding of the problem domain and the distinction between different algorithms. Complex coding problems often require sophisticated algorithms with intricate optimization techniques, and AI may need help to navigate the landscape of available algorithms to identify the most suitable one. Additionally, AI algorithms rely on predefined shortcuts or patterns in the data, which may only sometimes apply to complex coding problems that require customized solutions.
The lack of Resource Constraint:
Complex coding problems often involve resource constraints such as memory limitations, computational power, or time constraints. AI algorithms may need help to manage these constraints efficiently, leading to suboptimal solutions or even failure to find a solution within the provided resources. AI models can be used for large datasets and big tasks, but they may only sometimes generalize complex coding problems requiring real-time decision-making or optimal resource utilization. Moreover, AI algorithms may consume significant computer/coding resources, further deepening the resource limitations.
Limited Creativity and Novelty:
AI algorithms excel at pattern recognition and repeated tasks but may need more creativity and originality to solve complex coding problems. While AI models can generate solutions based on existing patterns in the data, they may need help to innovate or think outside the box to find novel solutions to complex coding challenges. Human programmers often rely on intuition, creativity, and expertise to create solutions to complex problems, which may be difficult for AI algorithms to replicate. Additionally, AI models are programmed on recorded data from the internet, like Google. They may need to be expanded by the scope of their training data, making it challenging to generate truly innovative solutions to coding problems.
I can’t solve complex problems because of how complex the problem is, and AI is still very young and isn’t yet sophisticated enough to develop accurate and complex code effectively.
“Artificial intelligence is plagued with numerous problems that hinder it from creating effective and reliable code. Many questions have been raised about the legal and ethical aspects of AI. Furthermore, with AI using open-source code and algorithms, it fails to replicate the creativity and complexity of coding.”
AI is too dependent on the data and algorithms it collects to write reliable code. “Code written by AI would be inaccurate or biased to the problem they are given. For example, ChatGPT won’t always produce the correct answer and, simultaneously, be bland and unoriginal while doing so. This also brings up how uncreative coding bots are.
One of the most significant qualities of coding is the ability to break down problems and find creative solutions.
However, all hope is still possible regarding the potential for AI coding as innovation continues to improve the practicality and adaptability of language models. Furthermore, despite its current unreliability at high levels, basic code can be written with the help of AI, which allows non-coders to get their foot in the door when it comes to software development and learning how to code. The future is bright for artificial intelligence, but we have to weather the current era of unreliability as this young technology continues to improve.
Problem Representation
When it comes to ‘why AI cannot solve complex coding problems,’ there are many factors that we have to consider, and one of those factors is problem representation in that specific topic.
Problem representation comes down to how it involves and understands the complex coding task and how the AI system understands the situation and tries to solve the coding problem. With problem representation in mind, there are points about ‘why AI cannot solve complex coding problems,’ one of them being the abstraction level. Diving deep into coding problems sometimes requires a more sophisticated understanding of the concept while also manipulating the code; it has to go from low-level operations to a high-level intricate design, which gives a more challenging job to the AI system to do while trying to provide an accurate answer to the complex problem.
Another reason is because of contextual understanding, most times AI may have a hard time grasping the bigger context of the complex problem, what that means is AI also has a problem implicating a real world way of solving but also its certain dependencies on the specific framework and the environments or libraries. Another essential reason would be uncertainty because AI systems must come up with a solution and be fast. Most times, the AI system has multiple different versions or interpretations of what it “thinks” the solution is, so the AI system will have a hard time navigating to what’s right when it's too complex and would lead to an incorrect solution because AI doesn’t have the capability of human creativity because it learns based on what its fed and learns to expand from that.
The last reason would be domain-specific knowledge. This means that when AI has to solve complex code, the AI system most often requires certain knowledge about other programming languages like data structures, algorithms, and, of course, software engineering principles. AI systems might lack this knowledge due to their limited understanding, which can be an issue when the AI system has to solve a complex coding problem.
“Many problems are solvable using a Turing machine and therefore can be solved on a computer, while many others are not. For example, the domino problem, a variation of the tiling problem formulated by Chinese American mathematician Hao Wang in 1961, is not solvable.” (Wang)
There are thousands of other solvable problems that are not known to have polynomial-time algorithms despite ongoing intensive efforts to find such algorithms. What separates us, humans, from machines is our ability to leverage experience and think literally. We demonstrate creativity and general intelligence that is missing from AI.
For example, programmers may intuitively interrogate ideas from diverse domains like consumer data structures and optimize algorithms. This is hard for AI to understand, but eventually, they’ll learn how to do that; as of right now, they aren’t able to.
“Also, humans are great at collaborating with multiple teams while AI technology is like AI agents are still a long way from replacing team collaboration”
AI systems can generate code but only understand it with a deeper comprehension of writing code. Maintaining and upgrading AI-written enterprises on a large scale is nearly impossible as of right now. AI is not a universal problem solver. It can only address the issues for which it has been specifically programmed and trained with relevant data, which is one point where it can’t solve complex problems yet. Still, eventually, in the future, it will just as of right now, they are unable to.
AI can generate new ideas and content, but it remains difficult for an eye to create something truly original and innovative. It indicates that it can only do stuff with an algorithm or code already programmed to know.
One example is that artificial intelligence would need help solving software problems. Software development involves high creativity, problem-solving, and adaptability—qualities that are tough to duplicate in AI like ChatGPT. Human programmers leverage their creativity and originality to develop innovative solutions and code and address challenges throughout development. While AI algorithms can assist with certain aspects of software development, they often fall short in tasks that require abstract thinking, lateral reasoning, and real-time adaptation to changing requirements.
Another example is deep learning algorithms like neural networks, which have succeeded in image recognition and natural language processing tasks. However, they may need to be better suited for tasks requiring symbolic reasoning or understanding complex algorithms. On the other hand, symbolic AI techniques, such as logic programming, excel at tasks involving logical reasoning and rule-based systems but may struggle with tasks requiring large amounts of data or statistical analysis. Various configurations may be considered even within a specific algorithm category, adding another layer of complexity to the selection process. While AI algorithms are promising, work must still be done to solve complex coding to be effective.
The lack of resource constraints can hinder AI’s ability to tackle complex coding problems effectively. Unlike humans, who often work within constraints such as time, memory, and the use of computer power, AI systems may have unlimited resource access. While this may seem favorable, it can lead to inefficient solutions and an over-reliance on brute-force approaches. For example, when faced with a complex problem, AI systems may resort to exhaustive search methods that explore every possible solution, regardless of computational cost. While this approach may eventually yield a solution, it may be prohibitively slow or resource-intensive, especially for large-scale problems. Moreover, the lack of resource constraint can also lead to overfitting, where AI systems memorize specific patterns in the training data rather than learning general solutions. This can be particularly difficult in coding tasks where the input space is vast and varied, requiring AI systems to generalize from limited examples.
