#### Why Computers Suck At Maths?

Computers are incredible at performing mathematical calculations with speed and accuracy far beyond human capabilities. Yet despite such numerical proficiency, computers actually lack true comprehension and reasoning skills when it comes to math. The roots of this dichotomy between calculation and understanding reveal inherent limits to computational thinking.

## The Processing Power of Computers

The development of transistors enabled computers to perform arithmetic operations orders of magnitude faster than people. Tasks that would have taken mathematicians months or years to compute by hand can be completed by computers in seconds. This raw computational power is why computers excel at math formulas and algorithms.

But speed alone does not equate to mathematical reasoning and problem-solving skills. Computers simply follow programmed instructions for computations – they don’t independently “understand” the logic behind those calcolatrice online scientifica.

## Lack of Math Insight

While computers can flawlessly calculate equations, they lack deeper insight into why those calculations make sense. Without a framework for mathematical reasoning, computers cannot comprehend concepts, extrapolate patterns, or make logical inferences about numbers and formulas.

Humans develop this intuitive math sense over years of learning, exploration and practice. But computers have no inherent abilities for abstract reasoning or contextual understanding. They miss the bigger picture behind calculations.

## Britain's Missing Brainpower

In 1967, John McCarthy referred to the inability of computers to exhibit general intelligence as “Britain's missing brainpower” – a reference to the techniques of symbolic logic focused on reasoning that were developed primarily by British mathematicians like George Boole and Augustus De Morgan in the mid-19th century. McCarthy argued that making real progress in artificial intelligence required implementing similar logical reasoning capabilities, not just computation.

## Limits of Computational Thinking

The traditional computer science approach emphasizes computational thinking – breaking problems down into programmed step-by-step instructions. But this process-focused paradigm does not encompass activities like conceptualization, intuition, inference, or other hallmarks of human reasoning.

While beneficial, over-reliance on computational thinking creates limitations. Real-world messiness cannot always be reduced to algorithms.

## The Need for Math Communication

Math comprehension requires not just calculating formulas, but explaining and justifying results. Humans advance mathematical understanding through discussion, debate and collaboration. We are forced to clarify ideas, make connections explicit, identify gaps, and evaluate solutions.

Without skills for mathematical communication, computers miss key context and reasoning behind the numbers. Their capabilities are confined to programmed instructions.

## The Complementary Strengths of Humans and Computers

Moving forward, the ideal approach leverages complementary strengths. People tackle conceptualization, reasoning and creative problem-solving, while computers handle complex, large-scale calculations and data processing.

As McCarthy originally asserted, progress lies not in mimicking human thinking, but combining human ingenuity with computational power. Together, humans and computers can achieve deeper mathematical insight than either can alone.

## Conclusion

While computers appear proficient at math because of incredible processing capabilities, they lack contextual comprehension. Computational thinking alone has limits. Developing true mathematical reasoning requires human attributes like insight, logic, inference making, abstraction and communication skills. Maximizing progress involves utilizing human and computational strengths in tandem.

## Key Takeaways

Computers can rapidly perform calculations but don't truly understand concepts.

Lacking reasoning skills, computers miss the bigger picture behind formulas.

Over-reliance on computational thinking creates limitations.

Math comprehension requires insight and communication abilities computers lack.

Combining human conceptual strengths with computational power holds promise

## Can computers truly understand mathematical concepts?

No, computers lack the reasoning and intuition required for conceptual understanding. They can perform computations accurately but don't comprehend the logic behind them.

### What key human attributes do computers lack when it comes to math?

Humans have abilities like pattern recognition, abstraction, inference making, and communication skills needed for deeper math comprehension. Computers miss this bigger picture context.

## How can over-reliance on computational thinking be limiting?

Reducing all problems to algorithmic, programmed steps overlooks the need for activities like conceptualization, creativity and reasoning in addressing real-world complexity.

### Why is mathematical communication important?

Explaining reasoning, debating solutions, and collaborating around math problems forces clarification and evaluation of concepts. This builds comprehension computers lack when simply processing computations.

### What is the ideal approach to leverage computers for math progress?

Have humans tackle conceptualization and reasoning while computers handle calculations and data processing. Combining these complementary strengths holds the most promise.

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