Artificial Intelligence (AI) has grown exponentially, yet it is still misunderstood and misrepresented. AI is not a magic bullet; it’s a tool that can perform specific tasks, but it’s not a panacea for all problems. It’s essential to understand that AI doesn’t ‘understand’ or ‘think’ in the human sense. Instead, it finds patterns in data and makes predictions based on those patterns.
Machine Learning (ML), a subset of AI, uses algorithms to analyse data, learn from it, and make decisions. Deep Learning, a further subset of ML, uses neural networks with many layers to learn complex patterns in large amounts of data.
AI’s effectiveness varies significantly depending on the problem at hand. It excels at tasks involving pattern recognition, such as image recognition, speech recognition, and translation. However, it struggles with tasks requiring common sense, creativity, or understanding of the world.
AI’s limitations stem from its dependence on data. It can only learn from the data it’s given and can’t understand or learn anything beyond that. This leads to issues like overfitting, where the AI becomes too specialised in the training data and fails to generalise to new situations.
AI is a potent tool, but it’s not without its flaws. It’s crucial to approach it with a realistic understanding of its capabilities and limitations.
Go to source article: https://blog.piekniewski.info/2020/06/08/ai-the-no-bullshit-approach/