Machine Learning vs Machine Reasoning: Know the Difference

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Today, Machine Learning systems can learn by themselves from preset data.

The next step in AI evolution towards human-level intelligence is machine reasoning, or the ability to apply prior knowledge to new situations.

Since ancient times, humans have been interested in finding systematic approaches to reasoning and logical thinking. Now, we want to make machines “think” like us and endow them with the reasoning ability that, unfortunately, we don’t quite understand ourselves.

But, why do we need machines that can deconstruct truths and validate reasons like we do? One of our most recent AI-related posts discusses the story of an AI system that can detect skin cancer more accurately than dermatologists. No doubt, this is a big deal in that an early diagnosis is one of the most effective methods for providing successful cancer treatments. It’s much easier to make an AI software that can recognize a set of data patterns to diagnose skin cancer than an AI that understands what skin cancer actually is. We want a Machine Reasoning AI that solves the problem, and before that, knows what the problem is.

Machine Learning Systems Aren’t Smart Enough

Many different AI systems can achieve performance comparable to that of humans without having to imitate human intelligence processes. Let’s take game AI as an example. The most advanced game-playing AI systems like Google’s AlphaGo can outperform humans, but can’t show human-like intelligence. Their systems mainly consist of a well-optimized game tree algorithm that assesses all possible moves and chooses the best according to the opponent’s move. While the end result looks like “intelligence”, in the background, it’s only a powerful combinatorial search engine. Human cognition doesn’t work this way.

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