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Common sense in AI remains elusive Posted on : Mar 28 - 2020

While AI and machine learning have made major improvements and advancements to computers, common sense in AI has proven to be a significant challenge.

Artificial intelligence has come a long way since the term was first coined in 1956. Today, we have computers that can carry out complex tasks, vehicles that can operate autonomously and computers that can recognize objects and people in images. AI and machine learning have helped create and improve these advances.

Machine learning gives computers a way to learn from data and examples. Existing forms of machine learning -- supervised, unsupervised and reinforcement -- provide a range of methods for a computer to take information and create generalizations that it can apply to new data it has not seen before. But there are many situations that you can't train a system to understand.

Without cracking the code on how humans reason and apply common sense, however, we will never be able to achieve the goals of Artificial General Intelligence (AGI). Without being able to learn common sense, a computer is less able to adapt to new situations or changes.

Why is common sense reasoning so hard?

One of the reasons why enabling computers with common sense is so difficult is that machines require data and need to find repetitive patterns in that data to learn. Without these patterns, the machine has no way of reasoning with the data. It is possible to teach an AI how to learn about a specific situation or area, such as playing chess or a video game, but it isn't possible to teach that same system how to learn to play different games with the current set of data it was trained on.

Humans have generalized intelligence, but machines are not able to handle this level of understanding with significant ability. Much progress has been made specifically on this area of machine learning, with research clearly showing the ability to process and understand unstructured data such as images and text.

However, accomplishing machine learning is not sufficient to achieve the goals of artificial intelligence. Machines that can learn on their own and generalize from previous experiences without specifically being trained on that data would open the possibility of a whole selection of new applications for AI. View More