The Present Applications of Artificial Intelligence (AI)

By StartUp City | Friday, February 22, 2019

According to the researchers at Waterloo University, there is no exact method to decide whether machine learning tools can solve a particular problem successfully. The tools present in the market are successful; nobody understands why they are successful, and there is no guarantee how long the tools will be successful. Machine learning algorithms are capable of performing simple tasks like answering yes or no, but when it comes to general setups; it cannot distinguish from learnable to un-learnable tasks.

In a study, researchers considered a learning model called estimating the maximum (EMX). This model captures multiple machine learning tasks. The researchers found that irrespective of mathematical method, it is not ensured that an AI-based tool could handle that task or not. This finding surprised the research community because they believed that if a task is provided, it can be determined if the ML algorithm can carry out the task.

Artificial Intelligence researchers and academics are using creativity and the ability to think laterally to produce original output. In a creative industry, AI can invent recipes and make movie trailers. The most significant achievement in machine learning up until now is Google’s Artificial Neural Network teaching itself to recognize a cat. The ANN can perform tasks that are mathematized and coded. Identifying human traits such as humor, empathy, and shared understanding are still not in the domain of AI. It is one of the biggest reasons why teaching subjectivity and sensitivity to a machine is challenging. 

AI researchers must strive to achieve artificial general intelligence (AGI). Generalization demands the need to perform conversations.  In realistic environments, a system must be flexible to adjust when the context of the discussion changes. The dialogue, however, should not be confined to only inanimate environments but should also be available for the three-dimensional cognition. In autonomous environments, agile and flexible models are required with the capabilities to perform in different domains if necessary and adapt according to the context. The social environment will likely be the most sophisticated system to understand the nuances of human behavior.

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