Google DeepMind researchers have proposed a new definition and taxonomy for artificial general intelligence (AGI). The team outlines five ascending levels of AGI, ranging from emerging to superhuman. They emphasize that AGI must be both general-purpose and high-achieving, capable of learning, assessing performance, and requesting assistance. The researchers also stress the importance of measuring AGI capabilities and highlight that AGI does not necessarily imply autonomy. The paper aims to bring clarity to the topic and address the confusion surrounding the term AGI.
Defining Artificial General Intelligence (AGI)
AGI, or artificial general intelligence, is a hot topic in the tech industry. However, there is a lack of consensus on what exactly AGI means. To address this, a team of researchers from Google DeepMind has developed a new definition and taxonomy of AGI.
The researchers outline five levels of AGI: emerging, competent, expert, virtuoso, and superhuman. Emerging AGI includes cutting-edge chatbots like ChatGPT and Bard, while superhuman AGI would outperform humans in a wide range of tasks, including those that humans cannot do.
This new definition provides much-needed clarity on the topic, as many people use the term AGI without fully understanding its meaning.
The Importance of a Clear Definition
As AGI becomes increasingly important, it is crucial to have a clear understanding of what it entails. The original term was coined around 20 years ago to describe AI that could perform multiple tasks well. However, over time, AGI has evolved into a property that computer programs can possess.
The DeepMind researchers emphasize that an AGI must be both general-purpose and high-achieving. It should be able to learn, assess its performance, and ask for assistance when needed. The focus should be on what an AGI can do, rather than how it does it.
Measuring the performance of AGI is challenging, as researchers debate the true measure of intelligence. The researchers suggest ongoing evaluation of AGI capabilities, rather than one-off tests.
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