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Most AI Dollars Go to Machine Learning
why axis chart ai dollars

Artificial intelligence is a term that sounds like it's way beyond you in IQ points and also in dollars. Right now, though, the reality of AI is less Ex Machina and more machine learning.

The Why Axis BugA study from Venture Scanner and Statista has found that the most of the funding in artificial intelligence is going toward machine-learning applications. So far, about $28.5 billion has been invested in applications that are accessible to many. When Google Photos sorts your images or you talk to your phone to send written texts, that's machine learning at work. The platforms that prop up those apps also get a fair share of funds and have received about $14.4 billion in spending.

The practicality and profitability of machine-learning applications is what makes them so popular. For businesses, the applications speed processes and profits by bypassing the need for humans for some rote tasks, analyzing marketing efforts, and keeping up with customer-service demands. When used by people, machine-learning applications can improve the conversation between man and machine. Just last week at Google I/O, Google demonstrated advances in AI, most of them centered on machine learning.

A replicant-filled world might await us. But for now, AI is far more mundane.

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why axis chart ai dollars

Artificial intelligence is a term that sounds like it's way beyond you in IQ points and also in dollars. Right now, though, the reality of AI is less Ex Machina and more machine learning.

The Why Axis BugA study from Venture Scanner and Statista has found that the most of the funding in artificial intelligence is going toward machine-learning applications. So far, about $28.5 billion has been invested in applications that are accessible to many. When Google Photos sorts your images or you talk to your phone to send written texts, that's machine learning at work. The platforms that prop up those apps also get a fair share of funds and have received about $14.4 billion in spending.

The practicality and profitability of machine-learning applications is what makes them so popular. For businesses, the applications speed processes and profits by bypassing the need for humans for some rote tasks, analyzing marketing efforts, and keeping up with customer-service demands. When used by people, machine-learning applications can improve the conversation between man and machine. Just last week at Google I/O, Google demonstrated advances in AI, most of them centered on machine learning.

A replicant-filled world might await us. But for now, AI is far more mundane.

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