Unlike humans, AI has computational power that allows it to process copious amounts of data. As AI’s tasks continue to become more advanced, there’s also an increase in computational power necessary-which can become expensive. In order to avoid this need, AI was made to be a specific purpose learner. If AI gains the ability to relate data that is similar, it should be able to more efficiently process the data and understand it with a lower amount of power.
AI makes you look better in selfies, prevents blindness, and can even
turn water into tastier beer. Tech giants and governments say we’re living in a
golden age of AI. Roll out the self-driving cars!
AI is already used in multiple disciplines and industries. These devices
are capable of reading, writing, speaking, seeing, and understanding. They are
able to recognize emotions, play games, and even debate. Due to these
capabilities, AI is often leveraged as a virtual assistant that can help plan
meetings; as a tool to simplify the job hunting and hiring processes; and much
more.
However, in order for this technology to be able to advance, it needs to
be able to learn.
AI systems rely on artificial neural networks (ANNs), which try to
simulate the way the brain works in order to learn.
ANNs can be trained to recognise patterns in information - including
speech, text data, or visual images - and are the basis for a large number of
the developments in AI over recent years.
Conventional AI uses input to 'teach' an algorithm about a particular
subject by feeding it massive amounts of information.
Practical applications include Google's language translation services, Facebook's
facial recognition software and Snapchat's image altering live filters.
The process of inputting this data can be extremely time consuming and
is limited to one type of knowledge.
A new breed of ANNs called Adversarial Neural Networks pits the wits of
two AI bots against each other, which allows them to learn from each other.
This approach is designed to speed up the process of learning, as well
as refining the output created by AI systems.
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