Post by anwenwilson on Jul 27, 2017 10:30:55 GMT
In 1956 those super smart people - pioneers - wanted to create complex machines that had the same characteristics as human intelligence.
Today, general AI as we know it (and if you have ever watched the Terminator movie, then that's it in a nutshell) is still a thing of science fiction but may not be one day.
Today we can do the “Narrow AI”: a concept of weak AI that means a machine can perform specific tasks as well as, or better than, a human can. Apple’s Siri, Amazon’s Alexa, Face recognition on Facebook etc are great examples. Others exists in all parts of our daily life.
Machine Learning (ML) :an approach to achieve artificial intelligence
Machine learning is the practice of using algorithms (people love using this in 121 pitches) to parse data, learn from it and make a prediction about something in the world.
No more hand coding, lots of software routines to do specific instructions to achieve something, the machine is instead trained using the large amounts of data and algorithms to give it them ability to learn how to perform a task. Cool huh? And from now on if you have read this and are pitching to me, you better be able to give me an example if ML is in your pitch.
So this all came about from different algorithmic approaches, clustering, inductive logic programming, decision tree learning and the list goes on. This was proper early AI crowds and none achieved the goal of general AI and even weak/narrow was pretty hard to achieve.
But the early adopters, those crossing the chasm, were not entirely wrong and it was just a case of time and the right learning algorithms.
Dell Support Number | Windows Customer Service
Today, general AI as we know it (and if you have ever watched the Terminator movie, then that's it in a nutshell) is still a thing of science fiction but may not be one day.
Today we can do the “Narrow AI”: a concept of weak AI that means a machine can perform specific tasks as well as, or better than, a human can. Apple’s Siri, Amazon’s Alexa, Face recognition on Facebook etc are great examples. Others exists in all parts of our daily life.
Machine Learning (ML) :an approach to achieve artificial intelligence
Machine learning is the practice of using algorithms (people love using this in 121 pitches) to parse data, learn from it and make a prediction about something in the world.
No more hand coding, lots of software routines to do specific instructions to achieve something, the machine is instead trained using the large amounts of data and algorithms to give it them ability to learn how to perform a task. Cool huh? And from now on if you have read this and are pitching to me, you better be able to give me an example if ML is in your pitch.
So this all came about from different algorithmic approaches, clustering, inductive logic programming, decision tree learning and the list goes on. This was proper early AI crowds and none achieved the goal of general AI and even weak/narrow was pretty hard to achieve.
But the early adopters, those crossing the chasm, were not entirely wrong and it was just a case of time and the right learning algorithms.
Dell Support Number | Windows Customer Service