Technology virtuosos like Elon Musk and the late Stephen Hawking have raised concerns about AI, declaring that it will turn out to be a menace to mankind
copyright by www.entrepreneur.com
Every day we hear and read about how machine learning is changing the face of technology. From social media to virtual assistants like Siri and Alexa, IoT, and even automobiles, algorithms analyze terabytes of data and make faster decisions.
Storing data and harnessing technology to make life easier has become cheaper. In an article for HBR magazine, Andrew Ng, the founder of Google Brain wrote, “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”
People often think of Artificial Intelligence (AI) and Machine Learning (ML) as the same. But there is a difference. AI means machines perform “intelligent” tasks – not only repetitive ones. They adapt to different situations and present us with outcomes accordingly.
ML is a more specific subset of AI. It works on the idea that machines can “think” for themselves and learn without our constant supervision.
Deep learning is ML’s most dominant technique. It’s essentially a statistical method to teach machines how to classify patterns using (artificial) neural networks. These networks memorize categories and apply them to similar situations in a roughly reliable manner.
Most people believe that if we add 100X more layers and 1000X more data, a neural net can to do anything a human being does. But not everything is copacetic right now.
Technology virtuosos like Elon Musk and the late Stephen Hawking have raised concerns about AI, declaring that it will turn out to be a menace to mankind.
Although possible, that future still is far away. Because Machine Learning itself has a lot of catching up to do when it comes to evolution.
Gary Marcus, professor of cognitive psychology at NYU, had a brief stint as director of Uber’s AI lab. He believes deep learning is “greedy, brittle, opaque, and shallow.”
Here’s why he thinks so, and how that currently pans out for mankind.[…]
read more – copyright by www.entrepreneur.com
Technology virtuosos like Elon Musk and the late Stephen Hawking have raised concerns about AI, declaring that it will turn out to be a menace to mankind
copyright by www.entrepreneur.com
Storing data and harnessing technology to make life easier has become cheaper. In an article for HBR magazine, Andrew Ng, the founder of Google Brain wrote, “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”
People often think of Artificial Intelligence (AI) and Machine Learning (ML) as the same. But there is a difference. AI means machines perform “intelligent” tasks – not only repetitive ones. They adapt to different situations and present us with outcomes accordingly.
ML is a more specific subset of AI. It works on the idea that machines can “think” for themselves and learn without our constant supervision.
Deep learning is ML’s most dominant technique. It’s essentially a statistical method to teach machines how to classify patterns using (artificial) neural networks. These networks memorize categories and apply them to similar situations in a roughly reliable manner.
Most people believe that if we add 100X more layers and 1000X more data, a neural net can to do anything a human being does. But not everything is copacetic right now.
Technology virtuosos like Elon Musk and the late Stephen Hawking have raised concerns about AI, declaring that it will turn out to be a menace to mankind.
Although possible, that future still is far away. Because Machine Learning itself has a lot of catching up to do when it comes to evolution.
Gary Marcus, professor of cognitive psychology at NYU, had a brief stint as director of Uber’s AI lab. He believes deep learning is “greedy, brittle, opaque, and shallow.”
Here’s why he thinks so, and how that currently pans out for mankind.[…]
read more – copyright by www.entrepreneur.com
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