Deep Learning AI Is Not a Magic Potion but Machine Learning

Deep Learning AI Is Not a Magic Potion but Machine Learning

Frankly, today’s AI technology is nothing less than magic. Algorithms deciphering images, videos, speeches and texts, translating languages in between, driving cars, identifying cancer, playing games have unleashed a new era of digital transformation that’s creating awe-inspiring milestones each day. It seems that AI is ravaging every part our industry verticals, and we can’t be more excited!

Apparently, AI algorithms are similar to conventional machine learning algorithms. Even the most robust systems feature more artistry than science, calls for a wide array of carefully curated data, does generalize beyond their own training area and contains several unknown glitches that even their developers have little knowledge about.

In the last half decade, Deep Learning AI renaissance has reached new heights. With groundbreaking innovations and phenomenal feats in ML technology, AI has come to hold almost a mythical significance. Quite frequently, academicians are conducting ‘intelligent systems’ conferences, analysts are etching an AI solution for every problem, media personalities are conjecturing AI’s keenness to replace human superiority – all of these instances are happening each day. Not a week passes by when a research publication or academic editorial doesn’t document another application or algorithmic achievement.

Quite obviously, the ‘future of machines’ is knocking at our doors…

The field of Machine Learning and AI is expanding. Going beyond the paradigms of human intelligence, artificial intelligence is making machines perform tasks, which were previously impossible. But, how do machines work? After all, there exists no spell that would cast its enigma and make machines perform like humans.

Interestingly, the machines learn and imbibe skills by experience and through inspirations from the human brain. Deep leaning is a fraction of machine learning wherein artificial neural networks and algorithms learn from huge volumes of data. Similarly, like how we learn from experience, the advanced algorithms follow our footsteps and perform a task repeatedly unless they succeed. Just, like us, they too believe in learning from experiences and mistakes. What’s more, the term ‘deep learning’ originates from the notion that networks have numerous deep layers and they boost learning.

At present, the world is churning out data at a phenomenal speed. A state of data explosion is not too far. Every day, a staggering – 2.6 quintillion bytes of data is being generated. And this is the fuel for deep learning. As deep learning algorithms need a humongous amount of data, the increase in the levels of data creation is one of the key reasons for which deep learning capabilities and resources have grown manifold in recent years.

In a nutshell, Deep Learning, AI and ML in conjunction are rapidly advancing. Cutting edge algorithms are making life and death decisions, but yes once again we would like to say, they are not some magic potion that casts its spell around just like that. Instead, they are advanced, well-built models that identify underlying patterns and implement those patterns to relevant data. The more experience they gather, the more productive they become.

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The blog has been sourced from ― www.forbes.com/sites/kalevleetaru/2018/11/14/todays-deep-learning-ai-is-machine-learning-not-magic/#142cc4276875

 

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November 24, 2018 3:43 pm Published by , , , , ,

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