Why has Deep Learning surpassed Machine Learning?

If you want to know why Deep Learning will revolutionize artificial intelligence, or you want to know how to make the most of this technology, you can’t miss this post. In this article, the paper writer describes the critical differences between Deep Learning and Machine Learning and what innovations have become the technological buzzword. Don’t miss the details of Deep Learning vs. Machine Learning.

Deep Learning vs. Machine Learning

Today we show you the two Artificial Intelligence systems, Deep Learning vs. Machine Learning, which is revolutionizing current technology and is bringing computers closer and closer to the functioning of the human brain and even, in some aspects, surpassing it.

Machine Learning would be the present (or almost past), and Deep Learning the future of Artificial Intelligence.

Both Artificial Intelligence systems work with large amounts of data and information (Big Data). Still, they are separated from the simpler systems by being able to learn by themselves and correct errors (Machine Learning) and the more innovative (Deep Learning) by making decisions from the data by itself. It could be said that the Deep Learning system is a more complex and perfected aspect of Machine Learning.

The key to differentiating them, apart from the technical perfection, is precisely the human intervention: With Machine Learning, we supervise all the processes and teach the computer to learn different data and classify them.

On the other hand, Deep Learning learns by itself with each new piece of data it receives. It may take a wrong part of data or category once, but it learns from that mistake and uses another amount of data to get closer to the correct result faster, faster, and more reliably.  

How Machine Learning works

AI systems arose from the need to give computer algorithms that work with rules and mechanisms to find answers among a large amount of data and at the same time be able to predict data or even suggest options.

In other words, to be more explicit, it works with patterns: if we are looking for a specific car, it will examine different categories: color, brand, horsepower and displacement, dimensions. And it will discard all the incorrect data to offer the relevant data. The downside of Machine Learning is that you have to guide the system at each stage so that it knows how to identify it automatically with practice. 

The autonomy of Deep Learning compared to Machine Learning

This system is much more sophisticated and works practically autonomously, requiring only one stage of human intervention: programming. After that stage, as its translation into English “deep learning” explains, it goes far beyond the technological possibilities of its predecessor and tries to imitate the functioning of the brain, working in layers or neuronal units. Each neuronal layer of the system processes the information and produces a result in weighting.

In this case, if we were to offer a computer many images of cars and search for a specific model detailing the main characteristics, Deep Learning would process the results and weight with a percentage of the possibility that it is or is not the model being searched for.

Each time the system performs a search, it learns by itself, and in the next one, it will be clear which data it has to search for first and which not to search for to improve the results. It is fundamental how it works with errors because each time we define that it has made a mistake, it incorporates new data to the neural network that it will not repeat in a similar search. 

Deep Learning Innovations

Now that you know more about Deep Learning vs. Machine Learning, we are going to tell you about the innovations that Deep Learning brings, don’t miss it:

• The most critical technology multinationals already use Deep Learning to solve problems in today’s world and get computers to act and think like humans. As usual, Google has been the pioneer in the field. Its Google Brain project tries to imitate brain functioning, which has revolutionized voice and image recognition. A few years ago, Andrew NG’s team needed 16,000 computers to recognize a particular cat among the more than 10 million videos on YouTube.
• An example we are all familiar with is the photo recognition used by Facebook to tag the different faces in a photo and at the same time to identify the location of an image.
• Not to be outdone, Twitter has chosen to use Deep Learning to improve the image quality of its streaming videos. Neural networks make it possible to compress the video further.
• They promise to facilitate the daily routine by deducing with a simple photo how many calories a dish may have, or even that cars can drive autonomously by recognizing driving patterns in the most ambitious AI idea. In sports, they already allow us to remember whether there is an offside play or who has crossed the finish line first in the 100 m sprint.

But everything has a process, and in the beginning, there are always many bugs and glitches. But for these ideas to be truly usable, they need the deepest refinement, 99% is no good if that 1% can cause harm to humans. We hope you have been able to check the differences between Deep Learning vs. Machine Learning.

Bio:

Elissa Smart is an omnipotent demiurge behind PaperHelp’s blog. Driven by seething creativity, not only she helps students with particular research and writing requests, but also finds the energy to share her extensive expertise via blog posts.