What Is Deep Learning?

Deep learning in theory is AI able to learn without human supervision, drawing from data that is both unstructured and unlabeled.

If artificial intelligence can be loosely interpreted to mean incorporating human intelligence to machines, we are still a long ways off from real AI or artificial general intelligence (AGI).

Deep learning techniques have improved the ability to classify, recognize, detect and describe – in one word, understand – but it’s still not real or sufficient to say it’s real AI.

The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. While deep learning has a lot of hype around it, it’s still pretty basic.

New machine learning approaches have improved the accuracy of models. New classes of neural networks have been developed that fit well for applications like text translation and image classification. That’s all very fair and well, but that’s not real lucid artificial intelligence.

Deep learning is a particularly good example in this regard. It’s related to – but not interchangeable with – the broader category of machine learning. Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. But this too is ponderous and requires massive amounts of data.

All deep learning means at this point is that it is a central subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.

So it’s nothing very aware or self-learning, simply a drone-like ability to improve. A deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. Still, that can lead to some impressive results.

Deep learning networks will often improve as you increase the amount of data being used to train them. Deep learning is essentially a branch of AI that closely tries to mimic how the human brain works but we can’t really consider it closer to AI. It’s more just an extension of machine learning.

A lot of computational power is needed to solve deep learning problems because of the iterative nature of deep learning algorithms, their complexity as the number of layers increase, and the large volumes of data needed to train the networks.

Deep learning is still trapped in fields of data, without any meaning, context or understanding of what it itself is doing.

Deep learning does enable reverse engineering of desired outcomes. For instance, provide a definition of the desired outcome and an example set of inputs, and the deep learning algorithm will backward solve the answer to your question.

Now non-technical people can create complex requests without knowing any programming. This however can lead to some pretty nefarious illustrations of early AI.

The intention of ML is to enable machines to learn by themselves using the provided data and then making accurate predictions. DL algorithms are roughly inspired by the information processing patterns found in the human brain.

In a perfect world DL is more “human” than ML and enables new developments in AI and AI products. Deep learning has evolved hand in hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. However DL has also not really changed the world as much or as quickly as some expected.

In deep learning, a computer model learns to perform classification tasks directly from images, text or sound. DL innovations certainly can lead to better products and new human-AI systems, but it’s a relatively slow crawl.

Real AI in a sense has yet to be born in 2020. Since deep-learning algorithms require a ton of data to learn from, this increase in data creation is one reason that deep learning capabilities have grown in recent years. But it’s also a bottleneck of what DL can actually do.

Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and interconnected.

This is clearly a level of processing above mere algorithms or ML. Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure.

It’s not clear what the upper ceiling of DL is, but its products have not radically changed the world like some imagined they would. Deep learning is used across all industries for a number of different tasks, but is likely not the future or end game of AI development and progress.

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