Deep learning and Natural Language Processing (NLP) are two buzzwords many people throw around without fully understanding their true meaning. When it comes to the processing of human language by machines, the line between the two concepts becomes even fuzzier.
One source of confusion around deep learning and NLP is the fact that the two are subdivisions of artificial intelligence (AI). Currently, AI has many sub-fields/ forms, such as:
· Computer vision
· Machine learning (deep learning is considered a subset of machine learning)
· NLP, loosely known as “AI for text.”
Both machine learning and deep learning use computer algorithms to help machines learn just like humans do with minimal or zero human input. But while machine learning uses predictive models to help computers analyze data, learn from that data, and spot trends and patterns in the specific data set, deep learning goes deeper.
Deep learning uses artificial neural networks (ANNs) to mimic the way the human brain thinks and learns in order to spot patterns with zero human input. In other words, there are several layers of algorithms that parse and learn from the data to reach complex conclusions. It is estimated that the current technology is so advanced that it may reach conclusions that cannot be foreseen even by its creators.
Deep learning already has real-world applications used on a global scale. For instance, the subscription-based streaming service Netflix uses deep learning to automatically detect what movies a particular user would enjoy based on his or her watch history and preferences. That is why Netflix recommendations have become more and more relevant. And the same goes for YouTube.
Also, music streaming services can predict with bone-chilling accuracy what tracks and artists you might enjoy, even if you had no previous knowledge of those tracks and artists. Google and Amazon have embedded deep learning in their voice and image recognition systems. Self-driving cars also use deep learning to improve their Autopilot feature and make it less reliant on human drivers.
One of the areas deep learning and NLP conflate is speech recognition. Two widespread real-world applications are state-of-the-art chatbots and machine translation.
Natural Language Processing (NLP) is traditionally defined as the automatic understanding and manipulation of human language (also known as “natural” language), be it speech or text, by machines.
While deep learning uses neural networks to understand how humans reason and learn in order to extract patterns from the processed data, NLP uses computer algorithms to mimic how humans communicate with one another. NLP is what makes smart machines in Sci-Fi flicks talk to humans, while deep learning is what enables those machines to reason and make decisions like humans do.
NLP stems from linguistics (the scientific study of language) and computer sciences, and has been perfected over the last 50 years. Decades ago, it took a machine around 7 minutes to process a single sentence; now it takes Google less than a second to process what is written in millions of web pages.
NLP’s applications in text to speech conversion, machine translation, and automated conversational agents. NLP-enabled systems can process various types of text, including web pages, emails, SMS, and signs. The final goal of NLP is to turn human-produced text or speech input into natural-language-like text or speech output. NLP is what made Amazon’s Alexa and Apple’s Siri possible.
There are two types of NLP: rule-based NLP and statistical NLP. Rule-based NLP is based on linguistic rules and patterns. It is used for simple tasks like identifying word order and parts of speech and counting word frequencies in a text to determine a pattern.
On the other hand, statistical NLP uses complex predictive models and algorithms to mimic human communication both in writing and speech. In recent years, statistical NLP has been paired with deep learning to help machines acquire natural language just like a baby to produce fluent conversation in natural language just like an adult would.
Statistical NLP and deep learning are the main reasons it is so hard to tell the friendly AI chatbot so eager to address your questions and concerns on various web pages from a human operator. Some chatbots are so “human-like” that companies are reluctant to unveil their identity in order not to alienate their customers.