Does AI Really Speak Our Language?

Does AI Really Speak Our Language?

Artificial intelligence has come a long way in handling tasks that involve language, but it's still missing most of the nuance and context that we humans take for granted.

By Ben Dickson December 17, 2018 8:00AM EST
Thanks to advances in deep learning—the subset of AI that solves problems by finding correlations between data points—computers have become very good at handling tasks that involve the processing and generation of language.
Robots that look and sound human, digital assistants that can place calls on our behalf and book restaurant tables, email apps that can predict and complete our sentences with decent accuracy, translator apps that can give you near-real-time translations, bots that can generate news stories—they're all powered by artificial intelligence.
All these developments can give the impression that AI has mastered language, one of the most complicated functionalities of the human mind. But there are fundamental differences between the way humans and AI handle language, and not understanding them can create false expectations about AI's language-processing capabilities.

Advances in the Field Are Real

Earlier approaches to natural language processing (NLP) involved a lot of manual coding. For instance, if you wanted to build an application that translated one language to another, you had to create an entire software library that defined the rules to extract the function of different words in a sentence, map those words to their target language, and generate a sentence that was both semantically and grammatically correct. The process was long, painful, and error-prone.
Deep learning uses a fundamentally different approach to machine translation. Translations based on deep learning and neural networks "extract a large number of latent patterns that significantly improve the quality of sentence translation over previous approaches," said David Patterson, head of AI for Aiqudo, a company that develops voice-enabled digital assistants.
The fundamental component of deep-learning algorithms is the neural network, a software structure roughly fashioned after how the human brain acquires knowledge. Give a neural network a set of English sentences and their corresponding French translations, and it extracts the common patterns between the examples and uses them to translate new sentences it hasn't seen before. So there's no need to hard-code rules.
In 2016, the New York Times ran a story in which it detailed how a switch to deep learning had revolutionized Google's translation service. "The A.I. system had demonstrated overnight improvements roughly equal to the total gains the old one had accrued over its entire lifetime," the Times wrote.
"When large amounts of training data are available, deep learning has done very well; for example, in machine translation, as compared to earlier statistical machine-translation models, [deep learning] improved the quality significantly, even though there is still a need for further improvements," said Salim Roukos, CTO of Translation Technologies at IBM.
According to Roukos, on a scale of 0 to 5, neural networks have helped improve machine translation by 0.5 to 1 point.
Beyond translation, deep learning has helped make great advances not only in translation but also in several other language-related fields, such as text summarization, question-answering, and natural-language generation.
Several companies have used deep learning to develop applications that enable users to accomplish tasks through conversational interfaces such as messenger apps. In recent years, AI-powered chatbots for banking, healthcare, customer service, and many other fields have become very popular. Companies including Facebook and Google have created platforms on which users can create smart chatbots without any coding—simply by providing them sample phrases.
Deep learning is also used in text-to-speech, the technology that enables us to send voice commands to our computers, and voice synthesis, the technology that enables computers to talk back to us in human-sounding voices.
There are now several AI-powered voice-synthesis platforms that let users generate natural-sounding voices. The technology can help solve different problems ranging from company branding to helping ALS patients regain their voice.

Deep Learning's Limited Grasp of Language

"It is possible to translate a sentence without understanding its meaning," Patterson said. This might sound absurd to a human translator, but for computers, the difference is very real.
Machine translation has nothing to do with understanding the source and target language. At its core, deep learning is a pattern-matching algorithm. When it finds similarities between a new sentence and the examples it has been trained on, it can provide an accurate translation. Otherwise, it could produce meaningless results, as Patterson pointed out.
"A related issue is that of robustness of the system when there is drift (or change) between the training data and the actual run-time data," Roukos said.
Despite advances, all current blends of artificial intelligence—including deep learning—are narrow AI, which means they can perform the very specific tasks they're trained on. Unlike humans, who can generalize knowledge and transfer concepts from one domain to another, neural networks perform poorly when applied to tasks that deviate from their specialized domain.
This means that while deep learning performs exceptionally well on simple translation and language-related tasks, it can fail spectacularly when those tasks require common sense and the understanding of abstract concepts.
Cognitive science professor Douglas Hofstadter unpacked some of the limits of deep learning–based translation in a feature published in The Atlantic in January. Hofstadter showed through examples how an AI translation system would miss some of the simplest concepts that a human translator would take for granted. For instance, any human reader would deduce that the following excerpt describes the house of a married couple: "In their house, everything comes in pairs. There's his car and her car, his towels and her towels, and his library and hers."
Accordingly, when translating the above excerpt to French, where possessive pronouns inherit their gender and number from the possessed item rather than the possessor, a human translator would add words and details to make sure that the reader would understand that the text is describing a man and a woman. But in Hofstadter's test, Google Translate generated a translation that completely missed the point, omitting details that would highlight the difference in gender between the two people who the original text was describing.
"We humans know all sorts of things about couples, houses, personal possessions, pride, rivalry, jealousy, privacy, and many other intangibles that lead to such quirks as a married couple having towels embroidered 'his' and 'hers.' Google Translate isn't familiar with such situations. Google Translate isn't familiar with situations, period," Hofstadter concluded.
"The subtleties of meaning in context, especially with a longer text, is still a challenging problem for machines. Also, there is a need to have more 'world knowledge' to help systems understand language in a way that is similar to human level," IBM's Roukos said.
Earlier this year, separate reports from The Wall Street Journal and Wired detailed how several companies that used deep learning for services such as smart email replies and intelligent chatbots were employing human operators to jump in and take over when the AI failed—which happened quite often. Facebook also used this practice with its abandoned M chatbot assistant, and Google is using it in the trial of its Duplex technology, which makes phone calls on behalf of users and books restaurants.
"We can't hope that real consciousness will become part of AI in the near future. For now, we need to use models of different real-world domains to help organize human-computer conversation on the level that 'simulates' human-human conversation," said Patterson. "In order to move from 'simulated' conversations to 'real' conversations, we need to figure out how to empower machines with things like common sense, understanding of context, and creativity. This is where the current research is focusing."
Scientists and researchers are working on different techniques that might complement deep learning or enable neural networks to do more than just match patterns. The hard truth is that natural language processing is a very complicated task. Some believe true NLP requires general artificial intelligence, AI that is on par with the human mind. And that is nowhere in sight. Until then, we have to acknowledge the limits of deep learning to make the most out of the tremendous progress we've made into teaching our computers to speak in our language.


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