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Imagine a world where satisfying human-computer dialogues exist. With the resurgence of interest in natural language processing (NLP) and understanding (NLU) – that day may not be far off.
In order to provide more satisfying interactions with machines, researchers are designing smart systems that use artificial intelligence (AI) to develop better understanding of human requests and intent.
Last year, OpenAI used a machine learning technique called reinforcement learning to teach agents to design their own language. The AI agents were given a simple set of words and the ability to communicate with each other. They were then given a set of goals that were best achieved by cooperating (communicating) with other agents. The agents independently developed a simple ‘grounded’ language.
Grounded vs. inferred language
Human language is said to be grounded in experience. People grasp the meaning of many basic words by interaction – not by learning dictionary definitions by rote. They develop understanding in terms of sensory experience — for example, words like red, heavy, above.
Abstract word meanings are built in relation to more concretely grounded terms. Grounding allows humans to acquire and understand words and sentences in context.
The opposite of a grounded language is an inferred language. Inferred languages derive meaning from the words themselves and not what they represent. In AI trained only on textual data, but not real-world representations, these methods lack true understanding of what the words mean.
What if the AI agent develops its own language we can’t understand?
It happens. Even if the researcher gives the agents simple English words the agent inevitably diverges to its own, unintelligible language. Recently researchers at Facebook, Google and OpenAI all experienced this phenomenon!
Agents are reward driven. If there is no reward for using English (or human language) then the agents will develop a more efficient shorthand for themselves.
That’s cool – why is that a problem?
When researchers at the Facebook Artificial Intelligence Research lab designed chatbots to negotiate with one another using machine learning, they had to tweak one of their models because otherwise the bot-to-bot conversation “led to divergence from human language as the agents developed their own language for negotiating.” They had to use what’s called a fixed supervised model instead.
The problem, there, is transparency. Machine learning techniques such as deep learning are black box technologies. A lot of data is fed into the AI, in this case a neural network, to train on and develop its own rules. The model is then fed new data which is used to spit out answers or information. The black box analogy is used because it is very hard, if not impossible in complex models, to know exactly how the AI derives the output (answers). If AI develops its own languages when talking to other AI, the transparency problem compounds. How can we fully trust an AI when we can’t follow how it is making its decisions and what it is telling other AI?
But it does demonstrate how machines are redefining people’s understanding of so many realms once believed to be exclusively human—like language. The Facebook researchers concluded that it offered a fascinating insight to human and machine language. The bots also proved to be very good negotiators, developing intelligent negotiating strategies.
These new insights, in turn, lead to smarter chatbots that have a greater understanding of the real world and the context of human dialog.
At SAS, we’re developing different ways to incorporate chatbots into business dashboards or analytics platforms. These capabilities have the potential to expand the audience for analytics results and attract new and less technical users.
“Chatbots are a key technology that could allow people to consume analytics without realizing that’s what they’re doing,” says Oliver Schabenberger, SAS Executive Vice President, Chief Operating Officer and Chief Technology Officer in a recent SAS Insights article. “Chatbots create a humanlike interaction that makes results accessible to all.” The evolution of NLP toward NLU has a lot of important implications for businesses and consumers alike.
Satisfying human-computer dialogues will soon exist, and will have applications in medicine, law, and the classroom-to name but a few. As the volume of unstructured information continues to grow exponentially, we will benefit from AI’s tireless ability to help us make sense of it all.
Further Resources:
Natural Language Processing: What it is and why it matters
White paper: Text Analytics for Executives: What Can Text Analytics Do for Your Organization?
SAS® Text Analytics for Business Applications: Concept Rules for Information Extraction Models, by Teresa Jade, Biljana Belamaric Wilsey, and Michael Wallis
Unstructured Data Analysis: Entity Resolution and Regular Expressions in SAS®, by Matthew Windham
SAS: What are chatbots?
Blog: Let’s chat about chatbots, by Wayne Thompson
Moving from natural language processing to natural language understanding was published on SAS Users.
| This post was kindly contributed by SAS Users - go there to comment and to read the full post. |