Artificial intelligence has brought about a revolution in countless aspects of our life. By now, many people have had an encounter with, or at least heard of Alexa, Siri, Google Assistant or Cortana. These and other neural conversation agents have made a foray into life as we know it thanks to advances in the machine learning chatbot.
What is a Machine Learning Chatbot?
As the name suggests, a chatbot is essentially a conversational tool which has the purpose of automating conversation. Though they come in countless forms and frameworks, they fall broadly into two main categories:
- Rule-based chatbots – These bots base their performance on the use of keywords to complete scripted actions. They have gained popularity in e-commerce not only because they are easier to build but also because they can get simple tasks done.
- Machine learning-based chatbots – These are more complex and tend to converse more naturally due to their use of neural networks.
The Rise of the Machine Learning Chatbot
The machine learning chatbot is becoming a more popular alternative to the rule-based models. To a great extent, this is attributable to breakthroughs in speech detection and analysis. Machine learning algorithms for chatbot are generally based on automated analytical model building, making it possible for the computer to learn from experience.
Basically, the idea is to integrate learning and experience to enable these algorithms to make better decisions without necessitating human intervention. It automates the building of analytical models based on the concept that computers are capable of learning. They learn from data to identify patterns and make autonomous decisions with the least human intervention. These brain-inspired models seek to emulate the manner in which the human brain learns in different ways.
Approaches for Creating Machine Learning Algorithms for Chatbot
ELIZA is one of the earliest examples of a chatbot based on the hard-coded rule-based system. She (it) was an MIT chatbot from back in the ‘60s who played the role of a therapist so well that some users actually thought it was an actual therapist. ELIZA uses a combination of a rule-based system based on pattern matching and substitution to simulate real-life conversations.
The digital assistants mentioned at the onset are more advanced versions of the same concept, a reflection of the evolution that has taken place over the years.
Neural conversation agents, unlike the rule-based models, are more intent at conversing in as natural a language as possible. Let us consider some common models on which these conversation agents are designed:
Retrieval is one of the most popular methods used to power a majority of chatbots today. It basically entails providing the model with a database of pre-defined responses to common questions. The model then uses prediction for dialogue selection, choosing the most appropriate response.
It could identify the best response through keyword matching or in more advanced systems, complex machine learning algorithms. Such systems require plenty of data pre-processing and hand engineering. At the same time, their databases run the risk of becoming obsolete, requiring manual updates. With this combination of factors, they have a hard time adapting to changing circumstances or use cases.
Generative models were built to address the weaknesses of previous models. In order to do so, the model would need to be intelligent enough to generate new content without precise engineering. Rather than having to draw on pre-defined responses, they use data from actual conversations for training. As a result, they are able to generate a new conversation that adheres to a similar pattern as their training data.
To build this machine learning chatbot model, all a developer requires is machine learning and data for training. There is no need for domain expertise or manual engineering. Therefore, the model is easier to scale in the long run and can more readily adapt to change.
Popular generative training approaches include reinforcement learning, supervised learning and adversarial learning. And the best part is that a developer can combine all three approaches in training a chatbot model.
The ultimate objective of creating a machine learning-based neural conversation agent is creating a model that can converse naturally about any given topic. Though this has to a large extent proved elusive, the ensemble approach is making some headway.
Ensemble learning incorporates elements of generative, retrieval and rule-based methods depending on the context. For instance, they can use a rule-based approach to sing, a generative method for unspecified tasks and a retrieval method to get the news.
But this approach is in the early stages and far from being capable of replacing human conversation.
External knowledge and context play a significant role in human conversation. To illustrate, when you tell a chatbot you are going to a restaurant, the bot will understand but not necessarily provide any insight. If, on the other hand, you tell that to a local, you could get a recommendation of the best dish there.
A human being will draw on context to build on the conversation and tell you something new. But such capabilities are not in your everyday chatbot, with the exception of grounded models. These are machine learning models trained to draw upon related knowledge to make a conversation meaningful and informative.
Grounded learning is, however, still an area of research and yet to be perfected.
Everyday Applications of the Machine Learning Chatbot
Though it may sound like a futuristic concept, the use of machine learning algorithms for chatbots is already gaining application in everyday situations. As mentioned at the onset, digital assistants have found a place in the smart home. These are goal-oriented chatbots which use natural language to help users solve everyday challenges. However, there are also general conversation chatbots which try to converse with users on a wider range of subjects.
In the modern-day business setting, it is possible to find chatbots that work on both ends of the spectrum. With such bots, it is possible to give online buyers the kind of attention that they would get in-store using a live chat interface. However, the kind of experience customers get will depend on the level of intelligence of a given chatbot. Such bots can answer questions and guide customers to find the items they want while maintaining a conversational tone.
Based on their practical application in such situations, the greatest success stories are in online marketing and e-commerce. Bots are said to have a higher capacity to re-engage with prospects, tell the brand story and even convert better than other automated approaches such as email.
As the field advances, the potential of machine learning in business goes way beyond e-commerce. Advanced models can access vast amounts of documentation to extract information and structure it while others listen to and analyze conversations.
What Does the Future Hold for the Machine Learning Chatbot?
A point of caution is that the technology is still in its nascent stages and chatbots may be prone to error and bias. This calls for due care in the deployment process to ensure your bot does not offend customers.
At the same time, a completely human-like conversation might still be in the future. But given the level and rate of progress taking place in the field, smooth conversations are not too far off.
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