Uses of AI in Energy and Utilities Industry
The energy and utilities industry is on the brink of a revolution, thanks to the immense potential of artificial intelligence (AI). With AI,...
Conversational AI has become immensely popular in recent years among businesses, users, and developers. With so many conversational AI platforms on the market, it can be difficult to tell which one offers the features you need for your business.
One popular platform is Google Dialogflow. It's most commonly used as a tool for building text-based chatbots, but it can do more than that. In fact, Dialogflow works behind the scenes for many of the most popular customer service solutions, including Mosaicx.
So if it's best known for text-based chatbots, why should you trust voice-based conversational AI using Google Dialogflow? Because Dialogflow is also widely used for another type of application: voicebots.
A voicebot is a conversational agent that uses artificial intelligence and natural language understanding (NLU) to interpret the intent and meaning in the speech of its conversational partner. Voicebots use input voice recognition and translation (commonly referred to as speech-to-text or STT) alongside a text-to-speech (TTS) engine to understand human speech and respond using everyday language.
Voicebots are similar to chatbots—which interact with users through text-based channels, such as websites and messaging apps—but they are designed to be used over the phone or other voice-enabled devices. This allows users to interact with voicebots in a more natural way, without having to type or use a keyboard. Some examples of voicebots include Siri, Alexa, Google Assistant, Cortana, and Bixby.
Voicebots can be used for a variety of purposes, such as:
Dialogflow is a powerful and versatile platform provided by Google that is widely used for building chatbots and voicebots. It offers a range of features that make it an excellent choice for developing conversational applications.
The platform is backed by Google's expertise in natural language processing and artificial intelligence. If you've ever talked to Google Assistant on a smartphone or smart speaker, you've experienced some of the best voice-recognition technology on the market. That same technology backs Dialogflow, which ensures that your voicebot can handle complex queries and provide a great user experience.
Other features that make Dialogflow well-suited for voicebots include:
Building a voicebot with Dialogflow starts with creating agents and defining intents. Intents are the reasons customers call. These intents should define what your voicebot can do, such as answering questions, providing information, or completing tasks. You will also need to create entities, which are the words or phrases that your voicebot can understand.
Once you have created your agents and defined your intents and entities, you must build conversation flows. Conversation flows are the sequences of steps that your voicebot will take to complete a task or answer a question.
All of this can time-consuming and confusing. First, you must discover why people call and be sure not to leave out any important intents. Secondly, people may phrase each intent 100 different ways. These are your entities. So If a Dialogflow voicebot responds to 50 intents, engineers must program up to 5,000 variant entities. For example, the voicebot must understand that “Los Angeles,” “Los Angeles International,” “LA,” and “LAX” all describe the same airport.
It's easy to miss intents and entities, but the Mosaicx team helps you navigate these pitfalls. We also continually modify intents and entities over time.
Once the voicebot is ready, you must make it accessible to the public. If your voicebot is great but the underlying infrastructure experiences frequent downtime, you'll create a poor customer experience. Mosaicx provides a robust, scalable infrastructure for some of the largest brands in the United States, allowing voicebots to serve their customers via call, text, and other channels.
Many small conversational AI platforms have popped up in recent years, but there are still only a couple true alternatives to DialogFlow:
Although all three are backed by some of the biggest names in tech, Dialogflow offers a number of advantages over its competitors. Here are a few reasons why we use Dialogflow in Mosaicx products:
In addition to these advantages, Dialogflow is also backed by Google's expertise in natural language processing and artificial intelligence. This means that you can be confident that your Dialogflow voicebot will be able to handle even the most complex queries and provide your users with a great experience.
Here is a table that compares Dialogflow to some of its top competitors:
Feature |
Dialogflow |
Amazon Lex |
IBM Watson Assistant |
---|---|---|---|
Ease of use |
Easy |
Medium |
Difficult |
Natural language processing |
Powerful |
Good |
Good |
Integrations |
Wide range |
Limited |
Limited |
Global reach |
Yes |
Yes |
Yes |
Scalability |
Yes |
Yes |
Yes |
At Mosaicx, we believe Dialogflow offers the best technology, experience, and value for voicebot projects of all sizes. That's why we incorporated Dialogflow into our own products. There's no need to create an all-new language model. Using Dialogflow allows us to do what we do best: create industry-specific, ready-to-use products and offer ongoing consultation, optimization, and support.
Speaking of language models, Dialogflow uses the BERT large language model (LLM) for its NLU capabilities. BERT was first introduced in 2018 by Google AI, and it has since become one of the most popular and widely used language models for natural language processing (NLP) tasks.
BERT is a bidirectional encoder representation from transformers model. This means that it can learn the context of words in a sentence, both before and after the word. This makes it more powerful than previous language models, which could only learn the context of words in a sentence from left to right.
Dialogflow CX, the newer version of Dialogflow, uses a BERT-based NLU model by default. Dialogflow ES, the older version of Dialogflow, can also use a BERT-based NLU model, but it is not enabled by default. To use a BERT-based NLU model in Dialogflow ES, you need to enable the "Experimental Features" flag in the Dialogflow console.
Here are some of the benefits of using the BERT language model in Dialogflow:
Overall, the use of the BERT language model in Dialogflow makes it a more powerful and versatile NLP platform for building conversational interfaces.
BERT is very large. The base version of BERT has 110 million parameters, and the larger version has 340 million parameters. This makes it more computationally expensive to train than smaller language models, but it also allows it to learn more complex relationships between words.
BERT has been shown to be effective at a variety of NLP tasks, including:
The best large language model is a matter of opinion, but some of the most popular and well-regarded models include:
Ultimately, the best large language model for you will depend on your specific needs and requirements. If you need a model that can perform a variety of tasks, GPT-4 or Turing NLG may be a good choice. If you need a model that can generate human-quality text, PaLM or LaMDA may be a better option. It is important to note that large language models are still under development, and they are constantly being improved.
Although GPT-4 has gotten a lot of attention, it's not always the best LLM. BERT excels at tasks that require understanding the context of words in a sentence, such as question answering and natural language inference. This is because BERT is a bidirectional model, which means that it can learn the context of words in a sentence from both before and after the word.
GPT-4 is better at tasks that require generating text, such as summarization and translation. This is because GPT is an autoregressive model, which means that it can predict the next word in a sequence given the previous words.
Here is a table that summarizes the key differences between BERT and GPT-4:
Feature |
BERT |
GPT-4 |
---|---|---|
Model type |
Bidirectional encoder |
Autoregressive |
Strengths |
Understanding the context of words, question answering, natural language inference |
Generating text, summarization, translation |
Weaknesses |
Generating text, summarization, translation |
Understanding the context of words, question answering, natural language inference |
In general, BERT is better for tasks that require understanding the meaning of text, while GPT is better for tasks that require generating text. These differences make BERT (and Dialogflow) a better option for chatbots and voicebots.
For all these reasons, we trust Dialogflow when building Mosaicx voicebots. But choosing a conversational AI platform is just step one. Next come the four steps to build, train, test, and deploy a conversational AI solution.
The energy and utilities industry is on the brink of a revolution, thanks to the immense potential of artificial intelligence (AI). With AI,...
How do customers want to get in touch with your company? Are phone numbers obsolete? What about email, chat, text, and social?
Call center attrition is a persistent challenge faced by many companies across industries. The high turnover rate of call center employees can have a...