Conversational AI is making life easier thanks to a number of handy applications. What makes it useful and inclusive is data – and lots of it.
People have talked to technology for years. From admonishing the toaster for burning breakfast, to offering encouraging words when starting a car on a cold morning and issuing an ultimatum to a vending machine that takes money but gives nothing in return.
In all these cases, however, the spoken words serve no purpose, other than to burn off some steam. This is all changing with the introduction of conversational artificial intelligence (AI) into modern technology. Now, when you speak, the machines listen.
Your application is only as effective as it is smart, and it needs to be taught all the different possible use cases in order to become as effective as possible.
Let’s take a look at what we mean by conversational AI, some examples, and how we can help improve your application.
What is conversational AI?
Conversational AI refers to a range of technologies that bring together machine learning, natural language processing (NLP), contextual awareness, and other advanced tools to facilitate a real-time dialog between human and computer.
At the heart of conversational AI is machine learning: algorithms that automatically improve as they acquire data.
Sophisticated voice-recognition systems require large volumes of speech data encompassing a broad spectrum of human interactions. In this way, they are able to recognize different people’s speech in a variety of circumstances, allowing conversation to be as natural as possible.
How is conversational AI used?
Let’s explore some of the practical settings where conversational AI is making a difference.
Smart speakers and smart assistants have grown hugely popular in recent years. Thanks to conversational AI, interacting with Siri, Google, Alexa, and the others is more like chatting to a friend than operating a machine.
These devices are more advanced than regular chatbots that are programmed with answers to certain questions. They’re configured to be more human-like, generating responses that are more natural and aligned to real human conversations.
You can ask for a weather update and get a response in seconds, for example, and you can program your Google Assistant for follow up questions too.
AI is increasingly being used in the medical and well-being sectors to deliver practical solutions for patients, doctors, nurses, and other clinical staff. Beyond providing basic information, modern systems enable personalized, actionable interactions that empower an individual’s healthcare decision making.
Lark, for example, is a friendly, voice-enabled weight-loss coach that cheers users on to eat healthier and get more active.
Human Resources (HR)
As well as partnering with employees to optimize their work experience and career progression, the scope of HR management also involves handling many recurring questions and routine processes.
Conversational AI can take on most of these tasks as well as collaborating with humans to screen job candidates, facilitate on-boarding, and generally free up HR to focus their specialist skills on issues that require them.
The benefits of conversational AI are even more evident with multi-site, multinational companies, where AI can help maintain consistent standards across the organization.
In most forms of learning, a great way to retain knowledge is by conversing about the subject. This is probably most true when studying a new language.
Duolingo is a language learning platform that uses AI chatbots to engage in real conversations with language students. The software speaks words and sentences, then listens to the user’s response, correcting any mistakes it hears and increasing the difficulty level as students progress.
Interacting with a shopper when they are viewing products and services is the ideal time to tackle any resistance to finalizing their purchase.
Human sales staff cannot possibly carry out this role in a global e-commerce environment. A smart and helpful chatbot, however, can answer questions, offer advice, and recommend complementary products and services before leading the shopper towards the checkout.
Unlike their human equivalent, AI chatbots are always there, ready to offer assistance 24/7, every day of the year. Chatbots, trained by conversational data sets, cut the cost of customer support while speeding up response times. Paradoxically, they can also humanize a brand, by providing shoppers with personalized assistance and engaging with them in a natural, human-like way.
A great example is Nike’s StyleBot, an AI chatbot that allows you to find shoes based on your preferences through product recommendations. Additionally, StyleBot gives you the ability to create your own personalized shoe designs.
In data-rich industries, such as banking and insurance, a great deal of the work involves gathering and processing data, in one form or another.
Conversational AI enables employees and customers to access and handle this data using the most natural and effortless method there is: their voice.
Complex tasks that would normally involve time spent working on a keyboard are now being accomplished by simply asking a question or requesting for the task to be completed.
In 2018, Bank of America launched its ‘talk, type, or tap’ virtual financial assistant, Erica, to provide customers with personalized, proactive support across a broad range of money management issues.
The system expands its capabilities by learning from bespoke external datasets as well as its own customer conversations. For now, Erica is only available in English, but it is currently using datasets to learn Spanish.
What makes conversational AI so smart?
AI is an avid learner. Speech recognition technology learns by absorbing a diverse assortment of material from a wide variety of sources, such as databases, websites, and text corpora (large, structured sets of texts), which enables voice-recognizing AI that is actually ‘conversational’, rather than controlled by prescribed spoken instructions.
If, for example, a user makes a request then adds a supplementary instruction, or changes their mind about what they want, a trained conversational AI interface will use its extensive database to make sense of the command and respond accordingly.
The same is true for other linguistic hurdles, such as synonyms, homonyms, jargon, and the use of vernacular.
Let’s Talk About Your Conversational AI Needs
Summa Linguae Technologies collects high-quality conversational data that, along with natural language processing, gives conversational AI systems sufficient operational elasticity to overcome all these challenges and engage in fluid, intuitive interactions with users.
There are also many ways to customize your speech data based on your specific needs.
To teach your technology to fully understand what’s being said, contact Summa Linguae today.
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