Gone are the days when conversational AI was slotted into the ‘hyped’ category in the tech realm. The update that has been fast doing the rounds is that it has very real advantages for modern businesses. This makes sense, as 85% of consumers worldwide would like to message with brands. Today’s blog contains a round-up of five trends that are set to rule the roost next year. Read on…
Conversational AI helps in generating comprehensive automatic reports of calls, that can of course be very insightful. A question that often strikes people is: Humans can fathom emotions – does AI? Doubts like these about whether AI can respond with emotional intelligence and empathy are likely to fade, thanks to sentiment analysis. It can definitely help you recognize your happiest customers, but it can also highlight negative statements that might be expressed by less happy customers who need your attention. Head over to this blog that is replete with insights into sentiment analysis.
Complex emotions can be anything like joy, anger, surprise, fear and these are reflected in human to human interactions across call centers. The AI will identify these emotions on call and can create a detailed report of how a conversation has gone.
You might ask, what is a language model? It is a tool that can incorporate lots of information in a concise manner that is reusable in a different context.
The third generation Generative Pre-trained Transformer (GPT-3) developed by OpenAI is a powerful neural network ML model trained using data to create any kind of text. All it needs is a chunk of input text to come up with huge amounts of sophisticated machine-generated text. GPT-3 is used to generate multiple sorts of creative writing (blog posts, advertisements, and poems) that is close to the style adopted by several famous writers. Not just that, it’s also used for text summarization.
Switch Transformer is the trillion-parameter pioneer that can support the creation of larger models with no rise in computational costs. With this neural net, Google made the routing process easier, so each token is sent to just one expert. This cuts down on computational and communication costs.
DALL·E is a smaller avatar of GPT-3 that combines the meaning of a penned sentence with the visual representations it could have. Its power is that it does not need examples to create images from scratch!
LaMDA which stands for Language Model for Dialogue Applications, represents the next generation of chatbots. Very interestingly, it can come up with smart, even unexpected responses and correct answers where facts are involved.
MUM stands for Multitask Unified Model. With its amazing multimodal capabilities, it is called the brain of the search engine! It can take complex search queries head-on and has been trained in as many as 75 languages.
Wu Dao 2.0 — You might have heard of it as possibly the most versatile AI today. It has a striking 1.75 trillion parameters. Wu Dao 2.0’s child, Hua Zhibing, is a virtual student who can learn continually, pen poetry, draw pictures, and may even code! Wu Dao 2.0 can learn multiple tasks over time, keeping ‘in mind’ all the learned content. It gives us the sense that AI is inching even closer to human memory and learning potential.
If you recall any recent experience of getting a document verified, you will concur that the manual way can be quite time-consuming. These days, be it document verification or payments, intelligent assistants come to the rescue. This software is handy as it can automate repeatable, multi-step business transactions. Do read this blog to understand what Business Process Automation (BPA) is all about.
Let’s take a closer look at social media monitoring, AI-based call centers, and internal enterprise bots.
It’s a well-known fact that any business would like to stay in the know about its industry 24/7. A key question is, how do you manage listening to lacs of conversations on the web and gleaning opportunities that matter? This can happen via social media monitoring which involves tracking all the elements relevant to your brand (like hashtags, keywords, and mentions). This monitoring is an algorithm-based tool that crawls sites and indexes them, successfully managing online conversations that are important to your business.
You are sure to have seen an AI assistant understanding customer requirements on a call and fetching relevant options automatically from a menu. That’s what happens on a regular basis in call centers powered by AI. AI comes into the picture to help customer service agents target a faster response time and better first-call resolution. This can result in more smiling faces across call centers, since they are less stressed dealing with the more sophisticated calls and can do their job well.
A time-saving resource, internal chatbots are AI solutions that automate internal enterprise processes, such as in Human Resources or Operations. The main ‘Why’ for leveraging an internal chatbot is that that task is done rarely and/or is ad hoc, and not very specialized or complex. Thanks to this kind of chatbot, any worries about accessing instructions vanish, because the bot acts as an instruction manual for teams to rely on. These bots are generally set up on platforms that a company’s people use daily, like the company website or the intranet. The underlying natural language processing technology is getting better and better, so the benefits of using this technology will grow as time goes on.
A voice application, or voice-based application, is an application that depends on speech requests to process a query and reacts to it with the expected action. Voice-enabled devices and the apps that control them are a thrilling new prospect for developers. While businesses think about how they are planning to leverage this new channel, they need to become familiar with some best practices for building and deploying on these various platforms. Do watch this space for a blog that will shed more light on best practices.
Interestingly, voice applications have become multilingual along with a complex understanding of language!
Quite often, chatbots that cover a variety of intents face poor performance because of intent overlap. What’s more, it is tough to autonomously retrain a chatbot considering the user feedback from live usage. Self-improving chatbots are challenging to achieve, as it is not very easy to select and prioritize metrics for chatbot performance evaluation. A dialog agent is needed to learn from the user’s experience and improve on its own.
There you have it – five trends in conversational AI that we should keep an eye out for in 2023! Before I wrap up today’s blog, I would like to share this interesting fact – three years hence, by 2026, conversational AI deployments within contact centers will reduce agent labor costs by $80 billion, according to Gartner, Inc.
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