Messaging at its full potential has become a necessity for almost every individual in the globe making it a major concern for tech companies involved in this sector looking towards improvement on the use of Artificial Intelligence in messaging. Most people prefer to share information on closed, private messaging environments like Facebook Messenger and WhatsApp. However, the experience on these platforms remains painfully circuitous. In order to share a single piece of content within a conversation, users do have to leave their active chat, open a new window to locate and copy the file, then re-enter the original chat to paste and share.
So more needs to be done in providing more intelligent ways to share content on messaging – whether the content is a funny animation, a dinner reservation, directions for getting somewhere and even simply chatting with friends. When AI is successfully applied to this area, we could see a whole expanse of brand new capabilities emerge in messaging, from intelligent recommendations for nearby restaurants or retailers, AR enabled communications, elevated video and audio messaging, live updates for upcoming trips or events, chatdesk for customers and their services or other convenience-focused features.
Artificial Intelligence in messaging has the potential to move AI in general to a greater height and even develop new sectors that has never been explored – that is, once it can fully understand what is being expressed in a conversation, or what is desired.
However, there are challenges in applying AI to the billions of online conversations happening worldwide.
Regardless of the recent advancements in technology, messaging remains one of the most challenging platforms for AI because it relies on the most significant aspect that sets humans and machines apart: The ability to understand context and nuance in interaction using language and communication.
Though AI has come a long way in the development of human activities and processes, there is still a huge gap between the general state of AI and AI required for intelligent messaging. Consumer-facing AI has come a long way from the first chatbots commonly used in customer service usually creating more problems than solutions. However, implementing AI in messaging requires even more sophisticated and instantaneous technical capabilities that, when coupled with the fine detailing of language and a small margin of error to work within, present a daunting challenge.
Challenges in AI involving privacy.
Another critical technological challenge that faces AI specific to messaging is that it must also be implemented securely. Data privacy has become a major determinant for users’ choice in using a particular piece of technology. AI used within messaging will not be successful if people lack trust in the way data from their personal conversations is managed. The good news is that, by design, messaging is one of the most secure use cases for AI. It requires AI to execute instantaneously and at scale, meaning there isn’t enough time (or power, on some devices) for AI to send information from chat apps, to another server, and back. Instead, AI processes information on the device, so conversation data should never need to be sent elsewhere to deliver results to users. Built-in AI in the users’ gadget is safer when it comes to data privacy and management even though it’s more of a task to develop.
A good model for AI application
- Understanding the intricacies of language and how it works should be a priority in the development of contextual AI coupled with understanding how language differs on a global scale – across multiple dialects and demographics. For example, to intelligently deliver the right piece of content to communicate various emotions, AI must be trained on preferences for expressing this emotion in Brazil versus the U.K. versus Japan, then make a recommendation accordingly.
- AI models developed specifically for messaging environments and interactions have to receive, process, and deliver information at an incredibly fast rate. Many other forms of AI, like digital assistants and social feeds, are designed with a built-in buffer, allowing time for information to travel to a server for analysis before returning results to a user. In the use of Artificial Intelligence in Messaging, a conversation needs to flow instantaneously, meaning AI only has seemingly very little time even in milliseconds to collect and interpret data and then use that information to simply make the right decisions, whether that be completing a task or delivering content. This is a tight but crucial turnaround, as even the slightest lag or error can disrupt a conversation. The work clearly becomes developing AI that can work under these intense conditions and yield great results for users.
- AI models built for chat should not be keyword-focused instead messaging requires the use of contextual AI, which is more sophisticated but better mimics the way that we use language. Language is creative and complex, imbued with subtleties, inside jokes, sarcasm, and sincerity. So, while it’s much easier to train AI to generate a transactional response based on a specific word or prompt, messaging requires AI that understands context – picking up on nuance and adapting to conversations as they unfold – to surface information that’s relevant and useful to users on both sides of a chat.
Timeline for the use of this technology
Though some piece of this outstanding technology is been seen, from swipe-to-complete to recommended words and emojis within Gmail, iMessage, and other SMS providers. Any major technology company working on its contextual nuance in language and natural language understanding research is testing this technology today. So, basically there would be acceleration in the market within the next 12 months, and we should start seeing more tech services and products enabled with this functionality across different applications.
But even with the rapid advancement of AI in messaging, we still have about two to three years until mainstream application of this technology is fully integrated within the messaging environment and we begin to see AI’s near-instant understanding of context in language.
Even with the clear success of AI in messaging, this technology remains difficult to solve because successful application essentially combines the biggest challenges currently facing the industry as outlined already: It must be regionally and contextually intelligent, near-instantaneous and, most importantly, secured and private. However, despite these challenges, messaging is positioned to drive the AI market forward by attention to the use of Artificial Intelligence in Messaging, tackling key tech issues and identifying solutions that will very likely be applied across industry sectors in the future. When this technology begins to flourish among users and users begin to experience the positive effect of AI in their daily interactions, the AI market will see a rocketing growth in their demand and a need for even more capabilities in the industry.
With such great development in the world of technology today, what do you think such improvement will entail?
Do comment on the comment box below and share your thoughts on this.
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