The Pitch Avatar team shares their vision for the evolution of conversational AI tools and explores how artificial intelligence will ‘talk’ to us in the future.
Despite the growing popularity and widespread use of conversational AI tools, they still face several limitations and challenges. The training and natural language understanding of these tools, and consequently the quality of their responses, often fall short of user expectations. Conversational AIs frequently experience ‘machine hallucinations,’ struggle with information retrieval, and become confused with complex queries, especially those with subtext or slang.
Based on our experience, most teams developing conversational AI are currently focused on addressing these issues. As a result, we can anticipate that AI will improve understanding, communication, and overall effectiveness, leading to broader adoption in the near future.
AI will soon be able to learn autonomously and do so rapidly
Currently, training conversational artificial intelligence is a lengthy and costly process, taking several months. It involves collecting extensive text and data for the AI to ‘read’ and requires significant resources to identify and correct errors. An important and costly part of this process is identifying and correcting the errors that the AI makes. All of the above applies both to the basic training of AIs and to the creation of specialized conversational agents based on them, designed for various professional tasks. For example, AI consultants, AI salespeople, AI presenters, and others.
However, this traditional training approach will soon become obsolete. Automated training methods for conversational AI are being actively developed and implemented. These methods enable AI to access information sources, like the Internet, and self-learn continuously. This will allow AI to stay updated with the latest information and adapt to changes in natural language in real time.
AI will improve in understanding and formulating responses
Modern artificial intelligence still doesn’t understand us very well. It relies on the literal meaning of words and expressions , making it difficult to grasp subtext and emotion. Consequently, AI responses can be unexpected, and they often lack emotional depth and variation, let alone their responses are quite often monotonous and not emotionally colored in any way.
Significant efforts are now focused on enhancing deep learning techniques to make conversational AI more ‘human.’ This includes improving the AI’s ability to recognize user preferences, understand context, appreciate humor, and interpret the various semantic nuances of words and expressions.
In the near future, AI is expected to increasingly interact with people using audio and video data. By learning to interpret facial expressions and vocal intonation, conversational AI will advance in understanding emotional expression. As a result, AI responses will not only become more accurate and coherent but also more creative, varied, and emotionally colored.
AI will learn to handle multiple tasks simultaneously
Currently, most conversational AI operates on a ‘one question, one answer’ principle. Complex or multi-part questions often exceed the capabilities of these systems, leading to incomplete or unsatisfactory responses. Therefore, when communicating with modern AI, it is not recommended to give them tasks consisting of several items and requirements. As a rule, the shorter and more unambiguous the question given to a dialog AI, the faster and better it answers. In addition, modern AIs still require rather complex procedures to turn into specialized tools in various professional spheres. In other words, the adaptability of AI to new scenarios still leaves much to be desired.
To address these limitations, many researchers and developers are focused on making conversational AI more adaptive and capable of multitasking. This includes equipping AI with the ability to independently search for and integrate various tools to complete assigned tasks. Additionally, AI may seek assistance from other AI systems, enabling a form of self-improvement based on the tasks at hand.
In summary, the aforementioned advancements are not just isolated fixes but represent broader improvements. Once realized, these developments will elevate the quality of conversational AI to a fundamentally new level.
Good luck, success, and high profits to everyone!
