In this special guest feature, Adnan Masood, PhD, Chief AI Architect, UST, believes the ultimate goal of conversational AI is to let people interact naturally with business services through these interfaces, facilitating human-machine interaction, and he’s hopeful that we are on a path to achieving this. Dr. Masood is passionate about developing highly innovative breakthrough technologies and his expertise includes Scalable Enterprise Architecture, Machine Learning, and Cloud platforms especially Microsoft Azure, GCP, and AWS. Being a Microsoft’s Most Valuable Professional for Artificial Intelligence, Adnan has extensive experience in developing secure & compliant FinTech solutions, with publications around explainable AI, AutoML, machine learning, and application security.
Creating AI that is capable of flawlessly imitating a human has long been the gold standard for AI capabilities, with the well-known Turing Test being used to appraise just how lifelike AI has become. Generations of computer scientists, mathematicians, and linguists have devoted their careers to enabling human-machine conversation in natural language, and despite the emergence of virtual personal assistants such as Siri, Alexa, Google Assistant, and Cortana, it remains challenging to develop conversational agents which can handle multi-modal scenarios, personas, and different use cases with limited labelled training data.
The challenge is rooted in the fact that human intelligence (while qualia being difficult to quantify and define) is evident in the ability to communicate freely in natural language, making this pretty much a prerequisite of any real-world artificial intelligence system. A true conversational AI must enable the computer to achieve that same fluency, context, multi-turn comprehension, and dialog flow that humans exhibit so effortlessly.
Like any advanced technology, the complexity and specialized terminology associated with conversational AI can appear daunting to those who are not familiar with its inner workings. Further complicating matters, the use cases, and implementations of conversational AI vary across industries, domains, and technologies, making it nuanced and multifaceted. This brief primer will outline the capabilities of the technology and provide insight into future applications as well as the key difficulties that innovators are working to resolve.
Conversational AI in Practice
Conversational AI assistants also known as chatbots are designed to provide conversational dialogue to accomplish a multitude of tasks. Platforms based on conversational AI are becoming increasingly sophisticated, supporting multiple, diverse use cases, and multiple business domains across a variety of industries. But to be usable and effective, conversational AI must have advanced functionality and differentiation, including capabilities like context sensitive intent and entity recognition, self learning of conversation driven intelligence, multi-channel contextual response, multi-lingual, and multi-person conversations.
Though AI-based conversational systems can be used across a broad spectrum of industries and use cases, Retail, banking, insurance, HR, financial services, marketing, and healthcare are among the industries and sectors benefiting greatly from conversational AI. This is because conversation AI provides the foundation for virtual personal assistants, enterprise assistants, or customer assistants, automating a range of activities including loan origination, returns processing, onboarding, investment advisory, help desk operation, customer service answering, triage, routing and more.
But where we stand now in terms of the potential for artificial intelligence helping augment human labor is just tip of the iceberg.Innovators are constantly finding new applications for conversational AIs. Prominent examples can be found in the pre-trained language models deployed across large data sets, smart speakers and smartphones. Today’s conversational AI is being primarily driven by advent of large language models (LLM) such as GPT3, T5, PaLM, Microsoft Turing NLR etc, which is also the source of innovation in conversational AI. All these offer surprisingly human-like responses to users’ questions. The next generation of conversational AI systems will address the multiple ethical and technical challenges associated with generational conversational AI systems, including bias, safety, multi-turn context, consistency, knowledge management and synthesis.
The conversational AI bots of the future will be able to handle multiple entities and purposes in a single conversation and understand context from collected behaviors to appear as a personal assistant, enterprise employee, or customer service representative.
Areas for Improvement
Scale limitations represent one of the main challenges to the implementation of conversational AI in an enterprise setting due to the complexity of the area and its heavy dependency on IT. However, platforms from cloud AI providers offer self-serve and low-code/no-code features to address this issue. Conversational AIs must also improve their support for multimodality in dialog systems, ability to process and understand visual dialogs, engage in data-efficient dialog model learning (learning from smaller datasets) as well as use knowledge graphs, multi-lingual conversations, and collaborate with edge and IoT devices to maintain context.
Furthermore, conversational AI systems struggle with a range of different dialogues. Taking turns, managing multiple topics and participating in multiparty dialogs are some of the key issues. Research and development of future conversational AI must overcome these shortcomings to achieve the goal of intelligent general purpose and domain driven dialogues..
Fortunately, a number of strategies are already being implemented to make conversational Ais more effective and ensure that they are able to rapidly evolve. One of the most effective of these is the use of large language models, and fine-tuning them to help optimize conversational AIs. To pre-train an model, developers utilize a large-scale domain specific corpus of data to effectively set its parameters (weights). These parameters are then further adjusted during the fine-tuning phase which results in peak usability.
Chatbots are also being improved though the use of human monitoring in conjunction with machine learning. These Human-In-The-Loop (HITL) approaches reduce the potential for errors by providing useful guidance that can be utilized to train and retrain conversational AI. When humans supervise the conversation and correct errors, AI systems operate more effectively and learn even faster.
Looking Ahead
Conversational AI has come a long way since IRC bots, but users are still looking for the ideal AI solution. This demand for perfections is pushing research, comprehensive solutions that provide an intuitive, seamless, well-integrated experience that closely mimics human behavior. Despite the shortcomings of modern conversational AI chatbots, there is every reason to believe that the exponential rate of innovation in this field will continue to yield exciting solutions that transform what we perceive as possible.
One area that has shown promise is the increasing use of generative models to allow for a diverse range of lifelike responses. Unlike the retrieval-based systems in use today which are largely limited to predefined responses, the next generation of generative chatbots will be capable of engaging in conversational dialogue by analyzing advanced conversational training data, and generating customized, tone sensitive contents
The ultimate goal of conversational AI is to let people interact naturally with business services through these interfaces, facilitating human-machine interaction and I am hopeful that we are on a path to achieving this.
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