Such a template can be directly reused to create a turnkey AI assistant. As shown below, it can also be quickly customized to suit specific needs.
Reusable AI enabling no-code AI
Not only do reusable AI components or systems save time and effort when creating an AI solution, they also enable rapid, no-code customization of the reusable components. Because every AI solution may need certain customizations, no-code AI further democratizes the adoption of AI and accelerates AI’s time to market. Below are several examples of customizing reusable AI components with no coding.
No-code customization of AI solution templates
Assume that an HR recruiter wishes to create an AI job interviewer. The recruiter can reuse an AI interviewer template but edit the interview questions or job-related FAQs in the template to build a custom AI job interviewer (Figure 8a). The template reuse and no-code customization greatly simplify the creation of an end-to-end AI solution, especially for knowledge workers who do not know how to code or lack deep IT skills.
Juji
Figure 8a. No-code customization of an AI interviewer template so the AI can ask specific questions designed by an HR recruiter. Here the recruiter adds question T17, “What do you like the best in your current job?” An existing AI communication unit will handle the discussion on this topic automatically.
No-code customization of AI functional units
Continuing the above example, let’s assume that the recruiter wants the AI interviewer to ask job applicants the question, “What do you like the best in your current job?” If an applicant’s response is something similar to “interacting with customers,” then the recruiter wants the AI interviewer to ask a follow-up question, “Could you give me an example that you enjoyed interacting with your customer?”
Since none of the pre-built AI communication units handle this specific case, the recruiter will need to customize the AI communication. As shown in Figure 8b, the recruiter can easily do so by indicating the user response and the corresponding AI interviewer actions including the follow-up question (defined in T18). Again, all of the customization can be done with no coding.
Juji
Figure 8b. No-code customization of an AI communication unit. Here if a user’s response to the question in T17 is similar to “customer interaction,” the AI interviewer will respond and ask a follow-up question (T18).
The above examples show how no-code customization of reusable AI templates or components could allow any non-IT personnel to rapidly create custom AI solutions.
Future directions of no-code, reusable AI
No-code, reusable AI democratizes the creation and adoption of powerful AI solutions without requiring scarce AI talent or costly IT resources. To make the no-code, reusable AI the main paradigm for developing and adopting AI solutions, advances must be made in several areas.
The first area is to make reusable AI components and systems explainable. To help non-IT personnel reuse pre-trained or pre-built AI components and solutions, it is critical to unbox the “black box” and explain what is inside each component or solution, both pros and cons. Explainable reusable AI not only helps humans better understand and leverage existing AI components and systems, but also helps us avoid potential AI pitfalls. For example, it would be helpful for an HR recruiter to understand how personal insights are inferred before s/he uses such AI power to infer applicants’ insights.
The second area would be the support of automatic AI debugging. As AI solutions become more complex and sophisticated, it is difficult to manually examine potential AI behavior under diverse and complex circumstances. Non-IT users especially will need help in assessing an AI solution (e.g., an AI assistant) and improving it before formally deploying it. Although there is some initial research on profiling AI assistants, much more is needed going forward.
The third area would be ensuring the responsible uses of AI, especially considering the democratization of AI. For example, if someone can simply reuse an AI functional unit to elicit sensitive information from users, then who will protect the users and their sensitive information and how? In addition to measuring typical AI performance such as accuracy and robustness, new measures and usage guidelines will be needed to ensure the creation and deployment of trustworthy and safe AI solutions.
Dr. Michelle Zhou is a co-founder and CEO of Juji, a cutting-edge artificial intelligence company that powers cognitive AI assistants in the form of chatbots. She is an expert in human-centered AI, an interdisciplinary field that intersects AI and human-computer interaction (HCI). Zhou has authored more than 100 scientific publications and 45 patent applications on subjects including conversational AI, personality analytics, and interactive visual analytics of big data. Prior to founding Juji, she spent 15 years at IBM Research and the Watson Group, where she led the research and development of human-centered AI technologies and solutions, including IBM Watson Personality Insights.
Zhou serves as editor in chief of ACM Transactions on Interactive Intelligent Systems and as associate editor of ACM Transactions on Intelligent Systems and Technology. She is an ACM distinguished member and was formerly the steering committee chair for the ACM International Conference Series on Intelligent User Interfaces. She received a Ph.D. in computer science from Columbia University.
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