Dr. Lingjia Tang, CTO and Co-Founder of Clinc, is a professor of Computer Science at The University of Michigan. Dr. Tang’s research in building large-scale production infrastructure for intelligent applications is widely recognized and respected in the academic community. In addition to working at both Microsoft and Google, Lingjia received her PhD in Computer Science from the University of Virginia. Lingjia has recently received prestigious awards including ISCA Hall of Fame, Facebook Research Awards and Google research Award.
What initially attracted you to AI? When did you first discover that you wanted to launch an AI business?
In the mid-2000s I was performing research around large-scale systems that support various applications and how we can design servers as a software system to run those applications more efficiently. At the time, we were shifting from working with traditional web applications to more machine learning-driven functions. That’s when I started to pay attention to the algorithms associated with AI and gained interest in fundamentally understanding how AI applications work. Soon after, the research team I was working with decided to pivot and basically build our own AI applications as benchmarks to study, which is what led us to publishing our first few research papers and developing our first product, Sirius—an open end-to-end voice and vision personal assistant.
As an open source software, Sirius allowed people to build conversational virtual assistants on their own. At the time, this was a very limited capability for the general public and was really only controlled by the big companies, such as Google and Apple. However, we saw that we were filling a critical gap when we released the software and saw that it had tens of thousands of downloads in the first week! That was the turning point where we knew there was a lot of market demand for this type of software.
Come 2015, we launched Clinc with the mindset that we would be able to provide everyone – every developer, company, person—who wants to be able to build a virtual assistant with the access to expertise, tooling and innovation to be able to do that.
Clinc offers conversational AI solutions without relying on keywords or scripts. Could you go into some details regarding how this is achieved? What are some of the Natural Language Processing (NLP) challenges that had to be overcome?
What really sets Clinc apart from other conversational AI platforms on the market is its underlying AI algorithms that enable its “human in the room” experience, which understands messy and unscripted language. This allows for corrections to backtrack and “heal” mistakes made in human conversation and enables complex conversational flows—conversations that a real human would be able to understand. In contrast to a speech-to-text word matching algorithm, Clinc analyzes dozens of factors from the user’s input including wording, sentiment, intent, tone of voice, time of day, location and relationships, and uses those factors to deliver an answer that represents a composite of knowledge extracted from its trained brain. For example, if I ask my virtual assistant, “how much money did I spend on a burger?” it needs to understand that I am asking about money and spending, that I am asking specifically about a hamburger and that a hamburger is a type of food and should be matched to my recent spending at a restaurant.
Achieving this level of understanding is not easy. In general, we would break down conversational AI into two components: Natural Language Understanding (NLU) and dialog management. So, the challenge that we had to overcome was figuring out how to build a system that can extract key pieces of information accurately and can anticipate what the user is asking.
We are able to do this through sophisticated, contextual, top-down NLU, that is trained to be intuitive in nature to keep up with the natural flow of conversation, understanding slang and context. This is in comparison to competitive solutions that have a top down, rules-based approach to Natural Language Processing (NLP) that does not allow for conversational healing, meaning if the customer makes an error, the competitive solutions make them go back to square one, wasting time and only frustrating the user. We also use crowdsourcing to extract our language data to create a richer, diverse data set that can be immediately used to train AI models.
Could you discuss how deep learning is used with the Clinc AI system?
Clinc is using a hybrid approach to deep learning where we use the traditional old-school model to some degree and leverage deep learning where needed. Specifically, we use deep learning to understand words and languages to determine the dialogue flow. Generally, our entire dialogue is a combination of deep learning and symbolic AI. We don’t use deep learning for language generation yet because, when it comes to our customers which are primarily in the banking industry, there are a lot of regulations that the virtual assistant must follow that dictate what they can and cannot say to their customers. So, there is still a lot of uncertainty around whether or not the deep learning will be able to follow those set language restrictions.
As of right now, I don’t think the conversational AI community is completely ready to fully adopt deep learning whereas the academic community is 100% all in, but I do look forward to seeing what the new models can do.
What’s the process for a company that wishes to customize the AI’s responses to target a specific audience? Could you give some examples of how Clinc is currently being used by clients?
We allow clients to either license a platform they can build on however they like, or take our fully built and trained chatbot, Finie, and customize it and integrate it into their apps or messaging services. Finie can handle matters related to balances, transactions, spending history, locating an ATM, making a transfer and more.
My favorite example of how a client has customized Clinc’s AI to target a specific audience is İşbank. As Turkey’s largest private bank, they turned to us to develop their digital banking assistant, Maxi, back in 2018. To infuse Maxi with a unique personality, İşbank held 14 focus groups to gauge what sort of traits and skills bank customers wanted in a virtual assistant. They also hired a voice actress to recite sentences in Turkish related to banking tasks. İşbank’s conversational banking team came up with these sentences by considering the way real people would phrase their needs. Upon our recommendation, the team paid participants on crowdsourcing marketplaces such as Amazon Mechanical Turk to supply different ways they might express the same questions, such as a request to view their balances (“what is my balance,” “how much money do I have in my account,” “show me the cash in my account”) or pay a bill (“pay my bill,” “bill payments”).
This example really shows how invested İşbank is in offering a digital banking assistant to help their customers better navigate their accounts. With Clinc, İşbank launched Maxi to more than 7.5 million people, in Turkish. Since its launch, İşbank has seen widespread adoption by more than 5.5 million users, with an average of 9.8 interactions per user. In recent months, as COVID-19 cases increased in Turkey, İşbank swiftly trained Maxi to be responsive to COVID-19-related queries. Since March 2020, Maxi has answered more than 1.2 million customer queries related to COVID-19, a more than 62% increase in usage.
What would you tell women who are interested in learning more about AI but are reluctant to get involved due to it being a male dominated field?
Off the bat, I don’t think there is any reason why AI is considered a male-dominated field. I think there are a lot of women pioneers in AI that are doing really well and are making an impact. I think AI coupled with social policy is a unique area that has the potential to have a lot of impact on people’s everyday lives. This is where I do think more diverse insights across the board would really benefit us, especially since there are a lot of conversations around AI bias involving race and gender. I believe that having a scoped community of AI developers will continue to have a disproportionate impact on society and policy.
For the women out there who are interested in joining the AI field, I highly recommend it especially if you are interested in making an impact. AI has had so much growth and innovation over the years and it really is an exciting time to be a part of it.
Is there anything else that you would like to share about Clinc?
Clinc is making huge strides right now. Personally, I have just stepped into a new role as CTO of Clinc and I am really excited to focus on how we can further work with developers and data scientists to grow the reach of our technology. As I look toward the future, I see the demand for AI-powered applications shifting to enable people who don’t have years of data science experience and machine learning background to be able to use it too. For example, you don’t have to have a graphic design degree to be able to use Photoshop. I think AI is heading in that direction where developers with no AI or machine learning training will be able to achieve results and produce high quality applications. Overall, we want to reiterate that we are not only devoted to the end-user but also to the developers, no matter what level, who show interest in our solution.
Thank you for the great interview, I look forward to followin your progress. Anyone who wishes to learn more should visit Clinc.