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Robert Weissgraeber, CTO & Managing Director at AX Semantics – Interview Series




Robert Weissgraeber is the Managing Director and CTO of AX Semantics, where he heads up product development and engineering. Robert is an in-demand speaker and an author on topics including agile software development and Natural Language Generation (NLG) technologies and a member of the Forbes Technology Council. He was previously Chief Product Officer at aexea and studied Chemistry at the Johannes Gutenberg University and did a research stint at Cornell University.

What initially attracted you to the space of Natural Language Generation (NLG)?

Writing and the way it has traditionally been executed, has not seen significant innovation since the advent of the typewriter 200 years ago and the word processor in the late 1960s. Three things attracted me to the NLG sector. First, witnessing the challenges and hardships people face and continuously endure with having to create vast quantities of content and text. For example, there are literally people who work in e-commerce that have to write hundreds of similar, yet unique t-shirt and clothing descriptions every month as new products come in. The amount of people needed to do this is astronomical, time-consuming, costly and impossible to scale. I knew the ability to utilize AI to automate content generation (vs. trying to produce content manually) would be a game changer for many industries who must regularly create mass volumes of content — not only in English but also many other languages.

Second, seeing the type of ‘low tech’ solutions others brought to the market — like spinning tools or poorly implemented NLG tools with ‘Enterprise UX’ — only solidified my attraction to the power of NLG.

Lastly, I wanted to work on something that wasn’t a rendition of the next online shop or the next “Uber for X”, but something capable of solving a really hard tech problem while also creating a solution for a real-world challenge. A perfect NLG solution with the ability to redefine content generation for the digital age can reduce ‘noise’ for all humanity, since it allows for super-precise communication.

Could you discuss some of the NLG solutions that are offered by AX Semantics?

AX Semantics is a 100% SaaS-based NLG solution with an easy to use UI (user interface). Customers build their own content generation machine by configuring their business application with our NLG tool, which automates content in 110 languages in a matter of minutes — including cross-data generation such as Chinese text from English data. As a result, companies can take data and information and create unique content rapidly and at scale regardless of perpetual business and cultural shifts.

There are a myriad of use cases for NLG technology. Different industries use it to solve content challenges unique to their sectors:

  • E-commerce: Most customers use our NLG software to generate large volumes of unique product descriptions (critical for SEO), category content or personalized emails like basket dropout recovery emails.
  • Brand/Customer Communications, including Social Media: Brands and content agencies use NLG to keep a steady flow of fresh blog content, or to create and populate unique social content across multiple social media channels — and can do so in 110 languages.
  • Media or ‘Robot Journalism’: Publishers use our NLG software for election reporting or data-based journalism such as pollution-level monitoring, stock table earnings, sports scores and crime blotters — freeing up journalists to work on more creative, engaging journalism or hard-hitting investigative stories. In many ways, content generation software is helping to revive local journalism, particularly for cash strapped small newspapers. Journalism has been in a tight spot since 2000 as newspapers have cut reporters and editors or shut down entirely. NLG is actually an unlikely ally in the push to save journalism.
  • Financial Services/Banking: Financial analysts, brokers, and executives face the demand to quickly update the content required by state and federal laws and regulations, such as details about investment plans, risk assessments, and financial filings — all of which must be updated regularly. Our NLG solution addresses the pain point of recurring financial reports, regulatory filings, executive summaries, and other written communication – all of which typically require massive amounts of financial data from disparate sources to be gathered, analyzed and translated into text customized for a broad range of audiences and languages. Banking and finance employees can effortlessly turn mountains of data into real-time actionable written narratives, create reports, descriptions of terms and loans, draft regulatory filings, and documents detailing investments — in more than 110 languages — all with minimal training — freeing up bandwidth for higher-value activities and responsibilities.
  • Pharmaceutical: Pharma companies use our HIPAA-compliant NLG software to generate regulatory Clinical Study Reports (CSRs) on medications up to 40% faster, by automating 30% of writing the CSR. This is crucial because the most challenging phase of bringing a drug to market is the human drug trial, or Phase III, during which time, clinicians must write a CSR that describes the pharmacological impacts and trial outcomes. Typically, data collected from the human drug trials is gathered and medical writing teams manually compile the report, however, this outdated, onerous and time-consuming process can potentially delay life-saving medications from coming to market sooner and cost pharmaceutical companies millions. A capacity challenge also exists. Writing a CSR report typically takes several months to complete, which limits the number of CSRs a team of medical writers can produce annually.

A writer’s voice is considered important in journalism and other types of writing, can you discuss the importance of drafting “personalities” for content generated by NLG?

With ‘data-to-text’ solutions like our NLG pipeline approach (in contrast to text-to-text, corpus-based stuff like GPT- 2/3), the writer is an essential and critical part of the creative process. The writer configures a lot of meaning levels between the data and adds ‘micro-templates’ for all aspects of the text, which allows the machine to select and combine all aspects in a ‘bag of words’ approach.

Can you discuss what hybrid content generation is and how employees can take advantage of this?

Hybrid content generation is where human and machine work together. Each actor focuses on the aspect they do best. Humans prioritize the creative part, writing style and specific content selection, including curation and definitions. The machine takes care of production, grammatical correctness building and scaling the content.

Hybrid content born from a partnership between man and machine fills a pressing need for fresh, vital content around the clock. Our software generates content that is almost indistinguishable from a human writer. Employees can use content generation to create new content that can be changed and updated at a moment’s notice. Working with content generation software allows them to not only fulfill, but exceed their job requirements and expectations.

With content being able to be created on the fly how will content quality be quantified?

Ultimately, it will be measured and quantified based on results. Early on, we had discussions about measuring quality aspects that were subjective to personal meaning, i.e. “I like this phrase or that,” etc., but this can now be measured objectively since scaled A/B testing is now possible. One of our customers, for example, struggled between the decision to use formal or informal languages (some non-English languages have that codified), and were able to test this out to see what worked best.

Can you share your opinions on how content will become decentralized?

No one source or market has a monopoly on content generation or the ability to scale content anymore. That’s the power of NLG technology and software — it delivers equal opportunity and equal access to companies large and small.

With access to valid data sources from all over the world along with NLG and publishing technology, businesses of all sizes can build scaled content, and adapt it to their own needs while using NLG technology to keep it continuously updated. For example, a customer service department can produce their own product descriptions with a focus on service-specific content, or an online marketer can tailor their content to be sales oriented — all without added maintenance or cost.

What are going to be some of the new “unlocked” business opportunities from this type of content generation?

First of all, we’re going to see totally new types of hyper-personalized content where lots of data sources are combined to produce content for a specific individual, such as a weather report that accounts for someone’s travel itinerary or financial services with individualized fund reporting. Second, as companies increasingly embrace the digital age, they’ll be able to utilize automated content generation to create a more robust online presence for their business.

Could you discuss some of the potential aspects for social good from NLG?

Consumers are inundated with a continuous stream of communication — texts, emails, countless ads and promotions — across multiple channels, including their mobile devices, computers and even the mail, they need to scroll through and read to find the information they want and need.

NLG allows for individualized, precise and noise-reduced communication. Imagine receiving only newsletters or reports that take your personal information into account and adapt it to your needs. NLG provides a better, more thoughtful way to reach customers in a way that matters to them.

What are some enterprises that are currently using AX Semantics?

Approximately three years ago after validating our solution with select clients, we began to introduce our NLG solution to the mass market. We had hundreds of customers try our software and build their individualized solution with AX Semantics. We then fine-tuned the necessary learning materials and onboarding process. A lot of those initially small customers now have scaled their content needs with us, including companies like Deloitte, Adidas, Nestlé, Otto and Beiersdorf.

Is there anything else that you would like to share about AX Semantics?

We’re very proud of the fact AX Semantics received recognition as one of the world’s top five providers of natural language generation platforms by Gartner, and that we were named a top emerging company in the NLG market by Forrester.

Lastly, in addition to our own client base and sales, we’re looking for companies that want to build their own vertical use cases on top of our technology, and we are actively supporting those companies with training, etc. So if you are building a new product or company and want to use content generation we’d love to speak with you.

Thank you for the great interview and the detailed answers regarding NLG, readers who wish to learn more should visit AX Semantics

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Antoine Tardif is a Futurist who is passionate about the future of AI and robotics. He is the CEO of, and has invested in over 50 AI & blockchain projects. He is the Co-Founder of a news website focusing on digital securities, and is a founding partner of unite.AI. He is also a member of the Forbes Technology Council.


Dimitris Vassos, CEO, Co-founder, and Chief Architect of Omilia – Interview Series




Dimitris Vassos is the CEO, Co-founder, and Chief Architect of Omilia, a global conversational intelligence company that provides advanced automatic speech recognition solutions to companies and organizations in North America, Canada, and Europe. Dimitris has significant experience in the field of applied speech and artificial technology, specifically, natural language understanding (NLU), speech recognition, and voice biometrics.

What initially attracted you to AI?

Human-Machine interfaces have mesmerized me since I was a child. In 1984, I had one of the first home computers. I remember I had programmed it to control our home lighting using sound recognition. Back then, there was no speech recognition technology available, so I had it recognize patterns of sound (i.e. clapping).

During my  studies, I learned about speech signal processing, and immediately saw this as the most promising human-machine interface technology. I had a mission to make machines understand humans using natural language understanding.

What was the inspiration behind founding Omilia?

My first job was with IBM in its voice solutions unit. I was developing and delivering voice interaction solutions for automated telephone inquiries. Very soon, I realized that technology and products available could be made better. After a few years of frustration, I decided to take things into my own hands. That’s when I met my partner, Pelias, and we founded Omilia with a mission to re-invent the voice automation industry.

What are some of the solutions currently offered by the Omilia Conversational AI?

Omilia provides human-like human-to-machine communication experiences and technologies in to help companies improve the customer care experience. Our main solution offerings are:

  1. Conversational AI Self-Service for Customer Care, over both voice and text channels
  2. Customer ID & Authentication using Voice Biometrics
  3. Transcription Services focused on Customer Service

The Omilia Cloud Platform (OCP) offers ready-to-consume micro-services that enable companies to integrate our state-of-the-art capabilities into their customer care processes, while offering critical solutions for those companies that wish to benefit from our team’s extensive experience in deploying large-scale conversational AI solutions for over 15 years now.

Our differentiator in the market is that our capabilities have evolved over time and with that experience are ahead of the curve, offering robust solutions that deliver real business value.

Could you discuss some of the speech recognition challenges that may be faced from having a global clientele speaking in different languages and with different accents/pronunciations?

A language is a system of communication defined by people and influenced by their country or community. As such, accents and pronunciations become a significant part of any language. So, developing a language for AI technology involves collecting a large sample of spoken language from a group of native speakers and using it for training our AI models. It requires considerable effort to perform this process. Our diverse clientele propelled us to strategically invest in building out capabilities in various languages. Today, our solutions support over 24 languages, and the upfront investment and assets that we have accumulated are very significant.

Why do you believe that voice biometrics is important?

In customer service, perhaps the most challenging part of a caller’s experience is authentication –  the process by which a customer representative verifies the identity of the caller. We all have called in various contact centers and have spent time answering trivial questions that are designed to prove our identity. One facet of the problem is that today, such personal information is easily discoverable by anyone making security of authentication processes alike compromised. At the same time, the authentication process itself is often very tedious, lengthy and adds frustration to the customer experience.

Voice Biometrics solves this problem. While one’s voiceprint is not 100% unique and secure, it is significantly more accurate and secure than knowledge-based information. When done right, voice biometrics can provide a more satisfactory customer experience. For me, “done right” is defined as seamless for the customer.

At Omilia, we specialize in adapting the technology to the customer. Good tech is invisible tech. This is the design principle that guides our products and services. With our deepVB solution, we are achieving ultra-high accuracy rates, from the first few seconds of the customer speaking, and without any need for the customer to say anything specific. It is an innovative breakthrough that is solving the problems of traditional biometric deployments with tedious enrolment processes and lengthy speech samples. Omilia’s Voice Biometrics works seamlessly across all voice channels.

Voice Biometrics is an important ingredient in the customer service experience, making the authentication process seamless, and thus improving overall customer experience and business value.

Voice biometrics has often been poorly deployed in the marketplace, how does Omilia tackle this challenge?

The most common mistake when deploying voice biometric services is designing the customers’ experience around the limitations of the technology. This is wrong. If customers were to use the technology exactly in the way it was designed, without deviations, it would perform well, but the reality is that customers have their own objectives they are set to accomplish on a call and technology can either meet those objectives, or not. The solutions must be designed around the customer experience, not the other way around.

So far in the market, most biometric deployments in customer service have not delivered the benefits they promised. In contact centers, most deployments have relied upon legacy technology, which hasn’t kept pace. As with voice self-service, at Omilia we persistently refuse to settle for anything less than stellar customer experience. So, we set out to define what such an experience would look like and what the technology enabling that experience should be able to do. The result is our deepVB engine, which is at the core of our services and solutions, and which not only leverages the latest in Deep Learning, but also works hand-in-hand with our Speech Recognition engine and Dialogue Manager, to smartly get informed from the context of the interaction. We have solved the Conversational Authentication problem, not just voice biometrics.

Being able to understand intent in speech analytics can be tricky, how is Omilia able to do this so successfully?

The ancient Greeks had a saying “good things are acquired by expending labor”. This is true for everything we do at Omilia, and it is certainly true for our Natural Language technology. For more than 15 years now, we have been painstakingly designing, building and refining a unique approach to Natural Language Understanding (NLU), which is not a singular technology, but rather a combination of multiple technological approaches, all fused together to deliver Conversational NLU for Customer Service. Conversational AI companies are continuously looking for the best Machine Learning algorithm to solve for NLU. However, most of the time, what makes a difference is not the best algorithm in the lab. What has rendered results for us is the approach of going the extra mile to find solutions to real world problems, not technological ones, utilizing the best tools.

I often remind our team of how long it took us to get here. And develop the technological capabilities we have. We were successful because we were sought out to solve the problems that companies and their customers are experiencing, as opposed to simply looking at the technology.

Conversational AI is becoming a hyper competitive industry, what do you believe differentiates Omilia from competing products?

The field of Conversational AI is very broad and is applied in different ways in different markets and industries.  In practice, only a small handful of providers can deliver sophisticated solutions that can compete with the most advanced solutions in the space in which Omilia is active. Omilia is one of those providers.

Omilia’s biggest  strengths are voice and dialogue. We can facilitate real-time, unconstrained, conversational dialogues with customers over various communication channels, with ultra-accuracy and stellar customer experience. We have been doing so for many years, and our experience has positioned us to achieve the highest number of conversational voice self-service solution deployments in the world to this day.

The “Forrester New Wave” 2019 report assessing customer service in Conversational AI found that Omilia’s strongest product differentiation is its omnichannel, voice and speech, vertical specialization (pre-trained models for specific industries) and security and authentication which enables seamless identification and authentication of customers, which otherwise is a point of high friction for customer service.

What type of enterprises are using Omilia products?

Omilia works in over 16 countries serving global companies in sectors with a large volume of customer service interactions such as finance, telecom, insurance, healthcare, energy, retail, utilities, public sector and transportation, to name a few. Omilia’s ability to achieve 96% semantic accuracy, across 24 ASR languages and do so at scale while ensuring consumer fraud prevention, makes Omilia one of the few companies that can meet the high customer service demand with human-like conversational experience, saving companies millions of dollars.

Omilia’s products fit all enterprises, big or small. We are serving mid-market customers with our cloud-based offering of OCP miniApps®, which we consider a real disruption in the Conversational AI space. We utilize sophisticated conversational voice and text self-service for a company of any size, at the click of a button and with zero-coding. Now, any company can provide the highest level of sophistication in customer service that previously only the large enterprise customers could afford.

Thank you for the interview, readers who wish to learn more should visit Omilia.


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Dr. Danny Lange, Senior VP of AI at Unity Technologies – Interview Series




Dr. Danny B. Lange is VP of AI and Machine Learning at Unity Technologies. Formerly, Danny was Head of Machine Learning at Uber where he led an effort to build the world’s most versatile Machine Learning platform to support Uber’s rapid growth.

What initially attracted you to Artificial Intelligence?

I built and programmed computers from a very young age and I was almost immediately fascinated by the idea of making these systems autonomous. What captivates me about autonomy is the challenges you as a developer have to overcome in creating a system made from sequences of rigid code that can safely respond to unpredictable and never-seen-before circumstances. The field of Artificial Intelligence (AI) has over the years provided us with increasing powerful tools from object-oriented programming, rule-based inference, to machine learning and more recently deep learning. It is the increased capabilities of these technologies that fuel the rapid progress in the field of AI.

You have been a leader in the space for many years such as being General Manager for Amazon Machine Learning in AWS, and Head of Machine Learning at Uber. What are some of the lessons that you have learned from these past experiences?

Machine learning is a truly transformative technology, but to realize the potential to its fullest, it is necessary to bring it to every corner of the enterprise. Repeatedly, machine learning has demonstrated its ability to create unimaginable optimizations and lift business operations to levels that cannot be achieved by ordinary human processes alone. However, true disruption only occurs when a critical mass of business processes are operated in this way. What organizations such as Amazon and Uber clearly have demonstrated is that if we make the machine learning systems broadly approachable and available to every team we experience a broad adoption that invariably leads to a virtuous cycle of continued improvements to the overall business as a network effect takes place

You’ve been the VP of AI at Unity Technologies since 2016. What was it about this company’s vision that excited you?

Unity is a fantastic place for the AI enthusiast. We have a remarkable culture of solving hard problems for our customers – with AI being one of the mightiest challenges that i can think of. Our leadership is committed to power and drive the future of AI. We have the technologies, resources, customers, and partners to do just that. I cannot imagine a better place to work on changing the world.

You’ve spoken before about the importance of synthetic data, could you share with us what this is precisely?

Synthetic data is created by an algorithm as opposed to data captured from the real world. A real-time 3D engine with a realistic physics emulator, is the ideal tool to create realistic yet synthetic training data for a wide variety of applications ranging from object recognition in computer vision systems to path planning for navigational robots.

What makes synthetic data so important when it comes to building machine learning systems?

I have on many occasions called Unity the perfect AI Biodome. And it is true. Working with AI in the real world and using real-world data can be outright scary. Do I have to mention self-driving vehicles on the streets of San Francisco or face recognition systems deployed in public spaces? There are worries about safety, bias is always lurking, and privacy concerns often collide with common use cases. And then there is the scarcity and high cost associated with collecting the necessary amounts of training data. With Unity, we have not only democratized data creation, you also have access to an interactive system for simulating advanced interactions in a virtual setting. Within Unity you can develop the control systems for an autonomous vehicle without the risk of hitting and injuring anyone.

Can you discuss how Unity Simulation can assist companies with the generation of synthetic data?

With Unity Simulation we have taken a real-time 3D engine designed for human consumption whether that is gaming, film, or engineering – and turned into an cloudoptimized instance that not only runs at unimaginable high frame rates, but also allows for scaling to thousands of instances running in parallel. In this way Unity Simulation allows developers to generate experiences for their AI systems orders of magnitude faster than wallclock time. Until recently, this scale of data generation was only available to a few privileged corporations, but with Unity Simulation we have truly levelled the playing field.

Recently, Unity has teamed up with The Institute for Disease Modeling (IDM) to build real-time 3D in-store simulations that model COVID-19 spread. Can you discuss how Unity Simulation can effectively simulate the spread of COVID-19?

Computer simulation has been used for decades by researchers, engineers, problem solvers, and policy makers in many fields, including the study of infectious disease. Unity Simulation enables a special kind of real-time spatial simulation that can be scaled on the cloud to holistically study large, complex, and uncertain systems. We built a simplified demonstration project to simulate coronavirus spread in a fictitious grocery store and explored the impact that store policy has on exposure rates. By running tenth of thousands of simulations, we were able to identify the behaviors and policies that appeared to have the greatest impact on the spread of this terrible infectious disease.

Recently, you’ve been speaking a lot about Artificial General Intelligence (AGI). Can you explain what emergent behavior is and why it’s important for the development of AGI?

In just 100,000 years, the human race went from surviving on picking berries in the wild to putting a person on the moon. We know from archaeogenetics that the human brain has not changed significantly during that period of time. You can say there were no significant hardware upgrades to the processor. So what was it then that was so transformative? The key should be found in our ability to accomplish something together. We use the term emergent behavior of a system that does not depend on its individual parts, but rather on their relationships to one another. Emergent behavior cannot be anticipated by investigating the individual parts of a system. It can only be predicted by understanding the relationships between the parts. Emergent systems are characterized by the observation that the whole is greater than the sum of the parts. While I have repeatedly shown entertaining examples of emergent behavior in relatively simple multi-agent systems, just imagine what will happen when you have a plethora of AI systems collaborating at the speed of light.

Do you believe that there is a possibility that we can achieve AGI within the next decade?

When it comes to AGI we have to remember that it is all about the journey and not the destination. Nobody knows exactly when AGI will happen as it will not be at a specific moment in time, but rather a gradual change over time. It is in the nature of AGI that it will be hard for us humans to pinpoint just how intelligent a system at any given moment. Looking at the progress made over the last decade, I am sure that this decade will bring us plenty of interesting progress towards AGI.

Is there anything else that you would like to share about Unity Technologies?

At Unity, we continue to see ourselves powering the future of AI and playing a significant role in the advancement of AI technologies. As our relationship with DeepMind is a clear demonstration of our technology is the perfect environment for researchers and developers to safely push the boundaries of AI. We are gearing up to support our customers and partners in creating virtual environments that operate at previously unseen scale to solve the challenges of tomorrow whether that is climate change, logistics, or health challenges.

Thank you for the amazing interview, I enjoyed learning about Unity and your views on AGI. Anyone who wishes to learn more should visit Unity Technologies.

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Dr. Lingjia Tang, CTO and Co-Founder, Clinc – Interview Series




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.

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