Better Data for Smarter Chatbots

 Chatbots, computer programs that interact with users through natural language, have become extraordinarily popular due to technological advances. Among various types of chatbots, the need of conversational AI chatbots has become acute in order to facilitate human computer interactions through messaging applications, phones, websites, or mobile apps. In fact, a chatbot is just one of the typical examples of AI systems, many of which are powered by machine learning.

Not-so-intelligent Chatbots

According to a survey by Usabilla in 2019, 54% of respondents said they would prefer a chatbot to a human customer support representative if it saves them 10 minutes. Moreover, 59% of consumers in a PWC survey mentioned they want to have more humanized experiences with chatbots. Although customers have positive feelings toward AI solutions for the efficiency, many not-so-intelligent chatbots which are not smart enough to engage in fundamental conversations are seen in several industries.

Most chatbots that we see today are based on machine learning. They incorporate the ability to understand human language and get themselves trained. With machine learning, the computer systems can learn by being exposed to many examples: the training dataset. The dataset can be thought of as a set of examples. The chatbot’s algorithms extract and save patterns with each input of data. In this way, a chatbot uses training data to understand user behavior and presents the most applicable conversation for a personalized experience.

If not properly considered and developed, chatbots may contain massive potential failures. For example, when a customer starts a conversation with the word “howdy”, if the chatbot only has greeting words “hello”, “hi”programmed in the training dataset, unfortunately it doesn’t have a clue how to respond.

The quality of training data is key

Ben Virdee-Chapman, head of product at Kairos.com, once said that “the quality of the training data that allows algorithms to learn is key.” Preparing the training dataset for chatbots is not easy. For a customer service chatbot, a dataset that contains a massive amount of discussion text between customers and human-based customer support need to be collected, cleaned and labeled to make it understandable for NLP and develop the AI-enabled chatbot so as to communicate with people.

Conversational AI agents such as Alexa and Siri are built with manually annotated data. The accuracy of the ML models can be constantly improved by manually transcribed and annotated data. However, large-scale manual annotation is usually expensive and time-consuming. Thus, abundant and useful datasets are valuable assets for chatbot development.

Manual annotation gives the chatbot a competitive advantage and differentiates it from other competitors. AI and ML companies are seeking high quality datasets to train their algorithms. The choices among different labeling services can make an enormous impact on the quality of the training data, the amount of time and cost required.

Chatbots needs sufficient data to understand human intention. Traditional data providers collect text data or transcribe audio data offline from all available resources and upload the total data onto a certain software, which in turn takes unnecessary communication cost. On the other hand, data quality is often not guaranteed. Thus, obtaining task-oriented text datasets and getting them annotated in a massive amount remains a bottleneck for developers.

ByteBridge.io is one of the leading data service companies that aim to transform the data labeling industry. With a unique and user-friendly platform, Bytebridge.io enables users to complete data labeling tasks online conveniently. Moreover, the blockchain-driven data company substitutes the traditional model with an innovative and precise consensus mechanism, which dramatically improves working efficiency and accuracy.

Partnered with over 30 different language speaking communities across the globe, ByteBridge.io now provides data collection and annotation services covering languages such as English, Chinese, Spanish, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Russian and more. With rich access to contractors worldwide, it ensures training data quality while expanding its service to a wider range of locations. ByteBridge’s high-quality data collecting and labeling service has empowered various industries such as healthcare, retail, robotics and self-driving, making it possible to integrate AI into such fields.

Chatbots are evolving and becoming increasingly sophisticated in an endeavor to mimic how people talk. Good chatbot applications can not only enhance customer experience, but also improve operational efficiency by reducing cost. To be successful, obtaining crucial datasets is valuable in training and optimizing the chatbot system.

Data matters for machine learning, but how to acquire the right data？

Over the last few years, there has been a burst of excitement for AI-based applications through businesses, governments, and the academic community. For example, natural language processing (NLP) and image analysis where input values are high-dimensional and high-variance are areas that deep learning techniques are highly useful. AI has shifted from algorithms that rely on programmed rules and logic to machine learning where algorithms contain a few rules and ingest training data to learn and training themselves. "The current generations of AI is what we call machine learning (ML) — in the sense that we’re not just programming computers, but we’re training and teaching them with data,” said Michael Chui, Mckinsey global institute partner in a podcast speech.

AI feeds heavily on data. Andrew Ng, former AI head of Google and Baidu, states data is the rocket fuel needed to power the ML rocket ship. Andrew also mentions companies and organizations that are taking AI seriously are working hard to acquire the right and useful data they need. Supervised learning needs more data than other model types in machine learning area. In supervised learning, algorithms learn from labeled data. Data needs to be labeled and categorized for training models. When the number of parameters and the complexity of problems increases, the need of data volumes grows exponentially. 

Data limitations: the new bottlenecks in machine learning

An Alegion survey reported that nearly 8 out of 10 enterprises currently engaged in AI and ML projects have stalled. The same study also revealed that 81% of the respondents admit the process of training AI with data is more difficult than they expected. According to a 2019 report by O’Reilly, the issue of data ranks the second-highest on obstacles in AI adoption. Gartner predicted that 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, the teams management, etc. The data limitations in machine learning include but not limited to: 

• Data collection. Issues like inaccurate data, insufficient representatives, biased views, loopholes, and ambiguity in data affect ML’s decisions and precision. Let along the hard access to large volumes of high quality datasets for model development, especially during Covid-19 when data has not been available for some demanding AI enterprises.  

• Data quality. Low-quality labeled data can actually backfire twice: first during training model building and again when the model consumes the labeled data to make future decisions. For example, popular face datasets, such as the AT&T Database of Faces, contain primarily light-skinned male images, which leaves systems struggling to recognize dark-skinned and female faces. To create, validate, and maintain production for high-performing machine learning models, ML engineers  need to use trusted, reliable data.

• Data labeling. Since most machine learning algorithms use supervised approaches, data is useless for ML applications which rely on computer visions and supervised learning approaches, unless it is labelled properly. The new bottleneck in machine learning nowadays is not only about the collection of qualified data anymore, but also about the speed and accuracy of the labeling process.

Solution

ML needs vast amounts of labeled high-quality datasets for model training to arrive at accurate predictions. Labeling of training data is progressively one of the primary concerns in the implementation of machine learning algorithms. AI companies are eager to acquire high quality labeled datasets to match their AI model requirements. Researches are showing ByteBridge.io, a data collection and labeling platform that allows users to train state-of-the-art machine learning models without manual marking of any training data themselves. ByteBridge.io's dataset includes diverse and rich data such as texts, images, audios and videos with full coverage of languages, races and regions across the globe. Its integrated data platform eliminates the intermediate processes such as labor recruitment for human in the loop, test, verification and so forth.

Automated data training platform

ByteBridge.io takes full advantage of the platform's consensus mechanism algorithm which greatly improves the data labeling efficiency and gets a large amount of accurate data labeled in a short time. The Data Verification Engine, equipped with advanced AI algorithms and the highly trained project management dashboard has automated the annotation process which fulfills the needs and standards of AI companies in a flexible and effective way.

“We believe data collection and labeling is a crucial factor in establishing successful machine learning models. We are committed to building the most effective data training platform and helping companies take full advantage of AI's capabilities,” said Brian Cheong, CEO of ByteBridge.io. “We have streamlined data collection and labeling process to relieve machine learning engineers from data preparation. The vision behind ByteBridge.io is to enable engineers to focus on their ML projects and get the value out of data.”

Compared with competitors, ByteBridge.io has customized for its automation data labeling system thanks to the natural language processing (NLP) enabled software. Its Easy-to-integrate API enables continuous feeding of high quality data into a new application system.

Both the quality and quantity of data matters for the success of AI outcome. Designed to power AI and ML industry, ByteBridge.io promises to usher in a new era for data labeling and collection, and accelerates the advent of the smart AI future .