In today’s world, data is a rich natural resource worth a fortune in relationship knowledge. One of the newest business approaches for understanding data more accurately is natural language processing or NLP. This AI field involves analyzing human speech and text to derive meaning from it.
Delving into NLP is all about gathering data — lots of it. Businesses and researchers mine it to gain insight into how they interact with their prospects and customers.
Expert.ai developed an innovative artificial intelligence platform for language understanding. Its unique approach to hybrid natural language combines symbolic human-like comprehension and machine learning. The goal is to extract useful knowledge and insight from unstructured data to improve decision-making.
The company started in a garage before it became a cliché. Today, it is a publicly-traded company (EXAI: IM) with offices in Europe and North America.
Its mission is to help global businesses, and government agencies turn language into data. Why? The simple response is to analyze complex documents, understand market risks and opportunities, and accelerate intelligent process automation to improve decision-making.
That may sound simple. But it takes AI and a lot more to make it work, noted Luca Scagliarini, chief product officer at Expert.ai.
“Of all the AI challenges, understanding natural language is one of the toughest. While most solutions can quickly crunch massive volumes of structured data, the multitude of meanings and nuances in language is a different matter,” he told TechNewsWorld.
Unique Platform Experience
Expert’s developers based the NLP platform on the company’s extensive experience in deploying hundreds of natural language understanding (NLU) solutions. It leverages the developers’ proprietary technology and integrates the most popular ML algorithms to offer a unique hybrid approach to NLU, Scagliarini offered.
The guiding principle behind its development was making it easier to create AI solutions or applications based on NLU. Yet, equally important, they designed the platform for ease of use by those who are not AI subject matter experts.
“By making our platform user-friendly and intuitive for people throughout an organization, we are able to help customers augment their business operations, accelerate and scale data science capabilities, and pave the way for AI adoption,” he said.
No other enterprise-ready, purpose-built platform for NLP and NLU exists that covers the complete workflow, he continued. That includes design, development, testing, and deployment of an NLP solution into production.
“We also make available a hybrid set of techniques to bring together the best of AI techniques from all worlds. Expert.ai can work off ML algorithms and draw from symbols to understand language as people do. We are the only platform proven to accomplish all this and do so at a level that enterprises need,” he said.
Transparency, the Big Differentiator
The platform also overcomes the single biggest impediment to AI progress. This is a black-box scenario common to ML.
The steps used to solve a problem are obscured and non-transparent. As a result, there is no insight into how it operates or what occurs between every input and output, explained Scagliarini.
“This produces results that cannot always be explained to ordinary users and is particularly problematic if customers feel they are being treated unfairly,” he said.
Expert.ai’s use of symbolic AI works from a rules-based approach, uniquely enabling the platform to provide full visibility into any given model. With this transparency, users can quickly detect errors in either the data or the algorithm and create new rules to correct them.
This approach streamlines AI projects and lowers costs. It also reduces the amount of data required to train the system and the risks inherent in data collection by shining a light on how it is being used. This can then be shared with customers or any other user base, shared Scagliarini.
Deciphering NLP for Business
Language is essential to all aspects of enterprise activity. Leveraging AI to scale the capability to take advantage of the data hidden in language is a critical success factor.
TechNewsWorld asked Scagliarini to demystify natural language processing as a vital component of modern business and the technology behind what Expert.ai does.
TechNewsWorld: What does Expert.ai’s NLP platform do?
Luca Scagliarini: Our platform for language understanding pairs simple and powerful tools with a proven hybrid AI approach. It combines symbolic and machine learning to solve real-world problems.
Our AI-based natural language capabilities have been deployed across a range of industries, including insurance, banking and finance, publishing, media, and defense, serving customers such as AXA XL, Zurich Insurance Group, Generali, The Associated Press, Bloomberg INDG, BNP Paribas, Rabobank, Gannett, and EBSCO.
What is unique about Expert’s hybrid platform approach?
Scagliarini: No NLU technique is a fit for every application. Rather, organizations must have the flexibility to implement the best technique that fits the unique needs of each application. We combine Symbolic AI and ML. They not only work together, but they excel when combined.
Symbolic AI mimics the human ability to read and comprehend the meaning of words in context. This capability mitigates some of the limitations of ML, and for this reason, the combined set of techniques is the most effective way to unlock the value of unstructured language data with the accuracy, speed, and scale required by today’s businesses.
For example, with insurance, deep understanding can extract data from all types of documents. This allows for automation of activities like claims processing, policy reviews, and risk assessments. All of this streamlines workflows and allows underwriters to process four times the volume of policy reviews while cutting their risk significantly.
How does mining data become useful for other business categories?
Scagliarini: In manufacturing, NL-based third-party risk mitigation can include sifting through millions of articles, posts, and social media monitoring data for “weak signals” like questionable practices by a supplier. This enables a company to take steps to improve operations and protect its reputation.
A retailer could also apply our approach to enhance analytics in customer communications. Retailers can then learn from emails, social media, or a chatbot. In turn, this gains a real-time grasp of buying behavior, products, and emerging trends.
What are typical use cases for Expert.ai’s artificial intelligence?
Scagliarini: Three main areas particularly help enterprises.
Intelligent process automation pulls unstructured language data from all types of documents, enabling automation of an array of tasks. Knowledge discovery extracts data quickly to support stronger, faster decision-making. Advanced text analytics applies our capabilities to any flow of information that is unstructured to provide insight into things like customer behavior and emerging trends.
We can help insurers streamline online processes through automation. Financial institutions enlist the technology to identify fraud. Publishers utilize knowledge discovery capabilities for content enrichment, data extraction, and categorization. The applications are endless.
What are the advantages of this platform?
Scagliarini: Language powers business. It fuels processes, shapes internal and external communication, offers visibility into target markets, and more.
The platform provides a deep understanding of language — from complex documents (e.g., contracts, emails, reports, etc.) to social media messages — turning it into knowledge and insight. This makes for faster and better decisions without all the manual, time-consuming, and costly work.
It is built to support the most challenging language-intensive processes and is simple enough for businesspeople to use. The platform uncovers the hidden language of an enterprise to drive any process or application that relies on language data. It does so with a hybrid approach that allows enterprises to harness the best of the AI world and apply it in uniquely powerful ways for additional competitive advantage.
What about the negative effects of using this technology?
Scagliarini: Most negative ideas revolve around AI technology in general. Foremost, the hype about AI has created the impression that machines can do everything humans can and do better. This is far from the truth.
Misconceptions have been driven by vendors and visionaries who predict far more than is possible and have set unrealistic expectations. AI enables people to do more and focus on tasks that bring greater value to their organization.
It is just another form of software. It needs to be programmed and tested. People must constantly be in the loop and ready to troubleshoot. It is hardly a set-it-and-forget-it situation. Nor can machines displace the people that make them run.
How are hybrid natural language and big data related?
Scagliarini: Big data refers to the common scenario for enterprises to have available large amounts of data. However, in the real world and for many processes, such as the one we described before, the data available or compliant with privacy requirements is not enough to effectively train a language model with pure ML.
With hybrid NL instead, you can address these limitations and gain tremendous value with a limited amount of data. This approach holds additional value because it can be applied to many broader language-based enterprise use cases.
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