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Natural Language Speaks Loudly About a Big Shift in AI

In today’s world, data is a rich natural resource worth a fortune in relational knowledge. One of the newest business approaches to understand data more precisely is natural language processing or NLP. This area of ​​AI is all about analyzing human speech and text to derive meaning.

Diving into NLP is all about collecting data – lots of it. Companies and researchers gain insights into how they interact with their prospects and customers.

Expert.ai developed an innovative platform for artificial intelligence for language understanding. Its unique hybrid natural language approach combines symbolic, human-like understanding and machine learning. The goal is to extract useful knowledge and insights from unstructured data to improve decision making.

The company started in a garage before becoming 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 voice into data. Why? The simple answer is to analyze complex documents, understand market risks and opportunities, and accelerate intelligent process automation to improve decision making.

That may sound easy. 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 most difficult. While most solutions can quickly handle large amounts of structured data, the variety of meanings and nuances in the language is another matter,” he told TechNewsWorld.

Unique platform experience

Expert developers built the NLP platform on the company’s extensive experience in delivering 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 that Scagliarini offers.

The guiding principle during development was to simplify the creation of AI solutions or applications based on NLU. But just as importantly, they have designed the platform to be easy to use even for those who are not experts in AI topics.

“By making our platform easy to use and intuitive for people across the organization, we can help customers extend their business operations, accelerate and scale data science capabilities, and pave the way for AI adoption,” he said .

There is no other enterprise-grade, purpose-built platform for NLP and NLU that covers the entire workflow, he continued. This includes the design, development, testing and deployment of an NLP solution to production.

“We also provide a hybrid set of techniques to bring together the best AI techniques from all worlds. Expert.ai can run ML algorithms and draw from symbology to understand language the way humans do. We are the only platform that has a proven track record of achieving all of this, at a level that businesses need,” he said.

Transparency, the big differentiator

The platform also overcomes the single greatest obstacle to AI progression. This is a black box scenario common to ML.

The steps to solve a problem are obscure and opaque. As a result, there is no insight into how it works or what happens between each entrance and exit, Scagliarini explained.

“This leads to results that cannot always be explained to the ordinary user and is particularly problematic when customers feel they have been treated unfairly,” he said.

Expert.ai’s use of symbolic AI is based on a rules-based approach, which uniquely enables the platform to provide full insight into any given model. With this transparency, users can quickly spot errors in either the data or the algorithm and create new rules to correct them.

This approach streamlines AI projects and reduces costs. It also reduces the amount of data required to train the system and the risks associated with data collection by providing insight into how it is being used. This can then be shared with customers or any other user base that shares Scagliarini.

Decode NLP for business

Language is essential to all aspects of business operations. Leveraging AI to scale the ability to leverage the data hidden within language is a critical success factor.

TechNewsWorld asked Scagliarini to demystify natural language processing as an essential component of modern business and the technology behind Expert.ai.

TechNewsWorld: What Does Expert.ai’s NLP Platform Do?

Luca Scagliarini: Our language understanding platform combines 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 clients 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?

Expert.ai CPO Luca Scagliarini

Scagliarini: No NLU technique is right for every application. Instead, organizations must be flexible enough to implement the best technology that meets the unique needs of each application. We combine symbolic AI and ML. They not only work together, but also excel in combination.

Symbolic AI mimics the human ability to read and understand the meaning of words in context. This capability mitigates some of ML’s limitations, and for this reason, combining techniques is the most effective way to unlock the value of unstructured language data with the accuracy, speed, and scalability required by today’s enterprises.

In insurance, for example, a deep understanding can extract data from all types of documents. This enables activities such as claims processing, policy reviews, and risk assessments to be automated. All of this streamlines workflows, allowing underwriters to process four times the volume of policy reviews while significantly reducing their risk.

How does mining data become useful for other business categories?

Scagliarini: In manufacturing, NL-based third-party risk mitigation can involve sifting through millions of articles, posts and social media monitoring data for “weak signals” such as questionable practices from a supplier. This allows a company to take steps to improve operations and protect its reputation.

A retailer could also apply our approach to improve customer communication analytics. Retailers can then learn from email, social media or a chatbot. This in turn enables real-time insight into buying behavior, products and emerging trends.

What are typical use cases for Expert.ai’s artificial intelligence?

Scagliarini: Three main areas help companies in particular.

Intelligent process automation pulls unstructured language data from all types of documents, enabling the automation of a range of tasks. Knowledge Discovery quickly extracts data to support stronger and faster decision making. Advanced Text Analytics applies our skills to any unstructured flow of information to gain insights into things like customer behavior and emerging trends.

We can help insurers streamline online processes through automation. Financial institutions use the technology to identify fraud. Publishers leverage knowledge discovery capabilities for content enrichment, data extraction, and categorization. The applications are endless.

What are the advantages of this platform?

Scagliarini: Language makes business. It drives processes, shapes internal and external communication, provides insight into target markets and much more.

The platform provides a deep understanding of language – from complex documents (e.g. contracts, emails, reports, etc.) to social media messages – and turns them into knowledge and insights. This enables faster and better decisions without all the manual, time-consuming and costly work.

It is designed to support the most demanding language-intensive processes and is simple enough for business people to use. The platform uncovers an organization’s hidden language to drive any process or application that relies on voice data. It does so with a hybrid approach that enables companies to take the best of the AI ​​world and apply it in uniquely powerful ways for additional competitive advantages.

What about negative effects of using this technology?

Scagliarini: Most negative ideas revolve around AI technology in general. In particular, the hype around AI has created the impression that machines can do everything humans can, and do it better. This is far from the truth.

Misunderstandings have been fueled by vendors and visionaries who predict far beyond what is possible and set unrealistic expectations. AI enables people to do more and focus on tasks that bring more value to their business.

It’s just another form of software. It has to be programmed and tested. Employees need to be constantly updated and ready to fix bugs. It’s hardly a situation where it can be set and forgotten. Neither can machines replace the people who make them work.

How are hybrid natural language and big data related?

Scagliarini: Big data refers to the common scenario where companies have large amounts of data. However, in the real world and for many processes like those described previously, the available or privacy-compliant data is not sufficient to effectively train a language model with pure ML.

Hybrid NL allows you to bypass these limitations and get tremendous value from a limited amount of data. This approach has added value because it can be applied to many broader language-based enterprise use cases.

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