Natural Language Indicates a Significant Change in AI

 

Data is a rich natural resource worth a fortune in connection knowledge in today's society. Natural language processing, or NLP, is one of the most recent corporate ways to better comprehending data. This AI area entails deriving meaning from human voice and text.

Exploring NLP is all on obtaining data - a lot of it. Businesses and researchers mine it for information on how they engage with prospects and customers.

Expert.ai created a novel artificial intelligence framework for language comprehension. Its novel hybrid natural language technique blends symbolic human-like comprehension with machine learning. The objective is to extract relevant information and insight from unstructured data in order to make better decisions.

Before it became a cliché, the firm began in a garage. It is now a public corporation (EXAI: IM) with offices throughout Europe and North America.

Its purpose is to assist global corporations and government organisations in converting language into data. Why? To enhance decision making, the easy solution is to evaluate complicated documents, comprehend market dangers and possibilities, and speed intelligent process automation.

That may appear to be straightforward. However, it needs AI and much more to make it work, according to Luca Scagliarini, chief product officer of Expert.ai.

"Understanding natural language is one of the most difficult AI issues." While most systems can swiftly analyse vast amounts of structured data, the plethora of meanings and subtleties in language is a different story."

Unusual Platform Experiment

The NLP platform was built on Expert's significant expertise in implementing hundreds of natural language understanding (NLU) solutions. Scagliarini explained that it uses the creators' own technology and blends the most prominent ML algorithms to provide a unique hybrid approach to NLU.

Its development was guided by the goal of making it easier to design AI solutions or applications based on NLU. However, they also intended the platform to be user-friendly for folks who are not AI subject matter experts.

"We are able to assist companies supplement their business processes, accelerate and expand data science skills, and pave the path for AI adoption by making our platform user pleasant and simple for individuals throughout an organisation," he added.

There is no other enterprise-ready, purpose-built platform for NLP and NLU that spans the entire workflow, he added. This involves the design, development, testing, and production deployment of an NLP system.

"We also provide a hybrid set of algorithms that combine the best of AI techniques from all worlds." Expert.ai can use ML techniques and symbolic representations to interpret language in the same way that humans do. "We are the only platform that has been proved to handle all of this at a level that corporations require," he added.

The Big Differentiator is Transparency.

The platform also addresses the single most significant hindrance to AI growth. This is a frequent black box scenario in machine learning.

The steps taken to remedy an issue are veiled and opaque. As a result, there is no understanding of how it works or what happens between each input and output, according to Scagliarini.

"This yields outcomes that are not always understandable to regular users and is especially troublesome if consumers believe they are being treated unjustly," he stated.

Expert.ai's symbolic AI employs a rules-based approach, allowing the platform to provide full insight into any given model. With this openness, users may spot flaws in the data or the algorithm immediately and develop new rules to remedy them.

This method speeds AI programmes and reduces expenses. It also decreases the quantity of data needed to train the system as well as the hazards associated with data gathering by casting a light on how it is used. Scagliarini explained that this may subsequently be shared with consumers or any other user base.

Understanding NLP for Business Language is critical to all parts of business activity. Using AI to grow the capacity to exploit the data buried in language is a vital success element.

Scagliarini explain natural language processing as a critical component of modern business and the science underlying what Expert.ai performs.

 

What exactly does Expert.ai's NLP platform do?

Scagliarini, Luca: Our language comprehension platform combines simple and powerful tools with a tried-and-true hybrid AI methodology. It solves real-world issues by combining symbolic and machine learning.

Our AI-powered natural language skills have been used by companies such as AXA XL, Zurich Insurance Group, Generali, The Associated Press, Bloomberg INDG, BNP Paribas, Rabobank, Gannett, and EBSCO.

What distinguishes Expert's hybrid platform approach?

Expert.ai's chief product officer is Luca Scaliarini.

Scagliarini: No NLU approach is suitable for all applications. Rather, businesses must be adaptable in order to use the appropriate approach for each application's specific demands. We mix Symbolic AI with Machine Learning. They not only collaborate, but they also flourish when joined.

Symbolic AI imitates humans' capacity to read and understand the meaning of words in context. Because this capacity mitigates some of ML's limitations, the combined set of methodologies is the most effective way to unlock the value of unstructured linguistic data with the accuracy, speed, and scale demanded by today's enterprises.

Deep understanding of insurance, for example, may extract data from various forms of papers. This enables the automation of tasks like as claims processing, policy reviews, and risk assessments. All of this simplifies workflows and allows underwriters to perform four times the amount of policy evaluations while considerably lowering risk.

How can mining data become beneficial for various types of businesses?

Scagliarini: In the industrial industry, NL-based third-party risk mitigation might include filtering through millions of articles, postings, and social media monitoring data for "weak signals" such as problematic supplier behaviour. This helps a corporation to enhance operations and defend its reputation.

A shop might also use our method to improve analytics in customer conversations. Retailers can then use emails, social media, or a chatbot to learn. As a result, this provides a real-time understanding of purchasing behaviour, items, and new trends.

What are some common applications for Expert.ai's artificial intelligence?

Scagliarini: Three major categories are particularly beneficial to businesses.

Intelligent process automation extracts unstructured linguistic data from many sorts of documents, allowing for the automation of a variety of operations. Knowledge discovery swiftly pulls data to help better, quicker decision making. Advanced text analytics uses our expertise to any unstructured flow of information to give insight into topics such as consumer behaviour and developing trends.

Through automation, we can assist insurers in streamlining their online procedures. Financial organisations use technology to detect fraud. Knowledge discovery skills are used by publishers for content enrichment, data extraction, and classification. The possibilities are limitless.

What are the benefits of using this platform?

Scagliarini: Business is fueled by language. It powers operations, influences internal and external communication, and provides insight into target markets, among other things.

From complicated papers (e.g., contracts, emails, reports, etc.) to social media communications, the platform enables deep comprehension of language, transforming it into knowledge and insight. This results in faster and better judgments without the need for manual, time-consuming, and expensive labour.

It is designed to facilitate the most difficult language-intensive operations while being easy enough for businessmen to utilise. The platform reveals an enterprise's hidden language to power any process or application that relies on language data. It accomplishes this through a hybrid strategy that enables organisations to harness the best of the AI world and use it in novel and powerful ways to gain a competitive edge.

What about the disadvantages of employing this technology?

Scagliarini: The majority of the negative notions focus around AI technology in general. First and foremost, AI hype has generated the notion that robots can do everything humans do and do it better. This could not be further from the truth.

Misconceptions have been fuelled by merchants and visionaries who have predicted far more than is attainable and established excessive expectations. AI empowers workers to perform more and concentrate on jobs that add more value to their firm.

It is simply another type of software. It must first be coded and tested. People must always be in the loop and prepared to troubleshoot. It's not a "set it and forget it" issue. Machines cannot replace the humans who make them run.

What is the relationship between hybrid natural language and big data?

Scagliarini: Big data refers to the frequent circumstance in which organisations have massive volumes of data available. However, in the real world and for many procedures, such as the one mentioned above, the data accessible or complying with privacy concerns is insufficient to train a language model using pure ML efficiently.

Instead, you may solve these restrictions with hybrid NL and achieve great benefit with a small quantity of data. This method is valuable because it can be used to a wide range of language-based corporate use cases.