Name: How to Reduce the Carbon Footprint of AI?
Start: 2021-03-25T16:00:00Z
End: 2021-03-25T16:00:29.000Z
Location: BrightTALK
Rating: 4.7

Cortical.io delivers highly efficient AI-based solutions that help enterprises unlock the value of unstructured text by leveraging a game-changing approach to Natural Language Understanding (NLU). Cortical.io SemanticPro is an intelligent document processing solution that accurately extracts, analyzes and classifies information based on meaning and builds the basis for document workflow automation. With more than 10 years expertise in implementing NLU solutions in the enterprise, Cortical.io has demonstrated its ability to solve the challenges of language ambiguity and variability across many use cases and verticals for Fortune 500 companies.

In this interview-like webinar, Andrew Sharp, AVP Quoting & Strategy at Unum, will answer questions about working with Cortical.io to automate the review of insurance plans for quoting group benefits, a traditionally labor-intensive and error-prone process that involves more than 100 quote specialists at Unum.

Register for this webinar if you want to learn how Unum, a major insurance carrier in the US, improved both quality and efficiency of the quoting process by implementing SemanticPro for intelligent document processing.

Improving Quoting Speed and Accuracy at Unum

Learn how Cortical.io IDP uses AI to:
 
- Improve efficiencies through automation and review
- Reduce errors and processing costs
- Provide an end-to-end IDP solution
 
using a real-world Insurance intake use case.

Automation? Human in the Loop? A real life example of Intelligent Doc Processing

We will explore some common themes in IDP including challenges of processing documents especially unstructured documents such as complex contracts. 

Dive into how companies are digitizing and automating their IDP document workflows, offer practical tips on adopting and IDP solution for your organization  and finally some real world examples of companies implementing an IDP solution for complex business challenges.

The State of Intelligent Document Processing with Alan Pelz-Sharpe

During this webinar we will discuss how intelligent Document Processing empowers organizations just like yours to streamline workflows, reduce organizational risks, and gain insights that maximize revenues. 

Learn from examples in Insurance & Financial Services, Management Consulting and Shipping & Logistics.

3 real-life business challenges solved with IDP

This webinar will explain, what is Intelligent Document Processing, discuss the importance and benefits of IDP for business leaders, and finally examine a few examples of corporations just like yours that have utilized Intelligent Document Processing to streamline workflows and gain insights that maximize revenues and reduce organizational risks.

The What, Why, How and Who of Intelligent Document Processing

Companies struggle with managing every aspect of their contracts with vendors, partners, and customers. Capturing significant dates and involved parties is a start, but companies need to understand and interpret information hidden within the contracts – clauses that can have a significant impact on a company’s bottom line. Combining contract lifecycle management with artificial intelligence-based contract intelligence can cost-effectively provide the visibility companies need.

Featuring guest speaker Andrew Bartels from Forrester, this expert panel discussion will help you:

> Understand the overall market trends of contract lifecycle management
> Explore the value of contract lifecycle management systems
> Learn more about contract intelligence and the role AI plays
> Discover the benefits of combining contract intelligence & contract lifecycle management

Anyone responsible for managing and processing a company’s contracts, or anyone interested in the impact of digital transformation can benefit from this webinar.

How AI-Based Contract Intelligence Supercharges Contract Lifecycle Management

While Artificial Intelligence (AI) has gained traction with enterprises in other industries, insurance providers have been slower to adopt. In 2016, research by Deloitte noted that less than 2% of insurance companies were currently investing in artificial intelligence technologies. This same research noted that overall funding in AI development was projected to reach $47 billion by 2020.
Today, as AI has become prevalent, more and more insurance companies are looking for advanced solutions that will help them effectively process their repositories of day-to-day paperwork and the unstructured data they contain. Intelligent Document Processing (IDP) platform solutions that leverage AI technology are uniquely positioned to help these companies realize significant gains. 

Listen to this talk to learn more about:
- the challenges of automating document processing 
- advances in natural language processing that overcome these challenges
- 3 example use cases from the insurance industry that can be streamlined with IDP:  quoting, policy compare, submissions & claims

Using AI to Streamline Document Processing in the Insurance Industry

According to Gartner, legal departments will increase their spend on legal technology threefold by 2025. A large share of these investments goes to contract management solutions that centralize all contracts in one single system, while assisting subject matter experts in managing the lifecycle of contracts, compliance requirements and more. 
These systems are more and more leveraging advanced technologies like machine learning and artificial intelligence, which remain obscure to most legal specialists, making it difficult for them to compare the different approaches. 
This question and answer session covers the steps required in the process of evaluating AI-based solutions to automate contract review and analysis:
- Search for training material
- Annotation of training material
- Methods to compare results
- Calculation of precision
- Identification of potential AI-based solutions
- Integration with an existing system

How to Evaluate AI-Based Solutions for Legal Tech

The goal of this panel discussion is to give some initial information to experts from the insurance industry who want to explore Natural Language Understanding (NLU) solutions as a means to improve the operational efficiency of their organization. 

In this panel discussion, four NLU experts talk about:
- the motivations for exploring AI & NLU solutions in the insurance sector
- the typical challenges encountered and examples of use cases
- how NLU can address these challenges
- how a solution is implemented in an enterprise environment.

Application of Natural Language Understanding in the Insurance Industry

More and more companies sitting on large collections of agreements and other documents recognize the importance of embracing automated review and analysis solutions. However, selecting an efficient and reliable tool that adds strategic value to the organization can be a challenge, especially when AI skills are not available.

This webinar goes through the top 5 criteria one needs to assess to make sure a contract analysis tool will improve efficacy and save time and money:

1) Can the solution identify terms and clauses with a high level of accuracy?
2) How easily and quickly can the solution be trained to meet company-specific requirements?
3) Can the solution correctly interpret and classify clauses based on meaning?
4) Does the solution allow for semantic search across documents?
5) Does the solution easily integrate with other systems?

5 Criteria to Evaluate Contract Review & Analysis Solutions

This video explains how an AI approach inspired by neuroscience can automatically handle the complexity of plan review and accurately extract information from a broad range of carrier contracts. The solution has been developed to meet the specific needs of plan review – for example, interpreting provisions, handling multiple products in a single plan, and managing different employee classes.

Key takeaways:
- Why Contract Intelligence can significantly improve the efficacy of operational processes
- How the solution helps reduce quoting efforts and get more timely and accurate quotes
- Which other processes can be transformed within the insurance industry

How to Transform the Insurance Quoting Process

By 2030, information and communication technologies will represent 21% of global energy needs. The environmental costs of the models being built constitute a major challenge of the application of machine learning to natural language. Mainstream approaches focus on very large models that manipulate language by means of statistics and require huge amounts of training data and computing power to get a superficial understanding of language. 

In his talk at AI Global Forum 2020, Francisco Webber, co-founder and CEO of Cortical.io, argues that the future of natural language understanding is to take an efficient approach towards AI leveraging what biology teaches us. By replicating the actual cognitive processes in the brain, Semantic Folding proposes a highly efficient NLU model that understands semantics at a fine-grained level.

Key takeaways:
- Why High Efficiency AI is the path for the future
- Which AI approach delivers orders-of-magnitude improvements in efficiency
- Why one should teach semantics rather than training data-intensive statistical models

High Efficiency AI: The Future of Information & Communication Technology

Contract Intelligence reduces the time and costs needed to review insurance documents. It uses AI-based natural language understanding to accurately search, extract and classify key information. It can be quickly trained by subject matter experts. 

In this video, you will see how Contract Intelligence automatically:
 
- extracts key terms and clauses based on meaning;
- classifies clauses and make inferences;
- deals with complex formatting including tables.

This demonstration also shows examples of extractions from long-term disability plans, as well as how easy it is to train a model by uploading new documents and creating extraction targets.

Online Demo: Contract Intelligence for the Insurance Industry

With all the excitement and hype around Artificial Intelligence, are there examples of it adding value in the enterprise? Michael Azoff, distinguished analyst at Ovum, provides an overview of AI technologies today. He examines the opportunities for Natural Language Understanding (NLU) and position the advantages of Cortical.io’s approach in this space. Steve Levine from Cortical.io highlights its Contract Intelligence solution and the innovative technology behind it. Sascha Demgensky, from PwC Germany describes use cases for Contract Intelligence.

Save Cost and Time Using AI for Contract Intelligence

Semantic Folding is a new paradigm for Natural Language Understanding (NLU) that overcomes the limitations of other Artificial Intelligence (AI) approaches. This video explains how Cortical.io mimics the way the brain processes information to convert text in a numerical representation, called a semantic fingerprint, that enables direct and intuitive text comparisons.

Semantic Folding: a new Model for Natural Language Understanding

Today, enterprises spend significant effort reviewing contracts and other legal documents. This expensive manual process is both tedious and error-prone - leaving companies open to financial and legal risk. Cortical.io Contract Intelligence is a ready-to-use, AI-based solution that provides up to an 80% reduction in the time it takes to extract, review and analyze key information from any type of legal document.

Contract Intelligence – How to Automate Contract Review with AI & NLU

A patented Artificial Intelligence approach to natural language understanding enables Cortical.io Contract Intelligence to quickly extract provisions and values, based on the meaning of words and sentences, rather than relying on keyword matches. This is the best way to extract the information accurately from a broad range of carrier contracts. 
The solution has been developed to meet the specific needs of plan review – for example, interpreting provisions, handling multiple products in a single plan, and managing different employee classes. This greatly reduces the time needed by a quote specialist and increases accuracy, resulting in more timely and higher quality quotes.

Increasing the Efficiency and quality of the Insurance Quoting Process

Training one single AI language model emits more carbon dioxide than 5 cars during their whole lifetime. While the AI community is becoming more and more aware of the sustainability issues of artificial intelligence and of the large training models, there is still no consensus about the best way to solve this problem.

This webinar explores:
   - The reasons behind AI energy-efficiency issues
   - Three approaches that try to solve this problem
      > Quantum computing
      > Hardware acceleration
      > Reverse engineering the brain
   - Semantic Folding, an approach that leverages reverse engineering the brain, and is already implemented in enterprise environments.

How to Reduce the Carbon Footprint of AI?

Artificial Intelligence

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Machine Learning

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How to Reduce the Carbon Footprint of AI?

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