Altilia is recognized as Major Player in 2023-2024 IDC MarketScape Worldwide Intelligent Document Processing Vendor Assessment

The lexicon of Artificial Intelligence – and how it can transform your organization

By altilia on April 28, 2023

The emergence of ChatGPT in recent months has prompted an unprecedented explosion of public hype and interest in Artificial Intelligence and its potential future uses.

Following industry luminaries such as Bill Gates describing AI as the most important technological advance in decades, there has been a torrent of predictions on how it will change the world of work.

Here at Altilia we have been building an AI-based platform, Altilia Intelligent Automation, that is at the forefront of a new revolution based around Intelligent Document Processing (IDP).

It is clear, however, that industry leaders anxious to understand this new phenomenon, are overwhelmed by the language, acronyms and blizzard of nascent technologies which leave them baffled and unsure on how to get started.

Our aim over a series of blogs is to unpack the lexicon of AI and explain what it all means, a useful, insightful and simplified guide for organizations interested in how AI will affect them.

What is Artificial Intelligence?

The term artificial intelligence (AI) is used loosely to refer to applications that perform complex tasks that previously required human intervention, such as communicating with customers online or playing chess.

The term is often used interchangeably with the terms machine learning (ML) and deep learning, although they (as we will discover) have different meanings.

Many companies are investing significantly in data science teams to take full advantage of AI, combining statistics, computer science and business knowledge to extract value from various data sources and enable problem-solving. Algorithms seek to create expert systems which make predictions or classifications based on input data.

Advanced functions can include the ability to see, understand and translate spoken and written language, analyze data, make recommendations and classify both structured and unstructured data.

Computers and machines are developed with the ability to reason, learn and act in a way that would normally require human intelligence, often at a scale that exceeds or speeds up what humans can deal with.

Benefits of AI:

  • Automation. AI can automate workflows and processes or work independently and autonomously from a human team.
  • Reduce human error. AI can eliminate manual errors in data processing, analytics, assembly in manufacturing, and other tasks through automation and algorithms that follow the same processes every single time.
  • Eliminate repetitive tasks. AI can be used to perform repetitive tasks, freeing human capital to work on higher impact problems.
  • Fast and accurate. AI can process more information more quickly than a human, finding patterns and discovering relationships in data that a human may miss.
  • Accelerated research and development. The ability to analyze vast amounts of data quickly can lead to accelerated breakthroughs in research and development.

Altilia are world experts in Intelligent Document Processing (IDP), so let’s have a look at how it builds on AI capabilities and developments to enhance organizational efficiency and accuracy.

What is Intelligent Document Processing?

At its most simplistic level, Intelligent Document Processing (IDP) converts unstructured and semi-structured data into structured usable information, thus enabling layers of automation to document-centric business processes.

As an example, many mortgage forms may be filled in with (unstructured) hand-written answers by an applicant, which would need human intervention to input that information into a financial services company’s systems.

IDP uses AI (and other technologies) to extract that information in a usable form, thus reducing time and manual labor.

Which AI fields are relevant for IDP?

A human reading and understanding a document needs to:

  • Have visual perception to recognize images, symbols and writing
  • Have a comprehensive understanding of the document’s language
  • Be able to understand new information and learn concepts
  • Be able to memorize concepts and the relations between them.

AI, therefore, needs to emulate these abilities to be capable of reading like a human – and for IDP, these are the most relevant application fields:

  • Computer Vision is the AI field that emulates human visual perception
  • Natural Language Processing (NLP) focuses on developing algorithms for general understanding of a language
  • Machine Learning (ML) is focused on training a machine to learn and conceptualise information as models
  • Knowledge Representation is focused on representing information in a way that is functional, not just to memorize the content, but relations between them.

We will continue this series explaining the lexicon of AI in the coming weeks. For more information on how Altilia can support your business, schedule a demo here .

By altilia on April 28, 2023

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Altilia is recognized as Major Player in the 2023-2024 IDC MarketScape Worldwide Intelligent Document Processing Vendor Assessment

Altilia, as a leading innovator in the field of Intelligent Document Processing (IDP), is proud to announce it has been recognized as a Major Player in the IDC MarketScape: Worldwide Intelligent Document Processing Software 2023–2024 Vendor Assessment (doc # US49988723, November 2023). We believe this acknowledgment represents yet another milestone for Altilia, reaffirming its position as a leader in the ever-evolving landscape of Intelligent Document Processing technology. With a dedicated team of over 50 highly experienced AI professionals, including scientists, researchers, and software engineers, Altilia aims to democratize the use of AI to help enterprises automate document-intensive business processes. As we celebrate this recognition from the IDC MarketScape, Altilia will continue its efforts to shape the future of document processing, bringing cutting-edge solutions to the forefront of the IDP market, and offering organizations unparalleled efficiency, automation, and knowledge management capabilities. About IDC MarketScape: IDC MarketScape vendor assessment model is designed to provide an overview of the competitive fitness of ICT (information and communications technology) suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each vendor’s position within a given market. IDC MarketScape provides a clear framework in which the product and service offerings, capabilities and strategies, and current and future market success factors of IT and telecommunications vendors can be meaningfully compared. The framework also provides technology buyers with a 360-degree assessment of the strengths and weaknesses of current and prospective vendors.

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How the technology behind Chat GPT can work for your organization

The explosion of interest and publicity in Artificial Intelligence in recent months has come from the advent of Large Language Models, specifically OpenAI’s ChatGPT, which set the record for the fastest-growing user base in January. Suddenly it seems like everyone is fascinated by the coming surge of AI with new applications, creating excitement and fear for the future. When Google’s so-called “Godfather of AI” Dr Geoffrey Hinton warned about “quite scary” dangers, it made headlines around the world. Behind the hype So, it is important to understand what is behind the hype and see how it works and what your organization can use to build future value. This blog is split into two: first we learn about Natural Language Processing, the branch of computer science concerned with giving machines the ability to understand text and spoken words in much the same way humans can. And then we will go deeper on Large Language Models (LLMs), which is what ChatGPT and others like Google’s Bard are using. NLP combines computational linguistics with statistical, machine learning, and deep learning models to enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes. There are two sub-fields of NLP: Natural Language Understanding (NLU) uses syntactic and semantic analysis of text and speech to determine the meaning of a sentence, similarly to how humans do it naturally. Altilia uses Large Language Models for this. Natural Language Generation (NLG) enables computers to write a human language text response based on data input. ChatGPT uses LLMs for NLG. Large Language Models (LLMs) LLMs are a relatively new approach where massive amounts of text are fed into the AI algorithm using unsupervised learning to create a “foundation” model, which can use transfer learning to continually learn new tasks. The key is using huge volumes of data. The training data for ChatGPT comes from a diverse set of text sources, including billions of web pages from the internet, a huge number of books from different genres, articles from news websites, magazines and academic journals and social media platforms such as Twitter, Reddit and Facebook to learn about informal language and the nuances of social interactions. The model is then able to predict the next word in a sentence and generate coherent text in a wide range of language tasks. Altilia does exactly the same, but uses this capability to provide enterprise tools for specific business use cases. Technology breakthrough Overall, NLP is the core technology to understand the content of documents. LLMs are a breakthrough in the field as they allow a shift from where an NLP model had to be trained in silos for a specific task to one where LLMs can leverage accumulated knowledge with transfer learning. In practice, this means we can apply a pre-trained LLM and fine-tune it with a relatively small dataset to allow the model to learn new customer-specific or use-case specific tasks. We are then able to scale up more effectively, it can be applied more easily for different use cases, leading to a higher ROI. For more information on how Altilia Intelligent Automation can support your organization to see radical improvements in accuracy and efficiency, schedule a demo here.

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Leveraging GPT and Large Language Models to enhance Intelligent Document Processing

The rise of Artificial Intelligence has been the talk of the business world since the emergence of ChatGPT earlier this year. Now executives around the world find themselves in need of understanding the importance and power of Large Language Models in delivering potentially ground-breaking use cases that can bring greater efficiency and accuracy to mundane tasks. Natural Language Generation (NLG) enables computers to write a human language text response based on human generated prompts. What few understand is that there is still a deep flaw in the ChatGPT technology: up to 20-30% of all results have inaccuracies, according to Gartner. What Gartner have found is that ChatGPT is “susceptible to hallucinations and sometimes provides incorrect answers to prompts. It also reflects the deficiencies of its training corpus, which can lead to biased or inappropriate responses as well as algorithmic bias.” To better understand this, it’s key to consider how LLMs work: hundreds of billions of pieces of training data are fed into the model, enabling it to learn patterns, associations, and linguistic structures. This massive amount of data allows the model to capture a wide range of language patterns and generate responses based on its learned knowledge. However, as vast training data can be, the model can only generate responses as reliable as the information it has been exposed to. If it encounters a question or topic that falls outside the training data or knowledge cutoff, responses may be incomplete or inaccurate. For this reason, and to better understand how best to use LLMs in enterprise environments, Gartner outlined a set of AI Design Patterns and ranked them by difficulty of each implementation. We are delighted to share that Altilia Intelligent Automation already implements in its platform two of the most complex design patterns: LLM with Document Retrieval or Search This provides the potential to link LLMs with internal document databases, unlocking key insights from internal data with LLM capabilities This provides much more accurate and relevant information, reducing the potential for inaccuracies due to the ability to the use of retrieval. Fine-tuning LLM The LLM foundation model is fine-tuned using transfer learning with an enterprise’s own documents or particular training dataset, which updates the underlying LLM parameters. LLMs can then be customized to specific use cases, providing bespoke results and improved accuracy. So, while the business and technology world has been getting excited by the emergence of ChatGPT and LLMs, Altilia has already been providing tools to enterprises to leverage these generative AI models to their full potential. And by doing so, thanks to its model’s fine-tuning capabilities, we are able to overcome the main limitation of a system like OpenAI’s ChatGPT, which is the lack of accuracy of its answers. For more information on how Altilia Intelligent Automation can help your organization, schedule a free demo here.

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