Learn how Altilia is leveraging GPT and Large Language Models to enhance its IDP platform
Altilia
At Crédit Agricole Italia, we’re working with startup Altilia to create new tools that will help us better understand and meet our customers’ needs. Altilia was chosen for this project through the ELIS OPEN ITALY program and will be integrating its platform into our Enterprise Banking ecosystem to improve our services and processes.
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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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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Welcome to our third blog of a series uncovering the key components of Artificial Intelligence to provide greater understanding for business leaders who may currently have FOMO (Fear Of Missing Out) from the blizzard of acronyms and hype. Here, we look at Computer Vision, one of the main applications of AI where computers can be made to gain high-level of understanding from digital images or videos. Critically, Computer Vision is concerned with automatic extraction of data, enabling documents that have handwriting and random layouts to become machine-readable. Huge data volumes Computer Vision needs a lot of data to be able to distinguish and recognize images. In a way, it looks like a jigsaw puzzle where you assemble all the scattered tiles to make an image. Neural networks for CV work on the same principle. Yet the computer does not have the final image, but it is fed hundreds, if not thousands of related images that train it to recognize specific objects. To identify a cat, the computer would not be shown individual elements such as ears, whiskers, tail etc, but millions of pictures of cats so that it can model the features of our feline friends. CV is used for visual surveillance, medical image processing for patient diagnosis and navigation by autonomous vehicles. But in Altilia’s development of Intelligent Document Processing (IDP), CV has several key roles to play. With Optical Character Recognition (OCR) and Intelligent Character Recognition (ICR), we are able to convert scanned documents into machine-readable PDFs and with Handwritten Text Recognition (HTR) are incorporate items such as signatures. End goal The end goal of an IDP solution is to extract meaningful information that are “hidden” in unstructured texts and documents, so we need to first break words down in a way that a machine can understand. This is especially relevant when the documents that need to be processed are (low quality) scans such as contracts, forms, invoices or ID cards. We then need to apply OCR to recognize both printed and handwritten text, using smaller units called tokens. To each token is added metadata, which is useful later in a search engine. In IDP, it is useful to distinguish a photo from text and to tag elements such as signatures, stamps and markings, saving human labor time by automating checks such as whether a contract is signed and marked. Finally, we focus on document layout analysis so that unsorted documents can be classified and then we can apply different machine learning algorithms and branch out different ML pipelines. These core capabilities allow Altilia’s solution to work as a general purpose platform, rather than a point solution for specific document types and formats. We have also developed a patented solution for document layout analysis. For more information on how Altilia Intelligent Automation can help your organization, schedule a free demo here.
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