How Machine Learning works – and what it means for your organization

By altilia on May 8, 2023

In our second blog of this series, where we unlock the lexicon of Artificial Intelligence for business leaders currently being overwhelmed by the hype of ChatGPT, we will focus on Machine Learning (ML).

What is Machine Learning?

People throw the terms machine learning and AI together and interchangeably, but they don’t mean the same thing. ML is a subset of AI that uses computers to learn or improve performance based on the data they use.

It’s a fascinating concept, straight out of science fiction: a computer uses algorithms to learn from the data provided. The more it develops, the more it learns: the more data it is fed, the better it gets.

It is where the concerns come that computers can become “more intelligent” than their human masters.

The reason ML has become more successful and prominent in the past decade, is the growth in volume, variety and quality of both public and privately-owned data, the availability of cheaper and more powerful data processing and storage capabilities.

Essentially ML models look for patterns in data and draw conclusions, which is then applied to new sets of data. They are not explicitly directed by people, as the machine learning capabilities develop from the data provided, particularly with large data sets. The more data used, the better the results will be.

So, where AI is the umbrella concept of enabling a machine to sense, reason or act like a human, ML is an AI application that allows computers to extract knowledge from data and learn from it autonomously.

How to train ML models

The key to machine learning (as much else in life) is training. ML computers need to be trained with new data and algorithms to obtain results.

Three training models are used in machine learning:

  • Supervised learning maps in a specific input to an output using labelled/structured training data. Simply, to train the algorithm to recognize pictures of cats, it feeds it labelled pictures of cats.
  • Unsupervised learning is based on unstructured (unlabelled) data, so that the end result is not known in advance. This is good for pattern matching and descriptive modelling. For example, Altilia uses Large Language Models (LLMs) as its foundation, which are trained on huge datasets using unsupervised learning.
  • Reinforcement learning can be described as “learn by doing”. An “agent” learns to perform a task by feedback loop trial and error until it performs within the desired range, receiving positive and negative reinforcement depending on its success. Altilia often uses Human-in-the-Loop (HITL) reinforced learning in its Altilia Review module.
  • Transfer learning enables data scientists to benefit from knowledge gained from a previous model for a similar task, in the same way that humans can transfer their knowledge on one topic to a similar one. It can shorten ML training time and rely on fewer data points. Altilia uses this technique to fine-tune pre-trained Large Language Models (LLMs) on a dataset provided by the client. We will focus on LLMs in a future blog.

Why not schedule a demo with Altilia to learn more about how we can help transform your organization? Click here to register. 

By altilia on May 8, 2023

Explore more stories like this one

Unlocking the Power of Unstructured Data: How AI is Revolutionizing Business Intelligence

In the ever-evolving landscape of digital business, data has become the new currency. Yet, not all data is created equal. While structured data has long been the cornerstone of business analytics, an ocean of unstructured data remains largely hidden. This is where Artificial Intelligence (AI) is making a difference, offering new ways to extract value from this digital goldmine. The Unstructured Data Difficulty Imagine for a moment the volume of information that flows through a modern enterprise: countless emails, social media interactions, customer service calls, images, videos, and documents. This is the realm of unstructured data, and it's growing at an astronomical rate. According to Gartner, a staggering 80% to 90% of data generated and collected by organizations is unstructured, with its volume expanding many times faster than structured data. The challenge lies not just in the volume, but in the nature of this data. Unlike structured data that neatly fits into predefined database fields, unstructured data is a mix of formats and sources. It's the difference between a meticulously organized filing cabinet and a room full of scattered papers, photos, and recordings. This lack of inherent organization has historically made unstructured data a huge challenge for traditional analytics tools. Enter Artificial Intelligence This is where AI emerges as a game-changer. Advanced machine learning algorithms and natural language processing capabilities are enabling businesses to sift through vast quantities of unstructured data, uncovering patterns, insights, and actionable information that were previously hidden from view. Altilia is pioneering this field, offering solutions that transform how businesses handle unstructured data. Altilia's platform represents a leap forward in our ability to extract meaningful information from unstructured documents. It's not just about converting text to digital format; Altilia’s IDP solution can understand context, categorize information, and even make inferences based on the content they process. Solving the Unstructured Data problem Altilia's platform combines various AI technologies to tackle complex document workflows. This innovative approach allows businesses to automate the ingestion and analysis of various document types, extracting relevant information and even flagging potential issues or inconsistencies. What sets Altilia apart is its focus on accessibility and continuous improvement. Their no-code platform democratizes AI technology, making it accessible to users without technical backgrounds. Furthermore, their “human-in-the-loop” continuous learning ensures that the system keeps improving over time, adapting to new document types and evolving business needs. The Power of IDP in Action Imagine a banking institution processing loan applications. Traditionally, this would involve manual review of numerous documents, a time-consuming and subject to errors process. With Altilia's IDP solution, the bank can automate this process, handling a wide range of document formats and integrating seamlessly with existing enterprise systems. This not only speeds up the process but also enhances accuracy and compliance. But the applications go far beyond banking. In the legal sector, Altilia's AI can filter through vast databases of case law, identifying relevant precedents. In customer service, it can analyze call transcripts and chat logs to identify common issues and measure customer sentiment. The possibilities are virtually endless. The Business Impact The impact of these capabilities on business operations and decision-making cannot be overstated. By leveraging Altilia's IDP platform, businesses can: Enhance Efficiency: Automating complex document workflows dramatically reduces manual labor and accelerates decision-making processes. Improve Accuracy: AI-powered document processing minimizes human error, ensuring more reliable data extraction and analysis. Scale Operations: Altilia's platform can handle large volumes of documents efficiently, allowing businesses to scale their operations without proportionally increasing costs. Drive Innovation: By uncovering hidden patterns and correlations in unstructured data, businesses can spark new ideas for products, services, or process improvements. The Road Ahead While the potential of AI in managing unstructured data is immense, implementation requires careful consideration. This is where Altilia's ethical AI practices come into play, ensuring that businesses can harness the power of AI responsibly and sustainably. As we stand on the brink of a new era in data analytics, one thing is clear: the ability to effectively harness unstructured data will be a key differentiator for businesses in the coming years. Altilia's IDP platform is not just a tool for efficiency; it's a gateway to a new world of business intelligence. Conclusion The organizations that can successfully navigate this new landscape - leveraging AI to turn the chaos of unstructured data into a wellspring of actionable insights - will be well-positioned to lead in their respective industries. As the volume and variety of unstructured data continue to grow, so too will the importance of advanced IDP solutions like Altilia's. The future of business intelligence is here, and it's powered by AI. With Altilia's innovative approach to IDP, businesses have a powerful ally in their quest to unlock the full potential of their unstructured data. By embracing these technologies, companies can not only keep pace with the data revolution but stay ahead of the curve, turning information into insight, and insight into action.

Read more
Altilia Recognized as Major Player in Unstructured Intelligent Document Processing Software by IDC

Altilia Recognized as Major Player in Unstructured Intelligent Document Processing Software by IDC

We are thrilled to announce our recognition as a Major Player in the IDC MarketScape for Unstructured Intelligent Document Processing (IDP) software  This recognition highlights our software's ability to streamline business operations by effectively managing complex unstructured document workflows, particularly in sectors like finance and public administration. From the beginning, we have focused on enhancing organizational knowledge management with a solution that processes both structured and unstructured data, acting as a co-assistant to employees. Our Human-in-the-Loop (HITL) AI approach ensures continuous model training and transparency, providing highly accurate and reliable results. With a dedicated team of over 50 professionals, including scientists, researchers, and software engineers, we are committed to democratizing the use of AI to help enterprises automate document-intensive business processes. This acknowledgment by IDC MarketScape reaffirms our position as a leader in the ever-evolving landscape of Intelligent Document Processing technology. Our platform is designed for quick deployment and intuitive workflow design, making it suitable for organizations of all sizes and across various industry verticals looking to implement unstructured IDP.  As we celebrate this recognition, we remain dedicated to shaping the future of document processing by bringing cutting-edge solutions to the forefront of the IDP market. Our goal is to offer organizations unparalleled efficiency, automation, and knowledge management capabilities.   What is Unstructured IDP ? Unstructured Intelligent Document Processing (IDP) refers to a class of software technologies that leverage a combination of traditional and generative AI (GenAI), advanced analytics, and business rules to automate the classification, extraction, analysis, and validation of data from unstructured, semi-structured, and structured document formats. These technologies are designed to handle the high variability, inconsistent formats, and mixed elements (e.g., text, tables, charts) characteristic of unstructured documents, making the data within these documents actionable and integrated into business workflows.   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.

Read more
AI implications on the workplace | Altilia

AI implications on the workplace

Introduction Artificial Intelligence (AI) is no longer a futuristic concept; it’s a present reality reshaping the modern workplace. Generative AI (GenAI), in particular, holds the promise of augmenting human work by automating routine tasks and enabling employees to focus on more impactful activities. This transformation is supported by recent research from Deloitte, which highlights the profound impact AI can have on workforce dynamics. The Role of GenAI in the Workplace Generative AI (GenAI) refers to advanced algorithms capable of creating content, analyzing data, and automating repetitive tasks. Unlike traditional AI, which makes predictions based on data to perform specific tasks and rules-based decisions, GenAI can generate new content and insights by learning patterns from data, making it a more versatile tool in a wider range of industries and use case applications. Current Applications In recent years, artificial intelligence has evolved into a suite of powerful technologies that offer significant competitive advantages to businesses across various industries. As companies rapidly adopt AI to meet their business objectives and stay ahead of competitors, many are uncertain about the outcomes and the level of acceptance these tools will receive from their employees. While employers are enthusiastic about the opportunities AI presents, the potential impacts on employee experience and trust cannot be overlooked. Benefits of GenAI for Employees Increased Efficiency One of the primary benefits of GenAI is its ability to streamline processes, reducing the time employees spend on repetitive tasks. This increased efficiency allows businesses to achieve more in less time, freeing up resources for strategic initiatives. Focus on High-Impact Tasks With GenAI handling routine tasks, employees can dedicate more time to high-impact activities such as strategic planning, creative problem-solving, and innovation. For instance, customer service representatives can focus on complex queries that require a human touch, while automated systems handle routine inquiries. By automating routine activities such as data entry, report generation, and basic customer service inquiries, GenAI allows businesses to operate more efficiently. GenAI not only accelerates workflows but also minimizes human error, ensuring more consistent and reliable outcomes Case Studies HSBC has leveraged AI to identify potential money laundering activities by analyzing transactional patterns, customer behavior, and risk indicators. This AI-driven approach has enabled the bank to flag suspicious transactions more effectively, reducing the number of alerts that require investigation by over 60% Bank of America has utilized AI to forecast the likelihood of companies defaulting by analyzing diverse data sources, including financial statements, credit histories, and market trends. This AI-powered model has improved the accuracy of lending decisions and enhanced the bank's ability to manage credit risk effectively Trade-offs in the Use of GenAI A recent report of Deloitte points out that while the benefits of GenAI are significant, implementing these systems is not without challenges. Organizations must invest in the necessary infrastructure, train employees to work with AI systems, and manage the change process effectively. 1 - Creative Inspiration vs. Diligence As human beings, we value original thinking to solve problems, to understand each other better, and ultimately to improve our society. Without new ideas, we would settle for the status quo and abandon a core part of our human identity: the pursuit of progress.  AI as an 'idea sparker' could enable employees to create multiple versions of their work in parallel and uncover perspectives they may not have thought of themselves. In the Deloitte’s report, over 69% of executives said they believe AI will improve employee creativity to some extent, with AI sparking new ideas and inspiration that will improve the quality of work. However, there is also a fear that an over-reliance on AI will sacrifice accuracy and thoroughness, with 42% of conversations citing concerns about a decline in work quality. From this place of uncertainty, leaders have an opportunity to redefine creativity in the workplace while maintaining human rigor by setting boundaries for the use of AI. At Altilia, we agree GenAI can be a catalyst to spark new ideas. However, we believe a potential decline in work quality can only be a concern if AI is seen as a replacement for human effort. In our approach, GenAI is meant to complement and assist human work, not to replace it. Successful GenAI implementations should foster a "collaborative" approach, where AI generates intermediate inputs that are meant to be verified, expanded and enhanced by human experts. When used correctly, Generative AI organizes data and information, enabling humans to make informed decisions more efficiently. This partnership enhances work quality and fosters an environment where creativity and diligence thrive. 2 - Efficiency vs. Inclusivity Businesses are eager to leverage AI to expedite routine tasks and remove the administrative burden for their employees. While leaders are optimistic about efficiency gains, nearly a third of conversations cited in the report concern the bias and inclusion challenges of AI, suggesting that the risk of further embedding systemic bias tempers their excitement. Biases emerge wherever humans go. Unchecked, our narrow reference of personal experience will build unconscious bias into everything we create. Given AI’s pace of evolution and “black box” decisioning processes, leaders are concerned that AI will entrench existing biases with no opportunity to reroute: To mitigate the risk of bias, Businesses should focus on empowering employees to effectively use AI and identify bias while also promoting open dialogue on how AI is used within their organizations. Secondly, there is a need for transparency and trustworthiness in the AI output. This can be translated in the concept of "explainability"—the ability for end users to understand how and why an AI model generated a particular response. Explainability enables the implementation of human oversight mechanisms to verify the correctness and impartiality of AI outputs. Therefore, ensuring explainability should be a primary driver in the design of GenAI-based applications. In the context of Intelligent Document Processing, where Altilia operates, we achieve explainability by allowing users to easily trace back to the sources of information underpinning the AI model's responses. This approach ensures that users can trust the outputs and maintain control over the decision-making process, ultimately enhancing both the reliability and the acceptance of GenAI solutions in the workplace. 3 - Personalization vs. Data Privacy GenAI enables highly personalized experiences for customers and employees by analyzing vast amounts of data to tailor interactions and recommendations. However, this level of personalization often requires extensive data collection, raising concerns about data privacy and the potential misuse of sensitive information.  At Altilia, we prioritize data privacy by ensuring our AI models operate within a closed and protected environment. This means that the data used is fully compliant with data processing regulations, as it is not accessed or shared through third-party APIs. Our approach guarantees that both input and output data remain within the company's control, avoiding the risk of customer data being processed by external entities (for example 3rd API-based GenAI services like OpenAI.) This closed environment enhances compliance with various data protection regulations and ensures that only company-generated data is used. By relying solely on internal data, our models deliver more accurate and consistent responses, further strengthening data security and regulatory adherence. Future Outlook  The workplace implications of AI are profound, offering both opportunities and challenges. GenAI has the potential to enhance productivity, allowing employees to concentrate on high-impact tasks. According to the Deloitte report, AI integration in the workplace will continue to grow, with more businesses adopting GenAI to stay competitive. This trend will likely lead to new job roles focused on managing and enhancing AI systems. At Altilia, we believe that successful AI applications in the near future will not only focus on efficiency and process automation but also on enriching companies' informational assets and stimulating creativity. We see GenAI as an assistant that increasingly supports knowledge workers by taking on more operational roles, such as information reprocessing and synthesis. Meanwhile, human knowledge workers will remain at the forefront of decision-making and strategic activities. This collaborative relationship will help companies build greater trust and exercise more control over AI-enhanced processes, while also making it easier for the workforce to accept GenAI as an opportunity rather than a threat. By positioning AI as a supportive tool, we can foster an environment where AI drives innovation and efficiency, ultimately leading to more dynamic and successful business operations. If you’re eager to find a solution for your business to streamline processes, reduce the time spent on tasks, and drive growth, we’re here to guide you through every step. Book a free consultation with an Altilia expert today, and let’s embark on this journey together, unlocking the potential of GenAI to bring your business into the future.

Read more