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Forbes
07-04-2025
- Business
- Forbes
Why Data Curation Is The Key To Enterprise AI
Nick Burling, Senior Vice President of Product at Nasuni. All the enterprise customers and end users I'm talking to these days are dealing with the same challenge. The number of enterprise AI tools is growing rapidly as ChatGPT, Claude and other leading models are challenged by upstarts like DeepSeek. There's no single tool that fits all, and it's dizzying to try to analyze all the solutions and determine which ones are best suited to the particular needs of your company, department or team. What's been lost in the focus on the latest and greatest models is the paramount importance of getting your data ready for these tools in the first place. To get the most out of the AI tools of today and tomorrow, it's important to have a complete view of your file data across your entire organization: the current and historical digital output of every office, studio, factory, warehouse and remote site, involving every one of your employees. Curating and understanding this data will help you deploy AI successfully. The potential of effective data curation is clear in the development of self-driving cars. Robotic vehicles can rapidly identify and distinguish between trees and cars in large part because of a dataset called ImageNet. This collection contains more than 14 million images of common everyday objects that have been labeled by humans. Scientists were able to train object recognition algorithms on this data because it was curated. They knew exactly what they had. Another example is the use of machine learning to identify early signs of cancer in radiological scans. Scientists were able to develop these tools in part because they had high-quality data (radiological images) and a deep understanding of the particulars of each image file. They didn't attempt to develop a tool that analyzed all patient data or all hospital files. They worked with a curated segment of medical data that they understood deeply. Now, imagine you're managing AI adoption and strategy at a civil engineering firm. Your goal is to utilize generative AI (GenAI) to streamline the process of creating proposals. And you've heard everyone in the AI world boasting about how this is a perfect use case. A typical civil engineering firm is going to have an incredibly broad range of files and complex models. Project data is going to be multimodal—a mix of text, video, images and industry-specific files. If you were to ask a standard GenAI tool to scan this data and produce a proposal, the result would be garbage. But let's say all this data was consolidated, curated and understood at a deeper level. Across tens of millions of files, you'd have a sense of which groups own which files, who accesses them often, what file types are involved and more. Assuming you had the appropriate security guardrails in place to protect the data, you could choose a tool specifically tuned for proposals and securely give that tool access to only the relevant files within your organization. Then, you'd have something truly useful that helps your teams generate better, more relevant proposals faster. Even with curation, there can be challenges. Let's say a project manager (PM) overseeing multiple construction sites wants to use a large language model (LLM) to automatically analyze daily inspection reports. At first glance, this would seem to be a perfect use case, as the PM would be working with a very specific set of files. In reality, though, the reports would probably come in different formats, ranging from spreadsheets to PDFs and handwritten notes. The dataset might include checklists or different phrasings representing the same idea. A human would easily recognize this collected data as variations of a site inspection report, but a general-purpose LLM wouldn't have that kind of world or industry knowledge. A tool like this would likely generate inaccurate and confusing results. Yet, having curated and understood this data, the PM would still be in a much better position. They'd recognize early that the complexity and variation in the inspection reports would lead to challenges and save the organization the expense and trouble of investing in an AI tool for this application. The opportunities that could grow out of organization-wide data curation stretch far beyond specific departmental use cases. Because most of your organization's data resides within your security perimeter, no AI model has been trained on those files. You have a completely unique dataset that hasn't yet been mined for insights. You could take the capabilities of the general AI models developed in training on massive, general datasets and (with the right security framework in place) fine-tune them to your organization's unique gold mine of enterprise data. This is already happening at an industry scale. The virtual paralegal Harvey has been fine-tuned on curated legal data, including case law, statutes, contracts, legal briefs and the rest. BioBERT, a model optimized for medical research, was trained on a curated dataset of biomedical texts. The researchers who developed this tool did so because biomedical texts have such a particular or specific language. Whether you want to embark on an ambitious project to create a fine-tuned model or select the right existing tool for a department or project team's needs, it all starts with data curation. In this period of rapid change and model evolution, the one constant is that if you don't know what sort of data you have, you're not going to know how to use it. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Yahoo
03-04-2025
- Science
- Yahoo
The Definition of Transfer learning
Credit - Getty Images This article is published by a partner of TIME. Transfer learning is a machine learning technique that allows a model trained on one task to be repurposed or fine-tuned for a related task, drastically reducing the amount of data and computational resources needed. This method leverages pre-trained models on large datasets to perform well in new, often smaller, domains with limited labeled data. It has become increasingly popular in fields such as natural language processing, computer vision, and speech recognition, where vast amounts of data and time are typically required for training models from scratch. Pre-trained models: In transfer learning, models are initially trained on large datasets, often unrelated to the target task. For example, models like BERT and GPT-4o in natural language processing are pre-trained on diverse text information. Fine-tuning: After training on a large dataset, the model is fine-tuned on a smaller, domain-specific dataset. This involves adjusting the weights of the neural network to optimize performance for the new task. Feature extraction: One key characteristic is that lower layers of a neural network trained on a large dataset capture general features, while higher layers are fine-tuned to specific features related to the target task. Domain adaptation: Transfer learning allows models to adapt to tasks in a different but related domain. For example, a model trained on general images can be fine-tuned to identify specific objects like medical images or satellite imagery. Image classification: A model trained on a large image dataset such as ImageNet can be repurposed for a new, smaller dataset. Natural Language Processing (NLP): In NLP, large models like GPT-4o and BERT are trained on billions of words from the internet. These pre-trained models can then be fine-tuned for specific tasks such as sentiment analysis, question-answering, or text summarization with a much smaller amount of task-specific data. Speech recognition: A speech recognition system trained on a broad dataset can be fine-tuned for recognizing specific accents or dialects in different languages. For example, a general English speech recognition system could be adapted to recognize Australian English or Indian English with limited labeled data. Reduced training time: Since the model has already learned general features from the pre-training phase, the training process for a new task is much faster, often requiring fewer resources and less time. Less data required: Transfer learning allows models to achieve high performance even with a limited amount of labeled data, making it particularly useful in situations where data collection is expensive or time-consuming. Better performance with small datasets: Transfer learning often results in better performance on smaller datasets than training a model from scratch, because the model has already learned a robust representation from the large dataset. Cross-domain applicability: It enables the use of knowledge from one domain (e.g., image recognition) to be applied to another related domain (e.g., medical imaging), enabling a wider range of applications for pre-trained models. Task similarity requirement: Transfer learning works best when the source task (the one used to pre-train the model) is similar to the target task. If the two tasks are very different, transfer learning may not be effective or may even degrade performance. Overfitting risk: When fine-tuning a model on a small dataset, there is a risk of overfitting, where the model becomes too specialized on the limited new data and fails to generalize well to unseen examples. Computational resource requirements for pre-training: Although transfer learning reduces the resources needed for fine-tuning, pre-training large models on vast datasets is still computationally expensive and often requires high-performance hardware such as GPUs or TPUs. Knowledge transfer limitations: Not all knowledge learned from one domain can be transferred effectively to another. For instance, a model trained on natural images may not transfer well to more specialized areas, like recognizing satellite images, where features are quite different. Transfer learning is a powerful technique in machine learning, allowing models to adapt to new tasks efficiently by leveraging pre-trained knowledge. This approach not only reduces the need for large amounts of labeled data but also accelerates the development of AI systems across various domains, from healthcare to NLP. However, it does have its limitations, especially when the source and target tasks are not closely related or when the pre-training phase is highly resource-intensive. Despite these challenges, transfer learning remains one of the most effective methods for improving model performance and accelerating AI research in numerous fields. Copyright © by All Rights Reserved Contact us at letters@
Associated Press
20-03-2025
- Science
- Associated Press
CHM Makes AlexNet Source Code Available to the Public
Mountain View, California, March 20, 2025 (GLOBE NEWSWIRE) -- In partnership with Google, the Computer History Museum (CHM), the leading museum exploring the history of computing and its impact on the human experience, today announced the public release and long-term preservation of the source code for AlexNet, the neural network that kickstarted today's prevailing approach to AI. 'Google is delighted to contribute the source code for the groundbreaking AlexNet work to the Computer History Museum,' said Jeff Dean, chief scientist, Google DeepMind and Google Research. 'This code underlies the landmark paper 'ImageNet Classification with Deep Convolutional Neural Networks,' by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, which revolutionized the field of computer vision and is one of the most cited papers of all time.' For more information about the release of this historic source code, visit CHM's blog post here. By the late 2000s, Hinton's graduate students at the University of Toronto were beginning to use graphics processing units (GPUs) to train neural networks for image recognition tasks, and their success suggested that deep learning could be a solution to general-purpose AI. Sutskever, one of the students, believed that the performance of neural networks would scale with the amount of data available, and the arrival of ImageNet provided the opportunity. Completed in 2009, ImageNet was a dataset of images developed by Stanford professor Fei-Fei Li that was larger than any previous image dataset by several orders of magnitude. In 2011, Sutskever persuaded Krizhevsky, a fellow graduate student, to train a neural network for ImageNet. With Hinton serving as faculty advisor, Krizhevsky did so on a computer with two NVIDIA cards. Over the course of the next year, he continuously refined and retrained the network until it achieved performance superior to its competitors. The network would ultimately be named AlexNet, after Krizhevsky. In describing the AlexNet project, Hinton told CHM, 'Ilya thought we should do it, Alex made it work, and I got the Nobel Prize.' Before AlexNet, very few machine learning researchers used neural networks. After it, almost all of them would. Google eventually acquired the company started by Hinton, Krizhevsky and Sutskever, and a Google team led by David Bieber worked with CHM for five years to secure its release to the public. About CHM Software Source Code The Computer History Museum has the world's most diverse archive of software and related material. The stories of software's origins and impact on the world provide inspiration and lessons for the future to global audiences—including young coders and entrepreneurs. The Museum has released other historic source code such as APPLE II DOS, IBM APL, Apple MacPaint and QuickDraw, Apple Lisa, and Adobe Photoshop. Visit our website to learn more. About CHM The Computer History Museum's mission is to decode technology—the computing past, digital present, and future impact on humanity. From the heart of Silicon Valley, we share insights gleaned from our research, our events, and our incomparable collection of computing artifacts and oral histories to convene, inform, and empower people to shape a better future. Carina Sweet Computer History Museum (650) 810-1059 [email protected]
