Page 15 – Advance Aii

Datumbox Machine Learning Framework 0.6.0 Released

May 4, 2015 Vasilis Vryniotis . No comments The new version of Datumbox Machine Learning Framework has been released! Download it now from Github or Maven Central Repository. What is new? The main focus of version 0.6.0 is to extend the Framework to handle Large Data, improve the code architecture and the public APIs, simplify data parsing, enhance the documentation and move to a permissive license. Let’s see in detail the changes of this version: Handle Large Data: The improved memory management and the new persistence storage engines enabled the framework to handle big datasets of several GB in size. Adding support of the MapDB database engine enables the framework to avoid storing all the data in memory and thus be able to handle large data. The default InMemory engine is redesigned to be more efficient while the MongoDB engine was removed due to performance issues. Improved and simplified Framework architecture: The level of abstraction is significantly reduced and several core components are redesigned. In particular the persistence storage mechanisms are rewritten and several unnecessary features and data structures are removed. New “Scikit-Learn-like” public APIs: All the public methods of the algorithms are changed to resemble Python’s Scikit-Learn APIs (the...

Lobe.ai just released for open beta and the short story is that you should go try it out. I was lucky and got to test it in the closed beta so I figured i should review a short review. Making AI more understandable and accessible for most people is something I spend a lot of time on and Lobe is without a doubt right down my alley. The tagline is “machine learning made simple” and that is exactly what they do. Overall great tool and I see it as an actual advance in the AI technology by making AI and deep learning models even more accessible than the AutoML wave is already doing. So what is Lobe.ai exactly? Lobe.ai is an Automl tool. That means that you can make AI without coding. In Lobe’s case they work with image classification only. So in short you give Lobe a set of images with labels and Lobe will automatically find the most optimal model to classify the images. Lobe is also acquired by Microsoft. I think that’s a pretty smart move by Microsoft. The big clouds can be difficult to get started with and especially Microsoft’s current AutoML solutions is first of...

object(WP_Post)#8426 (24) { [“ID”]=> int(40160) [“post_author”]=> string(2) “36” [“post_date”]=> string(19) “2025-05-26 02:54:37” [“post_date_gmt”]=> string(19) “2025-05-26 02:54:37” [“post_content”]=> string(3706) “ Across the communications landscape, teams are being asked to do more with less, while staying aligned, responsive and compliant in the face of complex and often shifting stakeholder demands. In that environment, how we track, report and manage our relationships really matters. In too many organisations, relationship management is still built around tools designed for customer sales. CRM systems, built for structured pipelines and linear user journeys, have long been the default for managing contact databases. They work well for sales and customer service functions. But for communications professionals managing journalists, political offices, internal leaders and external advocates, these tools often fall short. Stakeholder relationships don’t follow a straight line. They change depending on context, shaped by policy shifts, public sentiment, media narratives or crisis response. A stakeholder may be supportive one week and critical the next. They often hold more than one role, and their influence doesn’t fit neatly into a funnel or metric. Managing these relationships requires more than contact management. It requires context. The ability to see not just who you spoke to, but why, and what happened...

January 2, 2016 Vasilis Vryniotis . No comments The new version of Datumbox Machine Learning Framework has been released! Download it now from Github or Maven Central Repository. What is new? The main focus of version 0.6.1 is to resolve various bugs, reduce memory consumption and improve speed. Let’s see in detail the changes of this version: Bug Fixes: A minor issue related to Unreleased Resources has been detected and fixed on the Tests of Dataset class. Also a memory leak was detected and patched on the AutoCloseConnector class. Improved Memory Footprint: The shutdown hooks are now removed when close() is called; this improves memory usage. Also the TypeInference class has been updated to reduce memory consumption. Speed: The TextClassifier class has been refactored and few speed improvements have been released. Staying Up-to-date: All dependencies and maven plugins used in the project have been updated to the latest stable versions. Few more details on the most important dependencies: The framework now uses MapDB 1.0.8 and it is within my plans to move to MapDB 2.0 once a stable version is released. Moreover I created a Mavenized version of LIBSVM; we currently use the most updated version which is the 3.21....

If there’s one thing that characterizes driving in any major city, it’s the constant stop-and-go as traffic lights change and as cars and trucks merge and separate and turn and park. This constant stopping and starting is extremely inefficient, driving up the amount of pollution, including greenhouse gases, that gets emitted per mile of driving. One approach to counter this is known as eco-driving, which can be installed as a control system in autonomous vehicles to improve their efficiency. How much of a difference could that make? Would the impact of such systems in reducing emissions be worth the investment in the technology? Addressing such questions is one of a broad category of optimization problems that have been difficult for researchers to address, and it has been difficult to test the solutions they come up with. These are problems that involve many different agents, such as the many different kinds of vehicles in a city, and different factors that influence their emissions, including speed, weather, road conditions, and traffic light timing. “We got interested a few years ago in the question: Is there something that automated vehicles could do here in terms of mitigating emissions?” says Cathy Wu, the Thomas...

Companies that want to keep up with market developments require well-organised metadata at a very granular level. They need to embrace automated labelling to be ready for the future. But labels and metadata are used at various levels, which means it’s not easy to see the forest for the trees. In this blog series, we focus on content metadata. Now that we’ve discussed the CEFR, keyword extraction, and topic classification, let’s have a look at learning objectives tagging. A brief explanation In our view, learning objectives tagging is a form of topic classification. But it has a specific taxonomy: a curriculum, or a structured set of learning goals. As you can tackle labelling at different levels, you’re dealing with a hierarchical taxonomy. For example, you can label subjects, but you can also take it one step further and label the topics that fall under certain subjects. Why learning objectives tagging is useful People tend to learn at their own pace, which means every student is at a specific point in the curriculum. In the past, fast learners had to wait weeks or months for their peers to catch up. But learning objectives tagging has paved the way for personalising teaching...

A few months ago, I spoke to a solo estate planner based in Denver. Her caseload had doubled in the past year—but her headcount hadn’t. “I can’t afford to hire another full-time paralegal,” she said, “but I also can’t keep working weekends.” That tension—between growth and capacity—is something I’ve heard from dozens of small firms. And that’s where automation changes the game. David vs Goliath, Rewritten For decades, big firms held the upper hand: deep pockets, large teams, and custom-built systems. But automation is rewriting that script—and small firms are finally getting the tools to fight back. Speed of service: Firms using document automation report up to 83% faster drafting times (Lawyaw.com). That means what took hours now takes minutes, giving small firms a serious edge in responsiveness. Fewer errors: Automated templates reduce manual input, helping firms cut drafting errors by 30% (RocketMatter.com). That’s more accuracy, fewer revisions, and less back-and-forth with clients. Better margins: Efficiency gains from automation drive profitability. Firms using end-to-end practice software report profit margins 20–30% higher than average—and some even hit 60% margins (Clio.com). Scalability: One small firm scaled revenue by 1,400% in just one year by aggressively automating their workflows...

Most companies find that the biggest challenge to AI is taking a promising experiment, demo, or proof-of-concept and bringing it to market. McKinsey Digital Analyst Rodney Zemmel sums this up: It’s “so easy to fire up a pilot that you can get stuck in this ‘death by 1,000 pilots’ approach.” It’s easy to see AI’s potential, come up with some ideas, and spin up dozens (if not thousands) of pilot projects. However, the issue isn’t just the number of pilots; it’s also the difficulty of getting a pilot into production, something called “proof of concept purgatory” by Hugo Bowne-Anderson, and also discussed by Chip Huyen, Hamel Husain, and many other O’Reilly authors. Our work focuses on the challenges that come with bringing PoCs to production, such as scaling AI infrastructure, improving AI system reliability, and producing business value. Bringing products to production includes keeping them up to date with the newest technologies for building agentic AI systems, RAG, GraphRAG, and MCP. We’re also following the development of reasoning models such as DeepSeek R1, Alibaba’s QwQ, Open AI’s 4o1 and 4o3, Google’s Gemini 2, and a growing number of other models. These models increase their accuracy by planning how to solve...

In the ever-evolving landscape of e-commerce, staying ahead of the curve often means adopting the latest technologies to engage and attract customers. One such innovation making waves in the industry is the use of generative video AI models. We’ve had the opportunity to explore Hedra’s generative video AI to create interesting video mockups for an online store. Here’s a look at our journey, the results, and some key insights into using Hedra effectively. Why Video Mockups Matter Video content has become a cornerstone of modern digital marketing. It captures attention, conveys information quickly, and can significantly boost conversion rates. For e-commerce, video mockups can showcase products in dynamic ways, helping customers visualize their use and benefits more vividly. This is where Hedra comes into play, offering an advanced AI model that can generate high-quality video content tailored to specific needs. Our Experience with Hedra Setting Up the Project Our goal was to create engaging video mockups that highlight our online store’s products in action. Hedra’s generative video AI provided a perfect platform for this task. The setup process was straightforward: 1. Upload Product Images: We started by uploading high-quality images of the products we wanted to feature. 2. Select a...

I am co-organizing an intimately small in-person event where we’ll dive into old-school, in-person, thought-provoking discussions about shaping our future. It will be held on January 22nd, 2025, in Toronto’s South Etobicoke area. Please join us for an evening that includes insightful talks, interactive Q&A with the speakers, delicious bites, and networking opportunities. This in-person event is your chance to connect with like-minded individuals and explore the possibilities that lie ahead. Conversations with the Future: Visions & Destinations, Evening Symposium Welcome to an engaging event where we’ll dive into thought-provoking discussions about the future. Join us for an evening that includes insightful talks, interactive Q&A with the speakers, delicious bites, and networking opportunities. This in-person event is your chance to connect with like-minded individuals and explore the possibilities that lie ahead. Don’t miss out on this unique opportunity to be part of shaping the future together! Agenda-At-A-Glance: Arrival & Networking: 6:15 PM Light snacks & bottled water will be provided. Vegan option available. Program Start Time: 7:00 PM sharp Individual Speaker Sessions (20-minutes each) Eric Boyd: “Prefiguring A Future Where Humans, Technology, & Nature All Get Along” Nikola Danaylov: “Our Future is Framed: Unleashing the Power of the Context Effect” Maggie Greyson: “All...