Text embedding and reranking are foundational to modern information retrieval systems, powering applications such as semantic search, recommendation systems, and retrieval-augmented generation (RAG). However, current approaches often face key challenges—particularly in achieving both high multilingual fidelity and task adaptability without relying on proprietary APIs. Existing models frequently fall short in scenarios requiring nuanced semantic understanding across multiple languages or domain-specific tasks like code retrieval and instruction following. Moreover, most open-source models either lack scale or flexibility, while commercial APIs remain costly and closed. Qwen3-Embedding and Qwen3-Reranker: A New Standard for Open-Source Embedding Alibaba’s Qwen Team has unveiled the Qwen3-Embedding and Qwen3-Reranker Series—models that set a new benchmark in multilingual text embedding and relevance ranking. Built on the Qwen3 foundation models, the series includes variants in 0.6B, 4B, and 8B parameter sizes and supports a wide range of languages (119 in total), making it one of the most versatile and performant open-source offerings to date. These models are now open-sourced under the Apache 2.0 license on Hugging Face, GitHub, and ModelScope, and are also accessible via Alibaba Cloud APIs. These models are optimized for use cases such as semantic retrieval, classification, RAG, sentiment analysis, and code search—providing a strong alternative to existing solutions like Gemini Embedding and OpenAI’s embedding APIs. Technical Architecture Qwen3-Embedding models adopt a dense transformer-based architecture with causal attention, producing embeddings by extracting the hidden state corresponding to the [EOS] token. Instruction-awareness is a key feature: input queries are formatted as {instruction} {query}<|endoftext|>, enabling task-conditioned embeddings. The reranker models are trained with a binary classification format, judging document-query relevance in an instruction-guided manner using a token likelihood-based scoring function. The models are trained using a robust multi-stage training pipeline: Large-scale weak supervision: 150M synthetic training pairs generated using Qwen3-32B, covering retrieval, classification, STS, and bitext mining across languages and tasks. Supervised fine-tuning: 12M high-quality data pairs are selected using cosine similarity (>0.7), fine-tuning performance in downstream applications. Model merging: Spherical linear interpolation (SLERP) of multiple fine-tuned checkpoints ensures robustness and generalization. This synthetic data generation pipeline enables control over data quality, language diversity, task difficulty, and more—resulting in a high degree of coverage and relevance in low-resource settings. Performance Benchmarks and Insights The Qwen3-Embedding and Qwen3-Reranker series demonstrate strong empirical performance across several multilingual benchmarks. On MMTEB (216 tasks across 250+ languages), Qwen3-Embedding-8B achieves a mean task score of 70.58, surpassing Gemini and GTE-Qwen2 series. On MTEB (English v2): Qwen3-Embedding-8B reaches 75.22, outperforming other open models including NV-Embed-v2 and GritLM-7B. On MTEB-Code: Qwen3-Embedding-8B leads with 80.68, excelling in applications like code retrieval and Stack Overflow QA. For reranking: Qwen3-Reranker-0.6B already outperforms Jina and BGE rerankers. Qwen3-Reranker-8B achieves 81.22 on MTEB-Code and 72.94 on MMTEB-R, marking state-of-the-art performance. Ablation studies confirm the necessity of each training stage. Removing synthetic pretraining or model merging led to significant performance drops (up to 6 points on MMTEB), emphasizing their contributions. Conclusion Alibaba’s Qwen3-Embedding and Qwen3-Reranker Series present a robust, open, and scalable solution to multilingual and instruction-aware semantic representation. With strong empirical results across MTEB, MMTEB, and MTEB-Code, these models bridge the gap between proprietary APIs and open-source accessibility. Their thoughtful training design—leveraging high-quality synthetic data, instruction-tuning, and model merging—positions them as ideal candidates for enterprise applications in search, retrieval, and RAG pipelines. By open-sourcing these models, the Qwen team not only pushes the boundaries of language understanding but also empowers the broader community to innovate on top of a solid foundation. Check out the Paper, Technical details, Qwen3-Embedding and Qwen3-Reranker. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Asif RazzaqWebsite |  + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Coding Guide to Building an Iterative AI Workflow Agent Using LangGraph and GeminiAsif Razzaqhttps://www.marktechpost.com/author/6flvq/From Clicking to Reasoning: WebChoreArena Benchmark Challenges Agents with Memory-Heavy and Multi-Page TasksAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Mistral AI Introduces Mistral Code: A Customizable AI Coding Assistant for Enterprise WorkflowsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/NVIDIA AI Releases Llama Nemotron Nano VL: A Compact Vision-Language Model Optimized for Document Understanding

The Photography Seoul Museum of Art (PhotoSeMA) officially opened its doors in May 2025, marking a significant milestone as South Korea’s first public institution solely dedicated to the art of photography. Situated in the Dobong District of northeastern Seoul, this architectural marvel is the result of a collaboration between Austrian firm Jadric Architektur and Korean studio 1990uao Architects. The museum’s dynamic, twisting facade not only serves as a visual landmark but also symbolizes the fluidity and transformative nature of photography itself. Spanning six levels, four above ground and two below, the museum encompasses a total area of 7,048 square meters. Its design emphasizes the interplay between light and space, with broad concrete surfaces and filtered lighting creating an environment conducive to introspection and artistic appreciation. Designers: Jadric Architektur and 1990uao Architects Interior spaces are thoughtfully punctuated by voids and light wells, allowing natural light to shape the visitor experience throughout the day. The building’s exterior features a twisting monolithic shape that integrates seamlessly with the surrounding public space, inviting visitors to engage with the museum both inside and out. This design choice reflects the architects’ intention to create a “walk-in object” that fosters community interaction and cultural engagement. PhotoSeMA’s opening is marked by two inaugural exhibitions that delve into the history and evolution of Korean photography. The Radiance: Beginnings of Korean Art Photography showcases seminal works by artists such as Jung Haechang, Lim Suk Je, Lee Hyungrok, Cho Hyundu, and Park Youngsook. Drawing from a collection of over 20,000 works and archival materials dating from the 1920s to the 1990s, the exhibition offers a comprehensive look at the medium’s development in Korea. Meanwhile, Storage Story features contemporary artists Dongsin Seo, Won Seoung Won, Jihyun Jung, Joo Yongseong, Melmel Chung, and Oh Jooyoung. This exhibition explores the museum itself as a subject, examining themes of classification, memory, and the evolving role of cultural institutions in society. As part of the Seoul Museum of Art (SeMA) network, PhotoSeMA extends the institution’s reach and reinforces its commitment to diverse artistic expressions. The museum not only provides a dedicated space for photographic art but also contributes to the cultural revitalization of the Dobong District, aligning with the city’s broader efforts to establish a “cultural mile” in the area. With its innovative design and focused curatorial approach, the Photography Seoul Museum of Art stands as a testament to the dynamic intersection of architecture and photography, offering visitors a unique space to explore and appreciate the visual narratives that shape our world. The post South Korea’s first public museum for photography now open first appeared on Yanko Design.

Scientific research across fields like chemistry, biology, and artificial intelligence has long relied on human experts to explore knowledge, generate ideas, design experiments, and refine results. Yet, as problems grow more complex and data-intensive, discovery slows. While AI tools, such as language models and robotics, can handle specific tasks, like literature searches or code analysis, they rarely encompass the entire research cycle. Bridging the gap between idea generation and experimental validation remains a key challenge. For AI to autonomously advance science, it must propose hypotheses, design and execute experiments, analyze outcomes, and refine approaches in an iterative loop. Without this integration, AI risks producing disconnected ideas that depend on human supervision for validation. Before the introduction of a unified system, researchers relied on separate tools for each stage of the process. Large language models could help find relevant scientific papers, but they didn’t directly feed into experiment design or result analysis. Robotics can assist in automating physical experiments, and coding libraries like PyTorch can help build models; however, these tools operate independently of each other. There was no single system capable of handling the entire process, from forming ideas to verifying them through experiments. This led to bottlenecks, where researchers had to connect the dots manually, slowing progress and leaving room for errors or missed opportunities. The need for an integrated system that could handle the entire research cycle became clear. Researchers from the NovelSeek Team at the Shanghai Artificial Intelligence Laboratory developed NovelSeek, an AI system designed to run the entire scientific discovery process autonomously. NovelSeek comprises four main modules that work in tandem: a system that generates and refines research ideas, a feedback loop where human experts can interact with and refine these ideas, a method for translating ideas into code and experiment plans, and a process for conducting multiple rounds of experiments. What makes NovelSeek stand out is its versatility; it works across 12 scientific research tasks, including predicting chemical reaction yields, understanding molecular dynamics, forecasting time-series data, and handling functions like 2D semantic segmentation and 3D object classification. The team designed NovelSeek to minimize human involvement, expedite discoveries, and deliver consistent, high-quality results. The system behind NovelSeek involves multiple specialized agents, each focused on a specific part of the research workflow. The “Survey Agent” helps the system understand the problem by searching scientific papers and identifying relevant information based on keywords and task definitions. It adapts its search strategy by first doing a broad survey of papers, then going deeper by analyzing full-text documents for detailed insights. This ensures that the system captures both general trends and specific technical knowledge. The “Code Review Agent” examines existing codebases, whether user-uploaded or sourced from public repositories like GitHub, to understand how current methods work and identify areas for improvement. It checks how code is structured, looks for errors, and creates summaries that help the system build on past work. The “Idea Innovation Agent” generates creative research ideas, pushing the system to explore different approaches and refine them by comparing them to related studies and previous results. The system even includes a “Planning and Execution Agent” that turns ideas into detailed experiments, handles errors during the testing process, and ensures smooth execution of multi-step research plans. NovelSeek delivered impressive results across various tasks. In chemical reaction yield prediction, NovelSeek improved performance from a baseline of 24.2% (with a variation of ±4.2) to 34.8% (with a much smaller variation of ±1.1) in just 12 hours, progress that human researchers typically need months to achieve. In enhancer activity prediction, a key task in biology, NovelSeek raised the Pearson correlation coefficient from 0.65 to 0.79 within 4 hours. For 2D semantic segmentation, a task used in computer vision, precision improved from 78.8% to 81.0% in just 30 hours. These performance boosts, achieved in a fraction of the time typically needed, highlight the system’s efficiency. NovelSeek also successfully managed large, complex codebases with multiple files, demonstrating its ability to handle research tasks at a project level, not just in small, isolated tests. The team has made the code open-source, allowing others to use, test, and contribute to its improvement. Several Key Takeaways from the Research on NovelSeek include: NovelSeek supports 12 research tasks, including chemical reaction prediction, molecular dynamics, and 3D object classification. Reaction yield prediction accuracy improved from 24.2% to 34.8% in 12 hours. Enhancer activity prediction performance increased from 0.65 to 0.79 in 4 hours. 2D semantic segmentation precision improved from 78.8% to 81.0% in 30 hours. NovelSeek includes agents for literature search, code analysis, idea generation, and experiment execution. The system is open-source, enabling reproducibility and collaboration across scientific fields. In conclusion, NovelSeek demonstrates how combining AI tools into a single system can accelerate scientific discovery and reduce its dependence on human effort. It ties together the key steps, generating ideas, turning them into methods, and testing them through experiments, into one streamlined process. What once took researchers months or years can now be done in days or even hours. By linking every stage of research into a continuous loop, NovelSeek helps teams move from rough ideas to real-world results more quickly. This system highlights the power of AI not just to assist, but to drive scientific research in a way that could reshape how discoveries are made across many fields. Check out the Paper and GitHub Page . All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces ARM and Ada-GRPO: Adaptive Reasoning Models for Efficient and Scalable Problem-SolvingNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural Networks

This is how to use the attention mechanism in a time series classification framework The post Hands-On Attention Mechanism for Time Series Classification, with Python appeared first on Towards Data Science.

There are very few philosophers who become part of popular culture, and often, if their ideas become influential, people don’t know where they came from.Niccolò Machiavelli, the great 16th-century diplomat and writer, is an exception.I don’t know how many people have actually read Machiavelli, but almost everyone knows the name, and almost everyone thinks they know what the word “Machiavellian” means. It’s someone who’s cunning and shrewd and manipulative. Or as one famous philosopher called him, “the teacher of evil.”But is this fair to Machiavelli, or has he been misunderstood? And if he has been, what are we missing in his work?Erica Benner is a political philosopher and the author of numerous books about Machiavelli including my favorite, Be Like the Fox, which offers a different interpretation of Machiavelli’s most famous work, The Prince.For centuries, The Prince has been popularly viewed as a how-to manual for tyrants. But Benner disagrees. She says it’s actually a veiled, almost satirical critique of authoritarian power. And she argues that Machiavelli is more timely than you might imagine. He wrote about why democracies get sick and die, about the dangers of inequality and partisanship, and even about why appearance and perception matter far more than truth and facts.In another of his seminal works, Discourses on Livy, Machiavelli is also distinctly not authoritarian. In fact, he espouses a deep belief in republicanism (the lowercase-r kind, which affirms representative government).I invited Benner onto The Gray Area to talk about what Machiavelli was up to and why he’s very much a philosopher for our times. As always, there’s much more in the full podcast, so listen and follow The Gray Area on Apple Podcasts, Spotify, Pandora, or wherever you find podcasts. New episodes drop every Monday.This interview has been edited for length and clarity. The popular view of Machiavelli is that he wanted to draw this neat line between morality and politics and that he celebrated ruthless pragmatism. What’s incomplete or wrong about that view?What is true is that he often criticizes the hyper-Christian morality that puts moral judgments into the hands of priests and popes and some abstract kind of God that he may or may not believe in, but in any case doesn’t think is something we can access as humans. If we want to think about morality both on a personal level and in politics, we’ve got to go back to basics. What is the behavior of human beings? What is human nature? What are the drives that propel human beings to do the stuff that we call good or bad? He wants to say that we should see human beings not as fundamentally good or evil. We shouldn’t think that human beings can ever be angels, and we shouldn’t see them as devils when they behave badly.But the basic point is if you want to develop a human morality, you study yourself, you study other humans, you don’t put yourself above other humans because you’re one, too. And then you ask, What kind of politics is going to make such people coexist?I take it you think his most famous book, The Prince, is not well understood?I used to have to teach Machiavelli and I would just say, It’s a handbook for tyrants. But he wrote the Discourses, which is a very, very republican book. So that’s the first thing that sets people off and makes you think, How could he have switched so quickly from writing The Prince to being a super-republican writing the Discourses? So that’s a warning sign. When I started seeing some of the earliest readers of Machiavelli and the earliest comments you get from republican authors, they all see Machiavelli as an ally and they say it. They say he’s a moral writer. Rousseau says, “He has only had superficial and corrupt readers until now.” If you ever pick up The Prince and you read the first four chapters, and most people don’t read them that carefully because they’re kind of boring, the exciting ones are the ones in the middle about morality and immorality and then you come to chapter five, which is about freedom.And up to chapter four, it sounds like a pretty cruel, cold analysis of what you should do. Then you get to chapter five and it’s like, Wow! It’s about how republics fight back, and the whole tone changes. Suddenly republics are fighting back and the prince has to be on his toes because he’s probably not going to survive the wrath of these fiery republics that do not give up.So who is he talking to in the book? Is he counseling future princes or warning future citizens?It’s complicated. You have to remember that he was kicked out of his job and had a big family to support. He had a lot of kids. And he loved his job and was passionate about the republic. He was tortured. He doesn’t know what’s going to happen next. He’s absolutely gutted that Florence’s republican experiment has failed and he can’t speak freely. So what does a guy with a history of writing dramas and satire do to make himself feel better? It’s taking the piss out of the people who have made you and a lot of your friends very miserable, in a low-key way because you can’t be too brutally satirical about it. But I think he’s really writing to expose the ways of tyrants.Would you say that Machiavelli has something like an ideology or is he just a clear-eyed pragmatist?He’s a republican. And again, this is something that, if you just read The Prince, you’re not going to get. But if you read the Discourses, which was written around the same time as The Prince, it’s very, very similar in almost every way except that it praises republics and criticizes tyrants very openly. Whereas The Prince never once uses the words “tyrant” or “tyranny.” So if there’s a guiding political view, whether you call it “ideology” or not, it’s republicanism. And that’s an ideology of shared power. It’s all the people in a city, all the male people in this case. Machiavelli was quite egalitarian. He clearly wanted as broad of a section of the male population to be citizens as possible. He says very clearly, The key to stabilizing your power is to change the constitution and to give everyone their share. Everyone has to have their share. You might want to speak a little bit more for yourself and the rich guys, but in the end, everyone’s got to have a share.Should we treat Machiavelli like a democratic theorist? Do you think of him as someone who would defend what we call democracy today?If you think the main principle of democracy is that power should be shared equally, which is how I understand democracy, then yes. He’d totally agree with that. What kind of institutions would he say a democracy has to have? He’s pretty clear in the Discourses. He says you don’t want a long-term executive. You need to always check power. I realize we exist in a very different world than Machiavelli, but is he a useful guide to understanding contemporary politics, particularly American politics?This is a really Machiavellian moment. If you read The Prince and look not just for those provocative quotes but for the criticisms, and sometimes they’re very subtle, you start to see that he’s exposing a lot of the stuff that we’re seeing today. Chapter nine of The Prince is where he talks about how you can rise to be the ruler of a republic and how much resistance you might face, and he says that people might be quite passive at first and not do very much. But at some point, when they see you start to attack the courts and the magistrates, that’s when you’re going to clash. And he says, That’s when you as a leader — and he’s playing like he’s on the leader’s side — that’s when you’ve got to decide if you’re going to get really, really tough, or are you going to have to find other ways to soften things up a bit?What would he make of Trump?He would put Trump in two categories. He’s got different classifications of princes. He’s got the prince of fortune, somebody who relies on wealth and money and big impressions to get ahead. He would say that Trump has a lot of those qualities, but he’d also call him this word “astutia” — astuteness, which doesn’t really translate in English because we think of that as a good quality, but he means calculating shrewdness. Somebody whose great talent is being able to shrewdly manipulate and find little holes where he can exploit people’s weaknesses and dissatisfactions.This is what he thought the Medici were good at. And his analysis of that is that it can cover you for a long time. People will see the good appearances and hope that you can deliver, but in the long run, people who do that don’t know how to build a solid state. That’s what he would say on a domestic front. I think there’s an unsophisticated way to look at Trump as Machiavellian. There are these lines in The Prince about knowing how to deploy cruelty and knowing when to be ruthless. But to your deeper point, I don’t think Machiavelli ever endorses cruelty for cruelty’s sake, and with Trump — and this is my personal opinion — cruelty is often the point, and that’s not really Machiavellian.Exactly. I wouldn’t say Trump is Machiavellian. Quite honestly, since the beginning of the Trump administration, I’ve often felt like he’s getting advice from people who haven’t really read Machiavelli or put Machiavelli into ChatGPT and got all the wrong pointers, because the ones that they’re picking out are just so crude. But they sound Machiavellian. You’re absolutely right, though. Machiavelli is very, very clear in The Prince that cruelty is not going to get you anywhere in the long term. You’re going to get pure hate. So if you think it’s ever instrumentally useful to be super cruel, think again.This obviously isn’t an endorsement of Trump, but I will say that something I hear often from people is that the system is so broken that we need someone to smash it up in order to save it. We need political dynamite. I bring that up because Machiavelli says repeatedly that politics requires flexibility and maybe even a little practical ruthlessness in order to preserve the republic. Do you think he would say that there’s real danger in clinging to procedural purity if you reach a point where the system seems to have failed?This is a great question. And again, this is one he does address in the Discourses quite a lot. He talks about how the Romans, when their republic started slipping, had “great men” coming up and saying, “I’ll save you,” and there were a lot before Julius Caesar finally “saved” them and then it all went to hell. And Machiavelli says that there are procedures that have to sometimes be wiped out — you have to reform institutions and add new ones. The Romans added new ones, they subtracted some, they changed the terms. He was very, very keen on shortening the terms of various excessively long offices. He also wanted to create emergency institutions where, if you really faced an emergency, that institution gives somebody more power to take executive action to solve the problem. But that institution, the dictatorship as it was called in Rome, it wasn’t as though a random person could come along and do whatever he wanted. The idea was that this dictator would have special executive powers, but he is under strict oversight, very strict oversight, by the Senate and the plebians, so that if he takes one wrong step, there would be serious punishment. So he was very adamant about punishing leaders who took these responsibilities and then abused them.Listen to the rest of the conversation and be sure to follow The Gray Area on Apple Podcasts, Spotify, Pandora, or wherever you listen to podcasts.See More:

Shenzhen-based 3D printer manufacturer Elegoo has introduced a new RFID Ecosystem for its upcoming printer line, including the upcoming Elegoo Saturn 4 Ultra. This system integrates RFID-tagged resin bottles, an Elegoo-designed scanner, and cloud-connected print profiles. Elegoo has opened a public feedback solicitation on its website and GitHub page to refine the implementation and encourage community input. The company is currently testing several use cases, such as automatic profile loading, material usage tracking, and batch traceability. Elegoo says these features aim to streamline workflow, reduce errors, and assist in quality assurance. However, in a GitHub post, the company emphasized that its RFID system is optional and will not lock users into proprietary materials. An open approach to a closed-loop trend? The Elegoo RFID Ecosystem enters a broader conversation in the additive manufacturing (AM) industry regarding material-locking strategies and proprietary ecosystems. As discussed in a recent 3D Printing Industry analysis, the proliferation of closed systems has triggered renewed debate about interoperability, user autonomy, and long-term value for manufacturers and end-users alike. Elegoo appears to be taking a middle-ground approach: providing automation and traceability features via RFID while maintaining support for third-party materials. In the Elegoo RFID Tag Guide, developers are encouraged to create and test custom tags, with detailed instructions and example code provided to the open-source community. Developer-centric rollout The Elegoo Saturn 4 Ultra, which serves as the first testbed for the RFID system, uses a dedicated RFID reader to retrieve data from tags affixed to resin bottles. These tags store encoded information such as resin name, type, batch number, and print profile metadata. The printer’s firmware can automatically sync this information with cloud-hosted slicer settings for optimal prints. According to the company, future updates may include compatibility with other Elegoo printers and additional features like usage history logging, tamper detection, and resin validation for regulatory compliance. Color scheme guide possibly used for tag classification or UI indication in Elegoo’s RFID material system. Image via Elegoo. A call for collaboration In its official blog post, Elegoo invited users, developers, and material manufacturers to contribute feedback and propose new applications. The company has not yet announced a formal launch date for the ecosystem or its associated hardware. Elegoo, known for its budget-friendly resin and FDM printers, has been expanding its R&D efforts in recent years. With the RFID ecosystem, it now joins other AM firms experimenting with embedded metadata and smart materials integration to support traceability, security, and ease of use. Interoperability and user autonomy The debate about open vs closed ecosystems has increasingly intensified in additive manufacturing discussions. For example, Bambu Lab’s controversial firmware update that introduced new authentication protocols, sparking concerns about third-party compatibility and user autonomy. Subsequent coverage highlighted pushback from the open-source community, including Orca Slicer developers, who rejected integration with Bambu Connect over transparency and access concerns. These cases underscore how interoperability is not only a technical issue, but a strategic and ideological one shaping the future of the AM sector.RFID in 3D printing While RFID integration is more common in logistics and supply chain management, researchers and companies are beginning to explore its potential in 3D printing. Scientists at Swinburne University developed biosensing RFID tags using 3D printed hybrid liquids, enabling applications in health diagnostics and environmental sensing. Meanwhile, materials firm Supernova unveiled a new resin cartridge system embedded with RFID to improve compatibility and process control in high-viscosity 3D printing platforms. These developments suggest that RFID could play a growing role in material authentication, traceability, and automated workflow management within additive manufacturing ecosystems.Subscribe to the 3D Printing Industry newsletter to keep up with the latest 3D printing news. You can also follow us onLinkedIn and subscribe to the 3D Printing Industry YouTube channel to access more exclusive content. At 3DPI, our mission is to deliver high-quality journalism, technical insight, and industry intelligence to professionals across the AM ecosystem.Help us shape the future of 3D printing industry news with our2025 reader survey. Featured image shows Elegoo RFID system displayed on a resin bottle, designed to communicate encoded material data to the printer. Image via Elegoo.

Care management platform DexCare is applying artificial intelligence (AI) in an innovative way to fix health care access issues. Its AI-driven platform helps health systems overcome rising costs, limited capacity, and fragmented digital infrastructure. As Americans face worsening health outcomes and soaring costs, DexCare Co-founder Derek Streat sees opportunity in the crisis and is leading a push to apply AI and machine learning (ML) to health care’s toughest operational challenges — from overcrowded emergency rooms to disconnected digital systems. No stranger to using AI to solve health care issues, Streat is guiding DexCare as it leverages AI and ML to confront the industry’s most persistent pain points: spiraling costs, resource constraints, and the impossible task of doing more with less. Its platform helps liberate data silos to orchestrate care better and deliver a “shoppable” experience. The combination unlocks patient access to care and optimizes health care resources. DexCare enables health systems to see 40% more patients with existing clinical resources. Streat readily admits that some advanced companies use AI to enhance clinical and medical research. However, advanced AI tools such as conversational generative AI are less common in the health care access space. DexCare addresses that service gap. “Access is broken, and our fundamental belief is that there haven’t been enough solutions to balance patient, provider, and health system needs and objectives,” he told TechNewsWorld. Improving Patient Access With Predictive AI Achieving that balance depends on the underlying information drawn from health care providers’ neural networks, ML models, classification systems, and advancements in generative AI. These elements build on one another. Derek Streat, Co-founder of DexCare With the goal of a better customer experience (CX), DexCare’s platform helps care providers optimize the algorithm so everyone benefits. The focus is on ensuring patients get what matches their intent and motivations while respecting the providers’ capacity and needs, explained Streat. He describes the platform’s technology as a foundational pyramid based on data that AI optimizes and manages. Those components ensure high-fidelity outcome predictions for recommended care options. “It could be a doctor in a clinic or a nurse in a virtual care system,” he suggested. “I’m not talking about clinical outcomes. I’m talking about what you’re looking for.” Ultimately, that managed balance will not burn out all your providers. It will make this a sustainable business line for the health system. From Providence Prototype to Scalable Solution Streat defined DexCare as an access optimization company. He shared that the platform originated from a ground-floor build within the Providence Health System. After four years of development and validation, he launched the technology for broader use across the health care industry. “It’s well tested and very effective in what it does. That allowed us to have something scalable across organizations as well. Our expansion makes health care more discoverable to consumers and patients and more sustainable for medical providers and the health systems we serve,” he said. Digital Marquee for Consumers, Service Management for Providers DexCare’s AI works on multiple levels. It provides health care system or medical facility services as a contact center. That part attracts and curates audiences, consumers, and patients. Its digital assets could be websites, landing pages, or screening kiosks. Another part of the platform intelligently navigates patients to the safest and best care option. This process engages the accumulated data and automatically allocates the health system’s resources. “It manages schedules and available staff and facilities and automatically allocates them when and where they can be most productively employed,” explained Streat. The platform excels at load balancing. It uses AI to rationalize all those components. The decision engine uses AI to ensure that the selected resources and needed services match so the medical treatment can be done most efficiently and effectively to accommodate the patient and the organization. How DexCare Integrates With CRM Platforms According to Streat, DexCare is not customer relationship management software. Instead, the platform is a tie-in that infuses its AI tools and data services that blend with other platforms such as Salesforce and Oracle. “We make it as flexible as we can. It is pretty scalable to the point where now we can touch about 20% of the U.S. population through our health system partners,” he offered. Patients do not realize they are interacting with the DexCare-powered experience console under the brands Kaiser, Providence, and SSM Health, some of the DexCare platform’s health systems users. The platform is flexible and adapts to the needs of various health agencies. For instance, fulfillment technologies book appointments and supply synchronous virtual solutions. “Whatever the modality or setting is, we can either connect with whatever you’re using as a health system, or you can use your own underlying pieces as well,” said Streat. He noted that the intelligent data acquisition built into the DexCare platform accesses the electronic medical record (EMR), which includes patients’ demographics, medical history, diagnoses, medications, allergies, immunization records, lab results, and treatment plans. “The application programming interface [API] gives us real-time availability, allows us to predict a certain provider’s capacity, and maintains EMR as a source of truth,” said Streat. AI’s Long-Term Role in Health Care Access Health care management by conversational generative AI provides insights into where organizations struggle, need to adjust their operations, or reassign staff to manage patient flow. That all takes place on the platform’s back end. According to Streat, the front-end value proposition is pretty simple. It helps get 20% to 30% more patients into the health system. Organizations generate nine times the initial visit value in downstream revenue for additional services, Streat said. He assured that the other part of the value proposition is a lower marginal cost of delivering each visit. That results from matching resources with patients in a way that allows balancing the load across the organization’s network. “That depends on the specific use case, but we find up to a 40% additional capacity within the health system without hiring additional resources,” he said. How? That is where the underlying AI data comes into play. It helps practitioners make more informed decisions about which patients should be matched with which providers. “Not everybody needs to see an expensive doctor in a clinic,” Streat contended. “Sometimes, a nurse in a virtual visit or educational information will be just fine.” Despite all the financial metrics, patients want medical treatment and to move on, which is really what the game is here, he surmised. Why Generative AI Lags in Health Care Streat lamented the rapidly developing sophistication of generative AI, which includes conversational interfaces, analytical capability, and predictive mastery. These technologies are being applied throughout other industries and businesses, but are not yet widely adopted in health care systems. He indicated that part of that lag is that health care access needs are different and not as suited for conversational AI solutions hastily layered onto legacy systems. Ultimately, changing health care requires delivering things at scale. “Within a health system, its infrastructure, and the plumbing required to respect the systems of records, it’s just a different world,” he said. Streat sees AI making it possible for us to move away from searching through a long list of doctors online to booking through a robot operator with a pleasant accent. “We will focus on the back-end intelligence and continue to apply it to these lower-friction ways for people to interact with the health system. That’s incredibly exciting to me,” he concluded.

On this day in 1975, Industrial Light & Magic was officially signed into existence by George Lucas. By Lucas O. Seastrom ILM’s original crew for Star Wars: A New Hope (1977) poses in the front lot of their original studio (Credit: ILM & Lucasfilm). 50 years ago today on May 28, 1975, George Lucas signed a legal certificate issuing his formal shares of stock ownership in a new company: Industrial Light & Magic. It’s likely the founder affixed his signature without pomp or ceremony. There was too much to do. ILM, as it would come to be known for short, had less than two years to build a visual effects studio from scratch and create nearly 400 shots in a new space fantasy film called Star Wars. By that time in late May, Lucas had hired John Dykstra to supervise the film’s visual effects. The director had an audacious vision for creating dynamic images of spaceships dogfighting with each other. Lucas wanted the camera to move with the ships, as if the camera operators were up there to capture the action by hand. The idea broke many of the traditional rules in visual effects that typically required locked off cameras to allow for separate elements to be carefully blended together. Visual effects supervisor John Dykstra poses on the stage next to a TIE fighter miniature (Credit: ILM & Lucasfilm). John Dykstra was practically the only effects artist in Hollywood willing to buy into Lucas’s plans on the existing terms. He’d gained experience with the type of equipment that would be needed to realize the elaborate shots of custom-built miniatures. Dykstra was also a free thinker with a sense of adventure. There were only a handful of effects companies still operating, and none at a major studio. Most balked at the proposal, decrying its limited budget, tight schedule, and seemingly unattainable goals. So Dykstra was tasked with establishing a new operation. Lucas was a Northern Californian and planned to base the editorial side of post-production near his San Francisco Bay Area home. He wanted to do the same for visual effects. Dykstra argued otherwise, deciding to keep the new facility in Southern California where he had access to a network of talent and close proximity to third party film processing labs. So it was at some point in late May that Dykstra located and then leased a warehouse in Van Nuys, one of a number of towns that sprawled across the San Fernando Valley, a ways north of Hollywood proper, and conveniently removed from the overbearing presence of the established studios.  Located in an industrial park on Valjean Avenue, just a block from the south end of the Van Nuys Airport, ILM rented a building for $2,300 a month from owner Bill Hanna. It was two stories, made largely of stacked cinder blocks, with a large asphalt lot in front. Inside were a handful of unfurnished offices and open warehouse space with high ceilings ideal for hanging lights. Early on, Dykstra would drive his motorcycle through the building, leaving skid marks on the floor. It was often oppressively hot, even more so once the tungsten film lights were switched on, and Dykstra initially planned to construct a pool onsite, but later compromised with a cold tub that could hold multiple people. The exterior of ILM’s original studio in Van Nuys, CA. An explosion on the surface of the Death Star is photograped in the foreground (Credit: ILM & Lucasfilm). “It just popped into my head,” Lucas would recall about the name “Industrial Light & Magic.” “We were sitting in an industrial park and using light to create magic. That’s what they were going to do.” Initially, Dykstra worked out of Lucasfilm’s offices in a bungalow on the Universal Studios lot, a few minutes drive from Van Nuys. Soon he’d moved to Valjean, working off the floor before furniture was acquired. He was busy recruiting. By early June, modelmakers Grant McCune and Bill and Jamie Shourt were hired, as were production manager Bob Shepherd, technician Jerry Greenwood, first cameraman Richard Edlund, electronics designer Al Miller, and machinists Richard Alexander and Don Trumbull.  As former Lucasfilm executive editor J.W. Rinzler would note in The Making of Star Wars, “They all knew one another and had worked together before.” They’d worked on feature films with Douglas Trumbull (son of Don), or on commercials and other projects with Robert Abel and Associates. A later group would come from another commercial house, Cascade Pictures. Others came straight from universities where they’d studied everything from animation to industrial design. They brought with them aspects of the culture and methodology from these other places, together making something new and unique. Optical composite photography supervisor Robert Blalack’s Praxis Printer is loaded into the new ILM space (Credit: ILM & Lucasfilm). A view of one of ILM’s shooting stages in the rear of the original Van Nuys studio (Credit: ILM & Lucasfilm). Before anything else could happen, the Valjean warehouse needed to be converted into production space and workshops. Over six weeks into the summer, they first taped out sections and then constructed the designated areas themselves. On the first floor would be the optical and rotoscope departments, a model shop, machine shop, wood shop, two shooting stages in the rear, and production offices in the front. Upstairs would be home to the animation department, editorial, a screening room, and the art department.By July, optical composite photography supervisor Robert Blalack and animation and rotoscope supervisor Adam Beckett had been hired, as had a sound recordist and designer who would use ILM’s space as a sometime home base, Ben Burtt. By early August, artist Joe Johnston was setting up the art department (concept artists Colin Cantwell and Ralph McQuarrie had started much earlier, but each worked from home). Within a few months, a dozen people were on board, many of them attracted to join the project out of admiration for George Lucas, whose American Graffiti (1973) had made waves upon its release two years before. The second floor art department, with storyboard and concept artist Joe Johnston working at a desk in the background as modelmaker Paul Huston looks on (Credit: ILM & Lucasfilm). Film control coordinator Mary Lind in the upstairs editorial department (Credit: ILM & Lucasfilm). The spaces were ready by mid-summer, but ILM’s work had only just begun. It would take them nearly a year to successfully design and construct an entire visual effects facility and workflow, including miniatures, motion-control camera systems, optical printers, animation cameras, and blue screens. “There’s a significant difference between coming up with a good idea and executing it,” Dykstra would say. ILM’s initial budget was roughly $1.2 million. Although time was of the essence to build the various equipment, distributor 20th Century Fox was slow to provide any initial funds ahead of the main shoot, which would commence in the spring of 1976. So for much of its first year, ILM operated with George Lucas’s personal finances, thanks to the momentous commercial success of American Graffiti. Former ILM general manager Thomas G. Smith would explain in his 1986 book, The Art of Special Effects, how “Outside, it looked like all the other industrial-style buildings in the valley. Inside, it was staffed with very young technicians, some barely out of college, few over 30, some even under 20 years old…. The doors at ILM were open 24 hours a day; technicians and artists worked without regard to time clocks or job classifications. They were children of the ’60s, and many rebelled against authority figures and traditional work rules. There were no dress codes and no specified work hours; designers built models, and modelmakers ran cameras. But there was a strong esprit de corps and feeling of purpose in the building…. The involvement was with the cause rather than with the money; somehow the group felt they were a part of something really important.” John Dykstra inspects miniatures of X-wings, TIE fighters, and Y-wings (Credit: ILM & Lucasfilm). The first floor model shop in ILM’s original Van Nuys, CA studio (Credit: ILM & Lucasfilm). What this group was about to accomplish in less than two years was anything but certain that late spring of 1975. If anything, it was “a long shot,” as Dykstra himself would admit. “It was very, very hard to say specifically what was and what wasn’t going to work before we built it,” he told Cinefantastique in 1977. “So we just had to take a shot at it and all I could do was bluff it and say, ‘Oh yeah, everything’s gonna be fine!’”As would become the defining element of ILM’s success and endurance, it was the people who made all the difference. “It would be very hard to do Star Wars just by setting up an independent facility unless you had the personnel to do it,” Dykstra said. “The people who designed the equipment and constructed it made it all happen. Not only was it independent of studios but the people who were doing it are the best people in the industry right now.” What began quietly with a handful of people in a hot, mostly empty warehouse would ultimately do the impossible, not just in the sense of its accomplishments on screen or the resulting accolades, but in its ability to grow, adapt, and continue innovating time and again. That story continues today at the company’s studios around the world. Though ILM has long since outgrown its original warehouse, it still attracts the same intrepid, curious people who bring their passion for image-making and problem-solving to multiple art forms. Watch ILM’s new celebratory reel in honor of the company’s 50th anniversary: — Lucas O. Seastrom is the editor of ILM.com and a contributing writer and historian for Lucasfilm. Read more on the ILM.com Newsroom. Watch Light & Magic on Disney+.

About Equip Equip is the leading virtual, evidence-based eating disorder treatment program on a mission to ensure that everyone with an eating disorder can access treatment that works. Created by clinical experts in the field and people with lived experience, Equip builds upon evidence-based treatments to empower individuals to reach lasting recovery. All Equip patients receive a dedicated care team, including a therapist, dietitian, physician, and peer and family mentor. The company operates in all 50 states and is partnered with most major health insurance plans. Learn more about our strong outcomes and treatment approach at www.equip.health.Founded in 2019, Equip has been a fully virtual company since its inception and is proud of the highly-engaged, passionate, and diverse Equisters that have created Equip’s culture.  Recognized by Time as one of the most influential companies of 2023, along with awards from Linkedin and Lattice, we are grateful to Equipsters for building a sustainable treatment program that has served thousands of patients and families.About the role We are looking for a passionate Product Manager to join us in building Equip’s commercial and finance reporting, integration, and data science capabilities. Data is the backbone of Equip—we use it every day to support business processes, measure our success, and inform our decisions. As the Product Manager for Commercial and Finance analytics, you will collaborate with domain experts to identify opportunities to use data to improve our patient acquisition and engagement strategy and billing processes. We need someone who can apply a creative, rigorous, and efficient approach to product management. You will be a critical member of our team, collaborating with Data Analysts, Data Scientists, Data Engineers, and internal stakeholders. You will help develop and drive the roadmap for our data initiatives and work towards a strategy for enabling the organization to self-serve data needs as we scale. If you’re eager to work in a dynamic, fast-moving environment where you’ll strive to make a big impact at a mission-driven organization, then this might be the right fit for you!Responsibilities Be a thought partner with our Commercial and Finance departments to identify data initiatives that support both departmental and organizational goals.Advocate for the needs of your stakeholders when working with our application development teams, third parties, and other cross-functional stakeholders.Develop a strategy and roadmap for Commercial and Finance analytics, integrations, and data science that is outcome-driven and pursues organizational goals.Work closely with your Data Analyst, Engineering, and Data Scientist counterparts to come up with innovative ways to execute on the data roadmap, ensuring tight timelines and successful launches.Utilize testing, user research, and market analysis to iterate on features, derive insights, and enhance customer/user satisfaction.Design and prepare product requirement documents, including articulating the business case, value proposition, trade-offs, and user stories. Drive features from development to launch in an agile scrum environment by coordinating and participating in development activities including daily standups, backlog grooming, retrospectives, sprint planning, demos, and other scrum ceremonies.Monitor usage and satisfaction of our data products to identify gaps and opportunities.Work towards improving the data literacy in the organization and enabling meaningful self-service analytics.Demonstrate empathy and curiosity when approaching all complex issues (be they technical, societal, or personal) and share a genuine interest in solving problems for our users.Operate with a high degree of autonomy in a growing, early-stage work environment. Find ways to improve efficiency and excellence in our product development process.Perform other duties as assigned.Qualifications 4+ years of experience as a Product Manager within a high-growth company, ideally working on data products and/or products in the healthcare domain. Proven track record of successfully delivering features on time following agile methodologies and best practices. Strong analytical skills, including the ability to clean, visualize, and present compelling narratives using data.Familiarity with data modeling, schema design, ETL/ELT pipelines, data lineage, and storage architectures.The ability to read and write SQL. Demonstrated business sense and intellectual curiosity with the desire to become a subject matter expert in eating disorder treatments.Organizational and project management skills with a demonstrated ability to balance numerous projects while meeting tight deadlines.Attention to detail. Effective interpersonal skills, particularly in building relationships, working collaboratively, and influencing and driving alignment. Strong written and verbal communication skills.Comfortable working in a fast-paced environment with changing priorities.Benefits Time Off:Flex PTO policy (3-5 wks/year recommended) + 11 paid company holidays.Medical Benefits: Competitive Medical, Dental, Vision, Life, and AD&D insurance.Equip pays for a significant percentage of benefits premiums for individuals and families.Maven, a company paid reproductive and family care benefit for all employees. Employee Assistance Program (EAP), a company paid resource for mental health, legal services, financial support, and more! Other BenefitsWork From Home Additional Perks: $50/month stipend added directly to an employee’s paycheck to cover home internet expenses. One-time work from home stipend of up to $500. Physical Demands Work is performed 100% from home with requirement to travel once or twice a year for in-person meetings. This is a stationary position that requires the ability to operate standard office equipment and keyboards as well as to talk or hear by telephone. Sit or stand as needed.At Equip, Diversity, Equity, Inclusion and Belonging (DEIB) are woven into everything we do. At the heart of Equip’s mission is a relentless dedication to making sure that everyone with an eating disorder has access to care that works regardless of race, gender, sexuality, ability, weight, socio-economic status, and any marginalized identity. We also strive toward our providers and corporate team reflecting that same dedication both in bringing in and retaining talented employees from all backgrounds and identities. We have an Equip DEIB council, Equip For All; also referred to as EFA. EFA at Equip aims to be a space driven by mutual respect, and thoughtful, effective communication strategy - enabling full participation of  members who identify as marginalized or under-represented and allies, amplifying diverse voices, creating opportunities for advocacy and contributing to the advancement of diversity, equity, inclusion, and belonging at Equip.As an equal opportunity employer, we provide equal opportunity in all aspects of employment, including recruiting, hiring, compensation, training and promotion, termination, and any other terms and conditions of employment without regard to race, ethnicity, color, religion, sex, sexual orientation, gender identity, gender expression, familial status, age, disability, weight, and/or any other legally protected classification protected by federal, state, or local law. Apply NowLet's start your dream job Apply now Meet JobCopilot: Your Personal AI Job HunterAutomatically Apply to Remote Sales and Marketing JobsJust set your preferences and Job Copilot will do the rest-finding, filtering, and applying while you focus on what matters. Activate JobCopilot

John Edwards, Technology Journalist & AuthorMay 26, 20255 Min ReadLuis Moreira via Alamy Stock PhotoStructured data, such as names and phone numbers, fits neatly into rows and columns. Unstructured data, however, has no fixed scheme, and may have a highly complex format such as audio files or web pages. Unfortunately, there's no single best way to effectively manage unstructured data. On the bright side, there are several approaches that can be used to successfully tackle this critical, yet persistently elusive challenge. Here are five tested ways to achieve effective unstructured data management from experts who participated in online interviews. Tip 1. Use AI-powered vector databases combined with retrieval-augmented generation "One of the most effective methods I've seen is using AI-powered vector databases combined with retrieval augmented generation," says Anbang Xu, founder of AI video generator firm Jogg.AI. A former senior software engineer at Google, Xu suggests that instead of forcing unstructured data into rigid schemas, using vector databases will allow enterprises to store and retrieve data based on contextual meaning rather than exact keyword matches. "This is especially powerful for text, audio, video, and image data, where traditional search methods fall short," he notes.  For example, Xu says, organizations using AI-powered embeddings can organize and query vast amounts of unstructured data by meaning rather than syntax. "This is what powers advanced AI applications like intelligent search, chatbots, and recommendation systems," he explains. "At Jogg.AI, we’ve seen first-hand how AI-driven indexing and retrieval make it significantly easier to turn raw, unstructured data into actionable insights." Related:Tip 2. Take a schema-on-read approach Another innovative approach to managing unstructured data is schema-on-read. "Unlike traditional databases, which define the schema -- the data's structure -- before it's stored, schema-on-read defers this process until the data is actually read or queried," says Kamal Hathi, senior vice president and general manager of machine-generated data monitoring and analysis software firm at Splunk, a Cisco company. This approach is particularly effective for unstructured and semi-structured data, where the schema is not predefined or rigid, Hathi says. "Traditional databases require a predefined schema, which makes working with unstructured data challenging and less flexible." The key advantage of schema-on-read is that it enables users to work with raw data without needing to apply traditional extract-transform-load (ETL) processes, Hathi states. "This, in turn, allows for working with the diversity typically seen in machine-generated data, such as system and application telemetry logs." Related:Tip 3. Look to the cloud Manage unstructured data by integrating it with structured data in a cloud environment using metadata tagging and AI-driven classifications, suggests Cam Ogden, a senior vice president at data integrity firm Precisely. "Traditionally, structured data -- like customer databases or financial records -- reside in well-organized systems such as relational databases or data warehouses," he says. However, to fully leverage all of their data, organizations need to break down the silos that separate structured data from other forms of data, including unstructured data such as text, images, or log files. This is where the cloud comes into play. Integrating structured and unstructured data in the cloud allows for more comprehensive analytics, enabling organizations to extract deeper insights from previously siloed information, Ogden says. AI-powered tools can classify and enrich both structured and unstructured data, making it easier to discover, analyze, and govern in a central platform, he notes. "The cloud offers the scalability and flexibility required to handle large volumes of data while supporting dynamic analytics workloads." Additionally, cloud platforms offer advanced data governance capabilities, ensuring that both structured and unstructured data remain secure, compliant, and aligned with business objectives. "This approach not only optimizes data management but also positions organizations to make more informed and effective data-driven decisions in real-time." Related:Tip 4. Use AI-powered classification and indexing One of the best ways to get a grip on unstructured data is to use AI-powered classification and indexing, says Adhiran Thirmal, a senior solutions engineer at cybersecurity firm Security Compass. "With machine learning (ML) and natural language processing (NLP), you can automatically sort, tag, and organize data based on its content and context," he explains. "Pairing this approach with a scalable data storage system, like a data lake or object storage, makes it easier to find and use information when you need it." AI takes the manual work out of organizing data, Thirmal says. "No more wasting time digging through files or struggling to keep things in order," he states. "AI can quickly surface the information you need, reducing human error and improving efficiency. It's also excellent for compliance, ensuring sensitive data -- like personal or financial information -- is properly handled and protected." Tip 5. Create a unified, sovereign data platform An innovative approach to managing unstructured data goes beyond outdated data lake methods, says Benjamin Anderson, senior vice president of technology at database services provider EnterpriseDB. A unified, sovereign data platform integrates unstructured, semi-structured, and structured data in a single system, eliminating the need for separate solutions. "This approach delivers quality-of-service features previously available only for structured data," he explains. "With a hybrid control plane, organizations can centrally manage their data across multiple environments, including various cloud platforms and on-premises infrastructure." When it comes to managing diverse forms of data, whether structured, unstructured, or semi-structured, the traditional approach required multiple databases and storage solutions, adding operational complexity, cost, and compliance risk, Anderson notes. "Consolidating structured and unstructured data into a single multi-model data platform will help accelerate transactional, analytical, and AI workloads." About the AuthorJohn EdwardsTechnology Journalist & AuthorJohn Edwards is a veteran business technology journalist. His work has appeared in The New York Times, The Washington Post, and numerous business and technology publications, including Computerworld, CFO Magazine, IBM Data Management Magazine, RFID Journal, and Electronic Design. He has also written columns for The Economist's Business Intelligence Unit and PricewaterhouseCoopers' Communications Direct. John has authored several books on business technology topics. His work began appearing online as early as 1983. Throughout the 1980s and 90s, he wrote daily news and feature articles for both the CompuServe and Prodigy online services. His "Behind the Screens" commentaries made him the world's first known professional blogger.See more from John EdwardsWebinarsMore WebinarsReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like