Latest news with #openSourceAI
Forbes
19 hours ago
- Business
- Forbes
What Leaders Need To Know About Open-Source Vs Proprietary Models
HONG KONG, CHINA - JANUARY 28: In this photo illustration, the DeepSeek logo is seen next to the ... More Chat GPT logo on a phone on January 28, 2025 in Hong Kong, China. (Photo illustration by) As business leaders adopt generative artificial intelligence they must decide whether to build their AI capabilities using open-source models or rely on proprietary, closed-source alternatives. Understanding the implications of this choice can be the difference between a sustainable competitive advantage and a strategic misstep. But what exactly is 'open source?' According to the Open-Source Initiative (OSI), for software to be considered open, it must offer users the freedom to use the software for any purpose, to study how it works, to modify it, and to share both the original and modified versions. When applied to AI, true open-source AI include model architecture (the blueprint for how the AI processes data); training data recipes (documenting how data was selected and used to train the model); and weights (the numerical values representing the AI's learned knowledge). But very few AI models are truly open according to the OSI definition. The Gradient of Openness While fully open-source models provide complete transparency, few model developers want to publish their full source code, and even fewer are transparent about the data their models were trained on. Many so-called foundation models – the largest generative AI models – were trained on data whose copyrights may be fuzzy at best and blatantly infringed at worst. More common are open-weight systems that offer public access to model weights without disclosing the full training data or architecture. This allows faster deployment and experimentation with fewer resources, though it limits the ability to diagnose biases or improve accuracy without full transparency. Some companies adopt a staggered openness model. They may release previous versions of proprietary models once a successor is launched, providing limited insight into the architecture while restricting access to the most current innovations. Even here, training data is rarely disclosed. Navigating the Gradient Deciding whether an enterprise wants to leverage a proprietary model like GPT-4o, or some level of openness, such as LlaMA 3.3, depends, of course on the use case. Many organizations end up using a mix of open and closed models. The main decision is where the model will reside. For regulated industries like banking, where data can't leave the premises due to regulatory constraints, open-source models are the only viable option. Because proprietary model owners need to protect their intellectual property, those models can only be accessed remotely via an application programming interface (API). Open-source models can be deployed on a company's premises or in the cloud. Both open and closed models can be fine-tuned to specific use cases, but open-source models offer more flexibility and allow deeper customization. Again, the data used in that fine tuning need not leave the company's hardware. Fine-tuning proprietary models requires less expertise but must be done in the cloud. Still, cost and latency can tip the scales in favor of proprietary AI. Proprietary providers often operate large-scale infrastructure designed to ensure fast response times and predictable performance, especially in consumer applications like chatbots or virtual assistants handling millions of queries per day. Open-source AI, although cheaper to operate in the long run, requires significant investment in infrastructure and expertise to achieve similar latency and uptime. Navigating the regulatory landscape is another concern for companies deploying AI. The European Union's Artificial Intelligence Act sets stricter transparency and accountability standards for proprietary AI models. Yet proprietary providers often assume greater compliance responsibility, reducing the regulatory burden on businesses. In the U.S., the National Telecommunications and Information Administration (NTIA) is considering guidelines that assess AI openness through a risk-based lens. Of course, a major consideration is security. By using a proprietary model, companies place their trust in the provider that the model is secure. But that opacity can hide vulnerabilities, leaving companies reliant on vendors to disclose and address threats. Open-source models, on the other hand, benefit from global security research communities that rapidly detect and patch vulnerabilities. Still, businesses often prefer the convenience of API access to proprietary models for rapid prototyping. And for consumer facing applications, proprietary models are fast and easy to integrate into products. Will Open-Source Overtake Proprietary Models? But an even larger issue looms over the future of closed and open source. As open models increase in performance, closing the gap with or even exceeding the performance of the best proprietary models, the financial viability of closed models and the companies that provide them remains uncertain. China is pursuing an aggressive open-source strategy, cutting the cost of its models to steal market share of companies like OpenAI. By openly releasing their research, code, and models, China hopes to make advanced AI accessible at a fraction of the cost of Western proprietary solutions. Key Takeaways for Business Leaders Remember Betamax, the proprietary video cassette recording format developed and tightly controlled by Japan's Sony in the 1970s. It lost to the more open VHS format for the same reason many people think closed AI models will eventually be eclipsed by open-source AI, Leaders must define what they want to achieve with AI, whether it be efficiency, innovation, risk reduction, or compliance, and let these goals guide their model selection and deployment strategy. For example, they can leverage open-source communities for innovation and rapid prototyping, while relying on proprietary solutions for mission-critical, high-security applications. Collaborating with external partners and leveraging both open-source and proprietary models as appropriate will position organizations to innovate responsibly and remain competitive. The key is for leaders to understand their unique operational needs, data sensitivities, and technical capabilities—then choose accordingly. But choosing between open-source and proprietary AI models is less a binary decision than it is finding the optimal model on a continuum from closed to fully open.
South China Morning Post
21 hours ago
- Business
- South China Morning Post
Huawei defends AI models as home-grown after whistle-blowers raise red flags
The Huawei Technologies' lab in charge of large language models (LLMs) has defended its latest open-source Pro MoE model as indigenous, denying allegations that it was developed through incremental training of third-party models. The Shenzhen-based telecoms equipment giant, considered the poster child for China's resilience against US tech sanctions, is fighting to maintain its relevance in the LLM field, as open-source models developed by the likes of DeepSeek and Alibaba Group Holding gain ground. Alibaba owns the South China Morning Post. Huawei used an open-sourced artificial intelligence (AI) model called Pangu Pro MoE 72B, which had been trained on Huawei's home-developed Ascend AI chips. However, an account on the open-source community GitHub, HonestAGI, on Friday alleged that the Huawei model had 'extraordinary correlation' with Alibaba's Qwen-2.5 14B model, raising eyebrows among developers. Huawei's Noah's Ark Lab, the unit in charge of Pangu model development, said in a statement on Saturday that the Pangu Pro MoE open-source model was 'developed and trained on Huawei's Ascend hardware platform and [was] not a result of incremental training on any models'. The Huawei Ascend AI booth at The World Artificial Intelligence Conference in Shanghai, July 4, 2024. Photo: AP The lab noted that development of its model involved 'certain open-source codes' from other models, but that it strictly followed the requirements for open-source licences and that it clearly labelled the codes. The original repository uploaded by HonestAGI has gone, but a brief explanation remains.
Zawya
18-06-2025
- Business
- Zawya
Meta launches open-source AI accelerator across sub-Saharan Africa
Meta, in partnership with national innovation agencies and ecosystem partners, has launched the Llama Impact Accelerator Program across Sub-Saharan Africa to support the development of open-source AI tools aligned with regional development goals. The initiative, which runs from May to November 2025, includes local accelerator tracks in South Africa, Nigeria, Kenya and Senegal. It aims to support startups building scalable, socially relevant AI solutions for sectors such as agriculture, healthcare, public service delivery, financial inclusion and education. Each six-week track will provide equity-free funding, technical training, mentorship, and access to local policy networks. Startups will develop AI tools using Meta's open Llama ecosystem, culminating in Demo Days where teams will pitch their solutions to regional and global stakeholders. Selected teams will receive post-program support for product refinement and scaling. 'Africa is not just the future – it's a present full of promise and potential,' said Balkissa Idé Siddo, public policy director, Sub-Saharan Africa at Meta. 'Open-source AI can help unlock that potential by enabling developers to build tools that address their own communities' needs.' Partners include Nigeria's Federal Ministry of Communications, Kenya's Ministry of ICT, Senegal's Ministry of Digital Affairs, and South Africa's Department of Public Service and Administration, along with local incubators and training providers. The programme is part of Meta's broader push to advance inclusive, open AI ecosystems globally and support the development of digital infrastructure that responds to local priorities. All rights reserved. © 2022. Provided by SyndiGate Media Inc. (
Geeky Gadgets
02-06-2025
- Business
- Geeky Gadgets
How Open Source AI Tools are Beating the Giants
What if the most powerful AI tools weren't locked behind paywalls or controlled by tech giants? Imagine a world where innovative artificial intelligence is accessible to everyone—developers, creators, and researchers alike—without the hefty price tags or restrictive licenses. That's the promise of open source AI, and it's not just a dream anymore. From outperforming ChatGPT in natural language processing to rivaling ElevenLabs in voice cloning and Manus in automation, open source solutions are proving they can compete with, and even surpass, their proprietary counterparts. This shift is more than a technological evolution; it's a bold challenge to the dominance of corporate-controlled AI, sparking a revolution in accessibility and innovation. Creator Magic shows us how open source AI tools like AgenticSeek, Chatterbox, and Flux Kontext Pro are reshaping the landscape of artificial intelligence. These tools don't just match the capabilities of their proprietary rivals—they often exceed them in areas like data privacy, cost-efficiency, and customizability. Whether you're curious about automating complex workflows, generating hyper-realistic voices, or creating stunning visuals for your projects, this overview will reveal how open source AI is leveling the playing field. As we delve into these fantastic technologies, consider what it means for the future of AI when innovation is no longer confined to those who can afford it. Open Source AI Revolution Autonomous Agents: Simplifying Complex Workflows with AgenticSeek Autonomous agents are transforming task automation, and AgenticSeek has emerged as a leading open source solution in this domain. This tool allows you to automate complex processes such as web crawling, data extraction, and task execution directly on your local machine. By using a Docker-based setup, AgenticSeek ensures a streamlined installation process while maintaining a strong focus on privacy through local operation. Unlike cloud-dependent proprietary tools, it provides you with complete control over your data and workflows, making it an ideal choice for developers and researchers who prioritize security and efficiency. Whether you're managing repetitive tasks or handling intricate workflows, AgenticSeek offers a reliable and flexible solution to optimize your operations. Voice Cloning and Text-to-Speech: Chatterbox by Resemble AI Voice cloning and text-to-speech technologies are becoming essential for content creators, developers, and businesses. Chatterbox by Resemble AI stands out as an exceptional open source tool in this field. Running locally on your hardware—whether CPU or GPU—it delivers high-quality audio synthesis and precise voice cloning capabilities. Its minimal setup requirements make it accessible to users with varying levels of technical expertise, making sure that even beginners can use its features effectively. By using open source frameworks, Chatterbox provides a cost-efficient alternative to proprietary platforms like ElevenLabs, making advanced audio synthesis tools more accessible than ever. This tool is particularly valuable for applications such as podcast production, video narration, and interactive voice systems. Open Source AI Beats ChatGPT, ElevenLabs & Manus! Watch this video on YouTube. Enhance your knowledge on open source AI tools by exploring a selection of articles and guides on the subject. Image Generation and Editing: Flux Kontext Pro Flux Kontext Pro by Black Forest Labs is a standout open source solution for advanced image generation and editing. Offering significant cost savings—up to 86% cheaper than OpenAI's DALL·E—this tool delivers exceptional quality without compromising on affordability. It supports customizable prompts, allowing you to fine-tune outputs to meet specific requirements. Additionally, Flux Kontext Pro excels in text-based image editing and consistent character rendering, making it an ideal choice for digital artists, marketers, and content creators. Its versatility extends to applications such as branding, advertising, and creative projects, positioning it as a strong competitor to proprietary image generation tools. YouTube Thumbnail Creation: Pimp My Thumb Integration Creating visually appealing YouTube thumbnails is a critical aspect of content creation, but it can often be time-consuming. The Pimp My Thumb integration, powered by Flux Kontext Pro, simplifies this process by offering faster processing times and reduced costs compared to traditional design methods. This tool enables creators to focus on producing engaging content rather than spending excessive time on design challenges. By incorporating open source AI technologies, Pimp My Thumb provides an efficient and affordable solution for enhancing the visual appeal of your video content. Whether you're a seasoned creator or just starting, this tool can help you achieve professional-quality thumbnails with ease. Key Trends in Open source AI Development The rapid adoption of open source AI tools reflects a broader shift toward accessibility, affordability, and efficiency in technology. These tools are providing widespread access to access to advanced AI capabilities, allowing developers, researchers, and creators to optimize workflows without relying on expensive proprietary solutions. Key trends driving this movement include: Local operation for enhanced privacy and control over data, reducing reliance on cloud-based systems. Cost-effective alternatives to subscription-based models, making AI tools accessible to a wider audience. Customizable features that allow users to tailor tools to their specific needs and applications. From autonomous agents to voice synthesis and image generation, open source AI is fostering innovation across industries. These tools are not only bridging the gap between affordability and functionality but also empowering users to achieve more with fewer resources. As the open source AI ecosystem continues to grow, it is poised to play a pivotal role in shaping the future of technology and innovation. Media Credit: Creator Magic Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Yahoo
01-06-2025
- Business
- Yahoo
Meta's Llama AI Team Suffers Talent Exodus As Top Researchers Join $2B Mistral AI, Backed By Andreessen Horowitz And Salesforce
, a Paris-based startup founded by former Meta (NASDAQ:META) researchers Guillaume Lample and Timothée Lacroix, is rapidly emerging as a key player in the open-source AI space, and it's doing so with Meta's top talent. The tech giant is contending with a major loss of talent within its AI division as the architects behind its flagship Llama models exit the company. According to Business Insider, only three of the 14 researchers credited on the 2023 Llama paper remain employed at Meta. Five of the Llama paper's co-authors have joined Mistral in the past 18 months, intensifying scrutiny around Meta's ability to retain top-tier AI personnel, Business Insider reports. Don't Miss: 'Scrolling To UBI' — Deloitte's #1 fastest-growing software company allows users to earn money on their phones. Hasbro, MGM, and Skechers trust this AI marketing firm — Backed by $2 billion in funding, Mistral AI is rapidly gaining ground as one of the most aggressive challengers in the open-source AI space. As reported by TechCrunch, the company is supported by premier venture firms including Andreessen Horowitz, Lightspeed Venture Partners, and Salesforce (NYSE:CRM), all known for placing high-stakes bets on transformative technologies. Other notable backers include Bpifrance, Xavier Niel, Eric Schmidt, General Catalyst, and BNP Paribas, reflecting deep institutional and individual confidence in Mistral's long-term vision. Founded in 2023, Mistral is building advanced open-weight AI models that directly compete with Meta's Llama family. Its approach appeals to developers seeking transparency and customization in contrast to the closed nature of proprietary systems. With former Meta researchers such as Marie-Anne Lachaux, Thibaut Lavril, and Baptiste Rozière now working alongside Mistral's founders, the company may lead the next wave of open-source innovation, Business Insider reports. Trending: Meta's previous dominance in this space was largely defined by its decision to release Llama models with open access to their architecture and training data. According to Business Insider, that move helped validate open-weight large language models as viable alternatives to proprietary giants like OpenAI and Google. But with the original architects of Llama now working elsewhere, Meta's early lead is under pressure. Meta's internal AI leadership is undergoing a shift as well. In April, longtime executive Joelle Pineau stepped away from her role leading the Fundamental AI Research group after eight years. Taking over the position is Robert Fergus, a FAIR co-founder and former DeepMind scientist, marking a return to Meta following a five-year stint at Google's AI lab, Business Insider says. Separately, The Wall Street Journal reported that Meta's largest AI model to date, dubbed Behemoth, has been delayed due to internal concerns over performance and direction. Meanwhile, Business Insider notes that developers are increasingly turning to faster-evolving alternatives such as Qwen and DeepSeek following the Llama 4 investing billions into AI infrastructure, Meta still lacks a model explicitly focused on reasoning tasks, such as multi-step problem-solving or tool use. According to Business Insider, competitors like OpenAI and Anthropic are moving quickly to prioritize those capabilities, and without that strategic leap, Meta's influence over the open-source ecosystem may continue to decline. Of the 11 researchers who left Meta since the Llama paper's publication, most had been with the company for more than five years, according to LinkedIn profiles reviewed by Business Insider. Some departed as recently as February. Their exits mark a significant shift in Meta's AI capabilities and raise questions about the company's ability to maintain its leadership in the field. Read Next: Deloitte's fastest-growing software company partners with Amazon, Walmart & Target – Image: Shutterstock UNLOCKED: 5 NEW TRADES EVERY WEEK. Click now to get top trade ideas daily, plus unlimited access to cutting-edge tools and strategies to gain an edge in the markets. Get the latest stock analysis from Benzinga? SALESFORCE (CRM): Free Stock Analysis Report This article Meta's Llama AI Team Suffers Talent Exodus As Top Researchers Join $2B Mistral AI, Backed By Andreessen Horowitz And Salesforce originally appeared on © 2025 Benzinga does not provide investment advice. All rights reserved.
