Posts in: AI

#LLMs: “The worst-case scenario would probably be a dictatorship that writes its propaganda into models on a grand scale”

The phenomenon of “artificial intelligence” is fuelled by the fascination that emanates from a machine that is difficult to distinguish from humans when communicating. Even if human communication is only simulated, people react to this communication as they are used to as social beings.

So here is the thing: Through the training the models had, vertain values and believes are subtlely infused. Using methods from psychology, researchers have found that responses differ according to the gender with which the model is addressed. Political colouring can also be determined with these psychometric tests.

Scientific studies confirm that people are influenced by them. And this already happens through the writing assistants, who complete or change sentences, for example. They are thus part of the writing process and can subtly change the opinions of the writers.

Interesting: large language models LLMs can, for example, help supporters of conspiracy theories to reduce their belief in these theories. But this also harbours the danger that the opposite effect can occur if the models are systematically polluted.

The article in the magazine #ct delves deeper into this dicussion (German, paywall): www.heise.de/select/ct…

🚀 The rise of large language models (#LLMs) and machine intelligence applications comes with significant concerns about the substantial resource requirements of these technologies. The demand for expensive hardware and high energy consumption has sparked creative solutions—one of which is #quantization.

• Quantization reduces the size of large language models (LLMs) by lowering parameter precision (e.g., from 32-bit to 8-bit or 4-bit).
• Benefits:
  • Smaller storage requirements enable deployment on devices like smartphones.
  • Improved energy efficiency and lower computational costs.
• Techniques:
  • Post-training quantization (applied after model training).
  • Quantization-aware training (incorporates quantization during training for better accuracy).
• Challenges: Potential accuracy loss and increased implementation complexity.
• Advances: Research on 1-bit models and methods to avoid matrix multiplications to enhance efficiency.

Quantization is a encouraging approach to making LLMs more accessible and sustainable.

For an insightful explanation of this approach (German, paywall): www.heise.de/hintergru…

🚀 Eyes Wide Open, AIs Wide Open: Staying in Control in the Age of AI 🤖

This talk by Yann Lechelle was given at the recent Open Community Experience (OCX) 2024 in Mainz, Germany, and shed light on Europe’s position in the global AI landscape.

Video link: www.youtube.com/watch

My key takeaways:

• Strategic independence: Emphasized the need for Europe to strengthen its technological sovereignty in AI and reduce its reliance on non-European vendors.
• Open Source & Open Science: Emphasized the central role of open source projects and open science in fostering innovation and ensuring transparency in AI development.
• Unified European Approach: Called for coherent policies and investments to strengthen Europe’s AI capabilities, ensuring that they are consistent with universal values and ethical standards.

tl;dr: Europe must strengthen its AI sovereignty through open source collaboration and unified strategies to remain competitive and uphold its core values.

I spent some free time the last few days exploring no-code and low-code workflow automation.

A significant part of my learning involved hands-on experience with #n8n, an open source workflow automation tool. I successfully completed the beginner/level 1 and intermediate/level 2 online courses.

Why n8n?

• Open source: Gives flexibility and transparency.
• Self-hosted 😎 option: Provides steeper learning curve and control over data.

What’s in it for me?

You get a lot! It’s fun, and it can make some of your more mundane tasks, like cross-posting, easier.

And: The future of AI applications lies in AI-enhanced or even AI-agent workflow support. Users will be looking for AI-driven support that goes beyond simple prompts and aims for intelligent, adaptive systems that can make context-aware decisions.

While n8n is user-friendly, it does require some technical understanding. Therefore, the future may lean toward no-code or “assisted low-code” applications, making advanced automation accessible to a broader user base.

For a deeper dive into AI-powered workflows with #n8n, check out this post: blog.n8n.io/ai-agenti…

Another interesting approach to AI-powered workflow applications can be found here: www.entaingine.com

#AI #WorkflowAutomation #NoCode #LowCode #n8n #OpenSource #SelfHosted #AIWorkflows

Introducing #EuroLLM, the latest European addition to the 8B class of LLMs, with a strong emphasis on multilingual capabilities. 🗣️🌍

💡 Why it stands out: • Top performer: Scored the best Borda score (1.0) across multilingual benchmarks. 🏆 • Translation excellence: Shines in tasks like FLORES (88.87) and WMT24 (83.61). 📜➡️🌐 • Versatility: Supports 24+ languages, including Arabic, Chinese, and Russian, powered by 9 billion parameters and a cutting-edge architecture. 🤖✨

Developed by leading European institutions and supported by the EuroHPC infrastructure, EuroLLM showcases the strength of collaborative innovation. 🛠️🇪🇺

For more details and download: huggingface.co/blog/euro…

🚀 Why it matters: In areas of international cooperation, where local language support is key, these models can serve as a powerful asset, rivaling global leaders in the LLM space. 🌐🌟

But let’s keep things in perspective: EuroLLM is in the 8B class, which is impressive but still a step below the likes of GPT-4 and other “Champions League” models. While the size of the models alone is not the deciding factor, there’s still a long way to go - this progress is inspiring and gives us hope for the future. 🌟

#AIInnovation #LLM #EuroLLM #MultilingualAI #TechInEurope #Collaboration #MachineLearning.

In today’s fast-moving landscape, where technologies and customer demands evolve at lightning speed ⚡, traditional businesses face a critical challenge: keeping up. 📉 Their structures - designed for stability and efficiency - often make it difficult to pivot quickly enough.

A solution? Corporate Venturing. 🌟

By partnering with startups, launching internal ventures, or establishing accelerators and incubators, companies unlock a powerful edge: ✅ Access to cutting-edge technologies that reshape industries ✅ New business models to tap into emerging markets ✅ Speed and agility to respond to change faster than competitors ✅ A boost to entrepreneurial culture within the organization

Corporate venturing allows companies to combine their resources, expertise, and networks with the bold innovation of startups—a winning formula for long-term growth and resilience. 🌱

Of course, every opportunity comes with things to manage: 👉 Aligning corporate processes with startup agility 👉 Balancing risk with strategic goals 👉 Integrating new ideas into the core business

💡So will the future belong to those who combine the stability of established companies with the creativity and speed of startups?

Have you seen this approach drive success? Let’s hear your insights! 💬✨

🌟 #OpenSource #LLM Teuken-7B has been officially launched and is available for download 🎉 This milestone is part of the OpenGPT-X initiative, dedicated to the creation of large AI language models “Made in Germany” 🇩🇪, designed for both business and research needs. Even with limited resources - just 18% of the computing power used to train models like Meta’s Llama3 8B - Teuken-7B comes up with some interesting features 🎯

Here’s what makes Teuken-7B stand out:

🔑 Key Features: • Multilingual & Open Source: Built to support all 24 official EU languages, emphasizing European linguistic diversity. 🌍 • Trustworthy & Versatile: Tailored to Europe’s wide range of cultures and businesses, with a strong focus on openness and community collaboration. 🤝

⚙️ Technical Innovations: • Custom Multilingual Tokenizer: Specifically optimized for European languages for enhanced efficiency and performance. Might help also to include other languages into LLMs. • Efficient training: Developed using just 18% of the computing power required for models such as Meta’s Llama3 8B, and less than 1% for larger models such as Llama3 405B - making this an interesting approach for resource-constrained environments and lower energy consumption for training. 🌍💡

📚 Training Data: • Over 50% non-English content, with training content also in languages like Maltese. • Own benchmarks for multilingualism and achieving comparable quality of output in all supported languages.

With Teuken-7B, it seems that Europe is showing some degree of resilience in the context of a geostrategic competition for influence in the emerging AI market. 🚀

For more details, check out the project: 👉 Teuken-7B: opengpt-x.de/en/models…

#AI #OpenSource #Teuken7B #Innovation #MultilingualAI #OpenGPTX #MachineLearning #EuropeanTech 💻🌍

🚀 Are rapid technological advancements leaving IT departments playing catch-up? This isn’t a new challenge. It was similar when messenger first came out and corporate IT only provided email. It can be tough to balance day-to-day operations, endless red tape, compliance needs, and procurement processes!

So introducing #ShadowIT: an intriguing phenomenon in today’s workplaces. 🌐 It’s when employees use their own IT apps for work. These days, it could be AI tools that help them be more productive or tackle specific tasks. It’s clear that this is a bit of a mixed bag. There are pros and cons.

With the rapid adoption of Large Language Models (#LLMs), even as IT departments move quickly to bring these powerful tools into the office, employees have access to even cooler stuff on their personal devices. The pace of technological development outside the workplace is relentless these days!

This dynamic opens up again a discussion on how to leverage personal technological discoveries while maintaining security and efficiency at work. Thoughts on balancing these innovations with workplace policies? 💬 #TechInnovation

If you have to deal with what is called #AI in a professional environment these days, you will often come across the term #RAG. These Retrieval-Augmented Generation systems are intended to address many of the weaknesses of Large-Language Models (#LLM).

These are the core elements:

🔍 Retriever

• Fetches relevant documents from external knowledge bases.
• Utilizes vector representations for efficient text search.
• Employs methods like keyword-based search, semantic search, or vector search to find pertinent information.

🧩 Augmentation

• Integrates retrieved data into the model’s input.
• Filters and structures information for relevance.
• Prepares data to optimize the generation process.

💡 Generator

• The Large Language Model (LLM) processes both the query and augmented information.
• Generates responses conditioned on the retrieved data.
• Delivers the final, synthesized response of the RAG system.

RAG systems help create more accurate, contextual and efficient solutions. This makes them more useful in many areas. They are just one step in the increasingly meaningful application of machine intelligence to real-world use cases.

💡 A few weeks ago, I posted about #OpenWashing in #AI. What is OpenWashing? 🚨 It’s the practice of labeling content as open source to signal transparency, while the actual openness… leaves much to be desired.

🌟 Now, the Open Source Initiative (OSI) has released version 1.0 of its Open Source AI Definition 📜, outlining what it truly means for AI to be #OpenSource.

🔑 Built on the four freedoms of open source software (#OSS) – use, understand, modify, and redistribute – this definition extends these principles to AI systems, including their code, weights, parameters, and documented training data.

📢 The initiative’s goal? Promote transparency 🌍 and enable reproducibility 🔄, ensuring AI systems can be independently reconstructed and adapted. 💡 Open source AI should allow users to build on and adapt models, fostering innovation and trust.

⚖️ While the OSI cannot enforce compliance, it plans to publicly name and shame AI models that falsely claim open source status. This move could influence policy, including the EU’s AI Act🇪🇺, which introduces exemptions and obligations for open-source AI.

✨ Will this definition shape global legislative approaches? Only time will tell. ⏳

While the OSI can’t enforce compliance, it plans to publicly name and shame AI models falsely claiming open-source status. This bold move could influence policies like the EU’s AI Act 🇪🇺, which includes exemptions and obligations for open-source AI.

✨ Will this definition shape legislative approaches? Only time will tell. ⏳

📖 Read the OSI’s definition here: The Open Source AI Definition – 1.0