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🚀 Ever wondered how AI can transform programming? Discover the power of fine-grained parallelism with sub-nanosecond overhead in Zig. Dive into the future of efficient computing and innovation! 🧠✨ [Read more](https://lnkd.in/guzbn_WG)

These are the questions I want to ask speakers who are experts in AI at UphillConf on the 17th of May in Bern. If you want to answer these questions or add questions on top of them, please let me know 😊 1. Can you recommend any specific books, online courses, or resources that provide a good introduction to AI concepts and techniques? 2. Can you give us an example of the most impactful AI applications today? 3. What are the most common misconceptions about AI that you encounter, and how do you address them? 4. How do you foresee AI evolving over the next five years, particularly in terms of new opportunities for newcomers to the field? 5. What are the essential skills and knowledge areas that someone should focus on to effectively understand and work with AI technologies? 6. What industries do you think are being most transformed by AI, and where do you see the greatest potential for future innovation? 7. Could you share some examples of entry-level projects or practical exercises that someone can work on to gain hands-on experience in AI? 8. What are the biggest challenges currently facing AI researchers and practitioners, and how are they being addressed? 9. How can professionals from non-technical backgrounds transition into AI roles, and what advice would you give them to make this shift successful? Looking forward to attending talks from Joshua Starmer PhD, Elle O'Brien, Miquel Àngel Farré Leandro von Werra, Lisa Carpenter, Gerard Sans, 🤷 Nico Martin, Kyra Goud, Tobias Kluge, and Josephine Pfeiffer Thank you Stacy von Cranach for inviting me to the conference!  #ai #artificialintelligence #bern #switzerland #datavisualization 🤖

Enjoyed seeing this profile of Yann as part of the Time100 Impact Awards. I've personally seen the power of the open source movement in software, operating systems, and the Web and remain convinced that open research, open standards, and open systems will be the building blocks of the next generation of technology. AI systems 'will constitute basically the repository of all human knowledge. And you cannot have this kind of dependency on a proprietary, closed system.' https://lnkd.in/d2w28smC

Here is an engaging tweet for the "AI Related" article "Spice: Fine-grained parallelism with sub-nanosecond overhead in Zig": 🚀 Imagine your code running with sub-nanosecond speed! Spice redefines fine-grained parallelism in Zig. Curious about the future of high-performance computing? Dive in and let's innovate together! 🤓💡

Models are getting more transparent AND smaller. This is a good thing. Transparency in AI research is crucial for fostering innovation and collaboration. However, many open-source language models only release the model weights and architecture, leaving the training data and process as a black box. ChuXin is a new 1.6B parameter language model that lays everything out in the open—from the training data to the evaluation code. Through lightweight continual pretraining, the model's context length has been extended to a1M tokens, enabling strong performance on challenging long-form retrieval tasks. I'm glad to see more and more models in the 1.5-4B parameter range and the more we know about them, the better. ↓ Liked this post? Follow the link under my name and never miss a paper highlight again 💡

See what AI, developer happiness and productivity, and the future of code have to do with enterprise value delivery in this conversation with GitHub’s VP of Engineering. https://okt.to/09qenE

Happening right now - The Future of Software Engineering and Acquisition with Generative AI via the Software Engineering Institute | Carnegie Mellon University In this webcast, SEI researchers explore the future of software engineering and acquisition using generative AI technologies. https://lnkd.in/eXaeWy8u

Lots of interesting observations about how hallucinations/confabulations can be reduced by certain architectures in this article, but here's what I'm wondering about this morning: Suppose the models do end up solving these problems through what is, in effect, a division of labor: Models are specialized to answer queries within a defined subdomain. Do we then develop new models that are good at integrating answers across subdomains into a full solution that represents the best answer across domains? Even if we do, it seems like it will have to be humans that are playing that role in the near term. 1 for those who are good at context recognition and integrating perspectives across different domains — specialists in generalization. https://lnkd.in/g5RMaEx6

Exciting new blog post alert! Our latest article delves into the disruptive forces of generative AI, augmented reality, and virtual reality, and their profound impact on software engineering and social dynamics. We present a comprehensive research playbook to guide the formulation of pertinent research questions and empirical investigation strategies, with a focus on understanding the long-term implications of these technologies. Dive into the insightful discussion and discover how this playbook can shape the future of research in software engineering and beyond. Read the full post here: https://bit.ly/3UOwgcj

In this plain-language, I've simplified through research how artificial intelligence is revolutionizing the way we build software using classical engineering principles. Consider it my contribution to making this topic easier to understand