Saifr’s Post

#Google #DeepMind unveils ‘superhuman’ #AI system that excels in fact-checking, saving costs and improving accuracy #artificialintelligence #GenerativeAI #GenAI #LLM #SAFE #superhuman

Dive into the world of AI with our latest opinion piece: "AI is Everyone's New Subject" 📰 💡 Explore how AI transcends traditional boundaries and why it's becoming essential for everyone to understand. 📈 From tech enthusiasts to skeptics, this piece argues AI literacy is crucial in today's digital age. 👉 Read now and join the conversation. #ArtificialIntelligence #AITechnology #DigitalLiteracy #TechOpinion #VNClagoon

AI hype train vs. Regulatory reality = CRASH 💥 Serious businesses need compliant AI. Maxime Vermeir of ABBYY explains how the company uses its 35+ years of experience to make AI practical. Plus, we discussed how #RAG, pruning, knowledge distillation, quantization, and small language models (SMLs) are critical. #AI #Compliance #ResponsibleAI #RAG #ProcessMining #TheFutureIsNow #LLMs #modelops #reponsibleAI #regulations https://lnkd.in/gEWmFu6f

🧮 Large language models (LLMs) hold transformative potential for productivity, yet their limitations—hallucinations, inconsistencies, and harmful outputs—demand a cautious approach. 🔍 A systematic evaluation of tasks against a generative AI cost equation is essential. This involves disaggregating processes, assessing suitability for LLM adaptation, and piloting applications. 🏢 Organizations must balance the benefits of automation with the risks of inaccuracies and legal liabilities. Continuous reevaluation of tasks is crucial as LLM capabilities evolve. By strategically leveraging LLMs, businesses can enhance efficiency while managing inherent risks effectively. Read the full article: https://lnkd.in/dH-vG845 #AI #productivity #Automation #riskmanagement #llm

Size doesn’t matter: Bigger isn't always better in AI The article at CTech by Calcalist explains the different parameters in language models that make them ready for production for real life business use cases 👩💼 In the interview, Ori Goshen, Co-founder and Co-CEO of AI21 Labs, explained: “The reason is that we end up implementing these models within applications, and when the models are smaller, the cost is lower, and they are faster. This is a key consideration for those who build applications, whether corporate or consumer. Not long ago, we released a model called #Jamba, a relatively small model that combines the two architectures. We managed to produce a model that benefits from both worlds, efficiency and quality. Today it is already in its second version and can handle long contexts accurately.” Great job Shay Adler Yuval Partouche Shalom Tel Aviv https://lnkd.in/dFicKKX7 #Jamba #AI #GenAI #LLM

The dawn of a new era is upon us, marked by the unprecedented potential of artificial intelligence (AI) in which tech giants are locked in a fierce competition to lead this revolution. #supplychain #technology #artificialintelligence https://lnkd.in/gEz6PQTw

A recently published Nature article reports that ‘Larger and more instructable language models become less reliable’. The prevailing methods to make large language models more powerful and amenable have been based on continuous scaling up (that is, increasing their size, data volume and computational resources) and bespoke shaping up (including post-filtering, fine tuning or use of human feedback). Contradictory to that, Zhou et al. (2024) report that larger and more instructable language models have become less reliable and that there are no safe operating conditions that users can identify where these LLMs can be trusted. (Find the link to the paper in the comments.) For many experts, these results are anything but surprising. Anyone who wants to benefit from generative AI should be aware that: ⚠️ When it comes to LLMs - larger does not mean better. ⚠️ With probabilistic models there will always be some level of risk of unreliability. 👉 Find guidance on how to manage this risk and capitalize on LLMs in the carousel. Power your future by blending AI strategically: ask the right questions, make strategic choices, and build a future that’s both innovative and reliable. #ai #technology #future IBM Zhou, L., Schellaert, W., Martínez-Plumed, F., Moros-Daval, Y., Ferri, C., & Hernández-Orallo, J. (2024). Larger and more instructable language models become less reliable. Nature, 1-8.

The human experience in the age of #AI Our very own Maria Wan and Jarmo Parkkinen wrote a fascinating blog post about Human-Centered Artificial Intelligence (HCAI). The rise of Large Language Models (LLMs) like #ChatGPT has taken the world by storm, especially the world of work. In the first of three blog posts centered around the topic, they explore how AI could become more human-like in the future and our role in shaping it. Check it out below: https://efcd.co/4aLaue7 #DevOps

Retrieval-augmented generation (RAG) represents the significant ability to enhance the capability of AI agents. Read more 👉 https://lttr.ai/ATR4q #SoftwareDevelopment #RetrievalAugmentedGeneration #AI

In my view, we're witnessing a trend where there's a split focus in AI: incremental innovation in #GenAI and ambitious, long-term innovation towards #AGI. The emphasis on technologies like #SLM and AI-in-a-Box signals a push to deliver practical, affordable solutions with clear business cases for investors eager for #ROI. Meanwhile, significant investments continue to pour into advanced research. A few key players with the financial, intellectual, and computational resources are adopting an #ambidextrous approach, balancing both immediate gains and future breakthroughs https://lnkd.in/dhkpPsg9 https://lnkd.in/dM7P8W4C https://lnkd.in/dWKJXzYk