AceReason-Nemotron is a groundbreaking AI model developed by NVIDIA that redefines how we train large language models (LLMs) for math and coding tasks. Unlike traditional models trained through distillation, AceReason uses reinforcement learning (RL) guided by strict verification and binary rewards to push reasoning capabilities further—particularly for small and mid-sized models. Starting with math-focused RL and later fine-tuning on code, the model shows impressive cross-domain generalization: math-only training significantly boosts code performance before even seeing code-related tasks. The new strategies help AceReason-14B outperform strong baselines like DeepSeek-R1-Distill, OpenMath-14B, and OpenCodeReasoning-14B on benchmarks like AIME and LiveCodeBench. It even approaches the capabilities of frontier models like GPT-4 and Qwen-32B in specific reasoning domains. For AI researchers and recruiters, AceReason is a compelling case study in how reinforcement learning—when combined with rigorous training design—can unlock reasoning in smaller models that once seemed exclusive to ultra-large systems.

For the first time in history, we are encountering AI agents that can outperform humans in many tasks, heralding an unprecedented era of technological advancement. This shift presents both significant opportunities and formidable challenges. How will we adapt to a world where AI is an integral part of our daily lives? What strategies can we employ to ensure that the integration of AI leads to positive outcomes for society as a whole?

Radio LoRa, with its rich history and diverse programming in 20 different languages, provides an exceptional platform for this important dialogue. This community radio station has been a beacon of independent journalism and cultural diversity, making it the perfect venue for discussing how we can navigate one of the most significant revolutions in human history.

The landscape of AI language models has been dominated by proprietary systems requiring massive computational resources. However, a new contender, S1, is redefining what’s possible with efficient training techniques and open-source transparency. Developed by researchers from Stanford University, the University…

Reasoning Engineering is the next frontier in AI, optimizing how AI agents collaborate to enhance structured reasoning rather than relying solely on prompt engineering. This approach designs reasoning models, where multiple agents interact to refine inference depth, self-awareness, and response modulation.

For instance, to simulate shyness, an AI system combines emotional perception, self-consciousness modeling, uncertainty processing, and inhibition mechanisms. A RoBERTa model detects emotional triggers, a Bayesian agent estimates social scrutiny, and a GPT-4-based processor introduces hesitation. Finally, a Transformer inhibition model restricts emotional output, ensuring reserved, self-conscious responses, replicating human-like shyness in AI-driven interactions.

How Our Reliance on Artificial Intelligence Risks Eroding Human Reasoning and Shaping a Passive Future Artificial intelligence (AI) is heralded as a transformative force, reshaping industries and augmenting human capabilities. Yet, emerging research warns of a darker undercurrent: the erosion…

Extract knowledge from LLMs for training. Introspection might change the dynamics of learning The landscape of training language models (LLMs) is on the brink of a dramatic transformation. Insights into how LLMs can introspect—access and utilise their own internal knowledge—promise…

Dalith Steiger and Massimo Buonaiuto, two renowned experts in the field of artificial intelligence and internationally sought-after speakers, will be in Zurich on 31 October 2024 to shed light on the opportunities, risks and dangers of AI, the current possibilities and challenges, from the following perspectives.

Excited to share my latest conversation on “The Leadership Lab” podcast, now available on Spotify: how generative AI is fundamentally transforming our society.
We are at a pivotal moment, for the first time in human history, we face intelligences that surpass our own: we have created the very intelligent aliens we once imagined would land on our planet. That is Generative AI, and the impact for human society will be immense.

“AI Will Be a Billion Times Smarter 💡 Than Humans before 2037 ” (Mo Gawdat, Former Chief Business Officer of Twitter/X)

I’m thrilled to announce my participation at the Leaders Dialog conference in Kassel, Germany, on the 3rd and 4th of July 2024. This event is a pivotal gathering for DACH companies to explore the evolving landscape of digitalization. My talk will focus on the revolution of the Generative AI, offering insights into cutting-edge technology trends and presenting compelling business cases to inspire other leaders in this digital revolution.

AI’s rapid evolution marks a pivotal shift in human civilization, presenting dual potentials to either aid or exacerbate our ecological crisis. Beyond mere technological convenience, AI redefines our existence, challenging the core of societal norms through mastery of language and manipulation. This transformative force could influence every aspect of life, from culture and politics to personal identity, demanding a critical examination of its role in shaping future societies.

Generative AI is revolutionizing the automotive industry, enhancing design, supply chain management, and predictive maintenance. By optimizing designs and customizing features, AI is enabling rapid prototyping and improving operational efficiency. AI also boosts supply chain resilience by predicting disruptions and automating quality control, ensuring high standards. The technology’s role in developing autonomous vehicles through extensive scenario testing highlights its transformative impact.

In the ever-evolving battle against cancer, the integration of Self-Supervised Learning (SSL) with CT scan analysis emerges as a beacon of hope, illuminating new pathways for early and accurate diagnosis. SSL, a sophisticated facet of machine learning, thrives on the challenge of unlabeled data, teaching AI models to navigate through vast informational landscapes to uncover hidden patterns indicative of cancer. This pioneering approach not only promises to enhance the precision of cancer detection but also to streamline the operational efficiency of healthcare diagnostics. By leveraging the untapped potential of SSL, we stand on the cusp of revolutionizing how we identify and combat cancer, making strides towards a future where accurate diagnosis is both faster and more accessible.