AI-native development is creating a paradox: soaring demand for AI engineers, and unemployment for traditional CS graduates. Businesses want developers skilled in prompting, RAG, evals, and agentic workflows—yet most universities still teach 2022-style coding. The best engineers today pair computer science fundamentals with cutting-edge AI fluency. Like the shift from punchcards to terminals, AI-native coding is becoming the new baseline. Those who adapt will thrive. Those who don’t risk obsolescence.

Markov Chains, Markov Decision Processes (MDP), and Memory-augmented MDPs (M-MDP) form the mathematical backbone of decision-making under uncertainty. While Markov Chains capture stochastic dynamics, MDPs extend them with actions and rewards. Yet, real-world tasks demand memory—this is where M-MDPs shine. By embedding structured memory into the agent’s state, M-MDPs enable agentic AI systems to reason, plan, and adapt across long horizons. This blog post explores the mathematics, technicalities, and the disruptive role of M-MDPs in modern AI architectures.

AI agents are overtaking search: shopping visits driven by generative AI surged 4,700%, while retailers like Walmart deploy “super agents” that guide purchasing end-to-end. But agents bring risks—less visible brands, opaque decisions, and emerging trust deficits. To thrive, businesses must reorganise for agent interaction: reengineer SEO through semantic structures, track agent-led conversions, and build accountability into the agent flow. In short, we’re moving into a world where your brand needs to speak agent, not just user.

MIT’s 2025 report finds 95% of enterprise GenAI pilots fail, blocked by a “learning gap.” Tools that don’t adapt, remember, or integrate into workflows stall, while adaptive, embedded systems cross the GenAI Divide. The winners are startups, not big Companies then, focusing on narrow but high-value use cases, embedding in workflows, and scaling through learning. Again, generic SaaS tools and in-house builds fail. Leaders must focus on strategic partnerships with startups, adaptive systems, back-office ROI, and agentic readiness to ensure AI delivers measurable impact—not hype.

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.