The Chinese SpikingBrain is a new family of brain-inspired large language models that reimagines how AI can process information more efficiently. SpikingBrain models adopt a biological principle: neurons remain idle until an event triggers them to fire. This event-driven design …

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 …

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. …

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 …

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 …

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 …

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 …

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 …

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 …