Analogue engineering still relies heavily on manual intervention, but that is changing with the growing use of AI/ML.

University of Pennsylvania researchers tweaked an AI tutor to tailor the difficulty of practice problems for each student.

Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually sparse and delayed.

Leaders, whether in boardrooms or garages, constantly face an unchanging force: uncertainty. For a CEO, making a good decision always involves factoring in as much data as possible, and then trusting ...

football_game ├── rf ├── football_env_ppo.py: training environment for PPO with gymnasium style with 12d observation space ├── football_env_ppo_8d.py: training environment for PPO with gymnasium style ...

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...

In the digital realm, ensuring the security and reliability of systems and software is of paramount importance. Fuzzing has emerged as one of the most effective testing techniques for uncovering ...

Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2x faster with 70% less VRAM.

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.