Apple is taking its time with AI. While most tech companies are racing to push out AI features as fast as they can, Apple is doing the opposite. Its big announcement – Apple Intelligence – won’t arrive for most users until 2026. That’s a long delay in a market where speed seems to matter more…
The implementation of the EU’s AI General-Purpose Code of Practice has exposed deep divisions among major technology companies. Microsoft has signalled its intention to sign the European Union’s voluntary AI compliance framework while Meta flatly refuses participation, calling the guidelines regulatory overreach that will stifle innovation. Microsoft President Brad Smith told Reuters on Friday, “I…
How I taught my live sensor app to answer back, mixing Node.js, JavaScript, and a sprinkle of AI. Continue reading on Artificial Intelligence in Plain English »
Recent developments in LLM agents have largely focused on enhancing capabilities in complex task execution. However, a critical dimension remains underexplored: memory—the capacity of agents to persist, recall, and reason over user-specific information across time. Without persistent memory, most LLM-based agents remain stateless, unable to build context beyond a single prompt, limiting their usefulness in…
Generative reward models, where large language models (LLMs) serve as evaluators, are gaining prominence in reinforcement learning with verifiable rewards (RLVR). These models are preferred over rule-based systems for tasks involving open-ended or complex responses. Instead of relying on strict rules, LLMs compare a candidate response to a reference answer and generate binary feedback. However,…
The Agentic Age: How AI’s Digital Teammates Are Quietly Remaking Our Careers, Companies, and Economy The Agentic Age: How AI’s Digital Teammates Are Quietly Remaking Our Careers, Companies, and… was originally published in Artificial Intelligence in Plain English on Medium, where people are continuing the conversation by highlighting and responding to this story.
The development of large-scale language models (LLMs) has historically required centralized access to extensive datasets, many of which are sensitive, copyrighted, or governed by usage restrictions. This constraint severely limits the participation of data-rich organizations operating in regulated or proprietary environments. FlexOlmo—introduced by researchers at the Allen Institute for AI and collaborators—proposes a modular training…
LLMs have made impressive strides in generating code for various programming tasks. However, they mostly rely on recognizing patterns from static code examples rather than understanding how the code behaves during execution. This often leads to programs that look correct but fail when run. While recent methods introduce iterative refinement and self-debugging, they typically act…
The Growing Threat Landscape for LLMs LLMs are key targets for fast-evolving attacks, including prompt injection, jailbreaking, and sensitive data exfiltration. It is necessary to adapt defense mechanisms that move beyond static safeguards because of the fluid nature of these threats. Current LLM security techniques suffer due to their reliance on static, training-time interventions. Static…
A criticism about AI safety from an OpenAI researcher aimed at a rival opened a window into the industry’s struggle: a battle against itself. It started with a warning from Boaz Barak, a Harvard professor currently on leave and working on safety at OpenAI. He called the launch of xAI’s Grok model “completely irresponsible,” not…