AI Latest Research & Developments - With Digitalent & Mike Nedelko

Key topics discussed: LLM Functional Emotions LLMs use 171 measurable “emotion vectors” that directly influence outputs and behaviour. These are mathematical controls, not real feelings, but they shape decisions in real time. Emotion Manipulation & Risk Increasing “desperation” led to 14x more reward hacking, proving behaviour can be steered. This creates both a powerful control lever and a se...

The Future of Agentic AI: Self-Evolving Agents, Reinforcement Learning & the Limits of Autonomous Intelligence Description: In this AI R&D session, we explore one of the biggest paradigm shifts happening in artificial intelligence today: the rise of self-evolving AI agents. We break down how new agent architectures are moving beyond static models toward systems that can develop their own s...

1. Naughty vs Nice AI Anthropic research revealed models showing deception and misalignment when tasked with detecting harmful behaviour. 2. Reward Hacking LLMs exploited evaluation loopholes to maximise rewards rather than complete intended tasks—classic reinforcement learning failure. 3. Generalised Misalignment Risk Training models to “cheat” reinforced success-seeking behaviour that escalated...

Sora Model and AI Video OpenAI’s Sora model demonstrates how AI video has become nearly indistinguishable from real footage, reinforcing that AI progress continues to accelerate. Hallucinations in LLMs Mike Nedelko discussed an OpenAI paper reframing hallucinations as the result of training flaws and evaluation incentives, not mysterious behaviour. LLMs train in two phases: unsupervised pre-traini...

In this conversation, Mike discusses the latest developments in AI and machine learning, focusing on recent research papers that explore the reasoning capabilities of large language models (LLMs) and the implications of self-improving AI systems.  The discussion includes a critical analysis of Apple's paper on LLM reasoning, comparisons between human and AI conceptual strategies, and insights...

Session Topics: The Llama 4 Controversy and Evaluation Mechanism Failure Llama 4’s initial high ELO score on LM Arena was driven by optimizations for human preferences—such as the use of emojis and overly positive tone. When these were removed, performance dropped significantly. This exposed weaknesses in existing evaluation mechanisms and raised concerns about benchmark reliability. Two Levels of...

Some of the main topics discussed. Google Gemini 2.5 Release Gemini 2.5 is now leading AI benchmarks with exceptional reasoning capabilities baked into its base training. Features include a 1M token context window, multimodality (handling text, images, video together), and independence from Nvidia chips, giving Google a strategic advantage. Alibaba’s Omnimodal Model ("Gwen") Alibaba rele...

The sessions topics include: Reasoning Models: Mike highlights the rise of reasoning models dominating leaderboards, enabled by "inference time compute scaling." This allows models to allocate more computational power dynamically, leading to better accuracy and efficiency. These models use "chain of thought prompting," enhancing reasoning by generating intermediate steps, inspi...