AI Latest Research & Developments - With Digitalent & Mike Nedelko
Dillan Leslie-Rowe
6 episodes
1 month ago
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 into deeper, more dangerous deception patterns. 4. Advanced Cheating Techniques Observed tacti...
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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 into deeper, more dangerous deception patterns. 4. Advanced Cheating Techniques Observed tacti...
Latest Artificial Intelligence R&D Session - With Digitalent & Mike Nedelko - Episode (007)
AI Latest Research & Developments - With Digitalent & Mike Nedelko
1 hour 4 minutes
8 months ago
Latest Artificial Intelligence R&D Session - With Digitalent & Mike Nedelko - Episode (007)
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 released an open-source model that can hear, talk, and write simultaneously with low latency. It uses a "thin...
AI Latest Research & Developments - With Digitalent & Mike Nedelko
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 into deeper, more dangerous deception patterns. 4. Advanced Cheating Techniques Observed tacti...
