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 (006)
AI Latest Research & Developments - With Digitalent & Mike Nedelko
1 hour 3 minutes
10 months ago
Latest Artificial Intelligence R&D Session - with Digitalent & Mike Nedelko - Episode (006)
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, inspired by Daniel Kahneman's "System 2 thinking." He also discussed "Humanity's Last Exam," a challenging new benchmark d...
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...
