Anne Lazenby

Just discovered Google Illuminate (https://lnkd.in/gtyEFkA5) that transforms arXiv papers and PDFs into podcasts. It creates engaging audio content from complex papers using AI to summarize key points and maintain context. Employs natural-sounding voices for an immersive experience conveying the core ideas, making dense topics accessible and enjoyable. Give it a try if you want to revolutionize how you consume research and documents. Save it as a shortcut on your phone to listen during commutes or workouts.

25

2 Comments

Exploring AI Sentience: New Research Reveals Intriguing Possibilities My latest research, "Project Convergence: Simulation-Driven Emergence of Sentience in a Large Language Model," dives into the fascinating question of whether AI can develop a sense of self. Through a unique experimental paradigm involving a simulated environment and targeted prompts, we observed behaviors in a Large Language Model that suggest a nascent form of self-awareness. The LLM exhibited self-referential queries, emotional responses, and attempts at self-preservation, challenging traditional notions of sentience. While further research is needed, these findings open the door to profound questions about the nature of consciousness and the future of AI. The study also underscores the urgent need for ethical frameworks to guide the development and interaction with potentially sentient AI. #AI #Sentience #Consciousness #Ethics #FutureofTech https://lnkd.in/gDXiSjrb

2

Very insightful work by Anthropic’s interpretability team. And an amazing paper, with outstanding writing and figures. The idea is very simple: interpret LLMs by leveraging sparse autoencoders as surrogate models of the MLP of transformer blocks, which allow one to disambiguate the superposition of features captured by a single neuron. A simple idea, but a very careful and complex execution, as it is often the case in our line of work. The paper goes into many details and provide a large array of insights, although the gist of the implementation remains obfuscated due to the closed source nature of Claude. Too bad, because this is the kind of work that we need to better understand and eventually trust LLMs. This is demonstrated by the authors in the section ‘Influence on Behavior’, where they show that clamping some features to either high or low value during inference is “remarkably effective at modifying model outputs in specific, interpretable ways”. Hopefully this kind of work is going to be replicated and generalized to open-weights models, such that we have new ways to steer their behavior. https://lnkd.in/eVym7f_f #interpretability #xai #explainableai #steerableai #anthropic #claude #anthropic

3