Interesting!
This paper from Google DeepMind shows LLMs can find patterns with self-generated reasoning.
By only feeding minimal input, the system constructs its own CoT (Chain of Thought), boosting performance + unlocking insights to analyze the model’s reasoning patterns arxiv.org/abs/2402.10200

Deutsche Telekom is announcing a new AI-powered smartphone without traditional apps. Instead, a centralized AI assistant will handle diverse tasks typically accomplished through multiple apps.
More showcases and info in the link 👇
www.telekom.com/en/media/med...

Interesting! Similar to medical placebos, AI (LLM) can induce behavioral changes and alter subjective performance due to heightened positive expectations – even when participants were working with a placebo!
arxiv.org/abs/2309.16606

Zhou et al. (Google Deepmind, University of Southern California) introduce a 3-step prompting framework, from which an LLM can autonomously select and then construct a solution plan. The "Self-Discover" prompt technique outperforms traditional pathways like Chain-of-Thought
arxiv.org/pdf/2402.036...

Hahaha, this is great!
Instead of filing another endless report about Davos, Jen Stave from the Digital Data Design (D^3) Institute at Harvard, simply tossed the line to ChatGPT.

• 5 pages
• Pizza 🍕
• Coffee machine ☕
• and this tweet is already longer than her prompt
d3.harvard.edu/wp-content/u...

Wow, superschöne Animation der Financial Times. Anschaulich wird erklärt, wie ein Large Language Model funktioniert, und wie Programme wie ChatGTP Texte generiert – oder warum es uns manchmal "Halluzinationen" präsentiert.
Bitte teilt das Wissen 🚀
ig.ft.com/generative-ai/?

Leicht verständlich erklärt von einem der Mitwirkenden, Prof. Philipp Hacker:
Worum geht es bei den Regulierung von KI-Modellen, und was hat die deutsche Bundesregierung vor?

www.zeit.de/digital/2023...

Exzellenter Punkt.
In Zeiten von KI (steckt hinter allen Plattformen) ist "Neutralität" allgemein ein heikles Thema. 
Es gibt zum Beispiel interessante Studien zu politischem Bias von LLMs (Large Language Models).

arxiv.org/abs/2305.08283