Soccer players have to master a range of dynamic skills, from turning and kicking to chasing a ball. How could robots do the same? ⚽
We trained our AI agents to demonstrate a range of agile behaviors using reinforcement learning.
Here’s how. 🧵
🔮 New Google DeepMind paper exploring what persuasion and manipulation in the context of language models. 👀
Existing safeguard approaches often focus on harmful outcomes of persuasion. This research argues for a deeper examination of the process of AI persuasion itself to…
1. What are the ethical and societal implications of advanced AI assistants? What might change in a world with more agentic AI?
Our new paper explores these questions:
It’s the result of a one year research collaboration involving 50+ researchers… a🧵
Excited to share Penzai, a JAX research toolkit from
@GoogleDeepMind
for building, editing, and visualizing neural networks! Penzai makes it easy to see model internals and lets you inject custom logic anywhere.
Check it out on GitHub:
"One of the most exciting things that AI can do for humanity is advancing our ability to create new technologies.”
At
#FortuneAI
, our VP of Research
@ZoubinGhahrama1
explained how AI models could accelerate scientific discovery.
Researchers at
@GoogleDeepMind
used deep reinforcement learning to train humanoid bipedal robots to play agile one-on-one soccer with each other.
Read more in
@SciRobotics
:
New research from
@GoogleDeepMind
brings together soccer and robotics. Using reinforcement learning, robots display agile and reactive movements similar to a soccer player, no shin guards needed:)
Say hello to the new members of the Gemma family of open models, tailored to devs and researchers. Learn how they offer new and innovative options for coding tasks and research exploration. 💬➡️