In the first lecture of the series, Research Scientist Hado introduces the course and explores the fascinating connection between reinforcement learning and artificial intelligence: dpmd.ai/RLseries1
In lecture two, Research Scientist Hado explains why it's important for learning agents to balance exploring and exploiting acquired knowledge at the same time: dpmd.ai/RLseries2
In the third lecture, Research Scientist Diana shows us how to solve MDPs with dynamic programming to extract accurate predictions and good control policies: dpmd.ai/RLseries3
In lecture four, Diana covers dynamic programming algorithms as contraction mappings, looking at when and how they converge to the right solutions: dpmd.ai/RLseries4
In part two of the model-free lecture, Hado explains how to use prediction algorithms for policy improvement, leading to algorithms - like Q-learning - that can learn good behaviour policies from sampled experience: dpmd.ai/RLseries6
In this lecture, Hado explains how to combine deep learning with reinforcement learning for deep reinforcement learning. He looks at the properties and difficulties that arise when combining function approximation with RL algorithms: dpmd.ai/RLseries7
In this lecture, Research Engineer Matteo explains how to learn and use models, including algorithms like Dyna and Monte-Carlo tree search (MCTS): dpmd.ai/RLseries8
Our video generation model Veo gives more control over the camera. 📹
You can prompt for:
🔘 Extreme close up
🔘 Slow-motion crane shots
🔘 Timelapses
And more. 🧵
✍️ Prompt: “Timelapse of the northern lights dancing across the Arctic sky, stars twinkling, snow-covered landscape.”
✍️ Prompt: “A panning shot of a waterfall cascading down a rocky cliff, lush greenery surrounding the falls, mist rising from the crashing water.”
✍️ Prompt: “A fast-tracking shot down an suburban residential street lined with trees. Daytime with a clear blue sky. Saturated colors, high contrast.”
We're announcing TacticAI: an AI assistant capable of offering insights to football experts on corner kicks. ⚽
Developed with @LFC, it can help teams sample alternative player setups to evaluate possible outcomes, and achieves state-of-the-art results. 🧵 dpmd.ai/49PGq1b
📊 Corner kicks can be challenging for AI to model due to the limited availability of data - @premierleague matches only average about 10 a game.
TacticAI uses a geometric deep learning approach to tackle this problem. → dpmd.ai/43p5Gcc
🔍 Analysts need to rewatch many game replays to study rival teams and design future tactics.
TacticAI can help by automatically computing numerical representations of players, allowing them to efficiently look up relevant past routines. ↓ dpmd.ai/49PGq1b
Introducing Gemini 1.5: our next-generation model with dramatically enhanced performance. It also achieves a breakthrough in long-context understanding.
The first release is 1.5 Pro, capable of processing up to 1 million tokens of information. 🧵 dpmd.ai/3SEbw4p
Gemini 1.5 was designed using a new Mixture–of-Experts (MoE) architecture, making it much more efficient to train and serve.
When tested on a set of text, code, image, audio and video evaluations, 1.5 Pro outperforms 1.0 Pro on 87% of benchmarks used for developing our LLMs.
Through a series of machine learning innovations, Gemini 1.5 Pro now has the longest context window of any large-scale foundation model yet.
The bigger the context window, the more information it can take in from a prompt — making its output more consistent, relevant and useful.