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Google DeepMind Profile picture
@GoogleDeepMind
May 11, 2020 • 7 tweets • 5 min read • Read on X
We’re back with more suggestions from our researchers for ways to expand your knowledge of AI.

Today’s #AtHomeWithAI recommendations are from research scientist Kimberly Stachenfeld (@neuro_kim) (1/7) Image
She recommends “The Scientist in the Crib” [longer listen] by @AlisonGopnik, Andrew Meltzoff, & Patricia K. Kuhl for those who are interested in what early learning tells us about the mind.

Listen along here: adbl.co/2Wwp5pE #AtHomeWithAI
Want to explore intelligence by using an approach that integrates cognitive science, neuroscience, computer science and AI?

Kimberly suggests the Brains, Minds & Machines Summer Course, offered & taught by @MBLScience and @MIT_CBMM here: bit.ly/351M6V5 #AtHomeWithAI
Interested in computational systems neuroscience? @neuro_kim recommends the lecture series from @MBLScience to learn more about circuits and system properties of the brain.

Watch them here: bit.ly/2VzCez2 #AtHomeWithAI
@neuro_kim says Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems [longer read] by Peter Dayan & L.F. Abbott is a must read for anyone looking for an introduction to the topic.

Read here: bit.ly/2xLt2yv #AtHomeWithAI
Described as “a classic for anyone who wants to understand the roots of DL”, Kimberly recommends “The Appeal of Parallel Distributed Processing” [longer read] by James McClelland, the late David Rumelhart, & Geoffrey Hinton.

Read it here: stanford.io/2VCHbHr #AtHomeWithAI
Have your own list of favourite resources? Let us know below! #AtHomeWithAI

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More from @GoogleDeepMind

Google DeepMind Profile picture
Mar 19
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
Read 6 tweets
Google DeepMind Profile picture
Feb 15
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. Static image with text saying: "Introducing Gemini 1.5 Pro."
Read 9 tweets
Google DeepMind Profile picture
Jan 17
Introducing AlphaGeometry: an AI system that solves Olympiad geometry problems at a level approaching a human gold-medalist. 📐

It was trained solely on synthetic data and marks a breakthrough for AI in mathematical reasoning. 🧵 dpmd.ai/alphageometry
AlphaGeometry is a system made up of 2️⃣ parts:
🔵 A neural language model, which can predict useful geometry constructions to solve problems
🔵 A symbolic deduction engine, which uses logical rules to deduce conclusions

Both work together to find proofs for complex geometry theorems.Image
🟠 AI systems have struggled with tough geometry problems due to a lack of training data.

We overcome this by generating 100 million synthetic theorems and their solutions across various levels of complexity. AlphaGeometry is trained from scratch entirely on this data.
Read 6 tweets
Google DeepMind Profile picture
Jan 4
How could robotics soon help us in our daily lives? 🤖

Today, we’re announcing a suite of research advances that enable robots to make decisions faster as well as better understand and navigate their environments.

Here's a snapshot of the work. 🧵 dpmd.ai/advanced-robot…
To produce truly capable robots, two fundamental challenges must be addressed:
🔘 Improving their ability to generalize their behavior to novel situations
🔘 Boosting their decision-making speed

We deliver critical improvements in both areas. ↓ dpmd.ai/advanced-robot…
1️⃣ Our new system SARA-RT converts Robotics Transformer models into more efficient versions using a novel method: “up-training.”

This can reduce the computational requirements needed for on-robot deployment, increasing speed while preserving quality. dpmd.ai/advanced-robot…
Read 9 tweets
Google DeepMind Profile picture
Dec 14, 2023
Introducing FunSearch in @Nature: a method using large language models to search for new solutions in mathematics & computer science. 🔍

It pairs the creativity of an LLM with an automated evaluator to guard against hallucinations and incorrect ideas. 🧵 dpmd.ai/x-funsearch
🔎 FunSearch uses an evolutionary approach to find the “fittest” ideas, which are expressed as computer programs to be run and evaluated automatically.

An iterative procedure allows the LLM to suggest improvements to programs while the evaluator discards bad ones. Image
We pushed the boundary of this simple method to discover new results for hard open problems in mathematics and computer science.

FunSearch doesn't only find solutions, it outputs programs that describe how to build those solutions. ↓ dpmd.ai/x-funsearch
Read 6 tweets
Google DeepMind Profile picture
Dec 6, 2023
We’re excited to announce 𝗚𝗲𝗺𝗶𝗻𝗶: @Google’s largest and most capable AI model.

Built to be natively multimodal, it can understand and operate across text, code, audio, image and video - and achieves state-of-the-art performance across many tasks. 🧵 dpmd.ai/announcing-gem…
We’ve optimized Gemini 1.0 for three different sizes, meaning it can run on everything from data centers to mobile phones. 🔨

1️⃣ Ultra: our largest one for highly complex tasks
2️⃣ Pro: our best one for scaling across many tasks
3️⃣ Nano: our most efficient one for devices
Gemini Ultra outperforms human experts on MMLU (massive multitask language understanding): one of the most popular methods of benchmarking AI models.

It involves a combination of 57 test subjects from math to history to law and more. ↓ dpmd.ai/announcing-gem…
Read 6 tweets

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