This AI newsletter is all you need | #2
Last Updated on July 15, 2022 by Editorial Team
Author(s): Towards AI Editorial Team
Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.
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This AI newsletter is all you need | #1
What happened this week inΒ AI
Our AI highlight this week was definitely Minecraft. Yes, Minecraft, the video game. Minecraft is now being used to train multi-modal reinforcement learning (RL) models (models that can take multiple types of inputs like text, images, videos, tabular dataβ¦). OpenAI released a model called VPT and an open competition for NeurIPS 2022. This model aims to learn how to play Minecraft, a complex game quite close to the real world. Likewise, the competition aims to promote research in learning from human feedback to enable agents that can accomplish tasks without crisp, easily-defined reward functions. We think this competition is a great opportunity for anyone interested in RL and advancing the fieldβββalso, check out MineRL, Carnegie Mellonβs Minecraft RL research competition.
The news we share below, MineDojo (by NVIDIA), is also related to this initiative using Minecraft as an RL agent training platform, a promising framework to benchmark embodied AIΒ agents.
Training multi-modals models on Minecraft is near perfect because you have access to a gigantic amount of text and tabular data through the Wiki and Reddit pages, lots of videos and gameplay footage on YouTube, and even video-to-text data through these same YouTube videos with their transcriptsβ¦ All describe a single game the AI can βliveβ in and experiment with.
Hottest news
- MineDojo, a framework to benchmark embodied AI agents by NVIDIA!
Based on Minecraft, MineDojo features thousands of diverse, open-ended tasks and a massive, internet-scale knowledge base where AI agents can freely explore the many 3D worlds of Minecraft. - Canβt get enough of the Craiyon AI image generator? Try these other AI tools.
If youβve played with the Dalle-mini app like most of us, now renamed craiyon, you will most certainly love this new article sharing many cool AI-based apps you can play with, forΒ free. - GitHub Copilot has been made generally available to individual developers!
The AI programming assistant costs $10 per month or $100 a year and is initially free for students, available to everyone.
Most interesting papers of theΒ week
- (Deepmind) Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
DeepNash, an agent trained with model-free multiagent reinforcement learning capable of learning to play the imperfect information game Stratego from scratch, up to a human expertΒ level. - 3D-Aware Video Generation
Exploring 4D generative adversarial networks (GANs) that learn unconditional generation of 3D-aware videos with a GAN framework that synthesizes 3D video supervised only with monocular videos.Β Code - A Path Towards Autonomous Machine Intelligence
A very interesting paper distilling much of Yann LeCunβs thinking over the last 5β10 years about promising directions in AI. More details with an abbreviated section on his TwitterΒ page.
Enjoy these papers and news summaries? Get a daily recap in yourΒ inbox!
This issue is brought to you thanks to Anyscale:
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The Learn AI Together community section!
Meme of theΒ week!
Featured community post from theΒ Discord
SOCKS#6109 shared a very interesting paper about hiding malware in neural networks. Something all AI students should be carefulΒ about.
This basic technique is based on the modification of the float32 values (but can be adapted to float16), where we modify the fraction bits or part of the fraction.
A very interesting user mentioned in a RedditΒ post:
βAs I saw with my experiments, we could easily hide megabytes of code in a simple ResNet50 and get away with it. A well-trained (and generalized) network should not degrade in performance significantly. The testing of that is planned for a futureΒ post.
Also, this method could be used for watermarking neural network weights, which could help with copyright claims (e.g., someone is using your open-sourced (and appropriately licensed) weights out of the box in a commercial product).β
AI poll of theΒ week!
TAI CuratedΒ section
Article of theΒ week
Zero-shot vs. Few-shot Learning: 50 Key Insights with 2022 Updates: This article summarizes some of the key differences and relative advantages of zero-shot vs. few-shot learning setups. With zero-shot learning, a machine can learn from data without being explicitly taught how to do so, while with few-shot learning, a machine can learn from just a few examples.
Last week we published 18 new AI blogs and welcomed six new writers to our platform. If you are interested in publishing at Towards AI, please sign up here and we will publish your blog to our network if it meets our editorial policies and standards.
Featured Jobs thisΒ week
Senior ML EngineerβββAlgolia AI @ Algolia (HybridΒ remote)
Senior ML EngineerβββSemantic Search @ Algolia (HybridΒ remote)
Machine Learning Engineer @ Gather AI (RemoteβββIndia)
Deep Learning Engineer (R&DβββEngineering) @ Weights & BiasesΒ (Remote)
Machine Learning Intern @ Weights & Biases (RemoteβββUSA)
Interested in sharing a job opportunity here? Contact [email protected]!
This AI newsletter is all you need | #2 was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
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