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Artificial Intelligence

Top news of the week: 04.01.2023.

Learning
Neural network
Sub-orbital spaceflight
Cognitive architecture
Language model
Output

@stanfordnlp shared
On Jan 3, 2023
RT @yoavgo: Everyone are writing about LLMs so I did too. (It became longer than I expected. and also not long enough.) https://t.co/TVkvlsqufN https://t.co/DjrkwsHCDN
Open
LLMs.md · GitHub

LLMs.md · GitHub

GitHub Gist: instantly share code, notes, and snippets.

@hardmaru shared
On Dec 31, 2022
RT @SchmidhuberAI: As 2022 ends: 1/2 century ago, Shun-Ichi Amari published a learning recurrent neural network (1972) much later called the Hopfield network (based on the original, century-old, non-learning Lenz-Ising recurrent network architecture, 1920-25) https://t.co/wfYYVcBobg https://t.co/bAErUtNdfN
Open
Annotated History of Modern AI and Deep Learning

Annotated History of Modern AI and Deep Learning

[UNI] Certain RL problems can be addressed through non-neural techniques invented long before the 1980s: Monte Carlo (tree) search (MC, 1949),[MOC1-5] dynamic programming (DP, …

@rodneyabrooks shared
On Jan 1, 2023
Posted my fifth annual review of my dated predictions from Jan 1, 2018, about self driving cars, AI/ML & Robotics, and human spaceflight. They seemed pessimistic back then, but they are standing the test of time. BONUS five new curmudgeonly predictions. https://t.co/YbBDkKby2Q
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Predictions Scorecard, 2023 January 01

Predictions Scorecard, 2023 January 01

On January 1st, 2018, I made predictions about self driving cars, Artificial Intelligence, machine learning, and robotics, and about progress in the space industry. In Q3 electric …

@deliprao shared
On Jan 1, 2023
RT @AnimaAnandkumar: Top-10 things that happened in 2022. It has been an action-packed year! So thankful for the opportunities I have been given and the amazing people I am surrounded with https://t.co/Tsw6PGLcjw
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Top-10 Things in 2022

Top-10 Things in 2022

Using neural network surrogates such as neural operators to replace ODE solvers can significantly speed up sampling diffusion models, as seen in our recent work. Robust Vision and …

@Miles_Brundage shared
On Jan 1, 2023
E.g. just taking one example - https://t.co/KHM6DsmLsX The majority of claims here fall into categories like: - thing that improved in 2022 will improve in 2023 - people will make progress on thing they are openly working on - debate (which is happening already) will happen
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Twenty-Five Eye-Opening 2023 Predictions About Generative AI And ChatGPT Including A Splash Of AI Ethics And AI Law Tossed In

Twenty-Five Eye-Opening 2023 Predictions About Generative AI And ChatGPT Including A Splash Of AI Ethics And AI Law Tossed In

What is going to happen with generative AI and ChatGPT in 2023? Here's your answer. Filled with lots of helpful background and insights. Start the new year armed with the latest on where AI ...

@johnplattml shared
On Dec 28, 2022
@mmitchell_ai @BrianRhoArc Thanks M! It was wonderful to work with you on image captioning in 2015 (https://t.co/TH48LORIzR) People should remember your pioneering image-to-text Midge work in 2012 (https://t.co/vILE3W28QO). Look how far we have all come in 10 years!
Open
From Captions to Visual Concepts and Back

From Captions to Visual Concepts and Back

From Captions to Visual Concepts and Back Hao Fang∗ Saurabh Gupta∗ Forrest Iandola∗ Rupesh K. Srivastava∗ Li Deng Piotr Dolla´r† Jianfeng Gao Xiaodong He Margaret Mitchell John C. …

@stanfordnlp shared
On Dec 31, 2022
RT @justinsylee: Quick blog post to close out the year on my experience taking XCS224n (NLP with Deep Learning) this fall through the Stanford Center for Professional Development: https://t.co/hVEzEHER89 https://t.co/PCpZmQPtWN
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My Experience Taking "XCS224n: NLP with Deep Learning"

My Experience Taking "XCS224n: NLP with Deep Learning"

Justin Seonyong Lee

@Miles_Brundage shared
On Jan 1, 2023
Good paper from Pan/Yahkhmi/Iyer et al., "Comparing the Perceived Legitimacy of Content Mod. Processes" - https://t.co/XkizFlX1YE Key result IMO: "whether users agree with the decision ... has a greater impact on [perceived legitimacy] than the process itself." 🙃🙃🙃🙃🙃
Open
Comparing the Perceived Legitimacy of Content Moderation Processes: Contractors, Algorithms, Expert Panels, and Digital Juries

Comparing the Perceived Legitimacy of Content Moderation Processes: Contractors, Algorithms, Expert Panels, and Digital Juries

82 Comparing the Perceived Legitimacy of Content Moderation Processes: Contractors, Algorithms, Expert Panels, and Digital Juries CHRISTINA A. PAN∗, Stanford University, USA SAHIL …

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