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Artificial Intelligence

Top news of the week: 13.01.2022.

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Machine learning
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Reinforcement learning
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Research

@hardmaru shared
On Jan 12, 2022
A survey of Automated Reinforcement Learning! This timely review paper covers relevant work from several distinct subfields, ranging from meta-learning to evolution. https://t.co/RuLczPPRYI https://t.co/m98u3c76CD
Open
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems

The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents. ...

@RichardSocher shared
On Jan 11, 2022
Nice summary of the AI Economist paper in the Deep Learning Batch: https://t.co/hrmb2AGl9Q https://t.co/HSs7sxvAEg
Open
Ethics for an Automated Army

Ethics for an Automated Army

Key insight: Economic simulations often use pre-programmed agents to keep the computation manageable, but hard-coding makes it difficult to study the impact of tax rates on agent …

@rsalakhu shared
On Jan 8, 2022
Improving RL with Lookahead: Learning Off-Policy with Online Planning – Machine Learning Blog | ML@CMU | Carnegie Mellon University https://t.co/med2UMKqBb
Open
Improving RL with Lookahead: Learning Off-Policy with Online Planning

Improving RL with Lookahead: Learning Off-Policy with Online Planning

Overview of LOOP: LOOP reduces dependency on value errors by using an H-step Lookahead Policy that plans online using learned dynamics with a terminal value function. The value function is ...

@deliprao shared
On Jan 8, 2022
@marian_nmt There’s a KeyBERT if you want a transformer https://t.co/PnInuIKo59
Open
KeyBERT

KeyBERT

Minimal keyword extraction with BERT. Contribute to MaartenGr/KeyBERT development by creating an account on GitHub.

@mxlearn shared
On Jan 8, 2022
[D] Eric Jang on Robots Learning at Google and Generalization via Language https://t.co/iItBPjb049
Open
[D] Eric Jang on Robots Learning at Google and Generalization via Language

[D] Eric Jang on Robots Learning at Google and Generalization via Language

2 votes and 1 comment so far on Reddit

@weballergy shared
On Jan 10, 2022
RT @MaartenvSmeden: NEW PAPER Our review of guidance documents for AI based prediction models in healthcare is out. Guidance *not just* for development/validation, but also for data collection, software development, impact assessment and model implementation Open access: https://t.co/9FRyJRx5Iu https://t.co/YO9lxNBKXs
Open
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

While the opportunities of ML and AI in healthcare are promising, the growth of complex data-driven prediction models requires careful quality and applicability assessment before they are ...

@petewarden shared
On Jan 10, 2022
RT @Hacksterio: CFU Playground is designed to help create customized hardware solutions that can dramatically speed up the execution time of ML algorithms. https://t.co/Cem9tusSkT
Open
Failing Faster in TinyML

Failing Faster in TinyML

CFU Playground offers a framework to rapidly iterate on hardware machine learning accelerator designs.

@peteskomoroch shared
On Jan 11, 2022
RT @Accel: Today we’re celebrating @Trifacta’s acquisition by @Alteryx. Accel has been a partner to @seankandel, @joe_hellerstein, @jeffrey_heer, and @a_adam_wilson since the very beginning. Congrats, Trifacta team! Thank you for the incredible journey. #AccelFamily https://t.co/gBgLZbVIPx https://t.co/7uoyKWXWxq
Open
Alteryx Announces Acquisition of Trifacta

Alteryx Announces Acquisition of Trifacta

“With Trifacta, our combined cloud platform will serve the needs of entire enterprises from data analytics teams and IT/technology teams to line of business users.” Trifacta …

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