Intelligent computing: the latest advances, challenges, and future
Computing is a critical driving force in the development of human civilization. In recent years,
we have witnessed the emergence of intelligent computing, a new computing paradigm that …
we have witnessed the emergence of intelligent computing, a new computing paradigm that …
Doing more with less: meta-reasoning and meta-learning in humans and machines
Artificial intelligence systems use an increasing amount of computation and data to solve
very specific problems. By contrast, human minds solve a wide range of problems using a …
very specific problems. By contrast, human minds solve a wide range of problems using a …
[HTML][HTML] Green IoT and edge AI as key technological enablers for a sustainable digital transition towards a smart circular economy: An industry 5.0 use case
Internet of Things (IoT) can help to pave the way to the circular economy and to a more
sustainable world by enabling the digitalization of many operations and processes, such as …
sustainable world by enabling the digitalization of many operations and processes, such as …
Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning
R Desislavov, F Martínez-Plumed… - … Informatics and Systems, 2023 - Elsevier
The progress of some AI paradigms such as deep learning is said to be linked to an
exponential growth in the number of parameters. There are many studies corroborating …
exponential growth in the number of parameters. There are many studies corroborating …
Green ai
Green AI Page 1 54 COMMUNICATIONS OF THE ACM | DECEMBER 2020 | VOL. 63 | NO.
12 contributed articles ILL US TRA TION B Y LIS A SHEEHAN DOI:10.1145/3381831 …
12 contributed articles ILL US TRA TION B Y LIS A SHEEHAN DOI:10.1145/3381831 …
[图书][B] Synthetic data for deep learning
SI Nikolenko - 2021 - Springer
You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
A survey and critique of multiagent deep reinforcement learning
Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …
led to a dramatic increase in the number of applications and methods. Recent works have …
A domain-specific supercomputer for training deep neural networks
A domain-specific supercomputer for training deep neural networks Page 1 JULY 2020 | VOL.
63 | NO. 7 | COMMUNICATIONS OF THE ACM 67 DOI:10.1145/3360307 Google’s TPU …
63 | NO. 7 | COMMUNICATIONS OF THE ACM 67 DOI:10.1145/3360307 Google’s TPU …
Scaling laws for transfer
D Hernandez, J Kaplan, T Henighan… - arXiv preprint arXiv …, 2021 - arxiv.org
We study empirical scaling laws for transfer learning between distributions in an
unsupervised, fine-tuning setting. When we train increasingly large neural networks from …
unsupervised, fine-tuning setting. When we train increasingly large neural networks from …