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Call for AI-themed holiday videos, art and more

AIHub

You'd better not run, you'd better not hide, you'd better watch out for brand new AI-themed holiday material on AIhub! Drop your videos, AI generated art, pictures, poems, algorithms, datasets, or anything else you can think of, down our chimney at aihuborg@gmail.com We'll be posting the best content through December and January. You can also see our featured content from 2019 here.


Smart cities and AI

AIHub

More than 68% of the world's population live in highly densely built cities. Those cities are not only causing high emissions and urban heat island impacts on the environment, they are also the most vulnerable areas for the impacts of climate change. Thus, immediate climate adaptation of cities is necessary. AI-based methods not only allow us to automatize observations and make future predictions about the climate change related indicators of cities, they also help us to understand those indicators better by using explainable AI techniques. In this lecture, you will see a brief introduction to our studies in this field.


Congratulations to the NeurIPS 2021 award winners!

AIHub

The thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) will be held from Monday 6 December to Tuesday 14 December. This week, the awards committees announced the winners of the outstanding paper award, the test of time award and – for the first time – the best paper award in the new datasets and benchmarks track. Six articles received outstanding paper awards this year. A Universal Law of Robustness via Isoperimetry Sébastien Bubeck and Mark Sellke The authors propose a theoretical model to explain why many state-of-the-art deep networks require many more parameters than are necessary to smoothly fit the training data. On the Expressivity of Markov Reward David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael Littman, Doina Precup and Satinder Singh This paper provides a clear exposition of when Markov rewards are, or are not, sufficient to enable a system designer to specify a task, in terms of their preference for a particular behaviour, preferences over behaviours, or preferences over state and action sequences.



AIhub monthly digest: November 2021 – avoiding hype, musical dissonance, and AI thanksgiving

AIHub

Welcome to our November 2021 monthly digest where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. We have a bumper edition this month covering science communication, avoiding AI hype, events, awards, AI thanksgiving, and much, much more. We have not one, but two discussions to bring you this month. The first, AIhub coffee corner: are deep learning's returns diminishing?, was stimulated by an article that appeared recently in IEEE Spectrum. The article reported that deep-learning models are becoming more and more accurate, but that the computing power needed to achieve this accuracy is increasing at such a rate that, to further reduce the error, the cost and environmental impact is going to be unsustainably high.


What's coming up at NeurIPS 2021?

AIHub

The thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) is due to the kick-off on Monday 6 December, and run until Tuesday 14 December. There is a bumper programme of events, including invited talks, orals, tutorials, workshops, and socials. There are eight keynote speakers this year. Luis von Ahn – How Duolingo uses AI to assess, engage and teach better Mary Gray – The banality of scale: a theory on the limits of modeling bias and fairness frameworks for social justice (and other lessons from the pandemic) Daniel Kahneman – An interview Peter Bartlett – Benign overfitting Gabor Lugosi – Do we know how to estimate the mean? Find out more about the workshops here.


EMNLP 2021 in tweets

AIHub

The Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) took place from the 7th to the 11th of November both in Punta Cana and online. If you did not have time to check the papers and the keynotes at the main conference, here are the livetweeted keynotes and papers sorted by language. Live Notes of EMNLP 2021 #EMNLP2021 Keynote by Ido Dagan on 3 directions that #NLProc should pursue: https://t.co/LLeBjcffOP At #EMNLP2021 Evelina Fedorenko makes a strong case to defuse criticism that neural language models cannot "think". Neither can the human language modules in the brain, she argues, based on human brain studies.


AIhub coffee corner: AI thanksgiving

AIHub

This month, we take a look at all the things we are thankful for in the AI community. Joining the discussion this time are: Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Holger Hoos (Leiden University), Sarit Kraus (Bar-Ilan University), Michael Littman (Brown University) and Carles Sierra (Artificial Intelligence Research Institute of the Spanish National Research Council). Holger Hoos: I think one can be really grateful that progress in AI has come at a point where we really need it. I think we've maneuvered ourselves as humankind into a situation where the limitations of our own natural intelligence make it very likely that we're going to drive ourselves against the wall. Issues such as climate change are simply too complex for us to figure out, even if you bring lots of smart people together and give them lots of resources.


Interview with Tao Chen, Jie Xu and Pulkit Agrawal: CoRL 2021 best paper award winners

AIHub

Congratulations to Tao Chen, Jie Xu and Pulkit Agrawal who have won the CoRL 2021 best paper award! Their work, A system for general in-hand object re-orientation, was highly praised by the judging committee who commented that "the sheer scope and variation across objects tested with this method, and the range of different policy architectures and approaches tested makes this paper extremely thorough in its analysis of this reorientation task". Below, the authors tell us more about their work, the methodology, and what they are planning next. We present a system for reorienting novel objects using an anthropomorphic robotic hand with any configuration, with the hand facing both upwards and downwards. We demonstrate the capability of reorienting over 2000 geometrically different objects in both cases.


Machine learning in chemistry – a symposium

AIHub

Image from TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials, work covered in the first talk by Adrian Roitberg. Reproduced under a CC BY NC ND 4.0 License. Moderated by Seogjoo Jang (CUNY) and Johannes Hachmann (University at Buffalo, SUNY), the event comprised four talks covering: quantum chemistry, predicting energy gaps, drug discovery, and "teaching" chemistry to deep learning models. A Star Wars character beats Quantum Chemistry! A neural network accelerating molecular calculations Adrian Roitberg, University of Florida Abstract: We will show that a neural network can learn to compute energies and forces for acting on small molecules, from a training set of quantum mechanical calculations.