Twitter users will have seen the proliferation of “I have a joke” tweets in their feed over the past few days. The AI community produced some gems so we’ve collected a selection here for your amusement.

There were three invited talks at this year’s virtual ICML. The first was given by Lester Mackey, and he highlighted some of his efforts to do some good with machine learning. During the talk he also outlined several ways in which social good efforts can be organised, and described numerous social good problems that would benefit from the community’s attention.

By Misha Khodak and Liam Li

Neural architecture search (NAS) — selecting which neural model to use for your learning problem — is a promising but computationally expensive direction for automating and democratizing machine learning. The weight-sharing method, whose initial success at dramatically accelerating NAS surprised many in the field, has come under scrutiny due to its poor performance as a surrogate for full model-training (a miscorrelation problem known as rank disorder) and inconsistent results on recent benchmarks. In this post, we give a quick overview of weight-sharing and argue in favor of its continued use for NAS.

By Roger Taylor, Chair of the Centre for Data Ethics and Innovation

Failure to use data effectively means we cannot deal with the most pressing issues that face us today, such as discrimination. Addressing this requires institutions that are fit to enable responsible use of data and technology for the public good, engaging civil society and the public as well as industry and government.

The AIhub coffee corner captures the musings of AI experts over a 30-minute conversation. This month we discuss conferences and whether they will ever be the same again now we’ve had a taste of the virtual.

Marc Habermann received an Honorable Mention in the Best Student Paper category at CVPR 2020 for work with Weipeng Xu, Michael Zollhöfer, Gerard Pons-Moll and Christian Theobalt on “DeepCap: Monocular Human Performance Capture Using Weak Supervision”. Here, Marc tells us more about their research, the main results of their paper, and plans for further improvements to their model.

Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Abeba Birhane about “Robot Rights? Exploring Algorithmic Colonization”.

By Akhil Padmanabha and Frederik Ebert

Touch has been shown to be important for dexterous manipulation in robotics. Recently, the GelSight sensor has caught significant interest for learning-based robotics due to its low cost and rich signal. For example, GelSight sensors have been used for learning inserting USB cables (Li et al, 2014), rolling a die (Tian et al. 2019) or grasping objects (Calandra et al. 2017).

There was lots going on at the virtual ICML conference this week. The event was bookended by tutorials and workshops, with the invited talks and poster sessions happening mid-week. There were also numerous opportunities to get involved in socials, and a chance to have a say on the format for future editions at the Town Hall meeting. Here is a selection of tweets from attendees and organisers.