Other talks also reflected the shift towards a data-centric approach, with a focus on building quality data sets. The notion of data annotation quality was central, and many speakers discussed the challenges of achieving high-quality data sets. As a community, we have a clear understanding of how to measure the quality of models. However, the quality of the data set is somehow still a vague and largely unexplored problem. To bring some light to the topic, some speakers proposed measuring errors in a data set as one of the most important quality measurements.

The first week of the 35th conference on Neural Information Processing Systems (NeurIPS2021) saw eight fascinating invited talks, tutorials, affinity group workshops, and a new datasets and benchmarks track. There were also poster sessions, oral sessions, competitions, demonstrations, and more. With this compilation of tweets, we look back on the week. "The greatest violence is the product of remoteness from reality" – a great talk by Mary L. 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) at #NeurIPS2021 'How duolingo uses AI to Asses, Engage and Teach Better' session @NeurIPSConf is . The final #NeurIPS2021 keynote starts soon! Radhika Nagpal will speak about "The Collective Intelligence of Army Ants, and the Robots They Inspire" at 15:00 GMT (10am EST).https://t.co/hSBUpuwUI8

Consequences of Massive Scaling in Machine Learning opening panel #NeurIPS2021 agrees LLMs haven't yet shifted paradigm, but might do so when we find emergent intelligence *without* understanding.

Now NeurIPS reviews are assigned, given the changes that they made this year (desk rejects and etc.), I am wondering how reviewers find the process of assignment and bidding so far, and how many paper has been assigned to each reviewer?

As a global cornerstone of the artificial intelligence community, NeurIPS … "NeurIPS is the premier conference in machine learning, with prominence …