The film forms part of a research project undertaken by Alan Warburton which also includes a research paper and a series of satellite events. The film is based on doctoral research undertaken at Birkbeck's Vasari Centre for Art & Technology. It was commissioned by the National Videogame Museum in collaboration with the Open Data Institute (ODI) and Cambridge University's Leverhulme Centre for the Future of Intelligence . The ODI hosted a webinar on 6 May to discuss the content of the film. The panellists explored what AI can and can't do, what effects a collapse of real and virtual could have on visual culture, and if we're living in a post-truth world.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we learn about AI for science, delve into world models, research transparent and trustworthy AI, and hear about the lottery ticket hypothesis. The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants featured Ximing Wen who is researching transparent and trustworthy AI systems. We found out more about her work, her experience as a research intern, and what inspired her to study AI. In this wide-ranging conversation, Jonathan Frankle delves into empiricism versus theoretical proofs, how the approach to computer science has changed (even if the fundamental problems haven't), how younger researchers are rapidly adapting to a world that values impact above all else, and what it means to be a researcher.

Pruning the weights of randomly initialized neural networks plays an important role in the context of lottery ticket hypothesis. Ramanujan et al. [23] empirically showed that only pruning the weights can achieve remarkable performance instead of optimizing the weight values. However, to achieve the same level of performance as the weight optimization, the pruning approach requires more parameters in the networks before pruning and thus more memory space. To overcome this parameter inefficiency, we introduce a novel framework to prune randomly initialized neural networks with iteratively randomizing weight values (IteRand). Theoretically, we prove an approximation theorem in our framework, which indicates that the randomizing operations are provably effective to reduce the required number of the parameters. We also empirically demonstrate the parameter efficiency in multiple experiments on CIFAR-10 and ImageNet.

Considerable research efforts have recently been made to show that a random neural network $N$ contains subnetworks capable of accurately approximating any given neural network that is sufficiently smaller than $N$, without any training. This line of research, known as the Strong Lottery Ticket Hypothesis (SLTH), was originally motivated by the weaker Lottery Ticket Hypothesis, which states that a sufficiently large random neural network $N$ contains sparse subnetworks that can be trained efficiently to achieve performance comparable to that of training the entire network $N$.Despite its original motivation, results on the SLTH have so far not provided any guarantee on the size of subnetworks.Such limitation is due to the nature of the main technical tool leveraged by these results, the Random Subset Sum (RSS) Problem.Informally, the RSS Problem asks how large a random i.i.d.

Neural Information Processing Systems http://nips.cc/

Work was done when the author interned at Microsoft.

In recent years, deep learning has become a staple solution to different tasks, such as computer vision,bio-informatics,speechrecognition,andmanymore.

There has been an enormous interest in deploying deep neural networks (DNNs) into numerous real-world applications requiring strict security.

In recent years, the Lottery Ticket Hypothesis (LTH) [1] has drawn great attention and thorough researchefforts.