Peak is this summer's finest co-op game. The game, created in partnership with developers Aggro Crab and Landfall as part of a game jam, is currently in Steam's top five bestsellers. It sold over a million copies in its first week, and has now surpassed 8 million, according to Aggro Crab cofounder Nick Kamen. Now, as a result of its success, says Kamen, scammers are selling cheap, AI-made versions of it wherever they can. "We hate to see it," says Kamen.

Medical Imaging (MI) datasets are fundamental to artificial intelligence in healthcare. The accuracy, robustness, and fairness of diagnostic algorithms depend on the data (and its quality) used to train and evaluate the models. MI datasets used to be proprietary, but have become increasingly available to the public, including on community-contributed platforms (CCPs) like Kaggle or HuggingFace. While open data is important to enhance the redistribution of data's public value, we find that the current CCP governance model fails to uphold the quality needed and recommended practices for sharing, documenting, and evaluating datasets. In this paper, we conduct an analysis of publicly available machine learning datasets on CCPs, discussing datasets' context, and identifying limitations and gaps in the current CCP landscape.

Recently normalizing flows have been gaining traction in text-to-speech (TTS) and voice conversion (VC) due to their state-of-the-art (SOTA) performance. Normalizing flows are unsupervised generative models. In this paper, we introduce supervision to the training process of normalizing flows, without the need for parallel data. We call this training paradigm AutoEncoder Normalizing Flow (AE-Flow). It adds a reconstruction loss forcing the model to use information from the conditioning to reconstruct an audio sample. Our goal is to understand the impact of each component and find the right combination of the negative log-likelihood (NLL) and the reconstruction loss in training normalizing flows with coupling blocks. For that reason we will compare flow-based mapping model trained with: (i) NLL loss, (ii) NLL and reconstruction losses, as well as (iii) reconstruction loss only. Additionally, we compare our model with SOTA VC baseline. The models are evaluated in terms of naturalness, speaker similarity, intelligibility in many-to-many and many-to-any VC settings. The results show that the proposed training paradigm systematically improves speaker similarity and naturalness when compared to regular training methods of normalizing flows. Furthermore, we show that our method improves speaker similarity and intelligibility over the state-of-the-art.

This story is syndicated from the Substack newsletter Big Technology; subscribe for free here. In front of a packed house last week at Amsterdam's World Summit AI last week, I asked senior researchers at Meta, Google, IBM, and The University of Sussex to speak up if they did not want AI to mirror human intelligence. After a few silent moments, no hands went up. The response reflected the AI industry's ambition to build human-level cognition, even if it might lose control of it. AI is not sentient now--and won't be for some time, if ever--but a determined AI industry is already releasing programs that can chat, see, and draw like humans as it tries to get there.

Artificial intelligence (AI) has long been the preserve of science fiction narratives involving sentient machines and killer automatons. But for many years now, the term has been a major part of the technologists' lexicon. Everywhere you look, applications, games, services, transportation, cybersecurity and even consumer goods are leveraging the power of AI. It's become shorthand for automating systems that perform laborious tasks – often creatively – beyond the power of mere mortals. "I think artificial intelligence is a misnomer to a certain extent," argues Martine Bertrand, Senior AI Researcher at DNEG Montreal. "The reason why there's so much hype around these technologies is because, for most of us, we get convinced this is artificial intelligence in the form of a super-smart robot or a brain living in a cloud, that can suddenly do magical stuff.

The Pulitzer Prize-winning book Gödel, Escher, Bach inspired legions of computer scientists in 1979, but few were as inspired as Melanie Mitchell. After reading the 777-page tome, Mitchell, a high school math teacher in New York, decided she "needed to be" in artificial intelligence. She soon tracked down the book's author, AI researcher Douglas Hofstadter, and talked him into giving her an internship. She had only taken a handful of computer science courses at the time, but he seemed impressed with her chutzpah and unconcerned about her academic credentials. Mitchell prepared a "last-minute" graduate school application and joined Hofstadter's new lab at the University of Michigan in Ann Arbor.

The Pulitzer Prize-winning book Gödel, Escher, Bach inspired legions of computer scientists in 1979, but few were as inspired as Melanie Mitchell. After reading the 777-page tome, Mitchell, a high school math teacher in New York, decided she "needed to be" in artificial intelligence. She soon tracked down the book's author, AI researcher Douglas Hofstadter, and talked him into giving her an internship. She had only taken a handful of computer science courses at the time, but he seemed impressed with her chutzpah and unconcerned about her academic credentials. Mitchell prepared a "last-minute" graduate school application and joined Hofstadter's new lab at the University of Michigan in Ann Arbor.

This paper presents a novel unsupervised abstractive summarization method for opinionated texts. While the basic variational autoencoder-based models assume a unimodal Gaussian prior for the latent code of sentences, we alternate it with a recursive Gaussian mixture, where each mixture component corresponds to the latent code of a topic sentence and is mixed by a tree-structured topic distribution. By decoding each Gaussian component, we generate sentences with tree-structured topic guidance, where the root sentence conveys generic content, and the leaf sentences describe specific topics. Experimental results demonstrate that the generated topic sentences are appropriate as a summary of opinionated texts, which are more informative and cover more input contents than those generated by the recent unsupervised summarization model (Bra\v{z}inskas et al., 2020). Furthermore, we demonstrate that the variance of latent Gaussians represents the granularity of sentences, analogous to Gaussian word embedding (Vilnis and McCallum, 2015).

Companies looking to protect their innovations in artificial intelligence and machine learning face a dilemma. Data and algorithms are essential components to developing products based on these technologies, but it is notoriously difficult to protect them from an intellectual property perspective. There are two possible approaches here. One is to apply for a patent. Another is to seek trade secret protection.

Editor's note: Noah Wang is co-founder and chief marketing officer of TOP Network, a Silicon Valley-based tech firm developing a business-friendly public blockchain and the world's first blockchain-based cloud communication network. The article reflects the author's views, and not necessarily those of CGTN. Earlier in January, at the annual World Economic Forum in Davos, Switzerland, Bloomberg released the 2019 Bloomberg Innovation Index, which ranks the most innovative countries using criteria including R&D investment, manufacturing capability, and patent activity. China jumped three spots to the 16th compared with a year before, beating the UK for the first time. However, Bloomberg index showed that China lagged far behind its innovative peers such as six-time champion the Republic of Korea as well as the U.S. and Japan, which secured their places among the top 10.