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

The Pope's Warnings About AI Were AI-Generated, a Detection Tool Claims Pangram Labs' updated Chrome extension puts warning labels on AI slop as you scroll your social feeds. On Monday, a brand-new Reddit account popped up on the widely read forum r/AmItheAsshole, where users have their personal disputes arbitrated by strangers. This particular user asked if they had crossed a line by "refusing to babysit my stepmother's kids because I have my own job and responsibilities." The post itself was succinct, straightforward, and grammatically clean, explaining a situation in which the person's stepmother and father often expected them to provide childcare on little notice, eventually leading to an argument. "Now there's tension at home, and I'm starting to wonder if I handled it the wrong way," the redditor concluded.

What Gilgo Beach killer's wife REALLY knew: Prosecutor reveals chilling truth about life with monster husband... and the'interests' she couldn't ignore Texas bride airlifted back to US on emergency flight after suffering'life-threatening' illness on honeymoon in Japan I thought I'd quit my addiction to'tweakments' and Botox forever. Then, feeling particularly confident at a Dubai lunch, I asked a stranger to guess my age... The lie my husband told to stop me divorcing him is beyond unforgivable. Every woman must beware... otherwise you might never realize: DEAR JANE Elizabeth Smart stuns fans with new incredible bodybuilding photos: 'I refuse to be ashamed' Dark secrets Days of Our Lives star Patrick Muldoon took to his grave: He'tried to hide' truth for decades... now friends are all whispering the same thing after his shock death New'Hollywood dose' pill: A-listers hooked on'youth elixir' that dermatologists say is anti-aging, shrinks pores, smooths wrinkles... and even banishes rosacea Katie Holmes likes telling comment about ex Joshua Jackson who shot to fame with her on Dawson's Creek Trump threatens to'blow up the rest of' Iran and'its leaders' with new Strait of Hormuz ultimatum'Paranoid' Tiger Woods and Vanessa Trump make major shakeup in the wake of golf legend's DUI scandal Death row inmate Chadwick Willacy who burned Florida mom alive during burglary is executed in front of victim's son What has Adam Levine done to his face? Meta's Big Brother move: Mark Zuckerberg's firm starts tracking employees' mouse clicks and taking screenshots of their screens - as one worker calls it'very dystopian' Meta has revealed plans to start tracking its employees' keystrokes and mouse clicks.

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

Seeding - the task of finding initial cluster centers - is critical in obtaining highquality clusterings for k-Means. However, k-means++ seeding, the state of the art algorithm, does not scale well to massive datasets as it is inherently sequential and requires k full passes through the data. It was recently shown that Markov chain Monte Carlo sampling can be used to efficiently approximate the seeding step of k-means++. However, this result requires assumptions on the data generating distribution. We propose a simple yet fast seeding algorithm that produces provably good clusterings even without assumptions on the data. Our analysis shows that the algorithm allows for a favourable trade-off between solution quality and computational cost, speeding up k-means++seeding by up to several orders of magnitude.

This study introduces a novel feature selection approach CMICOT, which is a further evolution of filter methods with sequential forward selection (SFS) whose scoring functions are based on conditional mutual information (MI). We state and study a novel saddle point (max-min) optimization problem to build a scoring function that is able to identify joint interactions between several features. This method fills the gap of MI-based SFS techniques with high-order dependencies. In this high-dimensional case, the estimation of MI has prohibitively high sample complexity. We mitigate this cost using a greedy approximation and binary representatives what makes our technique able to be effectively used. The superiority of our approach is demonstrated by comparison with recently proposed interactionaware filters and several interaction-agnostic state-of-the-art ones on ten publicly available benchmark datasets.

In the past, voice assistants like Siri or the one in your satnav used so-called'synthetic voices'. These require voice actors to spend hours in the recording studio, meticulously sampling all the different words and phrases that the assistant might need. Voice clones, on the other hand, have revolutionised how synthetic voices are created, by using AI to digitally recreate someone's speech patterns. These clones can be created with as little as a few seconds of recorded audio, even using clips from social media or snippets of conversation as the raw material. This has sparked concerns that criminals using AI could easily impersonate friends, family, or co-workers to manipulate their targets . According to the National Trading Standards, criminals are already using AI to clone people's voices and set up unauthorised direct debits over the phone. In the study, the researchers created voice clones of human participants using just 120 pre-recorded sentences. Participants listened to 80 unique sentences - 40 spoken by a real person and 40 spoken by an AI voice clone. The researchers compared human (top) AI-generated (bottom) voice recordings to see why this might be the case, but couldn't find any clear explanation Can you tell which voices are AI?

The restricted isometry property (RIP) for design matrices gives guarantees for optimal recovery in sparse linear models. It is of high interest in compressed sensing and statistical learning. This property is particularly important for computationally efficient recovery methods. As a consequence, even though it is in general NP-hard to check that RIP holds, there have been substantial efforts to find tractable proxies for it. These would allow the construction of RIP matrices and the polynomial-time verification of RIP given an arbitrary matrix. We consider the framework of average-case certifiers, that never wrongly declare that a matrix is RIP, while being often correct for random instances. While there are such functions which are tractable in a suboptimal parameter regime, we show that this is a computationally hard task in any better regime. Our results are based on a new, weaker assumption on the problem of detecting dense subgraphs.

Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights. These models are expressive and allow efficient computation of samples and derivatives, but cannot be used for computing likelihoods or for marginalization. The generativeadversarial training method allows to train such models through the use of an auxiliary discriminative neural network. We show that the generative-adversarial approach is a special case of an existing more general variational divergence estimation approach. We show that any f-divergence can be used for training generative neural samplers. We discuss the benefits of various choices of divergence functions on training complexity and the quality of the obtained generative models.

This paper deals with price optimization, which is to find the best pricing strategy that maximizes revenue or profit, on the basis of demand forecasting models. Though recent advances in regression technologies have made it possible to reveal price-demand relationship of a large number of products, most existing price optimization methods, such as mixed integer programming formulation, cannot handle tens or hundreds of products because of their high computational costs. To cope with this problem, this paper proposes a novel approach based on network flow algorithms. We reveal a connection between supermodularity of the revenue and cross elasticity of demand. On the basis of this connection, we propose an efficient algorithm that employs network flow algorithms. The proposed algorithm can handle hundreds or thousands of products, and returns an exact optimal solution under an assumption regarding cross elasticity of demand. Even if the assumption does not hold, the proposed algorithm can efficiently find approximate solutions as good as other state-of-the-art methods, as empirical results show.