Flow models have recently made great progress at modeling quantized sensor data such as images and audio. Due to the continuous nature of flow models, dequantization is typically applied when using them for such quantized data. In this paper, we propose subset flows, a class of flows which can tractably transform subsets of the input space in one pass. As a result, they can be applied directly to quantized data without the need for dequantization. Based on this class of flows, we present a novel interpretation of several existing autoregressive models, including WaveNet and PixelCNN, as single-layer flow models defined through an invertible transformation between uniform noise and data samples. This interpretation suggests that these existing models, 1) admit a latent representation of data and 2) can be stacked in multiple flow layers. We demonstrate this by exploring the latent space of a PixelCNN and by stacking PixelCNNs in multiple flow layers.

In standard Turing test, a machine has to prove its humanness to the judges. By successfully imitating a thinking entity such as a human, this machine then proves that it can also think. However, many objections are raised against the validity of this argument. Such objections claim that Turing test is not a tool to demonstrate existence of general intelligence or thinking activity. In this light, alternatives to Turing test are to be investigated. Self-recognition tests applied on animals through mirrors appear to be a viable alternative to demonstrate the existence of a type of general intelligence. Methodology here constructs a textual version of the mirror test by placing the chatbot (in this context) as the one and only judge to figure out whether the contacted one is an other, a mimicker, or oneself in an unsupervised manner. This textual version of the mirror test is objective, self-contained, and is mostly immune to objections raised against the Turing test. Any chatbot passing this textual mirror test should have or acquire a thought mechanism that can be referred to as the inner-voice, answering the original and long lasting question of Turing "Can machines think?" in a constructive manner.

With the advent of huges volumes of data produced in the form of fast streams, real-time machine learning has become a challenge of relevance emerging in a plethora of real-world applications. Processing such fast streams often demands high memory and processing resources. In addition, they can be affected by non-stationary phenomena (concept drift), by which learning methods have to detect changes in the distribution of streaming data, and adapt to these evolving conditions. A lack of efficient and scalable solutions is particularly noted in real-time scenarios where computing resources are severely constrained, as it occurs in networks of small, numerous, interconnected processing units (such as the so-called Smart Dust, Utility Fog, or Swarm Robotics paradigms). In this work we propose LUNAR, a streamified version of cellular automata devised to successfully meet the aforementioned requirements. It is able to act as a real incremental learner while adapting to drifting conditions. Extensive simulations with synthetic and real data will provide evidence of its competitive behavior in terms of classification performance when compared to long-established and successful online learning methods.

A Dutch court has ordered the immediate halt of an automated surveillance system for detecting welfare fraud because it violates human rights, in a judgment likely to resonate well beyond the Netherlands. The case was seen as an important legal challenge to the controversial but growing use by governments around the world of artificial intelligence (AI) and risk modelling in administering welfare benefits and other core services. Campaigners say such "digital welfare states" – developed often without consultation, and operated secretively and without adequate oversight – amount to spying on the poor, breaching privacy and human rights norms and unfairly penalising the most vulnerable. The UN special rapporteur on extreme poverty and human rights, Philip Alston, applauded the verdict and said it was "a clear victory for all those who are justifiably concerned about the serious threats digital welfare systems pose for human rights". The decision "sets a strong legal precedent for other courts to follow", he added.

When you hear the word "cyborg," scenes from the 1980s films RoboCop or The Terminator might spring to mind. But the futuristic characters made famous in those films may no longer be mere science fiction. We are at the advent of an era where digital technology and artificial intelligence are moving more deeply into our human biological sphere. Humans are already able to control a robotic arm with their minds. Cyborgs--humans whose skills and abilities exceed those of others because of electrical or mechanical elements built into the body--are already among us.

Princeton WordNet (PWN) is a lexicon-semantic network based on cognitive linguistics, which promotes the development of natural language processing. Based on PWN, five Chinese wordnets have been developed to solve the problems of syntax and semantics. They include: Northeastern University Chinese WordNet (NEW), Sinica Bilingual Ontological WordNet (BOW), Southeast University Chinese WordNet (SEW), Taiwan University Chinese WordNet (CWN), Chinese Open WordNet (COW). By using them, we found that these word networks have low accuracy and coverage, and cannot completely portray the semantic network of PWN. So we decided to make a new Chinese wordnet called Multi-Fusion Chinese Wordnet (MCW) to make up those shortcomings. The key idea is to extend the SEW with the help of Oxford bilingual dictionary and Xinhua bilingual dictionary, and then correct it. More specifically, we used machine learning and manual adjustment in our corrections. Two standards were formulated to help our work. We conducted experiments on three tasks including relatedness calculation, word similarity and word sense disambiguation for the comparison of lemma's accuracy, at the same time, coverage also was compared. The results indicate that MCW can benefit from coverage and accuracy via our method. However, it still has room for improvement, especially with lemmas. In the future, we will continue to enhance the accuracy of MCW and expand the concepts in it.

The gradient boosting algorithm constructs a regression estimator using a linear combination of simple "base learners". In order to obtain a robust non-parametric regression estimator that is scalable to high dimensional problems we propose a robust boosting algorithm based on a two-stage approach, similar to what is done for robust linear regression: we first minimize a robust residual scale estimator, and then improve its efficiency by optimizing a bounded loss function. Unlike previous proposals, our algorithm does not need to compute an ad-hoc residual scale estimator in each step. Since our loss functions are typically non-convex, we propose initializing our algorithm with an $L_1$ regression tree, which is fast to compute. We also introduce a robust variable importance metric for variable selection that is calculated via a permutation procedure. Through simulated and real data experiments, we compare our method against gradient boosting with squared loss and other robust boosting methods in the literature. With clean data, our method works equally well as gradient boosting with the squared loss. With symmetric and asymmetrically contaminated data, we show that our proposed method outperforms in terms of prediction error and variable selection accuracy.

Self-exciting Hawkes processes are used to model events which cluster in time and space, and have been widely studied in seismology under the name of the Epidemic Type Aftershock Sequence (ETAS) model. In the ETAS framework, the occurrence of the mainshock earthquakes in a geographical region is assumed to follow an inhomogeneous spatial point process, and aftershock events are then modelled via a separate triggering kernel. Most previous studies of the ETAS model have relied on point estimates of the model parameters due to the complexity of the likelihood function, and the difficulty in estimating an appropriate mainshock distribution. In order to take estimation uncertainty into account, we instead propose a fully Bayesian formulation of the ETAS model which uses a nonparametric Dirichlet process mixture prior to capture the spatial mainshock process. Direct inference for the resulting model is problematic due to the strong correlation of the parameters for the mainshock and triggering processes, so we instead use an auxiliary latent variable routine to perform efficient inference.

Hierarchical reinforcement learning (HRL) helps address large-scale and sparse reward issues in reinforcement learning. In HRL, the policy model has an inner representation structured in levels. With this structure, the reinforcement learning task is expected to be decomposed into corresponding levels with sub-tasks, and thus the learning can be more efficient. In HRL, although it is intuitive that a high-level policy only needs to make macro decisions in a low frequency, the exact frequency is hard to be simply determined. Previous HRL approaches often employed a fixed-time skip strategy or learn a terminal condition without taking account of the context, which, however, not only requires manual adjustments but also sacrifices some decision granularity. In this paper, we propose the \emph{temporal-adaptive hierarchical policy learning} (TEMPLE) structure, which uses a temporal gate to adaptively control the high-level policy decision frequency. We train the TEMPLE structure with PPO and test its performance in a range of environments including 2-D rooms, Mujoco tasks, and Atari games. The results show that the TEMPLE structure can lead to improved performance in these environments with a sequential adaptive high-level control.

Stuart Russell, a leading artificial-intelligence (AI) researcher at the University of California, Berkeley, and author of the book Human Compatible (Penguin Random House, October 2019), sits down with McKinsey Global Institute chairman James Manyika to discuss our future as AI transforms our world. In this broad conversation, they explore the immense benefits ahead and what our role will be as AI becomes more pervasive. They also delve into potential challenges we may face with our current approach to AI, and how we can redefine AI to ensure it helps humanity achieve its full potential. James Manyika: When you look at the AI field today and you see all these announcements and breakthroughs, what excites you the most? Stuart Russell: With today's technology, delivering high-quality education to everybody on Earth is just the beginning. Even fairly simple AI tutoring tools have been shown to be very effective. So that can only get better if we figure out how to roll it out to the people who really need it.