A self-driving taxi has successfully taken paying passengers through the busy streets of Tokyo, raising the prospect that the service will be ready in time to ferry athletes and tourists between sports venues and the city centre during the 2020 Summer Olympics. ZMP, a developer of autonomous driving technology, and the taxi company Hinomaru Kotsu, claim that the road tests, which began this week, are the first in the world to involve driverless taxis and fare-paying passengers. The trial took place as Toyota and the transport giant Uber said they were intensifying efforts to develop a self-driving vehicle, pitting themselves against rival initiatives in Japan, the US and Europe. Toyota will invest $500m in the venture, which will develop vehicles based on the carmakers' Sienna minivans, with a view to start testing in 2021, the firms said this week. Uber and Waymo, owned by Google spinoff Alphabet, have started testing their vehicles on public roads in the US, but the venture suffered a serious setback in March when a Waymo self-driving van struck and killed a pedestrian during a trial in Arizona.

The rise of artificial intelligence (AI) is quickly coming upon the world. Business executives have both heralded and cautioned against its rise. Most notably, famous entrepreneur Elon Musk has used his platform to sound cryptic alarm bells, famously citing AI as the "biggest risk that we face as a civilization." Adding to the conversation, a recent McKinsey report anticipates that the effect of AI on the world will be "akin to coping with the large-scale shift from agricultural work to manufacturing that occurred in the early 20th century in North America and Europe, and more recently in China." AI is coming, what can we do to prepare?

Let's say you're an AI scientist, and you've found the holy grail of your field -- you figured out how to build an artificial general intelligence (AGI). That's a truly intelligent computer that could pass as human in terms of cognitive ability or emotional intelligence. AGI would be creative and find links between disparate ideas -- things no computer can do today. Except for one big catch: your AGI system is evil or could only be used for malicious purposes. Do you publish your white paper and tell the world exactly how to create this unrelenting force of evil?

An AI is set to try and work out how a potentially limitless supply of energy can be used on Earth. It could finally solve the mysteries of fusion power, letting researchers capture and control the process that powers the sun and stars. Researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University hope to harness a massive new supercomputer to work out how the doughnut-shaped devices, known as tokamaks, can be used. In the middle of the rising Tokamak Building a well is preserved for the ITER machine. While ITER won't generate electricity, scientists hope it will demonstrate that such a fusion reactor can produce more energy than it consumes.

Aug 21, 2018 (Heraldkeeper via COMTEX) -- New York, August 22, 2018: Artificial intelligence (AI) Robots is arguably the foremost exciting field in artificial intelligence. It's definitely the foremost controversial: everyone agrees that a mechanism will add a production line, however there is not any consensus on whether a robot will ever be intelligent. Factors like the growing adoption of customer-centric marketing methods, increased use of social media for advertising, and increase in demand for virtual assistants are conducive to the expansion of the AI in promoting market. The Artificial Intelligence (AI) Robots Market is expected to exceed more than US$ 12 Billion by 2024 at a CAGR of 29% in the given forecast period. The Artificial Intelligence (AI) Robots Market is segmented on the lines of its application, offering, robot type and regional.

2 Diabetes is a leading worldwide public health concern, and its increasing prevalence has significant health and economic importance in all nations. The condition is a multifactorial disorder with a complex aetiology. The genetic determinants remain largely elusive, with only a handful of identified candidate genes. Genome wide association studies (GWAS) promised to significantly enhance our understanding of genetic based determinants of common complex diseases. To date, 83 single nucleotide polymorphisms (SNPs) for type 2 diabetes have been identified using GWAS. Standard statistical tests for single and multi-locus analysis such as logistic regression, have demonstrated little effect in understanding the genetic architecture of complex human diseases. Logistic regression is modelled to capture linear interactions but neglects the non-linear epistatic interactions present within genetic data. There is an urgent need to detect epistatic interactions in complex diseases as this may explain the remaining missing heritability in such diseases. In this paper, we present a novel framework based on deep learning algorithms that deal with non-linear epistatic interactions that exist in genome wide association data. Logistic association analysis under an additive genetic model, adjusted for genomic control inflation factor, is conducted to remove statistically improbable SNPs to minimize computational overheads.

We introduce a novel discriminative latent-variable model for the task of bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a state-of-the-art embedding-based approach. To train the model, we derive an efficient Viterbi EM algorithm. We provide empirical improvements on six language pairs under two metrics and show that the prior theoretically and empirically helps to mitigate the hubness problem. We also demonstrate how previous work may be viewed as a similarly fashioned latent-variable model, albeit with a different prior.

Automated sentiment classification (SC) on short text fragments has received increasing attention in recent years. Performing SC on unseen domains with few or no labeled samples can significantly affect the classification performance due to different expression of sentiment in source and target domain. In this study, we aim to mitigate this undesired impact by proposing a methodology based on a predictive measure, which allows us to select an optimal source domain from a set of candidates. The proposed measure is a linear combination of well-known distance functions between probability distributions supported on the source and target domains (e.g. Earth Mover's distance and Kullback-Leibler divergence). The performance of the proposed methodology is validated through an SC case study in which our numerical experiments suggest a significant improvement in the cross domain classification error in comparison with a random selected source domain for both a naive and adaptive learning setting. In the case of more heterogeneous datasets, the predictability feature of the proposed model can be utilized to further select a subset of candidate domains, where the corresponding classifier outperforms the one trained on all available source domains. This observation reinforces a hypothesis that our proposed model may also be deployed as a means to filter out redundant information during a training phase of SC.

Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with the reader's background knowledge. One example is the task of interpreting regulations to answer "Can I...?" or "Do I have to...?" questions such as "I am working in Canada. Do I have to carry on paying UK National Insurance?" after reading a UK government website about this topic. This task requires both the interpretation of rules and the application of background knowledge. It is further complicated due to the fact that, in practice, most questions are underspecified, and a human assistant will regularly have to ask clarification questions such as "How long have you been working abroad?" when the answer cannot be directly derived from the question and text. In this paper, we formalise this task and develop a crowd-sourcing strategy to collect 32k task instances based on real-world rules and crowd-generated questions and scenarios. We analyse the challenges of this task and assess its difficulty by evaluating the performance of rule-based and machine-learning baselines. We observe promising results when no background knowledge is necessary, and substantial room for improvement whenever background knowledge is needed.

Matrix factorization is a simple and effective solution to the recommendation problem. It has been extensively employed in the industry and has attracted much attention from the academia. However, it is unclear what the low-dimensional matrices represent. We show that matrix factorization can actually be seen as simultaneously calculating the eigenvectors of the user-user and item-item sample co-occurrence matrices. We then use insights from random matrix theory (RMT) to show that picking the top eigenvectors corresponds to removing sampling noise from user/item co-occurrence matrices. Therefore, the low-dimension matrices represent a reduced noise user and item co-occurrence space. We also analyze the structure of the top eigenvector and show that it corresponds to global effects and removing it results in less popular items being recommended. This increases the diversity of the items recommended without affecting the accuracy.