"I have songwriting credits…even though I don't know how to write a song." 1 The speaker of this statement is not a musician and has no musical training. He helped create an app called Endel, which is self-described as "a cross-platform audio ecosystem." 2 Endel is part of a larger part of the current hot debate over works of art being "created" by computers using programs employing "artificially intelligent" modes of computer learning, or AI for short. "Dmitry Evgrafov, Endel's composer and head of sound design, says all 600 tracks were made'with a click of a button.' There was minimal human involvement outside of chopping up the audio and mastering it for streaming. Endel even hired a third-party company to write the track titles." 3 What makes this notable is that Endel has a record deal with Warner Bros. Music. 4 "Five Endel albums have already been released, and 15 more are coming this year -- all of which will be generated by code. In the future, Endel will be able to make infinite ambient tracks." 5 But didn't the Endel engineers create the software in question?

"We will work constructively with other WTO members to undertake necessary WTO reform with a sense of urgency," the statement said. Hiroshige Seko, minister of economy, trade and industry, said that it is significant that specifics concerning WTO reform were included in a G20 ministerial statement for the first time. Improving the organization's system for resolving disputes was one of the issues raised. "We agree that action is necessary regarding the functioning of the dispute settlement system consistent with the rules as negotiated by the WTO members," the statement said. Foreign Minister Taro Kono said it was a "big feat" for Japan, which places importance on this issue, that the need to address the system was explicitly mentioned in the statement.

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

We propose a nonparametric test of independence, termed OPT-HSIC, between a covariate and a right-censored lifetime. Because the presence of censoring creates a challenge in applying the standard permutation-based testing approaches, we use optimal transport to transform the censored dataset into an uncensored one, while preserving the relevant dependencies. We then apply a permutation test using the kernel-based dependence measure as a statistic to the transformed dataset. The type 1 error is proven to be correct in the case where censoring is independent of the covariate. Experiments indicate that OPT-HSIC has power against a much wider class of alternatives than Cox proportional hazards regression and that it has the correct type 1 control even in the challenging cases where censoring strongly depends on the covariate.

Artificial intelligence is quickly becoming a part of daily life. Enterprise implementations of AI-based technologies tripled in 2018, according to Gartner. At the same time, it's reaching ubiquity in consumer-facing applications, helping us write our emails, discover new music, and get on-demand customer support. At every touchpoint, our data is being collected and used to make machines faster and smarter, and that's driving calls for regulation from global citizens, governments, and companies who want to ensure deployments of machine and deep learning algorithms are safe and ethical. While implementing laws to protect consumers from "AI-gone-wild" may seem like a reasonable proposition, it's one that's doomed to fail.

"We will work constructively with other WTO members to undertake necessary WTO reform with a sense of urgency," the statement said. Hiroshige Seko, minister of economy, trade and industry, said that it is significant that specifics concerning WTO reform were included in a G20 ministerial statement for the first time. Improving the organization's system for resolving disputes is one of them. "We agree that action is necessary regarding the functioning of the dispute settlement system consistent with the rules as negotiated by the WTO members," the statement said. Foreign Minister Taro Kono said it was a "big feat" for Japan, which places importance on this issue, that the need to address the system was explicitly mentioned in the statement.

Right from contract review and management to legal outcomes anticipation and performing due diligence to conducting legal research, the impact of artificial intelligence (AI) in the legal industry is indeed worth mentioning. But lawyers, you are safe from the AI apocalypse, as of now. The intensifying buzz and madness around the groundbreaking and revolutionary technology, AI, shows a clear indication of the fact that AI is here to stay. While negative comments have been passed on the technology, the media and popular personalities share optimistic views on AI. To add to the list, the convenience that AI provides with its blend of automation and smartness simply pulls people's attention.

US federal government contract obligations and AI-related investments grew almost 75% to nearly $700 million between fiscal 2016 and 2018 [Federal News Network]. Of those that have adopted an AI-driven marketing solution, 74% reported using AI in an "assistive" fashion, which surfaces insights for marketers to consider during manual decision making. Only 26% of marketers reported using autonomous AI, which can act on its own insights and work collaboratively with marketers (without adding manual work) [Albert and Forrester]. Notable growth came in areas like food and consumer goods (48%), plastics and rubber (37%), life sciences (31%), and electronics (22%) [Robotic Industry Association]. Nearly eight out of 10 enterprise organizations currently engaged in AI and ML report that projects have stalled, and 96% of these companies have run into problems with data quality, data labeling required to train AI, and building model confidence; only half of enterprises have released AI/ML projects into production; 78% of their AI/ML projects stall at some stage before deployment; 81% admit the process of training AI with data is more difficult than they expected; 76% combat this challenge by attempting to label and annotate training data on their own; 63% go so far as to try to build their own labeling and annotation automation technology; 71% report that they ultimately outsource training data and other ML project activities [Alegion and Dimensional Research].

As followers of Christ, we are called to engage the world around us with the unchanging gospel message of hope and reconciliation. Tools like technology are able to aid us in this pursuit. We know they can also be designed and used in ways that dishonor God and devalue our fellow image-bearers. Evangelical Christians hold fast to the inerrant and infallible Word of God, which states that every human being is made in God's image and thus has infinite value and worth in the eyes of their Creator. This message dictates how we view God, ourselves, and the tools that God has given us the ability to create.

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics, but how to consider them both coherently is less studied. In addition, most existing methods treat output labels as independent methods, but ignore the hierarchical relations among them, leading to useful semantic information loss. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding words-matrix representation which preserves both the non-consecutive, long-distance and local sequential semantics. Then the words-matrix is input to the proposed attentional graph capsule recurrent CNNs for more effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches.