Artificial intelligence could one day organize the world. As if in anticipation of this, a maritime platform developer called Orca AI has just begun a research trial of new safety systems for autonomous ships, equipping a vessel with artificial intelligence that recognizes other ships to safely guide it through busy sea traffic, according to a recent press release from the company. Orca AI was founded in 2018 by a pair of naval tech experts, and designs software platforms with extreme specificity for maritime vessels. The firm blends existing safety systems with sensors to enhance the navigation and safety of vessels making their way through crowded (and sometimes dangerous) waterways. Orca AI is headquartered in Israel, and aims to link sea-bound vessels with 24/7 land-based AI insights.

An Israeli defense contractor on Monday unveiled a remote-controlled armed robot it says can patrol battle zones, track infiltrators and open fire. The unmanned vehicle is the latest addition to the world of drone technology, which is rapidly reshaping the modern battlefield. Proponents say such semi-autonomous machines allow armies to protect their soldiers, while critics fear this marks another dangerous step toward robots making life-or-death decisions. The four-wheel-drive robot presented Monday was developed by the state-owned Israel Aerospace Industries' "REX MKII." It is operated by an electronic tablet and can be equipped with two machine guns, cameras and sensors, said Rani Avni, deputy head of the company's autonomous systems division.

"In a world more and more driven by AI models, Data Scientists cannot effectively ascertain on their own the costs associated with the unintended consequences of False Positives and False Negatives. Mitigating unintended consequences requires the collaboration across a diverse set of stakeholders in order to identify the metrics against which the AI Utility Function will seek to optimize." I've been fortunate enough to have had some interesting conversations since publishing that blog, especially with an organization who is championing data ethics and "Responsible AI" (love that term). As was so well covered in Cathy O'Neil's book "Weapons of Math Destruction", the biases built into many of the AI models that are being used to approve loans and mortgages, hire job applicants, and accept university admissions are yielding unintended consequences that severely impact both individuals and society. AI models only optimize against the metrics against which it has been programmed to optimize.

Live streaming is becoming an increasingly popular trend of sales in E-commerce. The core of live-streaming sales is to encourage customers to purchase in an online broadcasting room. To enable customers to better understand a product without jumping out, we propose AliMe MKG, a multi-modal knowledge graph that aims at providing a cognitive profile for products, through which customers are able to seek information about and understand a product. Based on the MKG, we build an online live assistant that highlights product search, product exhibition and question answering, allowing customers to skim over item list, view item details, and ask item-related questions. Our system has been launched online in the Taobao app, and currently serves hundreds of thousands of customers per day.

Graph Attention Network (GAT) focuses on modelling simple undirected and single relational graph data only. This limits its ability to deal with more general and complex multi-relational graphs that contain entities with directed links of different labels (e.g., knowledge graphs). Therefore, directly applying GAT on multi-relational graphs leads to sub-optimal solutions. To tackle this issue, we propose r-GAT, a relational graph attention network to learn multi-channel entity representations. Specifically, each channel corresponds to a latent semantic aspect of an entity. This enables us to aggregate neighborhood information for the current aspect using relation features. We further propose a query-aware attention mechanism for subsequent tasks to select useful aspects. Extensive experiments on link prediction and entity classification tasks show that our r-GAT can model multi-relational graphs effectively. Also, we show the interpretability of our approach by case study.

Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to model their problems as constraint networks by classifying (partial) examples as positive or negative. For each negative example, the former focuses on one constraint of the target network, while the latter can learn a maximum number of constraints. Two bottlenecks of the acquisition process where both these algorithms encounter problems are the large number of queries required to reach convergence, and the high cpu times needed to generate queries, especially near convergence. In this paper we propose algorithmic and heuristic methods to deal with both these issues. We first describe an algorithm, called MQuAcq, that blends the main idea of MultiAcq into QuAcq resulting in a method that learns as many constraints as MultiAcq does after a negative example, but with a lower complexity. A detailed theoretical analysis of the proposed algorithm is also presented. %We also present a technique that boosts the performance of constraint acquisition by reducing the number of queries significantly. Then we turn our attention to query generation which is a significant but rather overlooked part of the acquisition process. We describe %in detail how query generation in a typical constraint acquisition system operates, and we propose heuristics for improving its efficiency. Experiments from various domains demonstrate that our resulting algorithm that integrates all the new techniques does not only generate considerably fewer queries than QuAcq and MultiAcq, but it is also by far faster than both of them, in average query generation time as well as in total run time, and also largely alleviates the premature convergence problem.

This work proposes an extensive analysis of the Transformer architecture in the Neural Machine Translation (NMT) setting. Focusing on the encoder-decoder attention mechanism, we prove that attention weights systematically make alignment errors by relying mainly on uninformative tokens from the source sequence. However, we observe that NMT models assign attention to these tokens to regulate the contribution in the prediction of the two contexts, the source and the prefix of the target sequence. We provide evidence about the influence of wrong alignments on the model behavior, demonstrating that the encoder-decoder attention mechanism is well suited as an interpretability method for NMT. Finally, based on our analysis, we propose methods that largely reduce the word alignment error rate compared to standard induced alignments from attention weights.

Human conversations consist of reasonable and natural topic flows, which are observed as the shifts of the mentioned concepts across utterances. Previous chatbots that incorporate the external commonsense knowledge graph prove that modeling the concept shifts can effectively alleviate the dull and uninformative response dilemma. However, there still exists a gap between the concept relations in the natural conversation and those in the external commonsense knowledge graph, which is an issue to solve. Specifically, the concept relations in the external commonsense knowledge graph are not intuitively built from the conversational scenario but the world knowledge, which makes them insufficient for the chatbot construction. To bridge the above gap, we propose the method to supply more concept relations extracted from the conversational corpora and reconstruct an enhanced concept graph for the chatbot construction. In addition, we present a novel, powerful, and fast graph encoding architecture named the Edge-Transformer to replace the traditional GNN architecture. Experimental results on the Reddit conversation dataset indicate our proposed method significantly outperforms strong baseline systems and achieves new SOTA results. Further analysis individually proves the effectiveness of the enhanced concept graph and the Edge-Transformer architecture.

The Pentagon is not the most inviting place for first-time visitors, and it was no different for Chris Lynch. When he rode the escalator out of the Pentagon metro station, Lynch was greeted by guard dogs and security personnel wearing body armor and toting machine guns. He lost cell service upon entering the building and was forced to run through more than a half mile of hallways to make his meeting in the office of the secretary of defense. He showed up late and out of breath, his hoodie and gym shoes soaked with sweat. It was a surreal experience, Lynch told me, and it marked the beginning of "the most delightful detour of my entire life." Lynch had just completed a 45-day posting in the United States Digital Service, an organization formed in 2014 to fill what many officials viewed as a crucial gap in the government's technology expertise. That year, the White House had launched HealthCare.gov to help enroll Americans in government health insurance, but it had been a technological debacle that almost derailed the Affordable Care Act. The website was so buggy that on its first day, only six people were able to sign up through the site. In response, and to prevent similar flops from occurring in the future, the White House created the USDS.

It began in early October 2017, when 108 stroke patients with significant arm and hand disabilities turned up for a peculiar clinical trial. The researchers would be surgically implanting a neurostimulator to their vagus nerve, the cranial nerve that runs along the groove in the front of the neck and is responsible for transmitting signals from the brain to other parts of the body. By the time the trial concluded, the subjects' once limited limbs had begun to come back to life. Somehow, pulses to that nerve combined with rehab therapy had given the patients improved use of their disabled limb--and done so faster and more effectively than any treatment before it, even on those who had responded to nothing else. This spring, the findings of the trial were published in The Lancet.