Multiple business scenarios require an automated generation of descriptive human-readable text from structured input data. Hence, fact-to-text generation systems have been developed for various downstream tasks like generating soccer reports, weather and financial reports, medical reports, person biographies, etc. Unfortunately, previous work on fact-to-text (F2T) generation has focused primarily on English mainly due to the high availability of relevant datasets. Only recently, the problem of cross-lingual fact-to-text (XF2T) was proposed for generation across multiple languages alongwith a dataset, XALIGN for eight languages. However, there has been no rigorous work on the actual XF2T generation problem. We extend XALIGN dataset with annotated data for four more languages: Punjabi, Malayalam, Assamese and Oriya. We conduct an extensive study using popular Transformer-based text generation models on our extended multi-lingual dataset, which we call XALIGNV2. Further, we investigate the performance of different text generation strategies: multiple variations of pretraining, fact-aware embeddings and structure-aware input encoding. Our extensive experiments show that a multi-lingual mT5 model which uses fact-aware embeddings with structure-aware input encoding leads to best results on average across the twelve languages. We make our code, dataset and model publicly available, and hope that this will help advance further research in this critical area.

In a sign that national security tech is a safe bet even during troubled economic times, defense- and security-focused VC firm Razor's Edge Ventures today announced the closing of its third startup investment fund at just under $340 million. It exceeded the initial target of $250 million, the firm notes, and will target companies developing autonomous systems, space technologies, cybersecurity, AI and machine learning, digital signal processing and other aerospace and defense technologies. Founded in 2010, Razor's Edge funds multistage startups with both commercial and government customers but specializes in ventures that "[help] national security community [members] solve difficult technology problems and advance critical missions," in its own words. The outfit's interest areas are informed by "strategic national security priorities," managing partner Mark Spoto tells TechCrunch, with the ostensible goal of helping the U.S. maintain "technological superiority." "While economic conditions in the broad financial markets are challenging at this time, spending for defense has grown significantly both in the U.S. and abroad; we face an increasingly complex and growing threat environment," Spoto said via email.

In a recent Wall Street Journal column, Ukrainian President Volodymyr Zelenskyy extolled his country's potential to become "a major hub for information technology." Certainly, all civilized people are rooting for Ukraine to repel the Russian invasion and have its war-shattered economy roar back stronger than ever. But before U.S. taxpayer dollars are spent on Ukraine's reconstruction, there's a serious problem that must be addressed: The People's Republic of China. The Ukrainian resistance has been truly heroic. Moscow expected it to crumble rapidly.

The lesser spotted eagle is endangered in Germany.Hinze, K/DPA via ZUMA Press This story was originally published by the Guardian and is reproduced here as part of the Climate Desk collaboration. Small in size, sensitive of constitution and with only 130 breeding pairs surviving locally in the wild, the lesser spotted eagle of the Oder delta lives up to its name. In Germany, key questions over the country's energy future hang on the question of whether artificial intelligence systems can do a better job of spotting the reclusive animal than birdwatchers do. Lesser spotted eagles (named after the drop-shaped spots on their feathers) are fond of riding thermals over many of the flatlands earmarked for a mass expansion of onshore windfarms by a German government under pressure to compensate for a pending loss of nuclear power, coal plants and Russian gas. Because lesser spotted eagles in mid-flight are unused to vertical obstacles, and keep their eyes focused on mice, lizard or frog-shaped prey below, conservationists say, they are known to occasionally collide with the rotor blades of wind turbines.

Imagine you are in a foreign country where you don't speak the language and your small child unexpectedly starts to have a fever seizure. You take them to the hospital, and the doctors use an online translator to let you know that your kid is going to be OK. But "your child is having a seizure" accidentally comes up in your mother tongue is "your child is dead." This specific example is a very real possibility, according to a 2014 study published in the British Medical Journal about the limited usefulness of AI-powered machine translation in communications between patients and doctors. Sometimes we need American-British translation, too.)

But the requirement has posed some compliance challenges. Unlike familiar financial audits, refined over decades of accounting experience, the AI audit process is new and without clearly established guidelines. Our Morning Risk Report features insights and news on governance, risk and compliance. "There is a major concern, which is it's not clear exactly what constitutes an AI audit," said Andrew Burt, managing partner at AI-focused law firm BNH. "If you are an organization that's using some type of these tools…it can be pretty confusing."

New York – Prime Minister Fumio Kishida has urged the world to reaffirm the importance of the rules-based international order amid the ongoing war in Ukraine and criticized Russia for threatening to possibly use nuclear weapons in the conflict. Addressing the U.N. General Assembly in New York on Tuesday, Kishida also said that specific reforms to the world body will be vital in restoring the credibility of the Security Council, which has been further jeopardized after veto-wielding permanent member Russia invaded its neighboring country earlier this year. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this support page.

We present a self-supervised method to disentangle factors of variation in high-dimensional data that does not rely on prior knowledge of the underlying variation profile (e.g., no assumptions on the number or distribution of the individual latent variables to be extracted). In this method which we call NashAE, high-dimensional feature disentanglement is accomplished in the low-dimensional latent space of a standard autoencoder (AE) by promoting the discrepancy between each encoding element and information of the element recovered from all other encoding elements. Disentanglement is promoted efficiently by framing this as a minmax game between the AE and an ensemble of regression networks which each provide an estimate of an element conditioned on an observation of all other elements. We quantitatively compare our approach with leading disentanglement methods using existing disentanglement metrics. Furthermore, we show that NashAE has increased reliability and increased capacity to capture salient data characteristics in the learned latent representation.

When learning from sensitive data, care must be taken to ensure that training algorithms address privacy concerns. The canonical Private Aggregation of Teacher Ensembles, or PATE, computes output labels by aggregating the predictions of a (possibly distributed) collection of teacher models via a voting mechanism. The mechanism adds noise to attain a differential privacy guarantee with respect to the teachers' training data. In this work, we observe that this use of noise, which makes PATE predictions stochastic, enables new forms of leakage of sensitive information. For a given input, our adversary exploits this stochasticity to extract high-fidelity histograms of the votes submitted by the underlying teachers. From these histograms, the adversary can learn sensitive attributes of the input such as race, gender, or age. Although this attack does not directly violate the differential privacy guarantee, it clearly violates privacy norms and expectations, and would not be possible at all without the noise inserted to obtain differential privacy. In fact, counter-intuitively, the attack becomes easier as we add more noise to provide stronger differential privacy. We hope this encourages future work to consider privacy holistically rather than treat differential privacy as a panacea.

AI is being increasingly used to aid response efforts to humanitarian emergencies at multiple levels of decision-making. Such AI systems are generally understood to be stand-alone tools for decision support, with ethical assessments, guidelines and frameworks applied to them through this lens. However, as the prevalence of AI increases in this domain, such systems will begin to encounter each other through information flow networks created by interacting decision-making entities, leading to multi-AI complex systems which are often ill understood. In this paper we describe how these multi-AI systems can arise, even in relatively simple real-world humanitarian response scenarios, and lead to potentially emergent and erratic erroneous behavior. We discuss how we can better work towards more trustworthy multi-AI systems by exploring some of the associated challenges and opportunities, and how we can design better mechanisms to understand and assess such systems. This paper is designed to be a first exposition on this topic in the field of humanitarian response, raising awareness, exploring the possible landscape of this domain, and providing a starting point for future work within the wider community.