Snafu's investor ABBA is looking for sounds from India. ABBA is a Swedish pop group that has anticipated its first album, Voyage, in 40 years. It will be streaming on the air on November 5. But before the release, the legendary comeback band sprinkled stardust on Snafu Records, a music label headed by an Indian. Snafu has introduced a new approach to search for music talent. Agnetha Fältskog, the ABBA singer, has joined a $6 million funding round for AI-powered record label Snafu records.

GENEVA – Japan, the United States and other countries have blocked any advancement in U.N. talks toward legally binding measures to ban and regulate the development and use of lethal autonomous weapon systems. The Sixth Review Conference of the Convention on Certain Conventional Weapons ended Friday in Geneva without progress, failing to reflect eight years of work and leaving countries and nongovernmental organizations that have called for legally binding rules expressing disappointment. Also referred to as "killer robots," autonomous weapons are artificial intelligence-powered weapons using facial recognition and algorithms. Once activated, the weapons can select and attack targets without the assistance of a human operator. They pose ethical, legal and security risks.

Multi-touch attribution (MTA), aiming to estimate the contribution of each advertisement touchpoint in conversion journeys, is essential for budget allocation and automatically advertising. Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint using counterfactual predictions. An assumption of these works is the conversion prediction model is unbiased, i.e., it can give accurate predictions on any randomly assigned journey, including both the factual and counterfactual ones. Nevertheless, this assumption does not always hold as the exposed advertisements are recommended according to user preferences. This confounding bias of users would lead to an out-of-distribution (OOD) problem in the counterfactual prediction and cause concept drift in attribution. In this paper, we define the causal MTA task and propose CausalMTA to eliminate the influence of user preferences. It systemically eliminates the confounding bias from both static and dynamic preferences to learn the conversion prediction model using historical data. We also provide a theoretical analysis to prove CausalMTA can learn an unbiased prediction model with sufficient data. Extensive experiments on both public datasets and the impression data in an e-commerce company show that CausalMTA not only achieves better prediction performance than the state-of-the-art method but also generates meaningful attribution credits across different advertising channels.

Measuring treatment effects in observational studies is challenging because of confounding bias. Confounding occurs when a variable affects both the treatment and the outcome. Traditional methods such as propensity score matching estimate treatment effects by conditioning on the confounders. Recent literature has presented new methods that use machine learning to predict the counterfactuals in observational studies which then allow for estimating treatment effects. These studies however, have been applied to real world data where the true treatment effects have not been known. This study aimed to study the effectiveness of this counterfactual prediction method by simulating two main scenarios: with and without confounding. Each type also included linear and non-linear relationships between input and output data. The key item in the simulations was that we generated known true causal effects. Linear regression, lasso regression and random forest models were used to predict the counterfactuals and treatment effects. These were compared these with the true treatment effect as well as a naive treatment effect. The results show that the most important factor in whether this machine learning method performs well, is the degree of non-linearity in the data. Surprisingly, for both non-confounding \textit{and} confounding, the machine learning models all performed well on the linear dataset. However, when non-linearity was introduced, the models performed very poorly. Therefore under the conditions of this simulation study, the machine learning method performs well under conditions of linearity, even if confounding is present, but at this stage should not be trusted when non-linearity is introduced.

Large-scale autoregressive language models such as GPT-3 are few-shot learners that can perform a wide range of language tasks without fine-tuning. While these models are known to be able to jointly represent many different languages, their training data is dominated by English, potentially limiting their cross-lingual generalization. In this work, we train multilingual autoregressive language models on a balanced corpus covering a diverse set of languages, and study their few- and zero-shot learning capabilities in a wide range of tasks. Our largest model with 7.5 billion parameters sets new state of the art in few-shot learning in more than 20 representative languages, outperforming GPT-3 of comparable size in multilingual commonsense reasoning (with +7.4% absolute accuracy improvement in 0-shot settings and +9.4% in 4-shot settings) and natural language inference (+5.4% in each of 0-shot and 4-shot settings). On the FLORES-101 machine translation benchmark, our model outperforms GPT-3 on 171 out of 182 translation directions with 32 training examples, while surpassing the official supervised baseline in 45 directions. We present a detailed analysis of where the model succeeds and fails, showing in particular that it enables cross-lingual in-context learning on some tasks, while there is still room for improvement on surface form robustness and adaptation to tasks that do not have a natural cloze form. Finally, we evaluate our models in social value tasks such as hate speech detection in five languages and find it has limitations similar to comparable sized GPT-3 models.

This may be remembered as the year when the world learned that lethal autonomous weapons had moved from a futuristic worry to a battlefield reality. It's also the year when policymakers failed to agree on what to do about it. On Friday, 120 countries participating in the United Nations' Convention on Certain Conventional Weapons could not agree on whether to limit the development or use of lethal autonomous weapons. Instead, they pledged to continue and "intensify" discussions. "It's very disappointing, and a real missed opportunity," says Neil Davison, senior scientific and policy adviser at the International Committee of the Red Cross, a humanitarian organization based in Geneva.

Shortly before 3 a.m. on July 19, 2016, U.S. Special Operations forces bombed what they believed were three Islamic State (IS) group "staging areas" on the outskirts of Tokhar, a riverside hamlet in northern Syria. They reported 85 fighters killed. In fact, they hit houses far from the front line, where farmers, their families and other local people sought nighttime sanctuary from bombing and gunfire. More than 120 villagers were killed. In early 2017 in Iraq, an American war plane struck a dark-colored vehicle, believed to be a car bomb, stopped at an intersection in the Wadi Hajar neighborhood of West Mosul. Actually, the car had been bearing not a bomb but a man named Majid Mahmoud Ahmed, his wife and their two children, who were fleeing the fighting nearby. They and three other civilians were killed. In November 2015, after observing a man dragging an "unknown heavy object" into an IS "defensive fighting position," U.S. forces struck a building in Ramadi, Iraq. A military review found that the object was actually "a person of small stature" -- a child -- who died in the strike. None of these deadly failures resulted in a finding of wrongdoing. These cases are drawn from a hidden Pentagon archive of the American air war in the Middle East since 2014. The trove of documents -- the military's own confidential assessments of more than 1,300 reports of civilian casualties, obtained by The New York Times -- lays bare how the air war has been marked by deeply flawed intelligence, rushed and often imprecise targeting and the deaths of thousands of civilians, many of them children, a sharp contrast to the U.S. government's image of war waged by all-seeing drones and precision bombs. The documents show, too, that despite the Pentagon's highly codified system for examining civilian casualties, pledges of transparency and accountability have given way to opacity and impunity. In only a handful of cases were the assessments made public. Not a single record provided includes a finding of wrongdoing or disciplinary action. Fewer than a dozen condolence payments were made, even though many survivors were left with disabilities requiring expensive medical care. Documented efforts to identify root causes or lessons learned are rare. The air campaign represents a fundamental transformation of warfare that took shape in the final years of the Obama administration, amid the deepening unpopularity of the forever wars that had claimed more than 6,000 American service members. The United States traded many of its boots on the ground for an arsenal of aircraft directed by controllers sitting at computers, often thousands of kilometers away. President Barack Obama called it "the most precise air campaign in history." This was the promise: America's "extraordinary technology" would allow the military to kill the right people while taking the greatest possible care not to harm the wrong ones. The IS caliphate ultimately crumbled under the weight of American bombing.

Offline reinforcement learning leverages previously-collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also desirable for multi-agent reinforcement learning (MARL) tasks, given the increased interactions among agents and with the enviroment. Yet, in MARL, the paradigm of offline pre-training with online fine-tuning has not been studied, nor datasets or benchmarks for offline MARL research are available. In this paper, we facilitate the research by providing large-scale datasets, and use them to examine the usage of the Decision Transformer in the context of MARL. We investigate the generalisation of MARL offline pre-training in the following three aspects: 1) between single agents and multiple agents, 2) from offline pretraining to the online fine-tuning, and 3) to that of multiple downstream tasks with few-shot and zero-shot capabilities. We start by introducing the first offline MARL dataset with diverse quality levels based on the StarCraftII environment, and then propose the novel architecture of multi-agent decision transformer (MADT) for effective offline learning. MADT leverages transformer's modelling ability of sequence modelling and integrates it seamlessly with both offline and online MARL tasks. A crucial benefit of MADT is that it learns generalizable policies that can transfer between different types of agents under different task scenarios. On StarCraft II offline dataset, MADT outperforms the state-of-the-art offline RL baselines. When applied to online tasks, the pre-trained MADT significantly improves sample efficiency, and enjoys strong performance both few-short and zero-shot cases. To our best knowledge, this is the first work that studies and demonstrates the effectiveness of offline pre-trained models in terms of sample efficiency and generalisability enhancements in MARL.

Datasets fuel AI models like gasoline (or electricity, as the case may be) fuels cars. Whether they're tasked with generating text, recognizing objects, or predicting a company's stock price, AI systems "learn" by sifting through countless examples to discern patterns in the data. For example, a computer vision system can be trained to recognize certain types of apparel, like coats and scarfs, by looking at different images of that clothing. Beyond developing models, datasets are used to test trained AI systems to ensure they remain stable -- and measure overall progress in the field. Models that top the leaderboards on certain open source benchmarks are considered state of the art (SOTA) for that particular task.

Country officials and campaigners have expressed disappointment after United Nations talks on autonomous weapons systems – known as "killer robots" – stopped short of launching negotiations into an international treaty to govern their use following opposition from manufacturing states. Unlike existing semi-autonomous weapons such as drones, fully-autonomous weapons have no human-operated "kill switch" and instead leave decisions over life and death to sensors, software and machine processes. The regulation of the industry has taken on new urgency since a UN panel report in March said the first autonomous drone attack may have occurred in Libya. This week, UN Secretary-General Antonio Guterres encouraged the 125 parties to the Convention on Certain Conventional Weapons (CCW) to come up with an "ambitious plan" on new rules. But on Friday, the Sixth Review Conference of the CCW failed to schedule further talks around the development and use of the Lethal Autonomous Weapon Systems, or LAWS.