Ukraine has developed drones that are capable of finding targets autonomously, a Ukrainian military leader has claimed, raising the prospect that the ongoing Russia-Ukraine war could see the first confirmed use of'killer robots' in armed conflict. Ukrainian Lieutenant Colonel Yaroslav Honchar gave details in an interview with Ukrainian news agency UNIAN on 13th October. Honchar is co-founder of Aerorozvidka ("Aerial Intelligence"), a team of around a thousand volunteer drone enthusiasts and technologists set up in 2014 to develop and use drones and other technology. Honchar says their drones already fly scout missions autonomously and mentions the possibility of automated strikes, but did not say such strikes had been carried out. Aerorozvidka declined to comment on the issue when asked by New Scientist.

Some technological advances are so great that they create ruptures in our understanding of what is possible. Eighty years ago, such a rift took place via the invention of the atom bomb, transforming the way we conceive of warfare and global order. From artificial intelligence to synthetic biology, experts and policymakers are beginning to dissect the potential consequences of adding unfamiliar, highly advanced, and potentially devastating new additions to the toolboxes of adversarial powers. When referring to world order, we often operate within a'Great Power' discourse and assume that geopolitical disruptions require geopolitical might. The democratisation of destructive technologies, however, will likely create the conditions for smaller non-state actors, and even individuals, to have a greater impact on an international level.

Eric Schmidt has prodded the Pentagon for years to hurry along its software-buying process. Today the AI tech investor and former Google CEO is more determined than ever to urge government decision-makers to pick up the pace, but not just when it comes to buying more software for the Defense Department. Schmidt wants the government to implement his sweeping blueprint to fight what he considers an existential threat to democracy posed by China's AI plans, an effort that could also bolster his own commercial AI interests. He says the U.S.'s national security and economic leadership are dependent upon spending billions to procure smarter software, bolster AI research, and build the country's computer science talent pool. And he says he knows better than the Pentagon itself how to remove the bureaucratic blockades preventing more agile use of AI by the government. But at the same time, Schmidt's venture capital firm Innovation Endeavors has invested in companies that have received multimillion-dollar contracts from federal agencies. Some of those investments and contracts -- reported here for the first time -- were granted between 2016 and 2021 while Schmidt chaired two influential government initiatives, the Pentagon's Defense Innovation Board and the National Security Commission on Artificial Intelligence.

Fusion, which promises practically unlimited, carbon-free energy using the same processes that power the sun, is at the heart of a worldwide research effort that could help mitigate climate change. A multidisciplinary team of researchers is now bringing tools and insights from machine learning to aid this effort. Scientists from MIT and elsewhere have used computer-vision models to identify and track turbulent structures that appear under the conditions needed to facilitate fusion reactions. Monitoring the formation and movements of these structures, called filaments or "blobs," is important for understanding the heat and particle flows exiting from the reacting fuel, which ultimately determines the engineering requirements for the reactor walls to meet those flows. However, scientists typically study blobs using averaging techniques, which trade details of individual structures in favor of aggregate statistics.

KYIV – Russian President Vladimir Putin said Russian strikes on Ukrainian infrastructure and a decision to freeze participation in a Black Sea grain export program were responses to a drone attack on Moscow's fleet in Crimea that he blamed on Ukraine. Putin told reporters on Monday that Ukrainian drones had used the same marine corridors that grain ships transited under the U.N.-brokered deal. 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.

While half of Twitter users try to figure out what is to come as billionaire Elon Musk takes over the social media platform, the other half are keeping themselves busy with a new AI tool. Designed to create a tweet that sounds as if it could come from the account holder, but didn't, Tweet Hunter's AI tweet generator has sparked attention. Google searches for "tweet generator," "AI tweet generator" and "Fake Elon Musk tweet generator" saw a huge uptick on Google as thousands rushed to try out the tool, inspired by other users. Putin Says Russia Tried Building Relations With West: 'Stop Being Enemies' Obama Praises Biden At Portrait Ceremony: Joe Is'America's Great Fortune' Where Is Donald Trump Holding Rallies Ahead Of The 2022 Midterms? The tool uses artificial intelligence (AI) to scrape through a Twitter user's previous content and build a picture of the phrases and sentences they may be likely to share.

In this study, learning modalities offered by public schools across the United States were investigated to track changes in the proportion of schools offering fully in-person, hybrid and fully remote learning over time. Learning modalities from 14,688 unique school districts from September 2020 to June 2021 were reported by Burbio, MCH Strategic Data, the American Enterprise Institute's Return to Learn Tracker and individual state dashboards. A model was needed to combine and deconflict these data to provide a more complete description of modalities nationwide. A hidden Markov model (HMM) was used to infer the most likely learning modality for each district on a weekly basis. This method yielded higher spatiotemporal coverage than any individual data source and higher agreement with three of the four data sources than any other single source. The model output revealed that the percentage of districts offering fully in-person learning rose from 40.3% in September 2020 to 54.7% in June of 2021 with increases across 45 states and in both urban and rural districts. This type of probabilistic model can serve as a tool for fusion of incomplete and contradictory data sources in support of public health surveillance and research efforts.

Regulations govern many aspects of citizens' daily lives. Governments and businesses routinely automate these in the form of coded rules (e.g., to check a citizen's eligibility for specific benefits). However, the path to automation is long and challenging. To address this, recent global initiatives for digital government, proposing to simultaneously express policy in natural language for human consumption as well as computationally amenable rules or code, are gathering broad public-sector interest. We introduce the problem of semi-automatically building decision models from eligibility policies for social services, and present an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. Despite the many open domain challenges, in this position paper we explore the enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.

Learning agents can make use of Reinforcement Learning (RL) to decide their actions by using a reward function. However, the learning process is greatly influenced by the elect of values of the hyperparameters used in the learning algorithm. This work proposed a Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) based method, which makes use of the Genetic Algorithm (GA) to fine-tune the hyperparameters' values. This method (GA+DDPG+HER) experimented on six robotic manipulation tasks: FetchReach; FetchSlide; FetchPush; FetchPickAndPlace; DoorOpening; and AuboReach. Analysis of these results demonstrated a significant increase in performance and a decrease in learning time. Also, we compare and provide evidence that GA+DDPG+HER is better than the existing methods.

The full power of human language-based communication cannot be realized without negation. All human languages have some form of negation. Despite this, negation remains a challenging phenomenon for current natural language understanding systems. To facilitate the future development of models that can process negation effectively, we present CONDAQA, the first English reading comprehension dataset which requires reasoning about the implications of negated statements in paragraphs. We collect paragraphs with diverse negation cues, then have crowdworkers ask questions about the implications of the negated statement in the passage. We also have workers make three kinds of edits to the passage -- paraphrasing the negated statement, changing the scope of the negation, and reversing the negation -- resulting in clusters of question-answer pairs that are difficult for models to answer with spurious shortcuts. CONDAQA features 14,182 question-answer pairs with over 200 unique negation cues and is challenging for current state-of-the-art models. The best performing model on CONDAQA (UnifiedQA-v2-3b) achieves only 42% on our consistency metric, well below human performance which is 81%. We release our dataset, along with fully-finetuned, few-shot, and zero-shot evaluations, to facilitate the development of future NLP methods that work on negated language.