Humans are accustomed to reading and writing in a forward manner, and this natural bias extends to text understanding in auto-regressive large language models (LLMs). This paper investigates whether LLMs, like humans, struggle with reverse modeling, specifically with reversed text inputs. We found that publicly available pre-trained LLMs cannot understand such inputs. However, LLMs trained from scratch with both forward and reverse texts can understand them equally well during inference. Our case study shows that different-content texts result in different losses if input (to LLMs) in different directions -- some get lower losses for forward while some for reverse. This leads us to a simple and nice solution for data selection based on the loss differences between forward and reverse directions. Using our selected data in continued pretraining can boost LLMs' performance by a large margin across different language understanding benchmarks.

Russia has launched several strikes across Ukraine, killing at least five people and wounding several, in an attack that appeared to target energy infrastructure. Ukraine also launched a drone attack on Russia's central region of Saratov, injuring four. The exchange began around midnight on Sunday and continued beyond daybreak on Monday. Ukraine's air force reported multiple groups of Russian drones moving towards its eastern, northern, southern, and central regions, followed by numerous cruise and ballistic missiles. Authorities in at least six Ukrainian regions said blasts had been heard.

Do bilingual Russian-French authors of the end of the twentieth century such as Andre\"i Makine, Val\'ery Afanassiev, Vladimir F\'edorovski, Iegor Gran, Luba Jurgenson have common stylistic traits in the novels they wrote in French? Can we distinguish between them and non-bilingual French writers' texts? Is the phenomenon of interference observable in French texts of Russian authors? This paper applies authorship attribution methods including Support Vector Machine (SVM), $K$-Nearest Neighbors (KNN), Ridge classification, and Neural Network to answer these questions.

In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for general image recognition tasks, application in industry is often precluded for three reasons: 1) large pre-trained DNNs are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre-training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal issues for companies. As a remedy, we study neural networks for CV that we train from scratch. For this purpose, we use a real-world case from a solar wafer manufacturer. We find that our neural networks achieve similar performances as pre-trained DNNs, even though they consist of far fewer parameters and do not rely on third-party datasets.

MOSCOW - Russian media say a contraption presented by Russian state television as a high-tech robot was in fact a man in a commercially available robot costume. The footage was shot at a high-tech show in the city of Yaroslavl that opened last Tuesday, featuring "Boris the Robot." Forum organizers used Boris to enliven the event, having him dance to a pop song. But a crew for Russian state television apparently thought Boris was real, and used footage of him dancing and speaking as an example of Russian technological prowess. Online TJournal noted the lack of sensors, human-like movements and other discrepancies, and revealed that Boris was in fact a human clad in a costume sold under the name Alyosha by the Russian company Show Robots.

Artificial intelligence (AI) has a growing number of applications in the security and military areas. It facilitates manoeuvres in the field, and can save lives when things go wrong. It also boosts the performance of armies by providing robot allies to combat forces. According to some experts, Lethal Autonomous Weapons Systems (LAWS) are creating a "Third Revolution" in warfare, after gunpowder and nuclear weapons. It is time we start worrying about the day when armies of robots are capable of conducting hostilities with full autonomy, without humans to command them.

The Russian president has become the latest person to warn of the dangers of artificial intelligence (AI), actually predicting that whoever masters the technology first can rule the world. Addressing students last week, Vladimir Putin said that there are legitimate concerns about AI and that its development will produce "colossal opportunities and threats that are difficult to predict now." Going further, Putin warned that "the one who becomes the leader in this sphere will be the ruler of the world." He added: "Artificial intelligence is the future, not only for Russia but for all humankind," according to Russia Today. Putin added that he does not want to see the technology "monopolized," and added that Russia would share it with the world if Moscow develops advanced AI first.

Artificial intelligence has increasingly been integrated into the weapons systems of the world's leading militaries, and at least one expert has said the futuristic technology may soon be the subject of a new Cold War. In a piece published Tuesday by The Conversation, North Dakota State University assistant professor Jeremy Straub argued that unlike the nuclear weapons that dominated much of the 21st century arms race between the U.S. and the Soviet Union, the use of cyberweapons and artificial intelligence largely remained "fair game," even as tensions again flared between the rivals. Both countries have invested heavily in developing new tools to wage war on this new front, but Russia particularly has sought to use it as an opportunity to upstage the more conventionally powerful U.S. Related: U.S. is losing to Russia and China in war for artificial intelligence, report says "Now, more than 30 years after the end of the Cold War, the U.S. and Russia have decommissioned tens of thousands of nuclear weapons. Any modern-day cold war would include cyberattacks and nuclear powers' involvement in allies' conflicts," wrote Straub, who was also associate director of the university's Institute for Cyber Security Education and Research, in his article. "It's already happening," he added.

About this course: Machine learning is transforming the world around us. To become successful, you'd better know what kinds of problems can be solved with machine learning, and how they can be solved. Don't know where to start? The answer is one button away. During this course you will: - Identify practical problems which can be solved with machine learning - Build, tune and apply linear models with Spark MLLib - Understand methods of text processing - Fit decision trees and boost them with ensemble learning - Construct your own recommender system.

IBM is making a 10-year, $240 million investment in artificial intelligence research through a new lab it's creating in partnership with the Massachusetts Institute of Technology. The investment will support research by IBM and MIT scientists at the newly created MIT-IBM Watson AI Lab in Cambridge, Mass., the two partners announced today. "Through this collaboration, we will target innovations that will move us beyond specialized tasks to more general approaches to solving more complex problems, with the added capability of robust, continuous learning," Dario Gil, IBM Research's vice president of AI and IBM Q, said in a blog post. Gil and MIT engineering dean Anantha Chandrakasan will be co-chairs of the lab, which will bring together more than 100 AI scientists, professors and students for joint research into AI hardware and software. The aim will be to take advantage of big data and find better ways to augment human intelligence, Gil said.