Why do we have five fingers and toes? It all goes back to our fishy ancestors. The answer to why we have five fingers and toes is surprisingly difficult to suss out. Breakthroughs, discoveries, and DIY tips sent every weekday. The popular nursery rhyme is an early childhood memory for many of us.

The Asahi, along with the Nikkei and the Yomiuri Shimbun, filed a lawsuit with the Tokyo District Court against Perplexity in August. "Journalism should not tolerate freeloading," said Shiro Nakamura, who is also the chair of the Japan Newspaper Publishers & Editors Association (Nihon Shinbun Kyokai, or NSK), during a news conference at the Foreign Correspondents' Club of Japan on Friday. Nakamura said Japan's publishers across the board were concerned about the impact generative AI is having on the news business. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

The first issue of the magazine Giant Robot I ever came across featured the Hong Kong actor Tony Leung Chiu-wai on the cover--this was enough to stand out on a crowded newsstand in the mid-nineteen-nineties. But what caught my attention were the teasers for a random assortment of other stories, about gangs, surfing, shaved ice, orgies. But who was I? I was a teen-ager and desperate to know. I suspected Giant Robot could help me figure it out. For anyone under the age of forty, this level of impressionability might sound a bit silly.

Symbolic Music Alignment is the process of matching performed MIDI notes to corresponding score notes. In this paper, we introduce a reinforcement learning (RL)-based online symbolic music alignment technique. The RL agent - an attention-based neural network - iteratively estimates the current score position from local score and performance contexts. For this symbolic alignment task, environment states can be sampled exhaustively and the reward is dense, rendering a formulation as a simplified offline RL problem straightforward. We evaluate the trained agent in three ways. First, in its capacity to identify correct score positions for sampled test contexts; second, as the core technique of a complete algorithm for symbolic online note-wise alignment; and finally, as a real-time symbolic score follower. We further investigate the pitch-based score and performance representations used as the agent's inputs. To this end, we develop a second model, a two-step Dynamic Time Warping (DTW)-based offline alignment algorithm leveraging the same input representation. The proposed model outperforms a state-of-the-art reference model of offline symbolic music alignment.

The article explores the cultural shift from recording to deleting information in the digital age and its implications on privacy, intellectual property (IP), and Large Language Models like ChatGPT. It begins by defining a delete culture where information, in principle legal, is made unavailable or inaccessible because unacceptable or undesirable, especially but not only due to its potential to infringe on privacy or IP. Then it focuses on two strategies in this context: deleting, to make information unavailable; and blocking, to make it inaccessible. The article argues that both strategies have significant implications, particularly for machine learning (ML) models where information is not easily made unavailable. However, the emerging research area of Machine Unlearning (MU) is highlighted as a potential solution. MU, still in its infancy, seeks to remove specific data points from ML models, effectively making them 'forget' completely specific information. If successful, MU could provide a feasible means to manage the overabundance of information and ensure a better protection of privacy and IP. However, potential ethical risks, such as misuse, overuse, and underuse of MU, should be systematically studied to devise appropriate policies.

In speech communication, how something is said (paralinguistic information) is as crucial as what is said (linguistic information). As a type of paralinguistic information, English speech uses sentence stress, the heaviest prominence within a sentence, to convey emphasis. While different placements of sentence stress communicate different emphatic implications, current speech translation systems return the same translations if the utterances are linguistically identical, losing paralinguistic information. Concentrating on focus, a type of emphasis, we propose mapping paralinguistic information into the linguistic domain within the source language using lexical and grammatical devices. This method enables us to translate the paraphrased text representations instead of the transcription of the original speech and obtain translations that preserve paralinguistic information. As a first step, we present the collection of an English corpus containing speech that differed in the placement of focus along with the corresponding text, which was designed to reflect the implied meaning of the speech. Also, analyses of our corpus demonstrated that mapping of focus from the paralinguistic domain into the linguistic domain involved various lexical and grammatical methods. The data and insights from our analysis will further advance research into paralinguistic translation. The corpus will be published via LDC and our website.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. One of WWE's premier events is back in front of a live audience after the coronavirus pandemic forced fans to stay home and watch from the comfort of their own homes. Survivor Series will take place at the Barclays Center in Brooklyn on Sunday with some of the top stars on both the Raw and SmackDown brands in action against each other. Big E is the RAW WWE champion.

Japan's hot startup stocks have two things in common: They do business in areas that could be described as mundane, and they've pushed their founders into the league of the ultrawealthy. Take AI Inside Inc., which helps turn handwritten documents into electronic files. Or Rakus Co., whose goal is to help small and midsize enterprises with their bookkeeping and emailing services. Their shares have all more than doubled in the past year, enriching their founders and leading to talk of a burgeoning tech scene that's very different from Silicon Valley. While the companies are using technologies like artificial intelligence and cloud computing, they're applying them in less sexy ways.

This paper addresses the problem of unknown delays in adversarial multi-armed bandit (MAB) with multiple play. Existing work on similar game setting focused on only the case where the learner selects an arm in each round. However, there are lots of applications in robotics where a learner needs to select more than one arm per round. It is therefore worthwhile to investigate the effect of delay when multiple arms are chosen. The multiple arms chosen per round in this setting are such that they experience the same amount of delay. There can be an aggregation of feedback losses from different combinations of arms selected at different rounds, and the learner is faced with the challenge of associating the feedback losses to the arms producing them. To address this problem, this paper proposes a delayed exponential, exploitation and exploration for multiple play (DEXP3.M) algorithm. The regret bound is only slightly worse than the regret of DEXP3 already proposed for the single play setting with unknown delay.

A Japanese language school whose stock soared almost 12-fold last year is planning to expand into new businesses as its chief executive officer tries to keep the rally alive. RareJob Inc., a Tokyo-based online English conversation school that uses teachers in the Philippines, will focus on areas including leadership training and job placement, Gaku Nakamura, the company's founder and chief executive officer, said in an interview. Nakamura said one of his goals is to boost the company's market value to ¥100 billion ($922 million) from its current level of about ¥25 billion. RareJob surged 1,093% in 2019, the second-best performance in Japan's Mothers market of smaller shares, after it surprised investors by saying earnings would jump. Analysts -- and history -- suggest it will be difficult to keep up those gains after the company's valuation exceeded 100 times estimated profit.