Tokenization is an important text preprocessing step to prepare input tokens for deep language models. WordPiece and BPE are de facto methods employed by important models, such as BERT and GPT. However, the impact of tokenization can be different for morphologically rich languages, such as Turkic languages, where many words can be generated by adding prefixes and suffixes. We compare five tokenizers at different granularity levels, i.e. their outputs vary from smallest pieces of characters to the surface form of words, including a Morphological-level tokenizer. We train these tokenizers and pretrain medium-sized language models using RoBERTa pretraining procedure on the Turkish split of the OSCAR corpus. We then fine-tune our models on six downstream tasks. Our experiments, supported by statistical tests, reveal that Morphological-level tokenizer has challenging performance with de facto tokenizers. Furthermore, we find that increasing the vocabulary size improves the performance of Morphological and Word-level tokenizers more than that of de facto tokenizers. The ratio of the number of vocabulary parameters to the total number of model parameters can be empirically chosen as 20% for de facto tokenizers and 40% for other tokenizers to obtain a reasonable trade-off between model size and performance.

Since graduating in 2014 with a master of fine arts from UCLA's design media arts program, artist Refik Anadol has become a worldwide sensation known for exhibitions that harness state-of-the-art artificial intelligence and machine learning algorithms to create mind-blowing multisensory experiences. His body of work though, is much more than simply mesmerizing feasts for the eyes and ears; it addresses the challenges and possibilities that our ubiquitous computing has imposed on humanity. On April 19, Anadol's latest piece, "Moment of Reflection" will debut on campus, where he also serves as a lecturer in the UCLA Department of Design Media Arts. It was in that department, he learned from innovative professors like Christian Moeller, Casey Reas, Jennifer Steinkamp and Victoria Vesna, all of whom use digital technology to help reshape conceptions of art. "Using data is a scientific approach to something very soulful and spiritual," Anadol said.

Sounds like the perfect technology for anyone who wants to advance Steve Bannon's'flood the zone with shit' strategy.

Experts in artificial intelligence (AI) trounced some of the world's best strawberry growers in a novel competition in China to see who could grow the most fruit. Three teams of top traditional growers were pitted against four teams of AI experts in the contest in Yunnan province organised by China Agricultural University and Chinese e-commerce platform Pinduoduo. BREAKING TRADITION: One of the expert traditional farmers who finished a distant second to teams using artificial intelligence (AI) in a novel contest in China last year. AgriFutures Australia managing director John Harvey said while there had been a big lift in capital investment into developing agrifood technologies, on-farm uptake was not rising at the same rate. Mr Harvey said the humans versus AI strawberry growing contest in China was a great example of the kind of work Australia needed to do to help farmers work out which technologies were going to deliver the greatest benefit and in what circumstances.

Australia's catastrophic east coast floods have been described by the NSW premier as a "one in 1,000-year event, a term that has created a great deal of confusion. Lengthy explanations that these terms are not the same as "occurring 1,000 years apart" or "once every 1,000 years" have only added to the confusion. The simplest explanation is that the actual meaning of "one in 1,000 years" is "having a probability of 0.1 percent in any given year" (1 in 1,000), which raises the question: why don't people simply say that? The main reason is that these terms date back to a time when most people didn't think in terms of probabilities, and even those who did were confused about how they worked. The daily weather forecast includes a percentage probability of rain, and longer-term forecasts give the probabilities of higher or lower than average rainfall according to El Nino and La Nina cycles.

A new cardiac arrest-detecting AI has been revealed. If this health technology is proven effective, it can reduce death cases caused by sudden heart dysfunction. Natalia Trayanova, the senior author of the latest study, explained that more than 20% of the deaths across the world are caused by cardiac arrest (cardiac arrhythmias). Because of this, they decided to create a new artificial intelligence that can detect if an individual is about to have heart failure. Now, will this new cardia arrest-detecting AI help reduce cardiac arrest deaths?

New Jersey (United States) – A2Z Market Research published new research on Global Artificial Intelligence Drug R&D covering the micro-level of analysis by competitors and key business segments (2022-2029). The Global Artificial Intelligence Drug R&D explores a comprehensive study on various segments like opportunities, size, development, innovation, sales, and overall growth of major players. The research is carried out on primary and secondary statistics sources and it consists of both qualitative and quantitative detailing. Various factors are responsible for the market's growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Artificial Intelligence Drug R&D market.

… market studies on Digital Earth Australia and automation of Victoria’s Vegetation Mapping Through Machine Learning initiative.

Latest survey on Artificial Intelligence for Blockchains Market is conducted to provide hidden gems performance analysis of Artificial Intelligence for Blockchains to better demonstrate competitive environment . The study is a mix of quantitative market stats and qualitative analytical information to uncover market size revenue breakdown by key business segments and end use applications. The report bridges the historical data from 2017 to 2022 and forecasted till 2027*, the outbreak of latest scenario in Artificial Intelligence for Blockchains market have made companies uncertain about their future outlook as the disturbance in value chain have made serious economic slump. If you are part of the Artificial Intelligence for Blockchains industry or intend to be, then study would provide you comprehensive outlook. It is vital to keep your market knowledge up to date analysed by major players and high growth emerging players.

Recently, federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. Nevertheless, directly applying federated learning to real-world tasks faces two challenges: (1) heterogeneity in the data among different organizations; and (2) data noises inside individual organizations. In this paper, we propose a general framework to solve the above two challenges simultaneously. Specifically, we propose using distributionally robust optimization to mitigate the negative effects caused by data heterogeneity paradigm to sample clients based on a learnable distribution at each iteration. Additionally, we observe that this optimization paradigm is easily affected by data noises inside local clients, which has a significant performance degradation in terms of global model prediction accuracy. To solve this problem, we propose to incorporate mixup techniques into the local training process of federated learning. We further provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability. Furthermore, we conduct empirical studies across different drug discovery tasks, such as ADMET property prediction and drug-target affinity prediction.