Carlos M. Meléndez is the COO and Co-Founder of Wovenware, an artificial intelligence and software development company. "These days, the reality is a bummer. Everyone is looking for a way to escape," said Wade Watts, the protagonist of the Ernest Cline novel Ready Player One, which was turned into a movie by Steven Spielberg in 2018. In the novel and film, which takes place in 2045, the world is on the verge of collapse, but a virtual reality universe, OASIS, has given people something to find hope in. While Ready Player One is clearly a science fiction story about a contest taking place in a virtual community, it draws many parallels to the rise of the metaverse.

A movie version of Mojang Studio's Minecraft is starting to come together. Action hero veteran Jason Momoa is in talks to star in an upcoming film adaptation of the popular worldbuilding game, reported The Hollywood Reporter. While no contract has been signed yet, the possible addition of Momoa is an encouraging sign of life for a film that has been on Warner Bros' backburner. Warner Bros originally planned to release the film in March 2022, but it was shelved due to production delays related to the pandemic, according to THR. The film's troubles pre-date Covid-19; its original director and screenwriters quit the movie in 2014 due to creative differences with Mojang.

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.

Transformers are the most eminent architectures used for a vast range of Natural Language Processing tasks. These models are pre-trained over a large text corpus and are meant to serve state-of-the-art results over tasks like text classification. In this work, we conduct a comparative study between monolingual and multilingual BERT models. We focus on the Marathi language and evaluate the models on the datasets for hate speech detection, sentiment analysis and simple text classification in Marathi. We use standard multilingual models such as mBERT, indicBERT and xlm-RoBERTa and compare with MahaBERT, MahaALBERT and MahaRoBERTa, the monolingual models for Marathi. We further show that Marathi monolingual models outperform the multilingual BERT variants on five different downstream fine-tuning experiments. We also evaluate sentence embeddings from these models by freezing the BERT encoder layers. We show that monolingual MahaBERT based models provide rich representations as compared to sentence embeddings from multi-lingual counterparts. However, we observe that these embeddings are not generic enough and do not work well on out of domain social media datasets. We consider two Marathi hate speech datasets L3Cube-MahaHate, HASOC-2021, a Marathi sentiment classification dataset L3Cube-MahaSent, and Marathi Headline, Articles classification datasets.

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.

When the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it already learned. As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a paper published in Science this week shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time. "The brains of living beings can continuously learn throughout their lifespan. We have now created an artificial platform for machines to learn throughout their lifespan," said Shriram Ramanathan, a professor in Purdue University's School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing.

A new machine learning method to teach robots how to deal with pliable substances such as pizza dough or fabric.

Seqwa's REST API is built on powerful machine learning models and … Its intelligent search technology leverages artificial intelligence to allow …

The Machine Learning in Utilities Market report studies the sales and consumption of the industry products/goods in the major geographic markets …