Imagine a world where you could have a beachside conversation with your colleagues, take meeting notes while floating around a space station, or teleport from your office in London to New York, all without taking a step outside your front door. Feeling under pressure with too many meetings scheduled today? Then why not send your AI-enabled digital twin instead to take the load off your shoulders? These examples offer but a glimpse into the future vision of work promised by "the metaverse," a term originally coined by author Neal Stephenson in 1992 to describe a future world of virtual reality. While defying precise definition, the metaverse is generally regarded as a network of 3-D virtual worlds where people can interact, do business, and forge social connections through their virtual "avatars."

In part two, we head to Venezuela, where AI data-labeling firms found cheap and desperate workers amid a devastating economic crisis, creating a new model of labor exploitation. The series also looks at ways to move away from these dynamics. In part three, we visit ride-hailing drivers in Indonesia who, by building power through community, are learning to resist algorithmic control and fragmentation. In part four, we end in Aotearoa, the Maori name for New Zealand, where an Indigenous couple are wresting back control of their community's data to revitalize its language. Together, the stories reveal how AI is impoverishing the communities and countries that don't have a say in its development--the same communities and countries already impoverished by former colonial empires.

Acusensus, based in Australia, is among the companies that employ artificial intelligence to address road safety.

The race is on to reduce fraud and continue improving payment flows. Artificial intelligence (AI) offers a winning strategy, says Chalapathy Neti, head, AI and machine learning platform, Swift. AI is out of the lab and already well on its way to delivering smarter tech solutions in our daily lives. Just look at the way Amazon and Netflix use machine learning algorithms to continually serve us fresh content and products based on our previous behaviours. We get a better, more personalised experience while they strengthen their business models.

Publicly traded companies are required to submit periodic reports with eXtensive Business Reporting Language (XBRL) word-level tags. Manually tagging the reports is tedious and costly. We, therefore, introduce XBRL tagging as a new entity extraction task for the financial domain and release FiNER-139, a dataset of 1.1M sentences with gold XBRL tags. Unlike typical entity extraction datasets, FiNER-139 uses a much larger label set of 139 entity types. Most annotated tokens are numeric, with the correct tag per token depending mostly on context, rather than the token itself. We show that subword fragmentation of numeric expressions harms BERT's performance, allowing word-level BILSTMs to perform better. To improve BERT's performance, we propose two simple and effective solutions that replace numeric expressions with pseudo-tokens reflecting original token shapes and numeric magnitudes. We also experiment with FIN-BERT, an existing BERT model for the financial domain, and release our own BERT (SEC-BERT), pre-trained on financial filings, which performs best. Through data and error analysis, we finally identify possible limitations to inspire future work on XBRL tagging.

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.