He praised the work of the UK ambassador, who Mr Meller said had "been working through the night to get Ai-Da released," but pointed out that her late release meant it would be difficult to get her ready for the display on Thursday. "We're right up to the wire now," he said.

Table 7. Rest of South and Central America Artificial Intelligence in Healthcare Market, by Component– Revenue And Forecast To 2027 (USD Million) Table 9. Rest of South and Central America Artificial Intelligence in Healthcare Market, by End User – Revenue and Forecast to 2027 (USD Million)

She has been described as "a vision of the future" who is every bit as good as other abstract artists today, but Ai-Da – the world's first ultra-realistic robot artist – hit a temporary snag before her latest exhibition when Egyptian security forces detained her at customs. Ai-Da is due to open and present her work at the Great Pyramid of Giza on Thursday, the first time contemporary art has been allowed next to the pyramid in thousands of years. But because of "security issues" that may include concerns that she is part of a wider espionage plot, both Ai-Da and her sculpture were held in Egyptian customs for 10 days before being released on Wednesday, sparking a diplomatic fracas. "The British ambassador has been working through the night to get Ai-Da released, but we're right up to the wire now," said Aidan Meller, the human force behind Ai-Da, shortly before her release. According to Meller, border guards detained Ai-Da at first because she had a modem, and then because she had cameras in her eyes (which she uses to draw and paint).

Applying artificial intelligence to big data can predict – and prevent – crime. When a social media site throws out an ad for a product you were just discussing over the phone, it's easy to jump to conclusions: They must be listening, surely. But the truth is that the site employed artificial intelligence (AI) to predict your behaviour. You searched for a yeast starter last week and commented on a friend's photo of sourdough bread yesterday. The ad for a bread-making course that seemingly pops up out of the blue was shown to you because the data predicted you might be interested in it – based on your own and previous users' behaviour.

The shelves are bare for consumer packaged goods (CPG) firms. Ongoing disruption to global supply chains continues to hurt food companies' ability to meet consumer demand for products. How might artificial intelligence (AI) helps CPG companies fight through the bottleneck? Most CPG companies are wrestling with a compelling challenge. The Coronavirus disease (COVID-19) pandemic is not going away anytime soon, if ever, which means that a CPG business must brace itself for ongoing disruptions caused by work slowdowns and outright shutdowns. For example, a factory in Malaysia was closed for 10 days because of a pandemic-related lockdown.

Got a moral quandary you don't know how to solve? Why not turn to the wisdom of artificial intelligence, aka Ask Delphi: an intriguing research project from the Allen Institute for AI that offers answers to ethical dilemmas while demonstrating in wonderfully clear terms why we shouldn't trust software with questions of morality. Ask Delphi was launched on October 14th, along with a research paper describing how it was made. From a user's point of view, though, the system is beguilingly simple to use. Just head to the website, outline pretty much any situation you can think of, and Delphi will come up with a moral judgement. Since Ask Delphi launched, its nuggets of wisdom have gone viral in news stories and on social media.

The design of widespread vision-and-language datasets and pre-trained encoders directly adopts, or draws inspiration from, the concepts and images of ImageNet. While one can hardly overestimate how much this benchmark contributed to progress in computer vision, it is mostly derived from lexical databases and image queries in English, resulting in source material with a North American or Western European bias. Therefore, we devise a new protocol to construct an ImageNet-style hierarchy representative of more languages and cultures. In particular, we let the selection of both concepts and images be entirely driven by native speakers, rather than scraping them automatically. Specifically, we focus on a typologically diverse set of languages, namely, Indonesian, Mandarin Chinese, Swahili, Tamil, and Turkish. On top of the concepts and images obtained through this new protocol, we create a multilingual dataset for {M}ulticultur{a}l {R}easoning over {V}ision and {L}anguage (MaRVL) by eliciting statements from native speaker annotators about pairs of images. The task consists of discriminating whether each grounded statement is true or false. We establish a series of baselines using state-of-the-art models and find that their cross-lingual transfer performance lags dramatically behind supervised performance in English. These results invite us to reassess the robustness and accuracy of current state-of-the-art models beyond a narrow domain, but also open up new exciting challenges for the development of truly multilingual and multicultural systems.

While large pre-trained models have enabled impressive results on a variety of downstream tasks, the largest existing models still make errors, and even accurate predictions may become outdated over time. Because detecting all such failures at training time is impossible, enabling both developers and end users of such models to correct inaccurate outputs while leaving the model otherwise intact is desirable. However, the distributed, black-box nature of the representations learned by large neural networks makes producing such targeted edits difficult. If presented with only a single problematic input and new desired output, fine-tuning approaches tend to overfit; other editing algorithms are either computationally infeasible or simply ineffective when applied to very large models. To enable easy post-hoc editing at scale, we propose Model Editor Networks with Gradient Decomposition (MEND), a collection of small auxiliary editing networks that use a single desired input-output pair to make fast, local edits to a pre-trained model. MEND learns to transform the gradient obtained by standard fine-tuning, using a low-rank decomposition of the gradient to make the parameterization of this transformation tractable. MEND can be trained on a single GPU in less than a day even for 10 billion parameter models; once trained MEND enables rapid application of new edits to the pre-trained model. Our experiments with T5, GPT, BERT, and BART models show that MEND is the only approach to model editing that produces effective edits for models with tens of millions to over 10 billion parameters. Increasingly large neural networks have become a fundamental tool in solving data-driven problems in computer vision (Huang et al., 2017) and natural language processing (Vaswani et al., 2017) in particular. However, a key challenge in deploying and maintaining such models is issuing patches to adjust model behavior after deployment (Sinitsin et al., 2020).

We study adaptive sensing of Cox point processes, a widely used model from spatial statistics. We introduce three tasks: maximization of captured events, search for the maximum of the intensity function and learning level sets of the intensity function. We model the intensity function as a sample from a truncated Gaussian process, represented in a specially constructed positive basis. In this basis, the positivity constraint on the intensity function has a simple form. We show how an minimal description positive basis can be adapted to the covariance kernel, non-stationarity and make connections to common positive bases from prior works. Our adaptive sensing algorithms use Langevin dynamics and are based on posterior sampling (\textsc{Cox-Thompson}) and top-two posterior sampling (\textsc{Top2}) principles. With latter, the difference between samples serves as a surrogate to the uncertainty. We demonstrate the approach using examples from environmental monitoring and crime rate modeling, and compare it to the classical Bayesian experimental design approach.

The robotic artist known as Ai-Da was scheduled to display her artwork alongside the great pyramids of Egypt on Thursday, though the show was nearly called off after both the robot and her human sculptor, Aidan Meller, were detained by Egyptian authorities for a week and a half until they could confirm that the artist was actually a spy. The incident began when border guards objected over Ai-da's camera eyes, which it uses in its creative process, and its on-board modem. "I can ditch the modems, but I can't really gouge her eyes out," Meller told The Guardian. The robot artist, which was built in 2019, typically travels via specialized cargo case and was held at the border until clearing customs on Wednesday evening, hours before the exhibit was scheduled to begin. "The British ambassador has been working through the night to get Ai-Da released, but we're right up to the wire now," Meller said, just before Ai-Da was sprung from robo-jail.