These cameras do not exist. As real as they might appear, they were created using an artificial intelligence system called DALL-E 2, which can make realistic images based only on text descriptions. These strange and insane-looking cameras were created using DALL-E 2, an artificial intelligence program created by OpenAI. The system was announced earlier this year and can create photo-realistic images based only on a brief description and allows a person to easily edit the image with simple tools. Not only can it create photos and images entirely from scratch, but it can also modify existing images.

Macro-operators (macros) are a well-known technique for enhancing performance of planning engines by providing "short-cuts" in the state space. Existing macro learning systems usually generate macros by considering most frequent action sequences in training plans. Unfortunately, frequent action sequences might not capture meaningful activities as a whole, leading to a limited beneficial impact for the planning process. In this paper, inspired by resource locking in critical sections in parallel computing, we propose a technique that generates macros able to capture whole activities in which limited resources (e.g., a robotic hand, or a truck) are used. Specifically, such a Critical Section macro starts by locking the resource (e.g., grabbing an object), continues by using the resource (e.g., manipulating the object) and finishes by releasing the resource (e.g., dropping the object). Hence, such a macro bridges states in which the resource is locked and cannot be used. We also introduce versions of Critical Section macros dealing with multiple resources and phased locks. Usefulness of macros is evaluated using a range of state-of-the-art planners, and a large number of benchmarks from the deterministic and learning tracks of recent editions of the International Planning Competition.

Microsoft inadvertently learned the risks of creating racist AI, but what happens if you deliberately point the intelligence at a toxic forum? As Motherboard and The Verge note, YouTuber Yannic Kilcher trained an AI language model using three years of content from 4chan's Politically Incorrect (/pol/) board, a place infamous for its racism and other forms of bigotry. After implementing the model in ten bots, Kilcher set the AI loose on the board -- and it unsurprisingly created a wave of hate. In the space of 24 hours, the bots wrote 15,000 posts that frequently included or interacted with racist content. They represented more than 10 percent of posts on /pol/ that day, Kilcher claimed. Nicknamed GPT-4chan (after OpenAI's GPT-3), the model learned to not only pick up the words used in /pol/ posts, but an overall tone that Kilcher said blended "offensiveness, nihilism, trolling and deep distrust."

Recent work suggests that convolutional neural networks of different architectures learn to classify images in the same order. To understand this phenomenon, we revisit the over-parametrized deep linear network model. Our analysis reveals that, when the hidden layers are wide enough, the convergence rate of this model's parameters is exponentially faster along the directions of the larger principal components of the data, at a rate governed by the corresponding singular values. We term this convergence pattern the Principal Components bias (PC-bias). Empirically, we show how the PC-bias streamlines the order of learning of both linear and non-linear networks, more prominently at earlier stages of learning. We then compare our results to the simplicity bias, showing that both biases can be seen independently, and affect the order of learning in different ways. Finally, we discuss how the PC-bias may explain some benefits of early stopping and its connection to PCA, and why deep networks converge more slowly with random labels.

Britons' trust in science is at an all-time high after the Covid pandemic, a new report reveals – but misinformation on social media continues to present a'threat to scientific credibility'. The 3M State of Science Index, published on Tuesday, reveals that 90 per cent of UK residents trust science in 2022, compared with 85 per cent in 2019. This stat also compares with 88 per cent of Europeans and 89 per cent of people globally who trust science in 2022. In the UK, 57 per cent of Brits say they are now more appreciative of science after the pandemic, likely due to the efforts of scientists in creating Covid vaccines. However, misinformation'is widespread' on social media and threatens the future of the public's understanding of science, the report says.

Several of 2022's most anticipated novels offer unique perspectives on society's thorniest issues, from racism to workplace harassment. Call it a summer fiction reading list for the socially engaged. Ms. Shibata is never officially assigned the menial tasks of her workplace--making coffee, tidying, answering the phones--but since she's the only woman on staff, her colleagues expect her to oversee them. Annoyed by the tedious sexism, Shibata announces that she is pregnant and unable to continue the extra work. We follow her fake pregnancy week by week, and though there is no child, something real grows within Shibata.

Top South African data scientists are solving critical challenges in society and making their mark on the global industry. From deliveries to cyber security, advanced analytics is driving change; but it's the leading thinkers behind these solutions that are really impressive. If you've ordered a delivery from South Africa's largest on-demand grocery delivery service, the driver's route was optimised by a "travelling salesman" algorithm that Kimberly Taylor originally developed as a Wits engineering student. She has since built a company and an award-winning app around this innovation which helps logistics companies scale their delivery volume. Multi-stop route optimisation is critical for perishable deliveries and through Taylor's solution, data science is helping on-demand delivery companies in the Quick Service Restaurant and grocery space keep their promise of 30 60 minutes.

Petuum, the creator of the world's first composable platform for MLOps, and the Inception Institute for Artificial Intelligence (IIAI), have agreed to partner on the development of revolutionary AI applications. Petuum has recently announced a limited release of the composable platform, which includes the AI OS, Universal Pipelines, Deployment Manager, and Experiment Manager, for select private beta partners. Through the partnership with Petuum, IIAI's enterprise AI/ML teams will operationalize and scale their applications into production. Founded in 2018, IIAI's mission is to build full-stack AI solutions and operating systems for enterprise businesses and developers. Besides being the research arm for G42, IIAI is also empowering stakeholders with AI applications and incubating new technology at the cutting edge of ML innovation.

A scheduling method in a robotic network cloud system with minimal makespan is beneficial as the system can complete all the tasks assigned to it in the fastest way. Robotic network cloud systems can be translated into graphs where nodes represent hardware with independent computing power and edges represent data transmissions between nodes. Time window constraints on tasks are a natural way to order tasks. The makespan is the maximum amount of time between when the first node to receive a task starts executing its first scheduled task and when all nodes have completed their last scheduled task. Load balancing allocation and scheduling ensures that the time between when the first node completes its scheduled tasks and when all other nodes complete their scheduled tasks is as short as possible. We propose a grid of all tasks to ensure that the time window constraints for tasks are met. We propose grid of all tasks balancing algorithm for distributing and scheduling tasks with minimum makespan. We theoretically prove the correctness of the proposed algorithm and present simulations illustrating the obtained results.

Polar bears and brown bears were still mating with each other long after they had split into two distinct species, a new study has found. The two species are known to have separated up to 1.6 million years ago, yet new genomic evidence suggests they have inherited traits from each other much more recently. Scientists from the USA, Mexico and Finland analysed the genomes of 64 modern polar and brown bears, as well as that of an ancient polar bear that lived up to 130,000 years ago. While evidence of evidence of hybridisation was found in both brown and polar bear genomes, the latter carried a particularly strong signature of DNA from brown bears. As global warming continued to melt Arctic sea ice, the two bear species may run into each other more frequently, their shared evolutionary history could become more significant.