This paper is a submission to the Open Philanthropy AI Worldviews Contest. In it, we estimate the likelihood of transformative artificial general intelligence (AGI) by 2043 and find it to be <1%. Specifically, we argue: The bar is high: AGI as defined by the contest - something like AI that can perform nearly all valuable tasks at human cost or less - which we will call transformative AGI is a much higher bar than merely massive progress in AI, or even the unambiguous attainment of expensive superhuman AGI or cheap but uneven AGI. Many steps are needed: The probability of transformative AGI by 2043 can be decomposed as the joint probability of a number of necessary steps, which we group into categories of software, hardware, and sociopolitical factors. No step is guaranteed: For each step, we estimate a probability of success by 2043, conditional on prior steps being achieved. Many steps are quite constrained by the short timeline, and our estimates range from 16% to 95%. Therefore, the odds are low: Multiplying the cascading conditional probabilities together, we estimate that transformative AGI by 2043 is 0.4% likely. Reaching >10% seems to require probabilities that feel unreasonably high, and even 3% seems unlikely. Thoughtfully applying the cascading conditional probability approach to this question yields lower probability values than is often supposed. This framework helps enumerate the many future scenarios where humanity makes partial but incomplete progress toward transformative AGI.

Smart grid data can be evaluated for anomaly detection in numerous fields, including cyber-security, fault detection, electricity theft, etc. The strange anomalous behaviors may have been caused by various reasons, including peculiar consumption patterns of the consumers, malfunctioning grid infrastructures, outages, external cyber-attacks, or energy fraud. Recently, anomaly detection of the smart grid has attracted a large amount of interest from researchers, and it is widely applied in a number of high-impact fields. One of the most significant challenges within the smart grid is the implementation of efficient anomaly detection for multiple forms of aberrant behaviors. In this paper, we provide a scoping review of research from the recent advancements in anomaly detection in the context of smart grids. We categorize our study from numerous aspects for deep understanding and inspection of the research challenges so far. Finally, after analyzing the gap in the reviewed paper, the direction for future research on anomaly detection in smart-grid systems has been provided briefly.

This research delves into the current literature on bias in Natural Language Processing Models and the techniques proposed to mitigate the problem of bias, including why it is important to tackle bias in the first place. Additionally, these techniques are further analysed in the light of newly developed models that tower in size over past editions. To achieve those aims, the authors of this paper conducted their research on GPT3 by OpenAI, the largest NLP model available to consumers today. With 175 billion parameters in contrast to BERTs 340 million, GPT3 is the perfect model to test the common pitfalls of NLP models. Tests were conducted through the development of an Applicant Tracking System using GPT3. For the sake of feasibility and time constraints, the tests primarily focused on gender bias, rather than all or multiple types of bias. Finally, current mitigation techniques are considered and tested to measure their degree of functionality.

Monitoring land cover using remote sensing is vital for studying environmental changes and ensuring global food security through crop yield forecasting. Specifically, multitemporal remote sensing imagery provides relevant information about the dynamics of a scene, which has proven to lead to better land cover classification results. Nevertheless, few studies have benefited such high spatial and temporal resolution data due to the difficulty of accessing reliable, fine-grained and high-quality annotated samples to support their hypotheses. Therefore, we introduce a temporal patch-based dataset of Canadian croplands, enriched with labels retrieved from the Canadian Annual Crop Inventory. The dataset contains 78,536 manually verified and curated high-resolution (10 m/pixel, 640 x 640 m) geo-referenced images from 10 crop classes collected over four crop production years (2017-2020) and five months (June-October). Each instance contains 12 spectral bands, an RGB image, and additional vegetation index bands. Individually, each category contains at least 4,800 images. Moreover, as a benchmark, we provide models and source code that allow a user to predict the crop class using a single image (ResNet, DenseNet, EfficientNet) or a sequence of images (LRCN, 3D-CNN) from the same location. In perspective, we expect this evolving dataset to propel the creation of robust agro-environmental models that can accelerate the comprehension of complex agricultural regions by providing accurate and continuous monitoring of land cover.

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.

Center for A.I. Safety Director Dan Hendrycks explains concerns about how the rapid growth of artificial intelligence could impact society. An expert and entrepreneur in the field of artificial intelligence warned that while the new technology has the potential for massive benefits, it could also prove "too powerful and too disruptive" for humanity, expressing doubt about the federal government's ability to address such a challenge. Kevin Baragona worked as a software engineer but recognized the potential impact of AI, which led him to start DeepAI in 2016 to help bring the new technology to fruition. The free online service is growing rapidly, with users increasing tenfold over the past year. DeepAI was the first company to offer an online AI text-to-image generator, which allows users to enter a description of the image they would like to create, select a theme and receive a custom image for download.

Code hidden inside PC motherboards left millions of machines vulnerable to malicious updates, researchers revealed this week. Staff at security firm Eclypsium found code within hundreds of models of motherboards created by Taiwanese manufacturer Gigabyte that allowed an updater program to download and run another piece of software. While the system was intended to keep the motherboard updated, the researchers found that the mechanism was implemented insecurely, potentially allowing attackers to hijack the backdoor and install malware. Elsewhere, Moscow-based cybersecurity firm Kaspersky revealed that its staff had been targeted by newly discovered zero-click malware impacting iPhones. Victims were sent a malicious message, including an attachment, on Apple's iMessage. The attack automatically started exploiting multiple vulnerabilities to give the attackers access to devices, before the message deleted itself.

Alarm over artificial intelligence has reached a fever pitch in recent months. Just this week, more than 300 industry leaders published a letter warning AI could lead to human extinction and should be considered with the seriousness of "pandemics and nuclear war". Terms like "AI doomsday" conjure up sci-fi imagery of a robot takeover, but what does such a scenario actually look like? The reality, experts say, could be more drawn out and less cinematic – not a nuclear bomb but a creeping deterioration of the foundational areas of society. "I don't think the worry is of AI turning evil or AI having some kind of malevolent desire," said Jessica Newman, director of University of California Berkeley's Artificial Intelligence Security Initiative.

The meeting is expected to help the agency's independent study team determine how to evaluate these mysterious sightings going forward. Astronaut Ed White became the first American to walk in space on this day in history, June 3, 1965. White, an engineer, a Lieutenant Colonel in the U.S. Air Force, a test pilot and NASA astronaut, made the spacewalk -- technically known as "Extravehicular Activity" or "EVA" -- while serving as the pilot on the Gemini 4 mission. Command pilot James McDivitt was the other member of the crew, and took pictures of White outside the vehicle. ON THIS DAY IN HISTORY, JUNE 2, 1953, QUEEN ELIZABETH II IS CROWNED IN LONDON'S WESTMINSTER ABBEY White spent about 20 minutes floating outside the Gemini 4 capsule, nearly double the time initially allowed by NASA for the spacewalk.

Recent work has shown that inducing a large language model (LLM) to generate explanations prior to outputting an answer is an effective strategy to improve performance on a wide range of reasoning tasks. In this work, we show that neural rankers also benefit from explanations. We use LLMs such as GPT-3.5 to augment retrieval datasets with explanations and train a sequence-to-sequence ranking model to output a relevance label and an explanation for a given query-document pair. Our model, dubbed ExaRanker, finetuned on a few thousand examples with synthetic explanations performs on par with models finetuned on 3x more examples without explanations. Furthermore, the ExaRanker model incurs no additional computational cost during ranking, and allows explanations to be requested on demand.