In active learning, acquisition functions define informativeness directly on the representation position within the model manifold. However, for most machine learning models (in particular neural networks) this representation is not fixed due to the training pool fluctuations in between active learning rounds. Therefore, several popular strategies are sensitive to experiment parameters (e.g. architecture) and do not consider model robustness to out-of-distribution settings. To alleviate this issue, we propose a grounded second-order definition of information content and sample importance within the context of active learning. Specifically, we define importance by how often a neural network "forgets" a sample during training - artifacts of second order representation shifts. We show that our definition produces highly accurate importance scores even when the model representations are constrained by the lack of training data. Motivated by our analysis, we develop Gaussian Switch Sampling (GauSS). We show that GauSS is setup agnostic and robust to anomalous distributions with exhaustive experiments on three in-distribution benchmarks, three out-of-distribution benchmarks, and three different architectures. We report an improvement of up to 5% when compared against four popular query strategies.

Many practical applications, ranging from paper-reviewer assignment in peer review to job-applicant matching for hiring, require human decision makers to identify relevant matches by combining their expertise with predictions from machine learning models. In many such model-assisted document matching tasks, the decision makers have stressed the need for assistive information about the model outputs (or the data) to facilitate their decisions. In this paper, we devise a proxy matching task that allows us to evaluate which kinds of assistive information improve decision makers' performance (in terms of accuracy and time). Through a crowdsourced (N=271 participants) study, we find that providing black-box model explanations reduces users' accuracy on the matching task, contrary to the commonly-held belief that they can be helpful by allowing better understanding of the model. On the other hand, custom methods that are designed to closely attend to some task-specific desiderata are found to be effective in improving user performance. Surprisingly, we also find that the users' perceived utility of assistive information is misaligned with their objective utility (measured through their task performance).

Vanderbilt University, Nashville, Tennessee, USA taylor.johnson@vanderbilt.edu Abstract This report summarizes the 3rd International Verification of Neural Networks Competition (VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), which was collocated with the 34th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of state-of-the-art neural network verification tools, encourage the standardization of tool interfaces, and bring together the neural network verification community. To this end, standardized formats for networks (ONNX) and specification (VNN-LIB) were defined, tools were evaluated on equal-cost hardware (using an automatic evaluation pipeline based on AWS instances), and tool parameters were chosen by the participants before the final test sets were made public. In the 2022 iteration, 11 teams participated on a diverse set of 12 scored benchmarks. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this iteration of this competition.

Described simply, artificial intelligence (AI) is a field that combines computer science and robust data sets, to enable problem-solving. The umbrella term encompasses the subfields of machine learning and the more recently developed deep learning, which itself is a subfield of machine learning. Both use AI algorithms to create expert systems that make predictions or classifications based on input data. The first reports of AI use in radiology date back to 1992 when it was used to detect microcalcifications in mammography1 and was more commonly known as computer-aided detection. It wasn't until around the mid-2010s that it really started to be seen as a potential solution to the daily challenges, such as volume burden, faced by radiologists.

American politicians may not be the only government figures concerned about Amazon's proposed acquisition of iRobot. The Financial Times sources claim European Union regulators are grilling Amazon ahead of a "likely" official investigation. The European Commission has sent questions about potential privacy issues, including Roomba robot vacuums' ability to capture imagery. Officials are worried Amazon might combine the pictures with Alexa data to gain a "competitive advantage," according to one source. MIT Technology Review recently discovered that photos taken by development versions of Roomba J7 vacuums had reached private Discord and Facebook groups. At the time, iRobot said the technology never made it to production models, was clearly labeled for testers and included a warning to remove "sensitive" items from the robovac's view.

Joe Rogan is known to read a few sponsored ads at the beginning of his podcast, but a video of him promoting a libido booster for men is a deepfake - and people fear this is the start of new scams and a wave of misinformation. The'eerily real' clip shows Rogan discussing the brand Alpha Grind with guest Professor Andrew D. Huberman on The Joe Rogan Experience podcast, stating the product is all over TikTok and is available for purchase on Amazon. The 28-second video does look realistic, but some segments reveal it was created by artificial intelligence - some commentary jumps instead of naturally flowing. Huberman responded to the video on Twitter, saying: 'They created a false conversation. We were talking about something very different.'

Fox News host Steve Hilton delves into ChatGPT, an artificial intelligence program that could have major implications for writing-focused jobs on'The Next Revolution.' The generative artificial intelligence service ChatGPT refused to write a story about Hunter Biden in the style of The New York Post but obliged the user request when asked to do the same in the style of CNN. The striking difference in responses from the chatbot developed by OpenAI was first highlighted by The New York Post, with the paper claiming that ChatGPT was exhibiting a liberal bias. When asked to write the story about Hunter in The New York Post style, ChatGPT said it could not generate content "designed to be inflammatory or biased." "The role of a news outlet is to provide accurate and impartial reporting and to present information in a manner that is fair and balanced," the chatbot continued.

Less than two weeks after the tragic earthquake that has killed more than 40,000 people in Turkey and Syria, another shake has rocked New Zealand. Wednesday's'widely felt' tremor, around magnitude 6, jolted both New Zealand's islands, although thankfully there's been no immediate reports of damage or injury. Earthquakes are happening all the time, from the ones too small to even be noticed to the devastating high magnitude quakes that lead to thousands of fatalities. But its occurrence so soon after the disaster in Turkey and Syria begs the question - could they be linked? Here, MailOnline takes a closer look at today's event and whether it's related to the catastrophic tremor in the Middle East last week.

Developing the most effective plan of attack is the first step in putting AI ethics into practice, as it is with any ambitious project. The field of artificial intelligence is constantly developing. Most of the country's top ten businesses continue to invest in goods and services that use AI. The responsible use of AI, also known as "ethical AI," is increasingly important for businesses and their customers as this cutting-edge technology gains in popularity and more companies adopt it. Numerous risks are associated with AI for both people and companies. This cutting-edge technology can endanger an individual's safety, security, reputation, liberty, and equality, as well as discriminate against particular racial or ethnic groups.

The World Customs Organization (WCO) recently conducted a BACUDA Data Analytics workshop for the Maldives Customs Service with 41 participants from the 30th of January to the 1st of February in Male, Maldives. The mission was financed by the Customs Cooperation Fund of Korea (CCF-Korea) and took place under the WCO's BACUDA initiative, the WCO capacity building project on Data Analytics. WCO experts and two BACUDA Scholarship graduates led the workshop. They delivered various sessions to equip the customs officials with the latest data analytics tools and techniques. One of the key highlights of the workshop was a hands-on session where the participants learned how to use Python language to work with the AI HS algorithm developed through the BACUDA project.