NBC News has discovered that Pinterest's recommendation algorithms are making it easier for pedophiles to create boards full of images of underage girls. After an initial search, Pinterest will start suggesting related searches that can easily be misused. The images themselves sometimes receive sexual comments. NBC notes that it didn't find child sexual abuse material (CSAM) during its investigation. However, the people creating the creepy boards sometimes had collections containing porn despite Pinterest's ban on that content.

As a white man in America with no discernible regional accent, I can simply assume that modern consumer technologies -- virtual assistants like Siri, Alexa or Assistant, and my phones' camera -- will work seamlessly out of the box. I assume this because, well, they do. That's namely because the nerds who design and program these devices overwhelmingly both look and sound just like me -- if even a little whiter. Folks with more melanin in their skin and extra twang on their tongue don't enjoy that same privilege. Tomorrow's chatbots and visual AIs will only serve to exacerbate this bias unless steps are taken today to ensure a benchmark standard of fairness and equitable behavior from these systems.

Machine learning is a powerful tool for digital marketing that uses data analysis to predict consumer behavior and improve marketing campaigns. Did you know Netflix uses machine learning to personalize its content recommendations, Amazon uses it to suggest products to customers, and Spotify uses it to curate personalized playlists for users? According to a survey by Salesforce, 51% of marketers already use Artificial Intelligence in some form, and another 27% plan to incorporate it into their strategies in the next two years. Machine learning algorithms can help you analyze customer journey, predict trends, and personalize content while saving you time and resources. This article will dive into machine learning and explore how it can revolutionize your digital marketing efforts. Whether you're a seasoned marketer or just starting, the power of machine learning can take your campaigns to the next level.

Conversational recommender systems (CRSs) are improving rapidly, according to the standard recommendation accuracy metrics. However, it is essential to make sure that these systems are robust in interacting with users including regular and malicious users who want to attack the system by feeding the system modified input data. In this paper, we propose an adversarial evaluation scheme including four scenarios in two categories and automatically generate adversarial examples to evaluate the robustness of these systems in the face of different input data. By executing these adversarial examples we can compare the ability of different conversational recommender systems to satisfy the user's preferences. We evaluate three CRSs by the proposed adversarial examples on two datasets. Our results show that none of these systems are robust and reliable to the adversarial examples.

We investigate how well traditional fiction genres like Fantasy, Thriller, and Literature represent readers' preferences. Using user data from Goodreads we construct a book network where two books are strongly linked if the same people tend to read or enjoy them both. We then partition this network into communities of similar books and assign each a list of subjects from The Open Library to serve as a proxy for traditional genres. Our analysis reveals that the network communities correspond to existing combinations of traditional genres, but that the exact communities differ depending on whether we consider books that people read or books that people enjoy. In addition, we apply principal component analysis to the data and find that the variance in the book communities is best explained by two factors: the maturity/childishness and realism/fantastical nature of the books. We propose using this maturity-realism plane as a coarse classification tool for stories.

You probably want a streaming device(Opens in a new tab) for your TV, whether you're a cord cutter(Opens in a new tab) or not. Roku is a popular choice, particularly as it ramps up its own original content(Opens in a new tab). Roku devices offer plenty of variety and portability, from the budget Roku Express(Opens in a new tab) to the feature-packed Roku Ultra(Opens in a new tab). Whichever one you have, there's more to know beyond the basics. Here's how to get more out of your streaming device.

An Arlo doorbell will work with Alexa, the Google Assistant, Siri, Samsung's SmartThings or IFTTT integrations. Unlike some smart home devices, Arlo plays nice. And right now, you can save $100 on the brand's wire-free version of the Essential Video Doorbell at Amazon. You can also get the same discount through Arlo's site directly. Get half off a no-wires-required video doorbell from a brand that works with whichever smart home assistant you prefer. The rechargeable battery inside makes the unit easy to install, particularly if your front entry isn't already wired for a doorbell.

It's the go-to dating app for many singletons, but it appears that Hinge is experiencing issues this afternoon. According to Down Detector, the problems started at around midday and are affecting users across the country. While the reason for the outage remains unclear, of those who reported issues, 99 per cent said they were struggling with the app, while the remaining one per cent said they were having problems logging in. One user posted a screenshot of the error message in the app, which reads: 'We'll be right back. Something went wrong, and we're working on fetching solutions.

NEEDHAM, Mass., March 7, 2023 – A new forecast from the International Data Corporation (IDC) Worldwide Artificial Intelligence Spending Guide shows that global spending on artificial intelligence (AI), including software, hardware, and services for AI-centric systems*, will reach $154 billion in 2023, an increase of 26.9% over the amount spent in 2022. The ongoing incorporation of AI into a wide range of products will result in a compound annual growth rate (CAGR) of 27.0% over the 2022-2026 forecast with spending on AI-centric systems expected to surpass $300 billion in 2026. "Companies that are slow to adopt AI will be left behind – large and small. AI is best used in these companies to augment human abilities, automate repetitive tasks, provide personalized recommendations, and make data-driven decisions with speed and accuracy," said Mike Glennon, senior market research analyst with IDC's Customer Insights & Analysis team. "Suppliers of AI technologies need to know which are the largest and fastest growing opportunities, but without data they become just another opinion. IDC's AI Spending Guide provides the foundation for marketing strategy through its comprehensive coverage of AI opportunities and gives a robust basis for a market focus that ties with companies' capabilities."

We address the issue of binary classification from positive and unlabeled data (PU classification) with a selection bias in the positive data. During the observation process, (i) a sample is exposed to a user, (ii) the user then returns the label for the exposed sample, and (iii) we however can only observe the positive samples. Therefore, the positive labels that we observe are a combination of both the exposure and the labeling, which creates a selection bias problem for the observed positive samples. This scenario represents a conceptual framework for many practical applications, such as recommender systems, which we refer to as ``learning from positive, unlabeled, and exposure data'' (PUE classification). To tackle this problem, we initially assume access to data with exposure labels. Then, we propose a method to identify the function of interest using a strong ignorability assumption and develop an ``Automatic Debiased PUE'' (ADPUE) learning method. This algorithm directly debiases the selection bias without requiring intermediate estimates, such as the propensity score, which is necessary for other learning methods. Through experiments, we demonstrate that our approach outperforms traditional PU learning methods on various semi-synthetic datasets.