New phishing technique embeds hidden Unicode characters between letters in email subjects, making malicious messages undetectable to keyword-based filters.

Contemporary e-mail services have high availability expectations from the customers and are resource-strained because of the high-volume throughput and spam attacks. Deep Machine Learning architectures, which are resource hungry and require off-line processing due to the long processing times, are not acceptable at the front line filters. On the other hand, the bulk of the incoming spam is not sophisticated enough to bypass even the simplest algorithms. While the small fraction of the intelligent, highly mutable spam can be detected only by the deep architectures, the stress on them can be unloaded by the simple near real-time and near zero-footprint algorithms such as the Bag of Synthetic Syllables algorithm applied to the short texts of the e-mail subject lines and other short text fields. The proposed algorithm creates a circa 200 sparse dimensional hash or vector for each e-mail subject line that can be compared for the cosine or euclidean proximity distance to find similarities to the known spammy subjects. The algorithm does not require any persistent storage, dictionaries, additional hardware upgrades or software packages. The performance of the algorithm is presented on the one day of the real SMTP traffic.

E-commerce marketplaces make use of a number of marketing channels like emails, push notifications, etc. to reach their users and stimulate purchases. Personalized emails especially are a popular touch point for marketers to inform users of latest items in stock, especially for those who stopped visiting the marketplace. Such emails contain personalized recommendations tailored to each user's interests, enticing users to buy relevant items. A common limitation of these emails is that the primary entry point, the title of the email, tends to follow fixed templates, failing to inspire enough interest in the contents. In this work, we explore the potential of large language models (LLMs) for generating thematic titles that reflect the personalized content of the emails. We perform offline simulations and conduct online experiments on the order of millions of users, finding our techniques useful in improving the engagement between customers and our emails. We highlight key findings and learnings as we productionize the safe and automated generation of email titles for millions of users.

The natural language capabilities demonstrated by large language models (LLMs) highlight an opportunity for new writing support tools that address the varied needs of people with dyslexia. We present LaMPost, a prototype email editor that draws upon our understanding of these needs to motivate AI-powered writing features, such as outlining main ideas, generating a subject line, suggesting changes, and rewriting a selection. We evaluated LaMPost with 19 adults with dyslexia, identifying promising routes for further exploration (such as the popular "rewrite" and "subject line" features), while also finding that the current generation of LLMs may not yet meet the accuracy and quality thresholds to be useful for writers with dyslexia. In addition, knowledge of the AI did not alter participants' perception of the system nor their feelings of autonomy, expression, and self-efficacy when writing emails. Our findings provide insight into the benefits and drawbacks of LLMs as writing support for adults with dyslexia, and they offer a foundation to build upon in future research. Dyslexia refers to a cluster of symptoms that result in challenges with word recognition, reading fluency, spelling, and writing population.8

Large Language Models (LLMs) are transforming the way people generate, explore, and engage with content. We study how we can develop LLM applications for online social networks. Despite LLMs' successes in other domains, it is challenging to develop LLM-based products for social networks for numerous reasons, and it has been relatively under-reported in the research community. We categorize LLM applications for social networks into three categories. First is knowledge tasks where users want to find new knowledge and information, such as search and question-answering. Second is entertainment tasks where users want to consume interesting content, such as getting entertaining notification content. Third is foundational tasks that need to be done to moderate and operate the social networks, such as content annotation and LLM monitoring. For each task, we share the challenges we found, solutions we developed, and lessons we learned. To the best of our knowledge, this is the first comprehensive paper about developing LLM applications for social networks.

While generative AI excels in content generation, it does not always increase user engagement. This can be attributed to two main factors. First, generative AI generates content without incorporating explicit or implicit feedback about user interactions. Even if the generated content seems to be more informative or well-written, it does not necessarily lead to an increase in user activities, such as clicks. Second, there is a concern with the quality of the content generative AI produces, which often lacks the distinctiveness and authenticity that human-created content possesses. These two factors can lead to content that fails to meet specific needs and preferences of users, ultimately reducing its potential to be engaging. This paper presents a generic framework of how to improve user engagement with generative AI by leveraging user feedback. Our solutions employ rejection sampling, a technique used in reinforcement learning, to boost engagement metrics. We leveraged the framework in the context of email notification subject lines generation for an online social network, and achieved significant engagement metric lift including +1% Session and +0.4% Weekly Active Users. We believe our work offers a universal framework that enhances user engagement with generative AI, particularly when standard generative AI reaches its limits in terms of enhancing content to be more captivating. To the best of our knowledge, this represents an early milestone in the industry's successful use of generative AI to enhance user engagement.

The idea that studios want actors to relinquish their digital doubles forever in exchange for a few tanks of gas (and that's before taxes!) was just too deliciously infuriating not to retweet or Thread. The scenario transformed the studios into creative labor–devouring supervillains. Even if you hadn't previously cared much about the looming strike, suddenly you were angry--the studios wanted to get away with replacing human actors for free. And who knew how they'd use those digital doubles! If you've watched the recent episode of Black Mirror in which a streaming site rationalizes all kinds of twisted uses of Salma Hayek's digital double based on a legal agreement, it's not hard to imagine the whole thing truly going off the rails.

Target Audience: Content creators, writers, bloggers, marketers, and businesses who want to optimize their content creation process and increase productivity. Reframe your prompts with assistants to generate more powerful results. Customizable presets and priority support to streamline your workflow. What would you like to generate? Target Audience: Content creators, writers, bloggers, marketers, and businesses who want to optimize their content creation process and increase productivity.

The thing that we have to come to grips with in a world of ubiquitous, powerful AI tools is how much it can do for us. The multiplier on human effort is unprecedented, and potentially disruptive. But this fact can often feel abstract. So I decided to run an experiment. I gave myself 30 minutes, and tried to accomplish as much as I could during that time on a single business project.

How many marketers' weapon of choice for distributing branded content to their customers is email marketing? And when they employ AI for email marketing, it takes their game to a whole new level. Email is a versatile marketing tool: brands can send out emails to their customers and prospects for just about anything. From saying the first'Hello' to lead generation, lead nurturing, sales-boosting, and personalized customer service, email is still the go-to tool for good reason. Given the significance of email marketing, businesses are readily embracing cutting-edge technologies to convert data into insights to both achieve better results and cut down on costs.