T erence Broad Creative Computing Institute University of the Arts London United Kingdom t.broad@arts.ac.uk Abstract The dead internet theory is a conspiracy theory that states that all interactions and posts on social media are no longer being made by real people, but rather by autonomous bots. While the theory is obviously not true, an increasing amount of posts on social media have been made by bots optimised to gain followers and drive engagement on social media platforms. This paper looks at the recent phenomenon of these bots, analysing their behaviour through the lens of computational creativity to investigate the question: is computational creativity flourishing on the dead internet? Introduction The dead internet theory is a conspiracy theory that emerged in the late 2010's or early 2020's that states that large parts of the internet, in particular on social media are no longer occupied by humans and human generated content, but rather posts by AI-driven bots that are designed to control or influence human behaviour (IlluminatiPirate 2021). Whist the theory emerges from the fringes of the internet, stemming in conspiratorial thinking as a way of explaining broad-based changes to society from nefarious actors, many commentators have observed that there is a grain of truth to the theory (Tiffany 2021).

Companies are looking at automation and other ways to boost their marketing efforts as working smarter or doing more with less become mantras hitting many departments. How could a chatbot marketing strategy help when it comes to attracting interest and driving sales or attracting customers, and as marketers look to make use of new technology tools? The marketing department is increasingly in charge of figuring out new ways of using current technology or adopting the latest innovations to give their marketing efforts an edge. Chatbots have been around in a mass-market sense for a good few years now, and there are plenty of positive marketing stories out there. The stats look good too, with some 47% of consumers happy to buy from a bot, and sales netting close to double the average order value.

Over the last few years, the team has built the course recommendation engine from the ground up and evolved it to serve recommendations using hyper-personalized models that learn billions of coefficients for our millions of members (Shivani Rao et al CIKM 2019, Polatkan et al blog post). A key goal of this recommendation engine is to surface the most relevant and personalized course recommendations, which can help learners develop new skills and drive engagement on the platform. In this two-part series, we'll show how Learning AI is recommending relevant courses to our members and helping drive engagement by using state-of-the-art AI technologies. In part 1, we'll share an overview of our recommendation engine design and then present a high-level explanation of the three main components of the engine. Later, in part 2, we'll delve deeper into each of the engine's components, providing insight into how we generate personalized course recommendations for every learner on the platform.

Plenty of smartphone users are more in-touch with their friends and family through SMS message apps than they are through social media and other tools, especially among the older demographics. Businesses are also using SMS for a range of uses, mostly around appointments and services, yet the rise of chatbots makes them a natural way to engage via SMS using Twilio and SnatchBot to build a clever service without the need for deep technical knowledge. The modern SMS app on an iPhone or Android device is just as effective as Facebook Messenger and other services, but it has some critical advantages. SMS works when 4G connections are down, it works in places where connectivity is spotty and every phone owner knows how to use it. Those advantages mean that any business looking to adopt a chatbot should consider running it as part of its SMS service as well as on websites, apps and social media messaging to ensure a way to engage and connect with users, whatever their preference and technology skills.

As language learning makes a sea change to online, users will come to expect personalized learning experiences. China's AI-powered online education market alone reached $568 million in 2017 and is expected to surpass $26 billion in 2022. At Sana Labs, we build AI technologies to power these learning experiences through easy to integrate APIs. This means that machine learning models for personalization as well as pronunciation, grammar, and overall fluency feedback can be production ready in days, not months. In this article, I'll highlight why deep learning will power this shift.

Your skills have always been important to your LinkedIn profile. They show your experience, serve as keywords to help you appear in Google and LinkedIn searches, and they show your target buyers and future employers your strengths. They also offer a way for you to get endorsements, which adds credibility and proves to your profile visitors that you do in fact have those skills. But soon there will be an even more important way for you to leverage your skills. You see, LinkedIn has unveiled a new feature that uses artificial intelligence to push your posts out to people who share your skills, based on the content of those posts.