Marketing with AI (Artificial Intelligence) may sound fancy; the term is thrown around in marketing tool ad copy by the marketing gurus and hyped by the media. Though the hype around what AI might do in the next few years is overstated, the reality of how it is used today in marketing is often under-recognized.

Though others have interviewed ChatGPT, I had some anxiety-riddled questions of my own: Will you take my job? Is the singularity upon us? These questions are half facetious, half serious. If you've been hidden away and somehow missed the ruckus, here's what all the commotion's about: In November, conversational AI tool ChatGPT took the world by storm, crossing one million users a mere five days after its release, according to its developer, San Francisco's OpenAI. If you are still one of those who think this is all hype, take it up with Microsoft (MSFT).

Have you ever heard of Midjourney? Well, in case you did not know, it's a standalone research laboratory responsible for developing an AI program of the same name. This program generates pictures based on textual descriptions, similar to OpenAI's DALL-E and Stable Diffusion. You probably don't know a lot about this, but today my aim is to change that. According to the company's founder (David Holz) the company was already profitable in August 2022.

Explicit reasonable utilization of artificial intelligence incorporates current web search tools, individual collaborator programs that figure out communication in language, self-driving vehicles, and suggestion motors, for example, those utilized by Spotify and Netflix. There are four levels or sorts of Artificial intelligence -- two of which we have accomplished, and two which stay hypothetical at this stage. The "theory of mind" phrasing comes from brain science, and this situation alludes to a simulated intelligence understanding that people have contemplations and feelings which then, at that point, thus, influence the simulated intelligence's way of behaving. ML is a lot of moving and terms these days. Machine Learning (ML) is a sub-section of Artificial intelligence.

So far we've spent a good 15 minutes with Joel and Ellie in conversations that weren't in the game. After cementing their relationship, the show finally returns to the game's story, except instead of Pittsburgh, the pair arrive in Kansas City, Mo., a more sensible, on-the-way location to Wyoming. In a scene ripped straight from the game, the pair encounter a man pretending to be hurt, and are jumped by the citizens of Kansas City, now freed from federal military rule. They crash the truck, and a firefight within a laundromat is lifted straight from the game. This echoes how enemy combatants from the second game react, calling out their fallen comrade's name in an attempt to humanize the "villains" of Joel and Ellie as other people trying to survive.

Personalized recommendations have become a common feature of modern online services, including most major e-commerce sites, media platforms and social networks. Today, due to their high practical relevance, research in the area of recommender systems is flourishing more than ever. However, with the new application scenarios of recommender systems that we observe today, constantly new challenges arise as well, both in terms of algorithmic requirements and with respect to the evaluation of such systems. In this paper, we first provide an overview of the traditional formulation of the recommendation problem. We then review the classical algorithmic paradigms for item retrieval and ranking and elaborate how such systems can be evaluated. Afterwards, we discuss a number of recent developments in recommender systems research, including research on session-based recommendation, biases in recommender systems, and questions regarding the impact and value of recommender systems in practice.

Differential framing of issues can lead to divergent world views on important issues. This is especially true in domains where the information presented can reach a large audience, such as traditional and social media. Scalable and reliable measurement of such differential framing is an important first step in addressing them. In this work, based on the intuition that framing affects the tone and word choices in written language, we propose a framework for modeling the differential framing of issues through masked token prediction via large-scale fine-tuned language models (LMs). Specifically, we explore three key factors for our framework: 1) prompt generation methods for the masked token prediction; 2) methods for normalizing the output of fine-tuned LMs; 3) robustness to the choice of pre-trained LMs used for fine-tuning. Through experiments on a dataset of articles from traditional media outlets covering five diverse and politically polarized topics, we show that our framework can capture differential framing of these topics with high reliability.

New events emerge over time influencing the topics of rumors in social media. Current rumor detection benchmarks use random splits as training, development and test sets which typically results in topical overlaps. Consequently, models trained on random splits may not perform well on rumor classification on previously unseen topics due to the temporal concept drift. In this paper, we provide a re-evaluation of classification models on four popular rumor detection benchmarks considering chronological instead of random splits. Our experimental results show that the use of random splits can significantly overestimate predictive performance across all datasets and models. Therefore, we suggest that rumor detection models should always be evaluated using chronological splits for minimizing topical overlaps.

Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs. However, existing methods are often tailored to specific GAN architectures and are limited to either discovering global semantic directions that do not facilitate localized control, or require some form of supervision through manually provided regions or segmentation masks. In this light, we present an architecture-agnostic approach that jointly discovers factors representing spatial parts and their appearances in an entirely unsupervised fashion. These factors are obtained by applying a semi-nonnegative tensor factorization on the feature maps, which in turn enables context-aware local image editing with pixel-level control. In addition, we show that the discovered appearance factors correspond to saliency maps that localize concepts of interest, without using any labels. Experiments on a wide range of GAN architectures and datasets show that, in comparison to the state of the art, our method is far more efficient in terms of training time and, most importantly, provides much more accurate localized control. Our code is available at: https://github.com/james-oldfield/PandA.

Apple cares a lot about music. Steve Jobs loved it so much that he invented the iPod and iTunes to let us bring all of it everywhere, and personally owned multi-thousand-dollar Swedish speakers in his sparsely-decorated living room. To this day, Apple Music is one of the best-sounding streaming services you can subscribe to thanks to lossless audio support. The headphones it makes, both itself and via Beats, are largely fantastic. It's a shame, then, that the company still fails to make a great full-size smart speaker.