The arrival of AI creation tools has greatly expanded the opportunities for content creators, but concerns remain about the use of such, and whether the work spat out by these apps and tools can actually, legally be used in your process. The answer, right now, is yes – but we are also seeing some cautionary tales and elements, which could influence your thinking around your adoption of AI creation tools in your process. In my view, AI creation tools should be used as supplementary elements, as tools that can help in your creation process, but should not be relied upon as sole facilitators of your content. But that is possible, and we're undoubtedly going to see an influx of AI-generated content across the web, as spammy SEO peddlers look to make a quick buck on the back of automated options. And really, the outputs of tools like ChatGPT will likely be better than what these scam sellers would have produced via outsourcing to human content farms anyway – but that's still not what you want for your site, and if anything, it could help to make your better quality content stand out, by providing more human, more accurate answers to people's queries.

In an experiment, the researchers discovered that one percent of the music the system produced directly copied the songs on which it was trained. This figure is high enough to make the business hesitant to release MusicLM in its current form, noted a TechCrunch report on Friday. The researchers highlighted the necessity for more future effort in addressing these hazards related with music generating and underlined the risk of potential creative content misappropriation linked with the use case. However, some people are still awwed by AI-audio bites released by the Google. "Impressed to see the quality of autogenerated vocals has gone way up! Sounds real but in a foreign language," wrote a Twitter user.

Then, suddenly, we flash forward to the modern day of 2023. Frank is wheelchair-bound and gravely ill, and Bill is old and frail. Frank is so sick that he's unable to feed himself or indulge in his pleasantries of painting. One morning, Frank decides he wants go die by his own choice, with Bill's assistance. Offerman as Bill flashes back the saddest face any man can possibly have, and Frank is heartbroken.

"Fudge," I remember saying, only I didn't say fudge, I said fuck, a word for adults. I was playing The Last of Us, a narrative video game for adults about a zombie apocalypse, and I had just died for what seemed like the thousandth time in the first room with a "clicker," the game lore's name for a medium-difficulty enemy. These "infected"--it's classier not to call them zombies, and this is a classy zombie-combat game, one with a story--had become misshapen thanks to a cordyceps brain infection, which devoured mankind almost overnight. The clicker was ghastlier than others, because it had lived long enough for the infection to fully engulf its formerly human face, fungal fibers enrobing it, teeth jutting out like barbs. An older infected is a more resilient one.

Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding of the performance trade-offs of different decisions made during the instruction-tuning process. These decisions include the scale and diversity of the instruction-tuning benchmark, different task sampling strategies, fine-tuning with and without demonstrations, training using specialized datasets for reasoning and dialogue, and finally, the fine-tuning objectives themselves. In this paper, we characterize the effect of instruction-tuning decisions on downstream task performance when scaling both model and benchmark sizes. To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks. Through the lens of this framework, we first present insights about instruction-tuning decisions as applied to OPT-30B and further exploit these insights to train OPT-IML 30B and 175B, which are instruction-tuned versions of OPT. OPT-IML demonstrates all three generalization abilities at both scales on four different evaluation benchmarks with diverse tasks and input formats -- PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG. Not only does it significantly outperform OPT on all benchmarks but is also highly competitive with existing models fine-tuned on each specific benchmark. We release OPT-IML at both scales, together with the OPT-IML Bench evaluation framework.

Real-time social media data can provide useful information on evolving hazards. Alongside traditional methods of disaster detection, the integration of social media data can considerably enhance disaster management. In this paper, we investigate the problem of detecting geolocation-content communities on Twitter and propose a novel distributed system that provides in near real-time information on hazard-related events and their evolution. We show that content-based community analysis leads to better and faster dissemination of reports on hazards. Our distributed disaster reporting system analyzes the social relationship among worldwide geolocated tweets, and applies topic modeling to group tweets by topics. Considering for each tweet the following information: user, timestamp, geolocation, retweets, and replies, we create a publisher-subscriber distribution model for topics. We use content similarity and the proximity of nodes to create a new model for geolocation-content based communities. Users can subscribe to different topics in specific geographical areas or worldwide and receive real-time reports regarding these topics. As misinformation can lead to increase damage if propagated in hazards related tweets, we propose a new deep learning model to detect fake news. The misinformed tweets are then removed from display. We also show empirically the scalability capabilities of the proposed system.

Running live music recommendation studies without direct industry partnerships can be a prohibitively daunting task, especially for small teams. In order to help future researchers interested in such evaluations, we present a number of struggles we faced in the process of generating our own such evaluation system alongside potential solutions. These problems span the topics of users, data, computation, and application architecture.

Language models are trained on large volumes of text, and as a result their parameters might contain a significant body of factual knowledge. Any downstream task performed by these models implicitly builds on these facts, and thus it is highly desirable to have means for representing this body of knowledge in an interpretable way. However, there is currently no mechanism for such a representation. Here, we propose to address this goal by extracting a knowledge-graph of facts from a given language model. We describe a procedure for ``crawling'' the internal knowledge-base of a language model. Specifically, given a seed entity, we expand a knowledge-graph around it. The crawling procedure is decomposed into sub-tasks, realized through specially designed prompts that control for both precision (i.e., that no wrong facts are generated) and recall (i.e., the number of facts generated). We evaluate our approach on graphs crawled starting from dozens of seed entities, and show it yields high precision graphs (82-92%), while emitting a reasonable number of facts per entity.

Given corpus-based semantic vectors (known as embeddings in computational linguistics and natural language processing) for pairs of base words and corresponding complex words, several methods have been proposed that take as input the semantic vector of the base word, and that produce as output the vector of the complex word. One such method is illustrated in Figure 1. Given the semantic vectors for two pairs of singulars and plurals (table/tables and pen/pens), and given the semantic vector for banana but no semantic vector for its plural, the semantic vector for bananas is obtained by first calculating the vectors that start at a singular and point to the corresponding plural (represented by blue vectors), and average these, resulting in an average shift vector (in red). This shift vector can then be applied to the vector of banana, resulting in the semantic vector for bananas (lower panel). Kisselew et al. (2015) calculated the average shift vector for each of a large set of German derivational affixes, and showed that this results in high-quality estimates of the meanings of derived complex words. Marelli and Baroni (2015) used a method based on matrix multiplication to obtain predicted semantic vectors for derived words, and showed that this method generated quantitative predictors that help explain variance in measures of lexical processing such as reaction times in visual lexical decision. Figure 1 The average of the shift vectors for given singular-plural pairs (table/tables, pen/pens) is used to calculate the semantic vector of the unknown plural vector of banana.

Figure 1: This spectrogram shows overlapped segments of two music tracks after being combined and reconstructed along a per-frequency seam (bright yellow). The tracks were beat and tempo matched, then overlapped by 64 beats. EXTENDED ABSTRACT The problem of transitioning smoothly from one audio clip to another arises in many music consumption scenarios; especially as music consumption has moved from professionally curated and live-streamed radios to personal playback devices and services. Classically, transitioning from one song to another has been reliant on either pre-mixed transitions on recorded digital or physical media, hardware or software crossfading on the playback device, or professional transitions by a host or disk jockey (DJ). While options for software crossfading are ubiquitous on music streaming platforms and media players alike, these transitions pale in quality when compared to those manually applied by an audio engineer or DJ who can harmonically and rhythmically align tracks--and importantly--manually apply equalizer (EQ) filters during transitions.