Black Mirror has captured the world's attention for more than a decade now, with its twisted take on the future of technology. But what was once just a dystopian Netflix series is now an eerie reality, as many of its visions begin to creep up on us. Surveillance, humanoids and even the reconstruction of thoughts are slowly becoming a part of our world thanks to huge advancements in technology. And it appears these predictions are not over just yet, with Black Mirror's sixth season on the way in just two months. Ahead of its release, MailOnline has revealed six occasions Black Mirror has given a glimpse into the future.

If you're a millennial, chances are you remember AIM's SmarterChild, an "intelligent" chatbot that simulated human conversation. Nowadays, ChatGPT's AI-infused chatbot is all the rage, providing smarter and more eloquent answers to just about any question you may have. As someone who bothered the heck out of SmarterChild as a kid, I was nostalgic for a similar experience, so I gave ChatGPT a try. So, did ChatGPT win over this skeptical contrarian? It felt like I was having a conversation with an actual human being, which SmarterChild just simply couldn't emulate during its heyday.

The artificial intelligence revolution is motoring forward at such a pace that it's hard to keep up with the torrent of news stories about it, let alone the technology itself. In recent weeks we've had AI newsreaders on Kuwaiti TV, an AI-generated photograph winning a major prize, an AI-generated interview with Michael Schumacher that got an editor sacked and, of course, numerous warnings that this all might spell the end of humanity itself. It's natural to feel apprehensive about these society-shaking developments. Even so, the reaction to a recent interview in which Joe Russo speculated on the future of AI-generated film seemed particularly intense. Russo – one half of Marvel-affiliated director duo the Russo brothers – was musing on how generative AI could invent a film catered to the whims of the viewer.

Seoul, South Korea – The news that Saint Von Colucci, a 22-year-old Canadian-Portuguese actor, singer, and songwriter with pull in South Korea's entertainment scene, died after undergoing surgeries to look like a K-pop star set media abuzz. Von Colucci was reported to have undergone 12 plastic surgeries, costing more than $200,000, to resemble BTS member Jimin and overcome discrimination "against his Western traits". He was said to have recently secured a role in an upcoming Korean drama. The only problem is that Von Colucci may have never existed. A raft of evidence suggests he is the product of an elaborate hoax using artificial intelligence that fooled dozens of media outlets, stretching from the United States and Canada to the United Kingdom, South Korea, India, Malaysia and the Philippines. The debacle appears to be the first known case of AI being used to trick media outlets en masse into spreading misinformation, heralding the dawn of a new era of computer-generated fake news.

A geography professor shared his method to detect AI-generated plagiarism with Fox News. He developed it after noticing that ChatGPT produced fake citations. A college professor said he found an easy way to catch AI-generated plagiarism after finding phony citations in some of ChatGPT's content. "It's very easy to identify the fake references," said Terence Day, a physical geography professor at Okanagan College in British Columbia. "All you need to do, really, is to check them up on the internet."

Fox News host Steve Hilton delves into ChatGPT, an artificial intelligence program that could have major implications for writing-focused jobs on'The Next Revolution.' Many experts worry that the rapid development of artificial intelligence may have unforeseen disastrous consequences for humanity. Machine learning technology is designed to assist humans in their everyday life and provide the world with open access to information. However, the unregulated nature of AI in its current state could lead to harmful consequences for its users and the world as a whole. Read below to find out the risks of AI.

Aiming at selecting a small subset of nodes with maximum influence on networks, the Influence Maximization (IM) problem has been extensively studied. Since it is #P-hard to compute the influence spread given a seed set, the state-of-the-art methods, including heuristic and approximation algorithms, faced with great difficulties such as theoretical guarantee, time efficiency, generalization, etc. This makes it unable to adapt to large-scale networks and more complex applications. On the other side, with the latest achievements of Deep Reinforcement Learning (DRL) in artificial intelligence and other fields, lots of works have been focused on exploiting DRL to solve combinatorial optimization problems. Inspired by this, we propose a novel end-to-end DRL framework, ToupleGDD, to address the IM problem in this paper, which incorporates three coupled graph neural networks for network embedding and double deep Q-networks for parameters learning. Previous efforts to solve IM problem with DRL trained their models on subgraphs of the whole network, and then tested on the whole graph, which makes the performance of their models unstable among different networks. However, our model is trained on several small randomly generated graphs with a small budget, and tested on completely different networks under various large budgets, which can obtain results very close to IMM and better results than OPIM-C on several datasets, and shows strong generalization ability. Finally, we conduct a large number of experiments on synthetic and realistic datasets, and experimental results prove the effectiveness and superiority of our model.

This paper targets the perceptual task of separating the different interacting voices, i.e., monophonic melodic streams, in a polyphonic musical piece. We target symbolic music, where notes are explicitly encoded, and model this task as a Multi-Trajectory Tracking (MTT) problem from discrete observations, i.e., notes in a pitch-time space. Our approach builds a graph from a musical piece, by creating one node for every note, and separates the melodic trajectories by predicting a link between two notes if they are consecutive in the same voice/stream. This kind of local, greedy prediction is made possible by node embeddings created by a heterogeneous graph neural network that can capture inter- and intra-trajectory information. Furthermore, we propose a new regularization loss that encourages the output to respect the MTT premise of at most one incoming and one outgoing link for every node, favouring monophonic (voice) trajectories; this loss function might also be useful in other general MTT scenarios. Our approach does not use domain-specific heuristics, is scalable to longer sequences and a higher number of voices, and can handle complex cases such as voice inversions and overlaps. We reach new state-of-the-art results for the voice separation task in classical music of different styles.

Attention-based neural networks, such as Transformers, have become ubiquitous in numerous applications, including computer vision, natural language processing, and time-series analysis. In all kinds of attention networks, the attention maps are crucial as they encode semantic dependencies between input tokens. However, most existing attention networks perform modeling or reasoning based on representations , wherein the attention maps of different layers are learned separately without explicit interactions. In this paper, we propose a novel and generic evolving attention mechanism, which directly models the evolution of inter-token relationships through a chain of residual convolutional modules. The major motivations are twofold. On the one hand, the attention maps in different layers share transferable knowledge, thus adding a residual connection can facilitate the information flow of inter-token relationships across layers. On the other hand, there is naturally an evolutionary trend among attention maps at different abstraction levels, so it is beneficial to exploit a dedicated convolution-based module to capture this process. Equipped with the proposed mechanism, the convolution-enhanced evolving attention networks achieve superior performance in various applications, including time-series representation, natural language understanding, machine translation, and image classification. Especially on time-series representation tasks, Evolving Attention-enhanced Dilated Convolutional (EA-DC-) Transformer outperforms state-of-the-art models significantly, achieving an average of 17% improvement compared to the best SOTA. To the best of our knowledge, this is the first work that explicitly models the layer-wise evolution of attention maps. Our implementation is available at https://github.com/pkuyym/EvolvingAttention.

We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain -- Reddit) for multi-level classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our models. We submitted a system in Task A, and it ranked 49th with F1-score of 0.82. This result showed to be competitive as it only under-performed the best system by 0.052% F1-score.