The lawsuit, which was filed Thursday, claims that the AI company unlawfully crawls, scrapes, copies, and distributes CNN's content from CNN Digital Platforms and third-party platforms. It also accuses the AI tools of reproducing verbatim copies of its articles, including paywalled stories, in query responses to users. Perplexity's AI tools allegedly have incorrectly attributed hallucinated content to CNN, which the company says in the suit violates its trademark. CNN's lawsuit stands for the proposition that Perplexity, a company valued at tens of billions of dollars, should not be able to steal from entities that create the original content Perplexity exploits, a CNN spokesperson said in a statement to the outlet. The public rely on high quality news journalism reported by human beings to understand their world, which is frequently dangerous and expensive to produce.

Taylor Swift has applied to trademark her voice and appearance in an apparent attempt to protect herself from artificial intelligence impersonations. The pop superstar has lodged three trademark applications in the US - one using a photo of herself on stage during her Eras Tour, and the other two being audio clips of her introducing herself while promoting her last album. AI-generated versions of Swift have cropped up in various ways in recent years - from explicit images to a fake election ad in which she appeared to urge people to vote for Donald Trump. The move comes after actor Matthew McConaughey became the first celebrity to use trademark rules to attempt to protect his voice and image from AI misuse earlier this year . Trademark applications are a relatively new way for celebrities to combat the growing issue of AI rip-offs.

Licence for the codes We use the code for MS-TCN [13], ASRF [24], LAS [9], all of which are under MITLicense according to https://opensource.org/licenses/MIT. For the Jigsaws [18] dataset, we follow the data use agreeement according to https://cs.jhu. Action classification: Action classification is the task of identifying a single action, as opposed to a sequence of actions. Several methods use 2DCNNs to extract frame-wise features from an input video, which are then combined to predict a coarse action taking place in the video [56, 39, 59]. There also exist several works that perform action classification from kinematic data [2, 12]. Action segmentation: Action segmentation is the problem of segmenting an input stream of data, labeling each frame according to the action that is being carried out. Earlier methods for action segmentation employed hidden Markov models [33, 22]. More recently, convolutional neural networks [58, 26] and recurrent neural networks [50] have been applied to this problem Inspired by the success of temporal convolutional networks (TCNs) in speech synthesis, [37] adapted these models to action segmentation. MS-TCN [13], which uses a multi-stage TCN architecture, has become one of the most widely used architecture for action segmentation. Although these methods achieve high frame-wise accuracy, they still produce a significant number of over-segmentation errors. In order to address this, several boundary-aware methods have been developed which perform temporal smoothing of the frame-wise predictions [57, 24]. These methods use ground-truth boundary information to train a binary classification network to perform boundary detection. The boundary estimates are then used to aggregate the frame-wise predictions either in a soft manner (boundary-aware pooling) or by setting a hard threshold. However, for elemental actions with a short duration, such as the functional primitives in the StrokeRehab dataset, the duration of each action is very short. As a result, the boundaries between actions can be hard to detect or even hard to define (see Figure 4). Sequence-to-sequence models: Our proposed method is based on sequence-to-sequence (seq2seq) models. These models allow us to learn a mapping of a variable-length input sequence to a variablelength output sequence [53].

Hey Meta workers, are you getting paid for those keystrokes? It's a very simple question that your bosses aren't inclined to answer. No longer content to subsume recognizable intellectual properties, the majority of the i ndexed internet and books ( basically all of them), AI will apparently now begin devouring its own workforce. A report in alleged that the keystrokes, mouse movements and clicks of Meta's workforce are to be captured for the purposes of training AI -- something the company's communications department was happy to confirmed as accurate! In a cheery missive, a company spokesperson told Engadget that If we're building agents to help people complete everyday tasks using computers, our models need real examples of how people use them [...] we're launching an internal tool that will capture these kinds of inputs on certain applications to help us train our models.

In this paper, we introduce IMPACT (Integrated Multimodal Patent Analysis and Creation Dataset for Design Patents), a large-scale multimodal patent dataset with detailed captions for design patent figures. Our dataset includes half a million design patents comprising 3.61 million figures along with captions from patents granted by the United States Patent and Trademark Office (USPTO) over a 16-year period from 2007 to 2022. We incorporate the metadata of each patent application with elaborate captions that are coherent with multiple viewpoints of designs. Even though patents themselves contain a variety of design figures, titles, and descriptions of viewpoints, we find that they lack detailed descriptions that are necessary to perform multimodal tasks such as classification and retrieval. IMPACT closes this gap thereby providing researchers with necessary ingredients to instantiate a variety of multimodal tasks. Our dataset has a huge potential for novel design inspiration and can be used with advanced computer vision models in tandem. We perform preliminary evaluations on the dataset on the popular patent analysis tasks such as classification and retrieval. Our results indicate that integrating images with generated captions significantly improves the performance of different models on the corresponding tasks. Given that design patents offer various benefits for modeling novel tasks, we propose two standard computer vision tasks that have not been investigated in analyzing patents as future directions using IMPACT as a benchmark viz., 3D Image Construction and Visual Question Answering (VQA).

Luke Littler, the youngest darts world champion in history, has applied to the Intellectual Property Office to trademark his face. The move is intended to prevent his face being reproduced, including by generative AI, without permission. Littler has won two World Championship titles in a row and has had his image used legally on darts merchandise, as well as by multiple brands such as KP Nuts. The 19-year-old joins celebrities such as actor Matthew McConaughey who have filed to protect their likeness from AI misuse in recent months. Littler has already trademarked his nickname the Nuke in the United States.

Tech companies believe in intellectual property, but not yours. In April 2024, Eric Schmidt, the former Google CEO and a current AI evangelist, gave a closed-door lecture to a group of Stanford students. If these young people hoped to be Silicon Valley entrepreneurs, Schmidt explained, then they should be prepared to breach some ethical boundaries. Yet Schmidt told the students to go ahead and download whatever they need to build an accurate "test" version of their AI product. If the product takes off, "then you hire a whole bunch of lawyers to go clean the mess up," he said.

Sir Paul McCartney was among the artists who spoke out on the issue. After significant backlash, the UK backed off from that position. We have listened, Technology Secretary Liz Kendall said on Wednesday. However, the government's new stance is, well, not a stance at all. It currently no longer has a preferred option about how to handle the issue.

They said the company'has shown it is willing to... steal the intellectual property ofAmerican creators.' After ByteDance suspended the global rollout of its new Seedance 2.0 AI video generator on the weekend, US senators have now told the company to immediately shut down the app. Seedance 2.0 poses a direct threat to the American intellectual property system and, more broadly, to the constitutional rights and economic livelihoods of our creative community, Senators Marsha Blackburn and Peter Welch wrote in a letter to the company . Responsible global companies follow the law and respect core economic rights, including intellectual property and personal likeness protections, the senators wrote. They cited Seedance AI examples including an AI generated Thanos and Superman battle, a rewritten ending and that famous (fake) Tom Cruise and Brad Pitt battle .