The team behind ETHforestAI strongly believes that education should be accessible, engaging, and empowering. By combining cutting-edge technology with a focus on gamification, they are creating a platform that not only aims to teach users about Web3 but also fosters their growth and development in the space. Armed with an AI chatbot, the team further aims to provide a fun way of getting personalized recommendations and answers to a wide variety of Web3 and Crypto related questions. At ETHForestAI, the team is motivating both creators and users to engage with Learn-To-Earn, Real Yield and Creator Economy! Join us and discover the future of digital education!

Conventional clustering methods based on pairwise affinity usually suffer from the concentration effect while processing huge dimensional features yet low sample sizes data, resulting in inaccuracy to encode the sample proximity and suboptimal performance in clustering. To address this issue, we propose a unified tensor clustering method (UTC) that characterizes sample proximity using multiple samples' affinity, thereby supplementing rich spatial sample distributions to boost clustering. Specifically, we find that the triadic tensor affinity can be constructed via the Khari-Rao product of two affinity matrices. Furthermore, our early work shows that the fourth-order tensor affinity is defined by the Kronecker product. Therefore, we utilize arithmetical products, Khatri-Rao and Kronecker products, to mathematically integrate different orders of affinity into a unified tensor clustering framework. Thus, the UTC jointly learns a joint low-dimensional embedding to combine various orders. Finally, a numerical scheme is designed to solve the problem. Experiments on synthetic datasets and real-world datasets demonstrate that 1) the usage of high-order tensor affinity could provide a supplementary characterization of sample proximity to the popular affinity matrix; 2) the proposed method of UTC is affirmed to enhance clustering by exploiting different order affinities when processing high-dimensional data.

As engineered systems grow in complexity, there is an increasing need for automatic methods that can detect, diagnose, and even correct transient anomalies that inevitably arise and can be difficult or impossible to diagnose and fix manually. Among the most sensitive and complex systems of our civilization are the detectors that search for incredibly small variations in distance caused by gravitational waves -- phenomena originally predicted by Albert Einstein to emerge and propagate through the universe as the result of collisions between black holes and other massive objects in deep space. The extreme complexity and precision of such detectors causes them to be subject to transient noise issues that can significantly limit their sensitivity and effectiveness. In this work, we present a demonstration of a method that can detect and characterize emergent transient anomalies of such massively complex systems. We illustrate the performance, precision, and adaptability of the automated solution via one of the prevalent issues limiting gravitational-wave discoveries: noise artifacts of terrestrial origin that contaminate gravitational wave observatories' highly sensitive measurements and can obscure or even mimic the faint astrophysical signals for which they are listening. Specifically, we demonstrate how a highly interpretable convolutional classifier can automatically learn to detect transient anomalies from auxiliary detector data without needing to observe the anomalies themselves. We also illustrate several other useful features of the model, including how it performs automatic variable selection to reduce tens of thousands of auxiliary data channels to only a few relevant ones; how it identifies behavioral signatures predictive of anomalies in those channels; and how it can be used to investigate individual anomalies and the channels associated with them.

We are delighted to announce that Oraichain Mainnet 2.0 has arrived on the scene since March 24, 2022 at 11:02:20 UTC! The revolutionary Oraichain Mainnet 2.0 endeavors to make headway towards the avant-garde vision of an AI Layer 1 for Data Economy and Oracle services, in quest of incorporating blockchain's decentralization and AI's absolute power into the future. The advent of Oraichain Mainnet 2.0 transcends known limits of its predecessor & other L1 chains with a more highly advanced AI-driven infrastructure facilitating various standalone yet securely scalable subnetworks. This historic milestone, furthermore, is marked with a major revamp for the Oraichain Mainnet 2.0's blockchain explorer -- Oraiscan. Since the integration of IBC into Oraichain Mainnet 2.0 to create seamless & secure communication between CosmosSDK-based chains, users can keep tabs on other IBC-compatible assets in addition to the Native ORAI tokens, such as ATOM, LUNA, UST, OSMO once these assets arrive at the Oraichain Mainnet 2.0 (via OraiDEX).

We have a planned maintenance outage from to UTC. Hence, we are unable to process your lab request. Please re-visit this page and request your lab after the outage. We have a planned maintenance outage from to UTC.You will be unable to connect and use your lab environment during this time. We have a planned infrastructure maintenance on 10-Jan-2020 from 15:30 PST to 18:30 PST.

Back in February, when AI conferences were still held in-person, Turing Award winners Geoffrey Hinton, Yann LeCun and Yoshua Bengio shared a stage in New York at an AAAI event, which Syncedcovered in detail. LeCun told the audience that, after decades of skepticism, he had finally joined Hinton in support of the idea that self-supervised learning may usher in AI's next revolution. Unlike supervised learning, which requires manual data-labelling, self-supervised learning (SSL) is an approach that can automatically generate labels. Recent improvements in self-supervised training methods have established SSL as a serious alternative to traditional supervised training. Google's language representation model ALBERT for example utilizes a self-supervised training framework to leverage large amounts of text. It's no surprise then that NeurIPS 2020 (the Conference on Neural Information Processing Systems) would find itself at the forefront of this trend.

Involving potential users of a particular technology in the research and development (R&D) process is a very powerful way to maximise success when such technology is deployed in the real world. In addition, this can speed up the R&D process because the researchers' perspective to the problem is combined with that of end-users. The non- profit project CYBATHLON was created by ETH Zurich as a way to advance R&D of assistive technology through competitions that involve developers, people with disabilities, and the general public. This 13th and 14th of November, the CYBATHLON 2020 edition is taking place. The event will be live-streamed, and it is completely open to the public.

The architecture of this package has been designed by Xavier Glorot (https://github.com/glorotxa), Update (Nov 13): the code for Translating Embeddings (see https://everest.hds.utc.fr/doku.php?id en:transe) has been included along with a new version for Freebase (FB15k). You need to install Theano to use those scripts. It also requires: Python 2.4, Numpy 1.5.0, The experiment scripts are compatible with Jobman but this library is not mandatory.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Classrooms are noticeably more hi-tech these days - interactive boards, laptops and online learning plans proliferate, but has the curriculum actually changed or are children simply learning the same thing on different devices? Some argue that the education this generation of children is receiving is little different from that their parents or even their grandparents had. But, in a world where artificial intelligence and robots threaten jobs, the skills that this generation of children need to learn are likely to be radically different to the three Rs that have for so long been the mainstay of education. The BBC went along to the Bett conference in London in search of different ways of teaching and learning. A stone's throw from the Excel, where Bett is held, stands a new school that is, according to its head Geoffrey Fowler, currently little more than a Portakabin.