'Making people feel powerful' All Will Rise. 'Making people feel powerful' All Will Rise. Why an up-and-coming indie developer is returning Microsoft's money Don't get Pushing Buttons delivered to your inbox? V ideo games are in a funding crisis. Investor money flowed freely during the pandemic gaming boom, but now the well has run dry.

A U.S. judge has ordered South Korean game developer Krafton to reinstate the head of one of its video game studios after ruling that he had been improperly removed as part of a takeover plan hatched by ChatGPT. WILMINGTON, DELAWARE - A Delaware judge on Monday ordered that South Korean game developer Krafton reinstate the head of one of its video game studios, ruling he had been improperly removed as part of a takeover plan hatched by ChatGPT. Krafton CEO Changhan Kim had largely followed the advice of artificial intelligence tool ChatGPT during a $250 million dispute with the leaders of the Subnautica game maker Unknown Worlds Entertainment, which Krafton had acquired, according to the ruling by Vice Chancellor Lori Will of the Court of Chancery in Delaware. Businesses and governments are scrambling for new ways to use AI, and the technology has been blamed for mass layoffs, fears of autonomous weapons and concerns about civil rights. Companies caught in takeover-related legal battles often spend millions of dollars on teams of attorneys and advisers from top-flight Wall Street firms. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

If taking a closer look at the MedDRA classification on the system organ level on its website, we can find a claim of "System Organ Classes (SOCs) which are groupings by aetiology (e.g. However, as claimed in the original paper, "It should be noted that we did not perform any preprocessing of our datasets, such as Tab. These datasets appear in MoleculeNet as well. As mentioned in the introduction in the main paper, there are also issues with inconsistent representations and undefined stereochemistry. We list an example for each in Figure 1 and Figure 1.

Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, W elQrate .

Nico (right) and Matteo Mucchetti pose with their homemade dark ride vehicle. We may earn revenue from the products available on this page and participate in affiliate programs. When 12-year-old Matteo Mucchetti mapped out an amusement-style attraction that he wanted to create in his family's basement and then showed it to his older brother Nico, the high-school sophomore was immediately sold. "This is amazing," said Nico. "Let's make it!" Matteo had sketched on paper a top-down view of the multi-room space in Bear, Delaware, where they live.

Understanding how the structure of language can be learned from sentences alone is a central question in both cognitive science and machine learning. Studies of the internal representations of Large Language Models (LLMs) support their ability to parse text when predicting the next word, while representing semantic notions independently of surface form. Yet, which data statistics make these feats possible, and how much data is required, remain largely unknown. Probabilistic context-free grammars (PCFGs) provide a tractable testbed for studying these questions. However, prior work has focused either on the post-hoc characterization of the parsing-like algorithms used by trained networks; or on the learnability of PCFGs with fixed syntax, where parsing is unnecessary. Here, we (i) introduce a tunable class of PCFGs in which both the degree of ambiguity and the correlation structure across scales can be controlled; (ii) provide a learning mechanism -- an inference algorithm inspired by the structure of deep convolutional networks -- that links learnability and sample complexity to specific language statistics; and (iii) validate our predictions empirically across deep convolutional and transformer-based architectures. Overall, we propose a unifying framework where correlations at different scales lift local ambiguities, enabling the emergence of hierarchical representations of the data.

Canonical Polyadic (CP) tensor decomposition is a workhorse algorithm for discovering underlying low-dimensional structure in tensor data. This is accomplished in conventional CP decomposition by fitting a low-rank tensor to data with respect to the least-squares loss. Generalized CP (GCP) decompositions generalize this approach by allowing general loss functions that can be more appropriate, e.g., to model binary and count data or to improve robustness to outliers. However, GCP decompositions do not explicitly account for any symmetry in the tensors, which commonly arises in modern applications. For example, a tensor formed by stacking the adjacency matrices of a dynamic graph over time will naturally exhibit symmetry along the two modes corresponding to the graph nodes. In this paper, we develop a symmetric GCP (SymGCP) decomposition that allows for general forms of symmetry, i.e., symmetry along any subset of the modes. SymGCP accounts for symmetry by enforcing the corresponding symmetry in the decomposition. We derive gradients for SymGCP that enable its efficient computation via all-at-once optimization with existing tensor kernels. The form of the gradients also leads to various stochastic approximations that enable us to develop stochastic SymGCP algorithms that can scale to large tensors. We demonstrate the utility of the proposed SymGCP algorithms with a variety of experiments on both synthetic and real data.

Tesla loses place as world's top electric vehicle seller to China's BYD Tesla has lost its place as the top global seller of electric vehicles to Chinese company BYD, capping a year defined by outrage over CEO Elon Musk's political manoeuvring and the end of United States tax breaks for customers. The company revealed on Friday that it had sold 1.64 million vehicles in 2025, compared with BYD's 2.26 million vehicles. The sales represented a 9 percent decline for Tesla from a year earlier. However, the market has become increasingly crowded with competitors, with China's electric vehicle market bounding ahead. Musk's embrace of US President Donald Trump in 2024 and subsequent spearheading of a controversial "government efficiency" panel (DOGE) behind widespread layoffs of federal workers has also proved polarising.

The company's shares are worth $1.4tn on the back of hopes for self-driving cars and robotics. The company's shares are worth $1.4tn on the back of hopes for self-driving cars and robotics. Tesla endured tough year in part thanks to some consumers' distaste for Elon Musk's embrace of rightwing politics Tesla has taken the unusual step of publishing sales forecasts that suggest 2025 deliveries will be lower than expected and future years' sales will be well below targets set by its chief executive, Elon Musk . The US electric vehicle maker published figures from analysts suggesting it will announce 423,000 deliveries during the fourth quarter of 2025, in a new "consensus" section on its investor website. That would represent a 16% decline from the final quarter of 2024.