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Look Up Back to School Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Say More Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Switch Off Mashable Voices Safety Net Versus All Series The Tetris Company had hinted at potential legal action. Matt Binder joined Mashable's tech vertical in 2018, where he covers social media, tech policy, cybersecurity, online scams, cryptocurrency, AI, creator news, weird tech, and other related tech beats. The White House has removed its Tetris clone. This is what the website looks like now without it. Last week, the White House unveiled a new government website called Arcade.gov, which hosted five web-based games that promoted President Trump's agenda.

Say More Mashable Selects Look Up Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Mashable Voices Safety Net Versus Gift Ideas For Everyone On Your List All Series The Tetris Company was not involved in the creation of'Build the Wall'. Matt Binder joined Mashable's tech vertical in 2018, where he covers social media, tech policy, cybersecurity, online scams, cryptocurrency, AI, creator news, weird tech, and other related tech beats. Tetris is speaking out against the anti-immigration puzzle game clone released by Trump's White House. The team behind Tetris, the classic 1984 puzzle video game, has responded to the White House over a knockoff game that the Trump administration released this week. The statement from Tetris hits back against Trump's White House over its recently released Tetris clone, Build the Wall.

Compressing neural networks by pruning weights with small magnitudes can significantly reduce the computation and storage cost. Although pruning makes the model smaller, it is difficult to get practical speedup in modern computing platforms such as CPU and GPU due to the irregularity. Structural pruning has attract a lot of research interest to make sparsity hardware-friendly. Increasing the sparsity granularity can lead to better hardware utilization, but it will compromise the sparsity for maintaining accuracy. In this work, we propose a novel method, TETRIS, to achieve both better hardware utilization and higher sparsity. Just like a tile-matching game, we cluster the irregularly distributed weights with small value into structured groups by reordering the input/output dimension and structurally prune them. Results show that it can achieve comparable sparsity with the irregular element-wise pruning and demonstrate negligible accuracy loss. The experiments also shows ideal speedup, which is proportional to the sparsity, on GPU platforms. Our proposed method provides a new solution toward algorithm and architecture co-optimization for accuracy-efficiency trade-off.

Compressing neural networks by pruning weights with small magnitudes can significantly reduce the computation and storage cost. Although pruning makes the model smaller,itisdifficult toget apractical speedup inmodern computing platforms such as CPU and GPU due to the irregularity.

Compressing neural networks by pruning weights with small magnitudes can significantly reduce the computation and storage cost. Although pruning makes the model smaller, it is difficult to get practical speedup in modern computing platforms such as CPU and GPU due to the irregularity. Structural pruning has attract a lot of research interest to make sparsity hardware-friendly. Increasing the sparsity granularity can lead to better hardware utilization, but it will compromise the sparsity for maintaining accuracy. In this work, we propose a novel method, TETRIS, to achieve both better hardware utilization and higher sparsity. Just like a tile-matching game, we cluster the irregularly distributed weights with small value into structured groups by reordering the input/output dimension and structurally prune them. Results show that it can achieve comparable sparsity with the irregular element-wise pruning and demonstrate negligible accuracy loss. The experiments also shows ideal speedup, which is proportional to the sparsity, on GPU platforms. Our proposed method provides a new solution toward algorithm and architecture co-optimization for accuracy-efficiency trade-off.

Tetris is a popular video game that has been widely used as a benchmark for various optimization techniques including approximate dynamic programming (ADP) algorithms. A close look at the literature of this game shows that while ADP algorithms, that have been (almost) entirely based on approximating the value function (value function based), have performed poorly in Tetris, the methods that search directly in the space of policies by learning the policy parameters using an optimization black box, such as the cross entropy (CE) method, have achieved the best reported results. This makes us conjecture that Tetris is a game in which good policies are easier to represent, and thus, learn than their corresponding value functions. So, in order to obtain a good performance with ADP, we should use ADP algorithms that search in a policy space, instead of the more traditional ones that search in a value function space. In this paper, we put our conjecture to test by applying such an ADP algorithm, called classification-based modified policy iteration (CBMPI), to the game of Tetris. Our extensive experimental results show that for the first time an ADP algorithm, namely CBMPI, obtains the best results reported in the literature for Tetris in both small $10\times 10$ and large $10\times 20$ boards. Although the CBMPI's results are similar to those achieved by the CE method in the large board, CBMPI uses considerably fewer (almost 1/10) samples (call to the generative model of the game) than CE.

We identify and address a fundamental limitation of sinusoidal representation networks (SIRENs), a class of implicit neural representations. SIRENs Sitzmann et al. (2020), when not initialized appropriately, can struggle at fitting signals that fall outside their frequency support. In extreme cases, when the network's frequency support misaligns with the target spectrum, a 'spectral bottleneck' phenomenon is observed, where the model yields to a near-zero output and fails to recover even the frequency components that are within its representational capacity. To overcome this, we propose WINNER - Weight Initialization with Noise for Neural Representations. WINNER perturbs uniformly initialized weights of base SIREN with Gaussian noise - whose noise scales are adaptively determined by the spectral centroid of the target signal. Similar to random Fourier embeddings, this mitigates 'spectral bias' but without introducing additional trainable parameters. Our method achieves state-of-the-art audio fitting and significant gains in image and 3D shape fitting tasks over base SIREN. Beyond signal fitting, WINNER suggests new avenues in adaptive, target-aware initialization strategies for optimizing deep neural network training. For code and data visit cfdlabtechnion.github.io/siren_square/.

Playing video games requires perception, memory, and planning, exactly the faculties modern large language model (LLM) agents are expected to master. We study the major challenges in using popular video games to evaluate modern LLMs and find that directly dropping LLMs into games cannot make an effective evaluation, for three reasons -- brittle vision perception, prompt sensitivity, and potential data contamination. We introduce lmgame-Bench to turn games into reliable evaluations. lmgame-Bench features a suite of platformer, puzzle, and narrative games delivered through a unified Gym-style API and paired with lightweight perception and memory scaffolds, and is designed to stabilize prompt variance and remove contamination. Across 13 leading models, we show lmgame-Bench is challenging while still separating models well. Correlation analysis shows that every game probes a unique blend of capabilities often tested in isolation elsewhere. More interestingly, performing reinforcement learning on a single game from lmgame-Bench transfers both to unseen games and to external planning tasks. Our evaluation code is available at https://github.com/lmgame-org/GamingAgent/lmgame-bench.

When game designer and entrepreneur Henk Rogers first encountered Tetris at the 1988 Las Vegas Consumer Electronics Show, he immediately knew it was special. "It was just the perfect game," he recalls. "It looked so simple, so rudimentary, but I wanted to play it again and again and again … There was no other game demo that ever did that to me." Rogers is now co-owner of the Tetris Company, which manages and licenses the Tetris brand. Over the past 30 years, he has become almost as famous as the game itself. The escapades surrounding his deal to buy its distribution rights from Russian agency Elektronorgtechnica (Elorg) were dramatised in an Apple TV film starring Taron Egerton.