In the revised version, we will make this clearer in the "Related Work" section. Figure 2 illustrates how the classification model (i.e. ( t 1) The parameter σ corresponds to the width of Gaussian kernel, which is fixed to be 1 in this paper (pp.3, footnote 1).

Chip designer Arm plans to unveil its own processor this year with Meta as the launch customer, The Financial Times reported. The chip would be a CPU designed for servers in data centers and would have the potential to be customized for clients. Manufacturing would be outsourced to a contract fab plant like TSMC (Taiwan Semiconductor Manufacturing Co.) and the first in-house chip could be revealed as early as this summer, according to the FT's sources. Last month, Arm parent Softbank announced the Stargate project, a partnership with OpenAI to build up to 500 billion worth of AI infrastructure. Arm, along with Microsoft and NVIDIA, is a key technology partner for the project.

In recent years, masked diffusion models (MDMs) have emerged as a promising alternative approach for generative modeling over discrete domains. Compared to autoregressive models (ARMs), MDMs trade off complexity at training time with flexibility at inference time. At training time, they must learn to solve an exponentially large number of infilling problems, but at inference time, they can decode tokens in essentially arbitrary order. In this work, we closely examine these two competing effects. On the training front, we theoretically and empirically demonstrate that MDMs indeed train on computationally intractable subproblems compared to their autoregressive counterparts. On the inference front, we show that a suitable strategy for adaptively choosing the token decoding order significantly enhances the capabilities of MDMs, allowing them to sidestep hard subproblems. On logic puzzles like Sudoku, we show that adaptive inference can boost solving accuracy in pretrained MDMs from $<7$% to $\approx 90$%, even outperforming ARMs with $7\times$ as many parameters and that were explicitly trained via teacher forcing to learn the right order of decoding.

Arm is a chip architecture company that licenses its designs to others, and its customers have shipped more than 100 billion chips in the past five years. The new architecture has processing that balances economics, design freedom, and accessibility advantages of general-purpose computing devices with specialized processors that handle tasks like digital signal processing and machine learning. At the current rate, 100% of the world's shared data will soon be processed on Arm; either at the endpoint, in the data networks or the cloud, Segars said. Back in 2011, Arm launched its 64-bit processing architecture, enabling Arm devices to make the leap from low-power mobile devices to high-end supercomputers. To address the greatest technology challenge today -- securing the world's data -- the Armv9 roadmap introduces the Arm Confidential Compute Architecture (CCA).

With the increase of data volume and the continuous development in deep learning, although more and more traditional machine learning techniques are outperformed by artificial neural networks, tree-based methods are still popular. Random forest (Breiman, 2001) is commonly used as a benchmark to evaluate the performance of nonparametric models, while XGBoost (Chen and Guestrin, 2016) performs well in Kaggle competitions and often competes with artificial neural networks. Also, instead of relying on a specific method, people prefer to make decisions based on a combination of multiple models, which shows a better performance than a single one. Therefore, identifying the importance of each model by weights assignment is critical.

Nvidia CEO Jensen Huang recently hosted yet another spring GTC event that drew more than 200,000 participants. And while he didn't succeed in acquiring Arm for $80 billion, he did have a lot of things to show off to those gathering at the big event. He gave an update on Nvidia's plans for Earth-2, a digital twin of our planet that -- with enough supercomputing simulation capability within the Omniverse –could enable scientists to predict climate change for our planet. The Earth 2 simulation will require the best technology -- like Nvidia's newly announced graphics processing unit (GPU) Hopper and its upcoming central processing unit (CPU) Grade. Huang fielded questions about the ongoing semiconductor shortage, the possibility of investing in manufacturing, competition with rivals, and Nvidia's plans in the wake of the collapse of the Arm deal. He conveyed a sense of calm that Nvidia's business is still strong (Nvidia reported revenues of $7.64 billion for its fourth fiscal quarter ended January 30, up 53% from a year earlier). Gaming, datacenter, and professional visualization market platforms each achieved record revenue for the quarter and year. He also talked about Nvidia's continuing commitment to the self-driving vehicle market, which has been slower to take off than expected. Huang held a Q&A with the press during GTC and I asked him the question about Earth-2 and the Omniverse (I also moderated a panel on the industrial metaverse as well at GTC). I was part of a large group of reporters asking questions. Question: With the war in Ukraine and continuing worries about chip supplies and inflation in many countries, how do you feel about the timeline for all the things you've announced? For example, in 2026 you want to do DRIVE Hyperion. With all the things going into that, is there even a slight amount of worry? Jensen Huang: There's plenty to worry about. I have to observe, though, that in the last couple of years, the facts are that Nvidia has moved faster in the last couple of years than potentially its last 10 years combined. It's quite possible that we work better, actually, when we allow our employees to choose when they're most productive and let them optimize, let mature people optimize their work environment, their work time frame, their work style around what best fits for them and their families. It's very possible that all of that is happening. It's also true, absolutely true, that it has forced us to put a lot more energy into the virtual work that we do. For example, the work around OmniVerse went into light speed in the last couple of years because we needed it. Instead of being able to come into our labs to work on our robots, or go to the streets and test our cars, we had to test in virtual worlds, in digital twins.

SoftBank's planned sale of the British semiconductor and software design company Arm to US chipmaker Nvidia has fallen through, but the Japanese technology investor immediately turned bullish on taking it public. SoftBank Group Corp said on Tuesday it plans an initial public offering of Arm after the intended sale to Nvidia failed due to regulatory problems. It said the IPO would come sometime in the fiscal year ending in March 2023. Chief Executive Masayoshi Son acknowledged he was disappointed but wasted no time in shifting to an aggressive sales pitch for Arm in its preparation to go public in the United States, likely on the Nasdaq exchange. Rather just being back, it's really going to grow explosively," Son told reporters. He said "a golden time" was coming because of Arm's "architecture", or technology for semiconductors, already widely used in mobile phones and adapted by internet giants like Amazon. Son said even bigger growth will come as the world shifts to electric vehicles because Arm products are energy-efficient. Earlier faltering results at Arm were merely because of a hefty investment in hiring engineers needed to keep such innovations going, Son said. Son said he was tapping new leadership to give Arm a fresh start, with Rene Haas, a semiconductor industry veteran, as chief executive, replacing Simon Segars. "With the uncertainty of the past several months behind us, we are emboldened by a renewed energy to move into a growth strategy and change lives around the world again," Haas said. Arm, which SoftBank acquired in 2016, is a leader in artificial intelligence, IoT, cloud, the metaverse and autonomous driving, with sales and profit growing in recent years. Its semiconductor design is widely licensed and used in virtually all smartphones, the majority of tablets and digital TVs. The company's business centres on designing chips and licensing the intellectual property to customers, rather than chip manufacturing, for which it relies on partners. Nvidia also confirmed the merger was no longer on, although it still had its 20-year licensing agreement with Arm. "Arm is at the centre of the important dynamics in computing.

US firm Nvidia is scrapping its $40 billion bid to buy UK mobile chip technology powerhouse Arm from SoftBank after persistent objections from regulators, the Financial Times reported Tuesday. Nvidia and SoftBank Group both declined to comment on the report, which cited three unnamed sources with direct knowledge of the deal. But the collapse would be no surprise, after recent speculation that the deal was on the verge of failure following pressure from US, UK and EU regulators concerned it would undermine competition. In December, US regulators filed a lawsuit seeking to block the merger, while British and European regulators had ordered probes into the deal. Japan's SoftBank Group announced in 2020 that it was selling Arm for up to $40 billion in a deal it hoped to complete in early 2022, subject to regulatory approvals. The value of the cash-and-shares deal has risen since as stock markets have rallied, with Nvidia's shares soaring.

After listening to early adopters, Boston Dynamics gave its robot dog a hardware boost and extended WiFi capabilities. It can be controlled remotely using the company's new web browser-based interface, Scout. It's the first Boston Dynamics device equipped with self-charging capabilities and a dock, which means it can be deployed for longer-term missions "with little to no human interaction," Boston Dynamics said. The previous version of Spot had around 90 minutes of battery life before requiring a manual charge.

Right: A picture of the prototype which has been retrofitted into a typical clinical-grade light microscope. Applications of deep learning in medical disciplines including ophthalmology, dermatology, radiology and pathology have recently shown great promise to increase both the accuracy and availability of high-quality healthcare. To further this technology, Google researchers have developed a tool that combines augmented reality with a deep learning neural network to provide pathologists with help in spotting cancerous cells on slides under a microscope. The prototype Augmented Reality Microscope (ARM) platform consists of a modified light microscope that enables real-time image analysis and presentation of the results of machine learning algorithms directly into the field of view. The ARM can be retrofitted into existing light microscopes found in hospitals and clinics by using low-cost, readily available components, and without the need for whole slide digital versions of the tissue being analyzed.