Major tech moguls had their claws out for each other on Wednesday, hissing at their rivals over enormous pledges to invest in AI that had been announced by Donald Trump the day before. Trump announced Stargate, a 500bn project to be funded jointly by OpenAI, Oracle and Softbank, on Tuesday. During the announcement, the president was flanked by the leaders of those companies: Sam Altman, Larry Ellison and Masayoshi Son, respectively. Son is slated to be the chair of the project. Absent from the photo op was a representative from MGX, Abu Dhabi's state AI fund, another principal investor.

Some of the biggest names in tech came together this week to announce "Stargate," a project they say will receive 500 billion in investment for US-based artificial intelligence infrastructure. The joint venture, spearheaded by OpenAI, Oracle, and SoftBank, aims to rapidly build out colossal new data centers crucial to future AI development. It will also prop-up new electricity plants needed to power these notoriously energy-intensive AI models. Stargate already has the blessing of newly-inaugurated president Donald Trump who this week said he has plans to "unleash" the US fossil fuel industry. Looser regulations on oil and gas extraction will make fossil fuels the obvious, cheapest choice to power Stargate's ambitious AI agenda.

President Donald Trump on Tuesday announced a 500 billion joint venture between OpenAI, Softbank, MGX and Oracle to build new datacenters to power the next wave of artificial intelligence (AI) – in an early signal that his Administration would embrace the technology. The plans, which predate the Trump Administration and involve no U.S. government funds, would result in the construction of large datacenters on U.S. soil containing thousands of advanced computer chips required to train new AI systems. "We want to keep it in this country; China's a competitor," Trump said of AI. "I'm going to help a lot through emergency declarations – we have an emergency, we have to get this stuff built." The message echoed recent talking points by the heads of AI companies like Sam Altman of OpenAI, who flanked him during the White House announcement. Altman has argued more vocally in recent months that the U.S. must race to build the energy and datacenter infrastructure in order to create powerful AI before China.

The paper proposes to perform Langevin dynamics in data space (as opposed to the latent space) of a deep generative model as a means to explore the data distribution. This reduces the difficult problem of estimating the data distribution to the slightly less difficult problem of estimating its gradients. The latter ones are estimated by different versions of score matching. This paper mainly builds on recent work on score matching by random projections. As a result, a new generative model is achieved whose sample quality is similar to GANs, while avoiding an adversarial training paradigm. This is a strong contribution.

US President Donald Trump has announced a private sector investment to fund infrastructure for artificial intelligence, with the goal of outpacing rival nations in the business-critical technology. Calling it the largest AI infrastructure project in history "by far", Trump said the joint venture called Stargate will build data centres and create more than 100,000 jobs in the United States. These companies, along with other equity backers of Stargate, have committed billions of dollars for immediate investment, with the remaining investment expected to occur over the next four years. Here's what you need to know about what Trump called "a resounding declaration of confidence in America's potential": It's a joint venture between OpenAI, Oracle, SoftBank and MGX that plans to invest up to 500bn over the next four years to build up new data centres needed for the development of AI projects in the US. A first injection of 100bn will start "immediately," according to an OpenAI statement.

OpenAI spent 1.76 million on government lobbying in 2024 and 510,000 in the last three months of the year alone, according to a new disclosure filed on Tuesday--a significant jump from 2023, when the company spent just 260,000 on Capitol Hill. The disclosure is a clear signal of the company's arrival as a political player, as its first year of serious lobbying ends and Republican control of Washington begins. While OpenAI's lobbying spending is still dwarfed by bigger tech players, the uptick comes as it and other AI companies are helping redraw the shape of AI policy. Forget massive steel tanks--some scientists want to make chemicals with the help of rocks deep beneath Earth's surface. New research shows that ammonia, a chemical crucial for fertilizer, can be produced from rocks at temperatures and pressures that are common in the subsurface.

OpenAI will build and open AI infrastructure worth 500 billion in the United States over the next four years in partnership with SoftBank. The two entities have teamed up to establish a new company called the Stargate Project to build AI data centers for the ChatGPT maker, and according to their announcement, it will "secure American leadership in AI" as well as "create hundreds of thousands of American jobs." SoftBank will finance the project, while OpenAI will be in charge of its operations. Masayoshi Son, the CEO of SoftBank, will serve as its chairman. While OpenAI and SoftBank will serve as the Stargate Project's lead partners, there are several other companies involved in the initiative. In addition to OpenAI, Arm, NVIDIA, Oracle and, of course, Microsoft will be its key initial technology partners.

Trying to model the physical reality by assembling various modalities: the image shows a couple of oranges seen through the lens of multiple modalities, with each slice showing a different way one might perceive and understand this scene. The modalities from left to right represent surface normals (the color represents surface orientation), depth (distance to the camera, red near, blue far), RGB (the original image), segmentation (distinct objects and image regions), and edges (object or texture boundaries). Large Language Models such as OpenAI's ChatGPT have already transformed the way many of us go about some of our daily tasks. These generative artificial intelligence chatbots are trained with language -- hundreds of terabytes of text'scraped' from across the Internet and with billions of parameters. Looking ahead, many believe the'engines' that drive generative artificial intelligence will be multimodal models that are not just trained on text but also can process various other modalities of information, including images, video, sound, and modalities from other domains such as biological or atmospheric data. Yet, until recently, training a single model to handle a wide range of modalities – inputs – and tasks – outputs – faced significant challenges.

Theorem 1 states "The [Recovery of a binary latent code from a two-layer ReLU] is NP-hard since it could be reduced to MAX-3SAT problem." For an NP-hardness proof, what one wishes to show is a reduction _from_ the known NP-hard problem _to_ the problem being studied. The proof in the appendix appears to correctly use the proposed recovery algorithm as a gadget in solving MAX3SAT, which would be sufficient for an NP-hardness proof, but the text is inconsistent with the theoretical results given. Additionally, while the binary reconstruction problem is interesting for the purpose of the complexity proof, it would be helpful to the reviewers if the rationale for this choice was made clearer outside of the context of the proof and while it is not strictly necessary for the theoretical results it would be useful if it could be shown that the binary latent variable case was representative of the complexity of the problem in general. It is unclear where the expansiveness requirement for the generator model fits into the proof of Theorem 2 The only competing method examined is a strawman version of gradient descent, it would be interesting to see how this method compares to other approaches. Should the inequality in equation 4 be wTz b 0? I'm confused about the dimensions of W and how it relates to both x and z.

This theoretical paper studied the invertibility of ReLU networks, as generative priors for denoising or compressive sensing. The invertibility of networks with random weights one layer at a time is also investigated and interesting stability bounds are also provided. Note: comments made by Reviewer #2 should be incorporated for the camera ready version.