Now, Activists Are Taking On Elon Musk's SpaceX IPO Activists, a leading labor union, and a big retirement fund are asking challenging questions about what's expected to be the largest-ever stock market debut. Elon Musk's SpaceX is facing protests against its expected initial public offering from some of the same advocacy groups that helped erase $600 billion from Tesla's market cap early last year. SpaceX's IPO is poised to be the largest ever, raising tens of billions of dollars for the Musk-founded company and valuing it above $2 trillion. If all goes as intended come June, the conglomerate that now owns a rocket manufacturer, a social media app, and an AI chatbot developer will instantly rank among the world's top 10 largest publicly-traded companies. On Wednesday, Randi Weingarten, president of the American Federation of Teachers, wrote to the US Securities and Exchange Commission urging it to scrutinize SpaceX's IPO preparations.

SpaceX combining with xAI would bring Elon Musk's rockets, Starlink satellites, the X social media platform and Grok AI chatbot under one roof. NEW YORK - SpaceX is exploring deals with other companies helmed by serial entrepreneur Elon Musk, leaving investors working through permutations between space, autonomous driving and artificial intelligence to analyze which combination makes the most sense. The rocket maker is in discussions to merge with xAI ahead of a blockbuster public offering planned for this year, Reuters reported on Thursday. The combination would bring Musk's rockets, Starlink satellites, X social media platform and Grok chatbot under one roof, according to a person briefed on the matter and two regulatory filings. The deal's value, timing or primary rationale could not be independently determined.

Musk's SpaceX in merger talks with xAI ahead of planned IPO, source says SpaceX combining with xAI would bring Elon Musk's rockets, Starlink satellites, the X social media platform and Grok AI chatbot under one roof. NEW YORK - Elon Musk's SpaceX and xAI are in discussions to merge ahead of a blockbuster public offering planned for later this year. The combination would bring Musk's rockets, Starlink satellites, the X social media platform and Grok AI chatbot under one roof, according to a person briefed on the matter and two recent company filings. The plan, which Reuters is reporting exclusively, would give fresh momentum to SpaceX's effort to launch data centers into orbit as Musk battles for supremacy in the rapidly escalating artificial intelligence race against tech giants like Google, Meta and OpenAI. Musk, the world's richest man, is the CEO of both the private space company SpaceX and the artificial intelligence company xAI, which controls his social media platform X.

OpenAI could be valued at $1tn if it launches an initial public offering, Reuters said. OpenAI could be valued at $1tn if it launches an initial public offering, Reuters said. The year of the'hectocorn': the $100bn tech companies that could float in 2026 Y ou've probably heard of "unicorns" - technology startups valued at more than $1bn - but 2026 is shaping up to be the year of the " hectocorn ", with several US and European companies potentially floating on stock markets at valuations over $100bn (£75bn). OpenAI, Anthropic, SpaceX and Stripe are among the big names said to be considering an initial public offering (IPO) this year. The success of their flotations - whether the shares maintain their value, rise or fall - could shape concerns about the AI race and whether the resulting market mania is a bubble .

In this very personal workography, I relate my 40-year experiences as a researcher and educator in and around Artificial Intelligence (AI), more specifically Natural Language Processing. I describe how curiosity, and the circumstances of the day, led me to work in both industry and academia, and in various countries, including The Netherlands (Amsterdam, Eindhoven, and Utrecht), the USA (Stanford), England (Brighton), Scotland (Aberdeen), and China (Beijing and Harbin). People and anecdotes play a large role in my story; the history of AI forms its backdrop. I focus on things that might be of interest to (even) younger colleagues, given the choices they face in their own work and life at a time when AI is finally emerging from the shadows.

The generated responses of large language models (LLMs) are often fine-tuned to human preferences through a process called reinforcement learning from human feedback (RLHF). As RLHF relies on a challenging training sequence, whereby a separate reward model is independently learned and then later applied to LLM policy updates, ongoing research effort has targeted more straightforward alternatives. In this regard, direct preference optimization (DPO) and its many offshoots circumvent the need for a separate reward training step. Instead, through the judicious use of a reparameterization trick that induces an \textit{implicit} reward, DPO and related methods consolidate learning to the minimization of a single loss function. And yet despite demonstrable success in some real-world settings, we prove that DPO-based objectives are nonetheless subject to sub-optimal regularization and counter-intuitive interpolation behaviors, underappreciated artifacts of the reparameterizations upon which they are based. To this end, we introduce an \textit{explicit} preference optimization framework termed EXPO that requires no analogous reparameterization to achieve an implicit reward. Quite differently, we merely posit intuitively-appealing regularization factors from scratch that transparently avoid the potential pitfalls of key DPO variants, provably satisfying regularization desiderata that prior methods do not. Empirical results serve to corroborate our analyses and showcase the efficacy of EXPO.

Pre-trained vision-language models like CLIP have remarkably adapted to various downstream tasks. Nonetheless, their performance heavily depends on the specificity of the input text prompts, which requires skillful prompt template engineering. Instead, current approaches to prompt optimization learn the prompts through gradient descent, where the prompts are treated as adjustable parameters. However, these methods tend to lead to overfitting of the base classes seen during training and produce prompts that are no longer understandable by humans. This paper introduces a simple but interpretable prompt optimizer (IPO), that utilizes large language models (LLMs) to generate textual prompts dynamically.

Video foundation models have achieved significant advancement with the help of network upgrade as well as model scale-up. However, they are still hard to meet requirements of applications due to unsatisfied generation quality. To solve this problem, we propose to align video foundation models with human preferences from the perspective of post-training in this paper. Consequently, we introduce an Iterative Preference Optimization strategy to enhance generated video quality by incorporating human feedback. Specifically, IPO exploits a critic model to justify video generations for pairwise ranking as in Direct Preference Optimization or point-wise scoring as in Kahneman-Tversky Optimization. Given this, IPO optimizes video foundation models with guidance of signals from preference feedback, which helps improve generated video quality in subject consistency, motion smoothness and aesthetic quality, etc. In addition, IPO incorporates the critic model with the multi-modality large language model, which enables it to automatically assign preference labels without need of retraining or relabeling. In this way, IPO can efficiently perform multi-round preference optimization in an iterative manner, without the need of tediously manual labeling. Comprehensive experiments demonstrate that the proposed IPO can effectively improve the video generation quality of a pretrained model and help a model with only 2B parameters surpass the one with 5B parameters. Besides, IPO achieves new state-of-the-art performance on VBench benchmark. We will release our source codes, models as well as dataset to advance future research and applications.

With consistent growth in Indian Economy, Initial Public Offerings (IPOs) have become a popular avenue for investment. With the modern technology simplifying investments, more investors are interested in making data driven decisions while subscribing for IPOs. In this paper, we describe a machine learning and natural language processing based approach for estimating if an IPO will be successful. We have extensively studied the impact of various facts mentioned in IPO filing prospectus, macroeconomic factors, market conditions, Grey Market Price, etc. on the success of an IPO. We created two new datasets relating to the IPOs of Indian companies. Finally, we investigated how information from multiple modalities (texts, images, numbers, and categorical features) can be used for estimating the direction and underpricing with respect to opening, high and closing prices of stocks on the IPO listing day.