Things to Do in L.A. Tap to enable a layout that focuses on the article. L.A.'s defense industry is booming. This is read by an automated voice. Please report any issues or inconsistencies here . L.A. defense-tech startups like Gambit face funding shortfalls as the Small Business Innovation Research program expired in September amid a Capitol Hill dispute.

This paper presents a framework for predicting rare, high-impact outcomes by integrating large language models (LLMs) with a multi-model machine learning (ML) architecture. The approach combines the predictive strength of black-box models with the interpretability required for reliable decision-making. We use LLM-powered feature engineering to extract and synthesize complex signals from unstructured data, which are then processed within a layered ensemble of models including XGBoost, Random Forest, and Linear Regression. The ensemble first produces a continuous estimate of success likelihood, which is then thresholded to produce a binary rare-event prediction. We apply this framework to the domain of Venture Capital (VC), where investors must evaluate startups with limited and noisy early-stage data. The empirical results show strong performance: the model achieves precision between 9.8X and 11.1X the random classifier baseline in three independent test subsets. Feature sensitivity analysis further reveals interpretable success drivers: the startup's category list accounts for 15.6% of predictive influence, followed by the number of founders, while education level and domain expertise contribute smaller yet consistent effects.

Early-stage startup investment is a high-risk endeavor characterized by scarce data and uncertain outcomes. Traditional machine learning approaches often require large, labeled datasets and extensive fine-tuning, yet remain opaque and difficult for domain experts to interpret or improve. In this paper, we propose a transparent and data-efficient investment decision framework powered by memory-augmented large language models (LLMs) using in-context learning (ICL). Central to our method is a natural language policy embedded directly into the LLM prompt, enabling the model to apply explicit reasoning patterns and allowing human experts to easily interpret, audit, and iteratively refine the logic. We introduce a lightweight training process that combines few-shot learning with an in-context learning loop, enabling the LLM to update its decision policy iteratively based on structured feedback. With only minimal supervision and no gradient-based optimization, our system predicts startup success far more accurately than existing benchmarks. It is over 20x more precise than random chance, which succeeds 1.9% of the time. It is also 7.1x more precise than the typical 5.6% success rate of top-tier venture capital (VC) firms.

Fox News correspondent William La Jeunesse joins'Fox News Sunday' to discuss the evolution of AI and the push lawmakers are making to regulate it. FIRST ON FOX: A select group of tech industry titans and venture capitalists will gather in Washington, D.C., this week to welcome the incoming Trump administration and celebrate new opportunities for global innovation in artificial intelligence and entrepreneurship. Presidents and CEOs from companies on the cutting edge of AI tech and their big financial backers, along with personnel from the incoming administration, will attend a dinner on Thursday organized by Outside the Box Ventures, a firm founded last year by journalist-turned-investment banker Katherine Tarbox, along with Laurent Bili, the French ambassador to the U.S. The list of those invited to Thursday's dinner includes "DOGE" chief Elon Musk, Silicon Valley investor and GOP mega-donor Peter Thiel, NVCA chief executive Bobby Franklin, incoming White House AI and crypto czar David Sacks, OpenAI's Sam Altman, investor Joe Lonsdale and Narya co-founder Colin Greenspon. "This gathering represents more than discussion. We hope it symbolizes a new chapter in public-private collaboration to harness technology's transformative power for the nation's future," a source close to the planning told Fox News Digital.

In the Venture Capital(VC) industry, predicting the success of startups is challenging due to limited financial data and the need for subjective revenue forecasts. Previous methods based on time series analysis or deep learning often fall short as they fail to incorporate crucial inter-company relationships such as competition and collaboration. Regarding the issues, we propose a novel approach using GrahphRAG augmented time series model. With GraphRAG, time series predictive methods are enhanced by integrating these vital relationships into the analysis framework, allowing for a more dynamic understanding of the startup ecosystem in venture capital. Our experimental results demonstrate that our model significantly outperforms previous models in startup success predictions. To the best of our knowledge, our work is the first application work of GraphRAG.

Far from the palm trees of Miami or Austin's taco trucks, Catalin Voss has headquartered his literacy start-up between a cannabis club and pawn shop in the heart of the Mission District. Voss rents a nondescript office building in one of San Francisco's most vibrant neighborhoods as a home base for Ello, a company he co-founded in 2020 that uses speech recognition technology, powered by artificial intelligence, to help struggling students develop their reading skills. The office is within walking distance of his Noe Valley apartment and only steps away from some of the city's best taquerias and cocktail bars. And those are just a few of the perks he recited in explaining why he is headquartered in San Francisco. Voss is part of a sizable cohort of San Francisco loyalists -- old and new -- who say they are flummoxed by the "all is lost" narrative propagated by conservative media hosts and more recently a vocal contingent of tech leaders that includes billionaire entrepreneur-turned-agitator Elon Musk.

On his office desk, Aleksandar Gavrilovic keeps two figurines: Vladimir Lenin, the Russian revolutionary, and Josip Broz Tito, the former communist leader of Yugoslavia. Gavrilovic is the founder of the video game company Gamechuck. Based out of a tiny office crammed with computers in Zagreb, the capital of Croatia, the company is organized around equality: Each worker earns the same salary and shares the profits of the games they create. All decisions are reached through anonymous voting on Discord: The 17-person collective recently voted to shorten workdays from eight hours to six. "We wanted to show that you don't actually have to work like everyone else to be successful," said Gavrilovic. Gavrilovic's company is an outlier in the gaming industry, known for its grueling hours, high turnover rates, and worker discontent.

Air Street Capital LLP (OC424177) (FRN 805476) is an Appointed Representative (AR) of Met Facilities LLP, which is authorized and regulated by the Financial Conduct Authority (FRN 587084).

Today we see clear movement and momentum to decarbonization and the green energy transition. In parallel, the rise in digital technology and advanced analytics provide unique opportunities to not only migrate to new energy technologies, but to monitor progress, predict performance, integrate systems, ensure reliability and resiliency – and improve sustainability by optimizing products, solutions, and services like never before. At the same time, we have changing dynamics in the sector that increase its complexity. Grids are moving from centralized to decentralized models. Energy producers have multi-OEM (original equipment manufacturer) solutions that must be monitored as a system to ensure uptime and output.

Data annotation, or the process of adding labels to images, text, audio and other forms of sample data, is typically a key step in developing AI systems. The vast majority of systems learn to make predictions by associating labels with specific data samples, like the caption "bear" with a photo of a black bear. A system trained on many labeled examples of different kinds of contracts, for example, would eventually learn to distinguish between those contracts and even extrapolate to contracts that it hasn't seen before. The trouble is, annotation is a manual and labor-intensive process that's historically been assigned to gig workers on platforms like Amazon Mechanical Turk. But with the soaring interest in AI -- and in the data used to train that AI -- an entire industry has sprung up around tools for annotation and labeling.