The AI lab is revamping its effort to build an AI coding tool, with two new executives joining from Cursor.

AI chatbots have been linked to suicides for years. Now one lawyer says they are showing up in mass casualty cases too, and the technology is moving faster than the safeguards.

The data infrastructure startup raised $5.3 million in seed funding led by Wischoff Ventures and South Park Commons.

At SXSW, Steven Spielberg said AI has uses in many fields, but not when it comes to replacing creative people in film and TV writing.

The AI industry is constantly churning out news, like major acquisitions, indie developer successes, public outcry, and existentially dangerous contract negotiations.

Gavriel Cohen is living an open source developer's dream as his project has achieved acclaim and a partnership with Docker in a matter of weeks.

When you edit your Taste Profile, you'll impact your personalized playlists like Discover Weekly, recommendations, and Wrapped.

According to Index Ventures Partner Shardul Shah, cybersecurity startup Wiz sits “at the center of three tailwinds: AI, cloud, and security spend.” Those tailwinds powered what just became the largest venture-backed acquisition in history — Google’s $32 billion deal, finalized after a declined 2024 offer, antitrust review on both sides of the Atlantic, and an […]

Peacock is betting on new AI-powered video experiences, vertical clips, and mobile games to help its growth.

The launch comes just two years after a major reboot at Hyundai-owned Motional. The service will start with a safety monitor, which Motional hopes to remove by year's end.

After selling his AI startup to AMD for $665 million, Peter Sarlin is back with Qutwo, a new venture building the infrastructure it believes enterprises will need when quantum computing finally arrives.

Caller identity platform Truecaller recently launched a new feature that lets one person become an admin of a family group, get alerts about fraud calls received by other members, and even end a call on their behalf if they suspect a family member might get scammed.

GTC is Nvidia's flagship annual event, where the chipmaker typically announces new products, partnerships, and its vision for the future of computing. Huang's keynote will focus on Nvidia's role in the future of computing and AI.

Rox, founded in 2024 by the former chief growth officer of New Relic, offers an AI-native alternative to CRM tools.

arXiv:2603.07295v1 Announce Type: new Abstract: This paper bridges neuroscience and artificial intelligence to propose a cortically inspired blueprint for modular perceptual AI. While current monolithic models such as GPT-4V achieve impressive performance, they often struggle to explicitly support interpretability, compositional generalization, and adaptive robustness - hallmarks of human cognition. Drawing on neuroscientific models of cortical modularity, predictive processing, and cross-modal integration, we advocate decomposing perception into specialized, interacting modules. This architecture supports structured, human-inspired reasoning by making internal inference processes explicit through hierarchical predictive feedback loops and shared latent spaces. Our proof-of-concept study provides empirical evidence that modular decomposition yields more stable and inspectable representations. By grounding AI design in biologically validated principles, we move toward systems that not only perform well, but also support more transparent and human-aligned inference.

arXiv:2603.07311v1 Announce Type: new Abstract: This chapter explores the evolution of data-driven hint generation for intelligent tutoring systems (ITS). The Hint Factory and Interaction Networks have enabled the generation of next-step hints, waypoints, and strategic subgoals from historical student data. Data-driven techniques have also enabled systems to find the right time to provide hints. We explore further potential data-driven adaptations for problem solving based on behavioral problem solving data and the integration of Large Language Models (LLMs).

arXiv:2603.07315v1 Announce Type: new Abstract: One common concern about advanced artificial intelligence is that it will prevent us from turning it off, as that would interfere with pursuing its goals. In this paper, we discuss an unorthodox proposal for addressing this concern: give the AI a (primary) goal of being turned off (see also papers by Martin et al., and by Goldstein and Robinson). We also discuss whether and under what conditions this would be a good idea.

arXiv:2603.07316v1 Announce Type: new Abstract: While Large Language Models (LLMs) can accelerate text-heavy tasks in alternative investment due diligence, a gap remains in their ability to accurately extract and reason over structured tabular data from complex financial spreadsheets. Progress is held back by the lack of real industry fund portfolio datasets for benchmarking, as private equity data rooms are confidential. To address this, we introduce FinSheet-Bench, a benchmark of synthetic financial portfolio data modeled on real private equity fund structures, designed to evaluate LLM performance on text-serialized spreadsheet question answering and numeric reasoning tasks. Our evaluation of ten model configurations from OpenAI, Google, and Anthropic on financial spreadsheets, including complex layouts, fund dividers, and multi-line column names, reveals that no standalone model achieves error rates low enough for unsupervised use in professional finance applications. The best-performing model, Gemini 3.1 Pro, achieves 82.4% accuracy across twenty-four evaluation files of varying complexity and structural layout (approximately 1 error per 6 questions), followed by GPT-5.2 with reasoning at 80.4%, Claude Opus 4.6 with thinking at 80.2%, and Gemini 3 Pro at 80.2%. Performance degrades substantially on larger, more complex spreadsheets: the largest spreadsheet (152 companies, 8 funds) yields an average accuracy of just 48.6% across all models, compared to 86.2% on the easiest evaluation file. These difficulty patterns are consistent across all ten models, indicating that they reflect LLM limitations rather than idiosyncratic model weaknesses. Reliable financial spreadsheet extraction will likely require architectural approaches that separate document understanding from deterministic computation.

arXiv:2603.07329v1 Announce Type: new Abstract: Contemporary artificial intelligence research has been organized around two dominant ambitions: productivity, which treats AI systems as tools for accelerating work and economic output, and alignment, which focuses on ensuring that increasingly capable systems behave safely and in accordance with human values. This paper articulates and develops a third, emerging ambition: the use of large language models (LLMs) as scientific instruments for studying human behavior, culture, and moral reasoning. Trained on unprecedented volumes of human-produced text, LLMs encode large-scale regularities in how people argue, justify, narrate, and negotiate norms across social domains. We argue that these models can be understood as condensates of human symbolic behavior, compressed, generative representations that render patterns of collective discourse computationally accessible. The paper situates this third ambition within long-standing traditions of computational social science, content analysis, survey research, and comparative-historical inquiry, while clarifying the epistemic limits of treating model output as evidence. We distinguish between base models and fine-tuned systems, showing how alignment interventions can systematically reshape or obscure the cultural regularities learned during pretraining, and we identify instruct-only and modular adaptation regimes as pragmatic compromises for behavioral research. We review emerging methodological approaches including prompt-based experiments, synthetic population sampling, comparative-historical modeling, and ablation studies and show how each maps onto familiar social-scientific designs while operating at unprecedented scale.

arXiv:2603.07335v1 Announce Type: new Abstract: High-performing vision language models still produce incorrect answers, yet their failure modes are often difficult to explain. To make model internals more accessible and enable systematic debugging, we introduce VisualScratchpad, an interactive interface for visual concept analysis during inference. We apply sparse autoencoders to the vision encoder and link the resulting visual concepts to text tokens via text-to-image attention, allowing us to examine which visual concepts are both captured by the vision encoder and utilized by the language model. VisualScratchpad also provides a token-latent heatmap view that suggests a sufficient set of latents for effective concept ablation in causal analysis. Through case studies, we reveal three underexplored failure modes: limited cross-modal alignment, misleading visual concepts, and unused hidden cues. Project page: https://hyesulim.github.io/visual_scratchpad_projectpage/