Lyte raises $165.0M Series C for robotics perception

themodernsoftware.dev

CS146S: The Modern Software Developer - Stanford University

cs329z.stanford.edu

Stanford CS329Z | Engineering AI Agents

Official course website for Stanford CS329Z: Engineering AI Agents — schedule, deadlines, coursework, project, and logistics.

arxiv.org

The Emergent Symbolic Structure of Artificial Neural Networks

Modern systems in artificial intelligence (AI) somehow excel in domains for which they seem poorly suited. Intelligence has traditionally been modeled as operating over structured combinations of symbols, such as logical formulas. However, the strongest modern AI systems are based on neural networks, which instead represent information in continuous vectors. Vectors seem inadequate for capturing the structure of language, logic, and other cognitive domains, yet neural networks achieve impressive performance in these areas. How do they do it? In this work, we propose a potential answer: Despite appearances, perhaps the internal representations of neural networks implicitly realize symbolic structure. In support of this hypothesis, we show that the vector representations of a variety of neural networks can be closely approximated with symbolic structures: we can replace the network's entire representation-generating process with a closed-form equation instantiating a symbolic structure, and the network's behavior remains largely unchanged. This finding holds for both small-scale neural networks trained to manipulate lists as well as large language models (LLMs) operating in four domains that are central in symbolic traditions: arithmetic, logic, computer code, and language. Further, our symbolic approximation allows us to modify an LLM's behavior in targeted ways via precise interventions on its internal representations, showing that the LLM's behavior is reliant on the symbolic structures we have identified. This work provides a potential way to reconcile longstanding symbolic conceptions of intelligence with the vector-based nature of modern AI.

signalundefied.bearblog.dev

i wanna live an npc life

the general consensus around being an npc is that you’re giving up your autonomy—essentially becoming a side character in someone else's story or watching li...

$3.5M

Fambot raises a $3.5M Pre-Seed round

Fambot is an AI company based in San Francisco that provides an automated chief of staff for family logistics. Founded by veterans from Uber, Instagram, and Google, it aims to reduce the administrative burden on parents by synthesizing fragmented communications into a single, actionable daily plan. The platform aggregates school emails, calendar invites, and chat messages into daily summaries, helping working parents manage their complex scheduling needs. Fambot currently serves over 1,000 families and continues to develop its AI-driven infrastructure.

$20.8M

Aslan Protects raises a $20.8M Seed round

Aslan Protects is a defense technology and cyber intelligence startup based in Washington, D.C. Founded in 2025, it develops a specialized cyber defense and intelligence platform using human-supervised, autonomous AI operatives. These operatives are designed to infiltrate hostile digital environments at internet scale, moving beyond static data collection. The platform targets cross-border smuggling, exposes foreign state actors, and maps international cyber-fraud networks.

$165.0M

Lyte raises a $165.0M Series C round

Lyte is an end-to-end integrated perception company for robotics and Physical AI, focused on enabling machines to see, understand, and operate safely in the physical world. Founded by experts behind Apple Face ID and Microsoft Kinect, it offers a unified sensing platform designed to integrate advanced sensing technologies for various robotic applications. Their flagship product, LyteVision, combines 4D sensing and motion awareness into a single framework for autonomous machines. The company aims to simplify robotics by eliminating the challenges of assembling perception systems from multiple vendors.