[AINews] AI Engineer World's Fair — Autoresearch, Memory, World Models, Tokenmaxxing, Agentic Commerce, and Vertical AI Call for Speakers
Summary
AIE World's Fair has announced its Wave 2 Call for Speakers for its summer 2026 event at Moscone West, aiming to double its size for the third consecutive year and serve over a million unique AI engineers monthly. The conference is expanding its schedule with new tracks including Autoresearch, Memory, World Models, Tokenmaxxing, Agentic Commerce, Vertical AI in Law, Healthcare, GTM, and Finance, and Robotics, which will feature free expo floor space for demos. A new Startup Battlefield event will also allow pre-Series A companies to pitch to VCs. The announcement encourages submissions, particularly for these new areas, and notes that accepted Wave 1 applicants will be notified, with others automatically considered in Wave 2. Travel and ticket refunds are available for successful applicants.
Key takeaway
For NLP Engineers and AI professionals looking to showcase their work or explore new frontiers, consider submitting a speaker proposal to AIE World's Fair 2026. Focus on projects relevant to emerging tracks like Autoresearch, Memory, World Models, or Agentic Commerce to maximize your visibility and contribute to the community's knowledge base. This is an opportunity to connect with peers, potential employers, and investors in a rapidly expanding field.
Key insights
AIE World's Fair 2026 expands tracks and speaker opportunities, emphasizing practical AI engineering and startup innovation.
Principles
- AI engineering requires continuous learning and adaptation.
- Practical application drives AI innovation and adoption.
Method
The conference actively solicits speakers for new, specialized tracks and offers a Startup Battlefield to foster emerging AI companies.
In practice
- Submit proposals for specialized AI engineering topics.
- Founders can pitch pre-Series A companies to VCs.
Topics
- AI Engineer World's Fair
- Agentic AI Systems
- LLM Capabilities & Benchmarking
- Model Interpretability
- AI Infrastructure
Code references
- Luce-Org/lucebox-hub
- ullahsamee/open-visual
- knoopx/pi
- deepseek-ai/Thinking-with-Visual-Primitives
- OpenSenseNova/SenseNova-U1
Best for: NLP Engineer, AI Engineer, Machine Learning Engineer, AI Scientist
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Editorial summary, takeaway, and curation by AIssential. Original article published by Latent.Space - Www.latent.space.