"The Age of Autonomous Systems" isn't a forecast. It's the stack being built right now: agents that act, transact, and get things done on your behalf. The premise underneath all of it is a trust layer, which is exactly what Kite is building. We are pleased to share that our Co-Founder & CEO @ChiZhangData will speak in the Tech Track at GTLC 2026 Bay Area Chinese Tech Leaders Summit, hosted by TGO Silicon Valley. She shares the stage with founders and researchers including: ▷ Yuandong Tian (@tydsh), Founder of Recursive Superintelligence, the $4.65B self-improving AI lab. Former Research Director at Meta FAIR, and earlier on Google X's self-driving car team (now Waymo). ▷ Yangqing Jia (@jiayq), creator of Caffe and Co-Founder & CEO of Lepton AI (acquired by NVIDIA). Former VP at NVIDIA, VP at Alibaba Group, and AI Director at Facebook. ▷ Junchen Jiang (@JunchenJiang), CEO of Tensormesh and Professor at UChicago, co-creator of the open-source LMCache. ▷ Bo Li, Co-Founder & CEO of Virtue AI and Professor at UIUC, a leading scholar in AI safety and trustworthy ML (IJCAI Computers and Thought Award, Sloan Fellow). ▷ Ryan Hanrui Wang, SVP Applied AI at Nebius and former Co-Founder & CEO of Eigen AI (acquired by Nebius for $643M), out of MIT's HAN Lab. ▷ Ding Zhao, Professor at CMU and director of the Safe AI Lab, focused on trustworthy and safe AI. ▷ Weiyan Shi (@shi_weiyan), Professor at Northeastern and AI2050 Fellow, who built the negotiation agent behind Meta's CICERO. 📅 July 17, 2026 · Tech Track 2:00 PM 📍 Q Bay Center, San Jose If you're in the Bay Area, come find us. 🪁 🔗

Trustworthy AI can’t exist without trustworthy data. And right now, the data behind AI is becoming harder to verify. Models are training on synthetic content, scraped datasets, and feedback loops that are difficult to trace. Human review still happens, but it is often anonymous, fragmented, and disconnected from any lasting record of who contributed, what they verified, or how reliable their work was. But by verifying contributors, tracking performance, and recording each validation step, AI data can become accountable. Experts can build reputation over time. High-quality work can be routed to higher-value tasks. Enterprises can see the human judgment behind the datasets their systems rely on. It’s why we’re building Perle Labs: expert-validated, human-verified, on-chain auditable data infrastructure for AI systems that need to be trusted in the real world.

China is not a trustworthy or reliable partner on trade or any other issues. Not as long as it persecutes its own citizens because of their faith. I thank @POTUS for raising religious prisoners with President Xi in the past and pray he will do so again.

Financial work depends on trustworthy sources, consistent definitions, accurate calculations and auditable outputs. Introducing Ling-3.0-flash-Fin, a finance-enhanced version of Ling-3.0-flash, developed with financial institutions and domain experts. With 124B total and 5.1B active parameters, it supports information retrieval, research, valuation modeling and report preparation across long reports, research materials and complex workbooks. The model showed competitive results across FinFIRST, FinSearchComp Verified, FinCRAFT, FinanceAgent v1.1/v2, APEX-Agents, SpreadsheetBench v1/v2 and τ³-Banking. We will open-source the model weights next week.

How do you build trustworthy AI diagnostic tools in one of medicine's most historically under-researched areas? In this episode of Just Now Possible, Teresa Torres talks with Tulsi Patel (Director of Product and Technology), Lorna Brightmore (Head of Data and AI), and Jack Pickard (Head of Engineering) at Hertility, a UK and Ireland-based women's health tech company. Hertility combines an in-depth online health assessment with at-home hormone testing and clinician-reviewed reports to help diagnose conditions spanning menstruation to menopause. Built on seven years of data linking symptoms, blood results, and pelvic ultrasound scans for over a million women, the team walks through two AI products in development: a Bayesian network that gives clinicians probability-based diagnoses instead of binary calls, and a scan automation pipeline that classifies ultrasound images, measures follicle counts and ovarian volume, and drafts clinical letters using an agentic loop that checks its own output against patient data before a human ever reviews it. You'll hear how the team guards against automation bias, builds clinician trust through transparency, minimizes PII before it ever reaches a model, and treats healthcare regulation as a design constraint from day one rather than a last-minute scramble. It's a detailed look at what it takes to bring AI into one of the most sensitive, tightly regulated corners of healthcare. Guests: - Tulsi Patel – Director of Product and Technology, Hertility - Lorna Brightmore – Head of Data and AI, Hertility - Jack Pickard – Head of Engineering, Hertility What we cover: - What makes Hertility's data set unique: seven years of linked symptoms, blood tests, and pelvic scans from over a million women - How uses a Bayesian network to give clinicians probability-based diagnoses instead of binary yes/no calls - Why showing clinicians the reasoning behind a diagnosis—not just the label—builds trust and speeds up triage - Guarding against automation bias with holdout sets and independent, fresh-eyes review - Inside the scan automation pipeline: classifying ultrasound images, detecting follicles, and measuring ovarian volume more precisely than manual methods - Using an agentic loop to check AI-drafted clinical letters against patient data and catch hallucinations before a human sees them - The infrastructure challenge of securely piping DICOM ultrasound images from third-party scan providers into Hertility's systems - How Hertility handles PII and PHI: pseudonymization, data minimization, and running models in-house on AWS Bedrock - Why treating healthcare regulation as a product requirement from day one makes AI products more scalable, not slower Key Takeaways: - Probabilistic, transparent AI outputs build more clinician trust than binary classifications. - Guardrails against automation bias are as important as the model itself. - Data minimization and in-house infrastructure make it possible to build AI responsibly with sensitive health data. - Treating regulation as a design constraint from day one makes AI products more defensible and scalable, not slower. Resources & Links: - Hertility — At-home hormone testing and reproductive health diagnostics for women in the UK and Ireland - AWS Bedrock — The platform Hertility uses to run LLMs in-house under its own governance and regulatory controls - PyTorch — The foundation for Hertility's in-house image classification and contouring models Chapters: 00:00 Meet the Team 00:13 What Hertility Does 01:51 How Customers Access It 04:06 A Unique Women's Health Dataset 07:03 Mission and Efficiency with AI 10:03 Why Long Assessments Convert 13:52 Before AI Workflows 16:52 Research Publications and Impact 18:48 GynAI Reducing Time to Diagnosis 21:21 Triage and Clinician Support 24:37 Keeping Patient UX the Same 26:12 Bayesian Network and Explainability 30:19 Multiple Diagnoses and Probabilities 32:37 Probabilistic Diagnosis Shift 33:50 Clinician Adoption and Workflow Fit 34:58 Communicating Medical Uncertainty 36:43 Scan Automation Overview 40:30 In House Image Analysis 44:25 DICOM Pipeline Engineering 47:30 Evals and Automation Bias 50:31 LLM Letter Guardrails 56:47 PHI Handling and Regulations 01:00:43 Infrastructure Choices and Wrap Up Listen on Spotify, Apple Podcasts, or watch on YouTube. Spotify: Apple Podcast: YouTube:

Exclusive: Meta is racing to make Hatch trustworthy after employees testing the AI agent found it could take unauthorized actions with users’ accounts and data. Read the full story:

Thrilled to share that our vision paper “Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On” has been accepted to the KDD Blue Sky Idea Track! 🎉✨ Trust in A2A networks should be baked in from the start, not patched on afterward. 🤖🔗🔐 📄 #KDD# #TrustworthyAI# #AIAgents# #LLM#