As Californian companies race to manufacture and deploy thousands of humanoid robots in the coming year, another new class of worker has been born

Encord is testing whether EEG signals can give robotics models something cameras don't: a hint of human intent when physical tasks get hard. The robots are stuck. Not in the way a chatbot gets stuck, with a bad answer and a shrug, but in the physical sense: hands, cups, cables, blocks, weight, friction. Language models had the internet. Robots have to learn from the world one task at a time, and Encord is now adding brain waves to that pile. TechCrunch reported on July 26, 2026, that Encord is running a trial at its warehouse in San Leandro, California, with Zander Labs, a German neuroscience company whose EEG headset measures brain activity while people perform manual tasks. In one scene from the report, Encord pilot Andrew Ceja pulls wooden blocks from a Jenga tower while wearing a headset with a camera and brain-wave sensors. It sounds like a lab stunt until you look at the problem underneath it. Robotics data is expensive because it has to be made. A person has to pour the coffee, stack the poker chips, plug in the ethernet cable, pick up the object and fail in ways the model can learn from. You can't scrape...

Join us at GitHub HQ to connect with fellow AI practioners, learn from industry experts, and enjoy a slice of pizza 🍕. Note, entry is first come, first serve!…

In this talk from Startup School Paris, Encord Co-CEO Eric Landau discusses leaving a lucrative quant career during the most profitable month his desk had ever seen, why PMF for Encord was a slow daily compound, and why founders should ride the emotional rollercoaster rather than fight it. Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs Chapters: 00:00 — Intro 00:31 — Physics, Quant, Founder 01:57 — Quitting at the Worst Possible M

Les patrons des plus grandes entreprises du Québec devraient gagner 236 fois plus que le salaire annuel moyen en 2026. Au deuxième jour de la nouvelle année, ils avaient déjà empoché l’équivalent de c...

Physical AI data infrastructure startup Encord lands $60M to accelerate intelligent robot and drone development - SiliconANGLE

Стартап Encord тестує зчитування мозкових хвиль для тренувальних даних роботів. Дізнайтесь деталі спільного проєкту з Zander Labs.

Labeling and annotation platforms might not get the attention flashy new generative AI models do. But they’re essential. The data on which many models train must be labeled, or the models wouldn’t be able to interpret that data during the training…

Artificial intelligence data annotation startup Encord, officially known as Cord Technologies Inc., wants to break down barriers to training multimodal AI models. To do that, it has just released what...

The purpose of this post is to introduce the “micro-model” methodology we use at Encord to automate data annotation. We have deployed this…

On the front line of physical artificial intelligence, this looks like Jenga laid out in San Leandro, California. There sits Encord – a company that develops data-collection tools needed to train artificial intelligence models. Andrew Ceja – the company’s pilot, i.e., its term for robotics trainers – carefully pulls wooden blocks from a wobbly tower, puts on a helmet with a camera that tracks what he sees. This device also measures brain activity as the structure is taken apart. Encord believes that the future frontier of robotics development will depend not on model architectures, but on the shortage of real-world data for interacting with the real world. So the company not only helps clients manage the data they have but also strives to create the data that is missing. The brain-wave helmet developed by Zander Labs – a German neuroscience startup – claims that measuring brain activity to identify errors, intentions, and surprises can help create a more useful dataset for training models. Currently, Encord is conducting a pilot with Zander to collect a baseline brain-wave dataset, test it on client robotics models, and assess whether performance improves before scaling up. Data simply doesn’t exist. – Vineeth Velmurugan Data as...

Exclusif : Encord regroupe les données multimodales de l'IA - y compris l'audio - sur une seule plateforme.

Physical AI transforms industries. Self-driving cars navigate roads. Delivery drones soar through skies. Manufacturing robots assemble products. This shift excites innovators. However, it creates new hurdles for AI teams. They must manage complex real-world data. Unlike large language models, physical AI demands sensor-rich datasets. These include video, audio, LiDAR, and telemetry. Older platforms falter under this load. Therefore, companies seek better solutions.

Before delving into how F1 has benefited from artificial intelligence, it is worth understanding just how the AI industry is interacting with the sport – and why.Alexandre Bonnet, the Lead ML Solutions Engineer for Encord told Autosport that the scope of what F1 offers as a platform for AI and machine learning is appealing to those who want to be involved.“The reason F1 is so attractive is

In this contributed article, Ulrik Stig Hansen, President and Co-Founder of Encord, discusses the reality – AI hallucinations aren’t bugs in the system—they’re features of it. No matter how well we bu...