Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI – Page 12

Sam Altman: The Future of OpenAI, ChatGPT’s Origins, and Building AI Hardware

A fireside with Sam Altman on June 16, 2025 at AI Startup School in San Francisco.

Sam Altman grew up obsessed with technology, broke into the Stanford mainframe as a kid, and dropped out to start his first company before turning 20.

In this conversation, he traces the path from early startup struggles to building OpenAI—sharing what he’s learned about ambition, the weight of responsibility, and how to keep building when the whole world is watching. He opens up about the hardest moments of his career, the limits of personal productivity, and why, in the end, it’s all still about finding people you like working with and doing something that matters.

Chapters (Powered by https://ChapterMe.co)
00:00 – We’re going for AGI
01:25 – Founding OpenAI Against the Odds.
05:00 – GPT-4o & the Future of Reasoning Models.
07:00 – ChatGPT Memory & the ‘Her’ Vision.
10:00 – GPT-5 & the Vision of a Multimodal Supermodel.
11:00 – Robots at Scale.
15:00 – Don’t Build ChatGPT — Build What’s Missing.
17:00 – Elon’s Harsh Email & Building Conviction.
26:00 – One Person’s Leverage in the Next Decade.
32:00 – AI for Science: Sam’s Personal Bet.

Post-Alcubierre Warp-Drives

Researchers are actively exploring and revising the concept of Alcubierre warp drive, as well as alternative approaches, to potentially make superluminal travel feasible with reduced energy requirements and advanced technologies ## ## Questions to inspire discussion.

Practical Warp Drive Concepts.

🚀 Q: What is the Alcubierre warp drive? A: The Alcubierre warp drive (1994) is a superluminal travel concept within general relativity, using a warp bubble that contracts space in front and expands behind the spacecraft.

🌌 Q: How does Jose Natario’s warp drive differ from Alcubierre’s? A: Natario’s warp drive (2001) describes the warp bubble as a soliton and vector field, making it harder to visualize but potentially more mathematically robust.

🔬 Q: What is unique about Chris Van Den Broeck’s warp drive? A: Van Den Broeck’s warp drive (1999) uses a nested warp field, creating a larger interior than exterior, similar to a TARDIS, while remaining a physical solution within general relativity. Energy Requirements and Solutions.

💡 Q: How do Eric Lent’s hyperfast positive energy warp drives work? A: Lent’s warp drives (2020) are solitons capable of superluminal travel using purely positive energy densities, reopening discussions on conventional physics-based superluminal mechanisms.

Continue reading “Post-Alcubierre Warp-Drives” | >

Tesla Robotaxi Vs. Waymo

Tesla is planning to launch a robo-taxi service in Austin, Texas, which is expected to disrupt the market with its competitive advantages in data collection, cost, and production, shifting the company’s business model towards recurring software revenue ## ## Questions to inspire discussion.

Tesla’s Robotaxi Launch.

🚗 Q: When is Tesla launching its robotaxi service? A: Tesla’s robotaxi launch is scheduled for June 22nd, marking a transformational shift from hardware sales to recurring software revenue with higher margins.

🌆 Q: How will Tesla’s robotaxi service initially roll out? A: The service will start with a small fleet of 10–20 vehicles, scaling up to multiple cities by year-end and millions of cars by next year’s end, with an invite-only system initially. Tesla vs. Waymo.

📊 Q: How does Tesla’s data collection compare to Waymo’s? A: Tesla collects 10 million miles of full self-driving data daily, compared to Waymo’s 250,000 miles, giving Tesla a significant data advantage for training AI and encountering corner cases.

🏭 Q: What production advantage does Tesla have over Waymo? A: Tesla can produce 5,000 vehicles per day, while Waymo has 1,500 vehicles with plans to add 200,000 over the next year, giving Tesla a substantial cost and scale advantage.

Continue reading “Tesla Robotaxi Vs. Waymo” | >

Hurricane-based company breaks ground on 20,000-square-foot robotics facility in Toquerville

TOQUERVILLE, Washington County — The Hurricane-based robotics company IME Automation recently announced the purchase of 6.5 acres of land at Anderson Junction in Toquerville, where the company has broken ground for its new 20,000-square-foot facility.

IME Automation develops custom robotic systems for manufacturing operations worldwide. This new facility will expand its capabilities and footprint in the region.

The land was acquired approximately eight months ago during the summer of 2024, brokered by sales agent Brandon Price with the commercial real estate agency NAI Excel. Price said he delayed putting out information about the acquisition until IME Automation was completely ready to break ground.

All-topographic neural networks more closely mimic the human visual system

Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a result, in addition to tackling various real-world computational problems, they could help neuroscientists and psychologists to better understand the underpinnings of specific sensory or cognitive processes.

Researchers at Osnabrück University, Freie Universität Berlin and other institutes recently developed a new class of artificial neural networks (ANNs) that could mimic the human visual system better than CNNs and other existing deep learning algorithms. Their newly proposed, visual system-inspired computational techniques, dubbed all-topographic neural networks (All-TNNs), are introduced in a paper published in Nature Human Behaviour.

“Previously, the most powerful models for understanding how the brain processes visual information were derived off of AI vision models,” Dr. Tim Kietzmann, senior author of the paper, told Tech Xplore.

Advanced algorithm to study catalysts on material surfaces could lead to better batteries

A new algorithm opens the door for using artificial intelligence and machine learning to study the interactions that happen on the surface of materials.

Scientists and engineers study the that happen on the surface of materials to develop more energy efficient batteries, capacitors, and other devices. But accurately simulating these fundamental interactions requires immense computing power to fully capture the geometrical and chemical intricacies involved, and current methods are just scratching the surface.

“Currently it’s prohibitive and there’s no supercomputer in the world that can do an analysis like that,” says Siddharth Deshpande, an assistant professor in the University of Rochester’s Department of Chemical Engineering. “We need clever ways to manage that large data set, use intuition to understand the most important interactions on the surface, and apply data-driven methods to reduce the sample space.”