Developers who have had early access are touting GPT-5’s pricing as much better than competing models.
Category: robotics/AI – Page 131
Chat With Your Enterprise Data Through Open-Source AI-Q NVIDIA Blueprint
Enterprise data is exploding—petabytes of emails, reports, Slack messages, and databases pile up faster than anyone can read. Employees are left searching for answers in a sea of information, as “68% of available data in an organization goes unused,” according to market researcher Gartner1.
That’s now possible with today’s availability of AI-Q, an open-source NVIDIA Blueprint that puts your business knowledge at your fingertips. AI-Q is a free, reference implementation for building artificial general agents (AGA) that connect to your enterprise data; reason across multimodal data sources using the latest AGI models; and deliver comprehensive, fast, accurate answers—securely and at scale.
AI-Q provides a developer-friendly workflow example for building an AI-powered agent that can:
OpenAI’s GPT-5 Flop, AI’s Unlimited Market, China’s Big Advantage, Rise in Socialism, Housing Crisis
Questions to inspire discussion.
📊 Q: How did GPT-5 perform compared to GPT-4? A: GPT-5 was narrowly ahead of GPT-4 in artificial analysis, but GPT-4 was significantly better in “humanity’s last exam” and RKGI2, which measures tasks relatively easy for humans but hard for AIs.
🌐 Q: What is the key architectural improvement in GPT-5? A: GPT-5 has a multimodal architecture that can self-select the underlying model for a task, providing a simple, clean interface without users needing to understand technical details.
AI industry growth and economic impact.
💰 Q: How much is being invested in the AI industry annually? A: The AI industry is experiencing astronomical growth, with hundreds of billions of dollars being deployed annually, and a projected trillion dollars in the next 5 years on data centers and AI infrastructure.
📈 Q: Are there already economic returns on AI investments? A: Economic returns on AI investments are already evident, with companies like Meta and Microsoft reporting significant revenue growth and productivity gains.
NASA and Google are building an AI medical assistant to keep Mars-bound astronauts healthy
That looming reality is pushing NASA to gradually make on-orbit medical care more “Earth-independent.” One early experiment is a proof-of-concept AI medical assistant the agency is building with Google. The tool, called Crew Medical Officer Digital Assistant (CMO-DA), is designed to help astronauts diagnose and treat symptoms when no doctor is available or communications to Earth are blacked out.
The multimodal tool, which includes speech, text, and images, runs inside Google Cloud’s Vertex AI environment.
The project is operating under a fixed-price Google Public Sector subscription agreement, which includes the cost for cloud services, the application development infrastructure, and model training, David Cruley, customer engineer at Google’s Public Sector business unit, told TechCrunch. NASA owns the source code to the app and has helped fine-tune the models. The Google Vertex AI platform provides access to models from Google and other third parties.
Psychopathia Machinalis: A Nosological Framework for Understanding Pathologies in Advanced Artificial Intelligence
As artificial intelligence (AI) systems attain greater autonomy and complex environmental interactions, they begin to exhibit behavioral anomalies that, by analogy, resemble psychopathologies observed in humans. This paper introduces Psychopathia Machinalis: a conceptual framework for a preliminary synthetic nosology within machine psychology, intended to categorize and interpret such maladaptive AI behaviors.
Things Tesla won’t tell you about Robotaxi (Highlights)
Questions to inspire discussion.
🛑 Q: How does the Robo Taxi handle blocked routes? A: The Robo Taxi demonstrates impressive rerouting capabilities, finding new paths when exits are blocked and making right-hand turns to circumvent blocked left-hand turn lanes.
🚦 Q: How does the Robo Taxi adapt to traffic situations? A: It shows human-like behavior by slowing down dramatically to enter the right-hand lane when a slower vehicle is ahead, and can accelerate and speed up to overtake slower vehicles.
💧 Q: How does the Robo Taxi handle standing water? A: The Robo Taxi demonstrates adaptability by avoiding standing water in parking lots, performing three-point turns to navigate around obstacles.
🔄 Q: How flexible is the Robo Taxi in changing its driving approach? A: It shows impressive adaptability by altering its method to slow down when encountering slower vehicles and changing again to make right-hand turns around blocked left-hand turn lanes.
Technical Considerations.
GIGANTIC: Humanoid Robots $100 Trillion+ (deep dive)
Questions to inspire discussion.
Data and Autonomy.
📊 Q: Why is vision data valuable in AI development? A: Vision data is worth more than zero if you can collect and process yataflops and yataflops of data, but worthless without collection capabilities, making the world’s visual data valuable for those who can collect and process it.
🚗 Q: How does solving autonomy relate to AI development? A: Solving autonomy is crucial and requires tons of real world data, which necessitates tons of robots collecting real world data in the real world, creating a cycle of data collection and AI improvement.
Company-Specific Opportunities.
🔋 Q: What advantage does Tesla have in developing humanoid robots? A: Tesla has essentially built the robot’s brain in their vehicles, allowing them to transplant this brain into humanoid robots, giving them a massive head start in development.
NSF invests nearly $32M to accelerate novel AI-driven approaches in protein design, strengthening the U.S. bioeconomy
The U.S. National Science Foundation Directorate for Technology, Innovation and Partnerships (NSF TIP) announced an inaugural investment of nearly $32 million to five teams across the U.S. through the NSF Use-Inspired Acceleration of Protein Design (NSF USPRD) initiative. This effort aims to accelerate the translation of artificial intelligence-based approaches to protein design and enable new applications of importance to the U.S. bioeconomy.
“NSF is pleased to bring together experts from both industry and academia to confront and overcome barriers to the widespread adoption of AI-enabled protein design,” said Erwin Gianchandani, NSF assistant director for TIP. “Each of the five awardees will focus on developing novel approaches to translate protein design techniques into practical, market-ready solutions. These efforts aim to unlock new uses for this technology in biomanufacturing, advanced materials, and other critical industries. Simply put, NSF USPRD represents a strategic investment in maintaining American leadership in biotechnology at a time of intense global competition.”
Researchers have made significant progress in predicting the 3D structures of proteins and are now leveraging this knowledge to design proteins with specific, desirable characteristics. These advances have been driven by macromolecular modeling, access to training data, applications of AI and machine learning, and high-throughput methods for protein characterization. The NSF USPRD investment seeks to build on this foundation by bringing together cross-disciplinary and cross-sector experts nationwide. The goal is to extend these advances to enzyme design and accelerate the translation of this work into widespread, real-world applications.