Inbolt raised $12.5 million (total funding now $34M) for its AI-powered 3D vision software for industrial robots.
Most robots on factory floors are effectively blind. They cannot handle part misalignment, tooling wear, or the small variations that happen on every real production line. When something shifts, they stall or produce defects.
Inbolt’s hardware-agnostic layer gives robots the ability to see, think, and adapt control loops in real time. It is already running on more than 200 robots across 100+ factories, including Bosch, Ford, Stellantis, and Toyota.
The investment logic is straightforward: a vision upgrade costs a fraction of a robot replacement and extends the useful life of equipment already deployed.
Capital is flowing into the software layer that makes existing robots useful — not just into new hardware.
Full analysis:
#IndustrialAI #Robotics #ComputerVision #SmartFactory
@ Nature this week calls out lack of real world data for AI medical algorithms.
AI-powered medical devices that inform clinical decision-making need rigorous, real-world testing equivalent to what’s required for drugs and self-driving cars.
- Since ChatGPT’s release in late 2022, generative AI has flooded into clinics rapidly — roughly 3 peer-reviewed articles on clinical AI publish every day, and 230+ million people weekly ask ChatGPT health questions.
- AI systems now handle admin tasks, order labs, help prescribe drugs, interpret X-rays/MRI/CT scans, and diagnose rare diseases.
- But regulatory oversight lags: some AI-powered medical products are being certified *without* real-world assessment.
Why current testing falls short:
- Most AI tools are novel, so there’s little existing clinical data to benchmark against (unlike a revised stethoscope or new bandage).
Pancreatic tumors vanished in roughly half of mice given an experimental mRNA therapy and did not return for up to a year after treatment stopped.
Researchers at UMass Chan Medical School developed an experimental treatment for pancreatic cancer that uses a cocktail of messenger RNAs (mRNA) to activate immune defenses, break through the protective tissue surrounding tumors, and help immune cells recognize cancer cells as threats. Inspired by work at the school’s RNA Therapeutics Institute and advances in COVID vaccines, the approach combines several immune signals and tumor-associated antigens in a single formulation.
The study, published in Nature Communications, combined mRNAs encoding five immune cytokines with three tumor-associated antigens in one injectable treatment. Approximately 50 percent of mice with pancreatic ductal adenocarcinoma experienced complete tumor regression and remained disease-free for a year, including after therapy was discontinued.
Let’s say you ask ChatGPT a question that stumps it, or a Waymo vehicle encounters something unusual in the road, or an autonomous factory faces an unexpected disruption. Most people would recognize that something unexpected has happened and adjust accordingly. For machines, it’s not always that simple.
Researchers at Georgia Tech’s H. Milton Stewart School of Industrial and Systems Engineering (ISyE) are addressing this challenge: How can autonomous systems recognize when something unexpected has happened, determine whether it matters and decide how to respond?
In a recent paper published in the INFORMS Journal on Data Science, the research team introduced a new framework called Mutual Information Surprise, designed to help machines identify meaningful surprises in complex environments.