🤖DIY Home Robot Training

MIT researchers develop new method that lets you train home robots using iPhone scans

Key Points 

• Train home robots by scanning your home with an iPhone and uploading the data into a simulation.

• The approach allows robots to practice tasks thousands of times without the consequences of failure.

• iPhone scans can help robots better adjust to the dynamic nature of home environments that simulations often struggle to capture accurately.

👨🏻‍🔬News - Researchers at MIT CSAIL have developed a new method that allows anyone to train home robots by scanning a part of their home with an iPhone and uploading the data into a simulation.

💁🏻‍♂️Not sold yet? Consider these pros - 

Simulation is a key part of robot training, allowing robots to practice tasks thousands or even millions of times in the same span of time it would take to do it once in real life, without the consequences of failing. For instance, teaching a robot to put a mug in a dishwasher in simulation means avoiding the cost of breaking multiple mugs.

Since simulations struggle with accurately capturing the dynamic nature of home environments, using an iPhone scan to simplify the process can greatly improve a robot's adaptability. Imagine teaching a toy robot to clean a room using just a computer game—it might not handle real changes well. But if you take a picture of your room with a phone and use that for training, the robot learns to manage real-life messes and adjustments, like moving furniture or leaving dishes out.

New astronomy-based method helps detect ‘deepfake’ AI faces

Key Points 

• Using methods designed for studying distant galaxies, researchers were able to analyze light reflections in eyes to determine if an image was manipulated.

• Logic: Real photos should exhibit “consistent physics,” meaning reflections in the left eye should be similar to those in the right eye, though not identical.

• This method has limitations; it requires a clear, close-up view of the eyes and may mistakenly identify real faces as deepfakes due to slight but genuine inconsistencies in reflections. 

☕News - Researchers are now using astronomy techniques to spot deepfake images. By applying methods usually used to study distant galaxies, they’re able to analyze how light reflects off people’s eyes to catch signs of image manipulation. 

Kevin Pimbblet from the University of Hull’s Centre of Excellence for Data Science, AI, and Modelling, who shared this approach at the UK Royal Astronomical Society’s National Astronomy Meeting on 15 July, explained that real photos should have “consistent physics.” This means reflections in the left eye should be similar to those in the right eye, though not identical. 

🤷🏻‍♂️Is this the ultimate deepfake detection fix? While this method is useful, it's not perfect. First, because it requires a clear, close-up view of the eyes, and second, because real faces can show inconsistent reflections too, leading to false positives. The researchers themselves acknowledge that their technique isn’t foolproof and may sometimes miss deepfakes or mislabel genuine faces.

We must also consider that as AI advances, it might get better at creating consistent eye reflections, making this method less effective. Brant Robertson, an astrophysicist at the University of California, Santa Cruz, pointed out that if you can measure how realistic a deepfake looks, AI models could use that to create even better deepfakes.

🤠In conclusion - Even with its limitations, this approach is a promising way to address the rise of AI-generated deepfakes. It's a clever use of technology from a different field and shows progress in keeping up with rapid AI advancements—definitely a step in the right direction.