A new AI tool can guess what you're looking at just by analyzing your brain scans--and recreate that image with remarkable precision. It can go the other way too, and predict a person's brain activity based on what they're looking at. In the image above, for example, the left-hand image of each pair is what the user actually saw--and its right-hand counterpart is what the model recreated based on the brain scan. Michal Irani, who developed the tool with her colleagues at the Weizmann Institute of Science in Rehovot, Israel, hopes her "mindreading" tool will ultimately reveal more about how the brain works, and could perhaps be used to help locked-in people communicate, or allow scientists to recreate the content of dreams. Judy Illes, a neuroethicist and professor of neurology at the University of British Columbia in Canada, who was not involved in the research, describes the work as "magnificent." "The idea [of using this approach] to help people with neurologic conditions therapeutically is tremendously exciting," she says. But other scientists warn that a similar approach could be used to reveal the inner thoughts and mental imagery of people, potentially without their consent. "The results seem very impressive," says Tommy Sprague, a neuroscientist at the University of California Santa Barbara. "But if there's a way to surreptitiously extract information about what you're thinking about, then 150 years of sci-fi can come true anytime, and that's worrisome in a lot of ways."

Coordinate-based Multilayer Perceptron (MLP) networks, despite being capable of learning neural implicit representations, are not performant for internal image synthesis applications. Convolutional Neural Networks (CNNs) are typically used instead for a variety of internal generative tasks, at the cost of a larger model. We propose Neural Knitwork, an architecture for neural implicit representation learning of natural images that achieves image synthesis by optimizing the distribution of image patches in an adversarial manner and by enforcing consistency between the patch predictions. To the best of our knowledge, this is the first implementation of a coordinate-based MLP tailored for synthesis tasks such as image inpainting, super-resolution, and denoising. We demonstrate the utility of the proposed technique by training on these three tasks. The results show that modeling natural images using patches, rather than pixels, produces results of higher fidelity. The resulting model requires 80% fewer parameters than alternative CNN-based solutions while achieving comparable performance and training time.

Imagine showing a photo taken through a storefront window to someone who has never opened her eyes before, and asking her to point to what's in the reflection and what's in the store. To her, everything in the photo would just be a big jumble. Computers can perform image separations, but to do it well, they typically require handcrafted rules or many, many explicit demonstrations: here's an image, and here are its component parts. New research finds that a machine-learning algorithm given just one image can discover patterns that allow it to separate the parts you want from the parts you don't. The multi-purpose method might someday benefit any area where computer vision is used, including forensics, wildlife observation, and artistic photo enhancement.

All around us people are slowly (or sometimes quickly) transitioning into the future of work. The full-time job (9 to 5, traditional career, etc.) is about to become a rarity; only available to a select group of people who represent the core of an organization, or who possess a very specific skill set. Because we live in a society increasingly shaped by tech. Automation will take over many of the tasks previously assigned to people. And the youth of today (and tomorrow) will have no problem transitioning into that situation.