Why you'see' things in the dark, according to an ophthalmologist Science explains why we see flickers of light and patterns in the darkness. Our eyes sometimes really do play tricks on us at night. Breakthroughs, discoveries, and DIY tips sent every weekday. In 1999, Daniel Myrick and Eduardo Sánchez shot one of the definitive horror films of the era on a budget of roughly $60,000. is a study in omission, in the conspicuous absence of the visual effects characteristic to the genre. In lieu of baroque prosthetic gore and over-the-top CGI effects, the movie leans into silence and darkness for much of its 81-minute run time.

On Friday, Elon Musk once again pledged to depart his role at DOGE, taking with him his bad personality, weird public behavior, complicated family life, troubled businesses, alleged regular illegal drug use, compulsive social media habits, exploding rockets, messianic conviction that he control all of earth's resources so as to colonize Mars, and a remarkably poor track record in his brief life as a quasi-public servant. He leaves behind the incredible destruction DOGE has wrought, and of course, DOGE itself, which will continue its work, as Project 2025 architect and Office of Management and Budget director Russell Vought reportedly floats making its cuts permanent without the approval of Congress. Even Trump says Musk is "really not leaving." But it would be a mistake to think that Musk's grip on the government is lessening; beyond his continued relationship with the Trump administration, Musk's companies will still have billions in lucrative and influential federal contracts. And as his recent travel shows, there are clear signs that Musk is also using his relationship with President Trump to pursue business, especially in the Middle East.

From uncertainty quantification to real-world object detection, we recognize the importance of machine learning algorithms, particularly in safety-critical domains such as autonomous driving or medical diagnostics. In machine learning, ambiguous data plays an important role in various machine learning domains. Optical illusions present a compelling area of study in this context, as they offer insight into the limitations of both human and machine perception. Despite this relevance, optical illusion datasets remain scarce. In this work, we introduce a novel dataset of optical illusions featuring intermingled animal pairs designed to evoke perceptual ambiguity. We identify generalizable visual concepts, particularly gaze direction and eye cues, as subtle yet impactful features that significantly influence model accuracy. By confronting models with perceptual ambiguity, our findings underscore the importance of concepts in visual learning and provide a foundation for studying bias and alignment between human and machine vision. To make this dataset useful for general purposes, we generate optical illusions systematically with different concepts discussed in our bias mitigation section. The dataset is accessible in Kaggle via https://kaggle.com/datasets/693bf7c6dd2cb45c8a863f9177350c8f9849a9508e9d50526e2ffcc5559a8333. Our source code can be found at https://github.com/KDD-OpenSource/Ambivision.git.

With his flowing locks, long beard, and worn robes, Jesus is one of the most instantly recognisable figures in the Western world. So it comes as no surprise that his face is also regularly spotted in inanimate objects. This is due to'face pareidolia' - a common brain phenomenon in which a person sees faces in random images or patterns. 'Sometimes we see faces that aren't really there,' explained Robin Kramer, Senior Lecturer in the School of Psychology, at University of Lincoln, in an article for The Conversation. 'You may be looking at the front of a car or a burnt piece of toast when you notice a face-like pattern. 'This is called face pareidolia and is a mistake made by the brain's face detection system.'

During World War I, Allied navies started implementing shocking, cubist-inspired "dazzle" paint jobs on ships. The now-iconic geometric designs were intended to throw off the visual perception of German U-boats crews and prevent them from accurately targeting ships with torpedoes. Conventional wisdom claims the bizarre camouflage pattern worked and helped turn the tide of Great War naval battles. But new research reevaluating one of the only rigorous studies testing that hypothesis suggests those conclusions were probably overblown. Researchers now claim another phenomena known as the "horizon effect" may have actually done more to throw off submarine gunners than the wacky aesthetic.

Simply by rearranging the regions of an image, we can create a new image of any subject matter. The definition of regions is user definable, ranging from regularly and irregularly-shaped blocks, concentric rings, or even individual pixels. Our method extends and improves recent work in the generation of optical illusions by simultaneously learning not only the content of the images, but also the parameterized transformations required to transform the desired images into each other. By learning the image transforms, we allow any source image to be pre-specified; any existing image (e.g. the Mona Lisa) can be transformed to a novel subject. We formulate this process as a constrained optimization problem and address it through interleaving the steps of image diffusion with an energy minimization step. Unlike previous methods, increasing the number of regions actually makes the problem easier and improves results. We demonstrate our approach in both pixel and latent spaces. Creative extensions, such as using infinite copies of the source image and employing multiple source images, are also given.

A new brainteaser claims only people with high IQs can solve it in seven seconds. The goal is to identify which string on the right side of the board leads to the one attached to the banana. It is important to carefully look through the image instead of making a snap decision to decide between four possible strings. These kind of puzzles can tell you a lot about how you think and view the world and can help you develop problem-solving and logical reasoning skills. The picture shows a string wrapped around a banana and extending toward a wooden board with four strings labeled '1, 2, 3, 4' on the other side.

A new brain teaser reveals the most respected aspects of your personality. The puzzle features images that can be interpreted in different ways, depending on your personal experiences, traits and mental state. Hidden in the picture is a lion, panther and bunch of dandelions, and the one you see first means you are either a natural-born leader, a problem solver or have a strong sense of conviction. The puzzle features images that can be interpreted in different ways, depending on your personal experiences, traits and mental state. Did you see a lion, panther or dandelions first?

The discovery of the quantum tunnelling effect -- the transmission of particles through a high potential barrier -- was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a novel deep neural network (DNN) architecture that processes information using the effect of quantum tunnelling. I demonstrate the ability of the quantum tunnelling DNN (QT-DNN) to recognise optical illusions like a human. Hardware implementation of QT-DNN is expected to result in an inexpensive and energy-efficient neuromorphic chip suitable for applications in autonomous vehicles. The optical illusions recognition tests developed in this paper should lay foundations for cognitive benchmarking tasks for AI systems of the future, benefiting the fields of sociophysics and behavioural science.

The advent of Vision Language Models (VLM) has allowed researchers to investigate the visual understanding of a neural network using natural language. Beyond object classification and detection, VLMs are capable of visual comprehension and common-sense reasoning. This naturally led to the question: How do VLMs respond when the image itself is inherently unreasonable? To this end, we present IllusionVQA: a diverse dataset of challenging optical illusions and hard-to-interpret scenes to test the capability of VLMs in two distinct multiple-choice VQA tasks - comprehension and soft localization. GPT4V, the best-performing VLM, achieves 62.99% accuracy (4-shot) on the comprehension task and 49.7% on the localization task (4-shot and Chain-of-Thought). Human evaluation reveals that humans achieve 91.03% and 100% accuracy in comprehension and localization. We discover that In-Context Learning (ICL) and Chain-of-Thought reasoning substantially degrade the performance of GeminiPro on the localization task. Tangentially, we discover a potential weakness in the ICL capabilities of VLMs: they fail to locate optical illusions even when the correct answer is in the context window as a few-shot example.