Dive deep into the trippy, terrifying art produced by a computer’s artificial brain

Over multiple iterations this process alters the input image, whatever it might be (e.g., a human face), so that it encompasses features that the layer of the DCNN has been trained to select (e.g., a dog). When applied while fixing a relatively low level of the network, the result is an image emphasizing local geometric features of the input. When applied while fixing relatively high levels of the network, the result is an image that imposes object-like features on the input, resembling a complex hallucination.

In the current study, we chose a relatively higher layer and arbitrary category types (i.e. a category which appeared most similar to the input image was automatically chosen) in order to maximize the chances of creating dramatic, vivid, and complex simulated hallucinations. Future extensions could ‘close the loop’ by allowing participants (perhaps those with experience of psychedelic or psychopathological hallucinations) to adjust the Hallucination Machine parameters in order to more closely match their previous experiences. This approach would substantially extend phenomenological analysis based on verbal report, and may potentially allow individual ASCs to be related in a highly specific manner to altered neuronal computations in perceptual hierarchies. What determines the nature of this heterogeneity and shapes its expression in specific instances of hallucination?

While the video footage is spherical, there is a bind spot of approximately 33-degrees located at the bottom of the sphere due to the field of view of the camera. After each video, participants were asked to rate their experiences for each question via an ASC questionnaire which used a visual analog scale for each question (see Fig. 2c for questions used). We used a modified version of an ASC questionnaire, which was previously developed to assess the subjective effects of intravenous psilocybin in fifteen healthy human participants31. Trained DCNNs are highly complex, with many parameters and nodes, such that their analysis requires innovative visualisation methods. Recently, a novel visualisation algorithm called Deep Dream was developed for this purpose24,25.

Google’s program popularized the term (Deep) “Dreaming” to refer to the generation of images that produce desired activations in a trained deep network, and the term now refers to a collection of related approaches. https://chat.openai.com/ Discover how Argil AI revolutionizes social media video production with AI clones, multilingual support, and dynamic editing. Google Deep Dream Generator generally refers to Deep Dream Generator.

In addition, the method carries promise for isolating the network basis of specific altered visual phenomenological states, such as the differences between simple and complex visual hallucinations. Overall, the Hallucination Machine provides a powerful new tool to complement the resurgence of research into altered states of consciousness. In two experiments we evaluated the effectiveness of this system.

Broadly, the responses of ‘shallow’ layers of a DCNN correspond to the activity of early stages of visual processing, while the responses of ‘deep’ layers of DCNN correspond to the activity of later stages of visual processing. These findings support the idea that feedforward processing through a DCNN recapitulates at least part of the processing relevant to the formation of visual percepts in human brains. Critically, although the DCNN architecture (at least as used in this study) is purely feedforward, the application of the Deep Dream algorithm approximates, at least informally, some aspects of the top-down signalling that is central to predictive processing accounts of perception.

How easy is it to use Deep Dream Generator for someone without art skills?

It is difficult, using pharmacological manipulations alone, to distinguish the primary causes of altered phenomenology from the secondary effects of other more general aspects of neurophysiology and basic sensory processing. Understanding the specific nature of altered phenomenology in the psychedelic state therefore stands as an important experimental challenge. Close functional and more informal structural correspondences between DCNNs and the primate visual system have been previously noted20,36.

Experiment 1 compared subjective experiences evoked by the Hallucination Machine with those elicited by both (unaltered) control videos (within subjects) and by pharmacologically induced psychedelic states (across studies). Comparisons between control and Hallucination Machine with natural scenes revealed significant differences in perceptual and imagination dimensions (‘patterns’, ‘imagery’, ‘strange’, ‘vivid’, and ‘space’) as well as the overall intensity and emotional arousal of the experience. Notably, these specific dimensions were also reported as being increased after pharmacological administration of psilocybin31. Experiment 1 therefore showed that hallucination-like panoramic video presented within an immersive VR environment gave rise to subjective experiences that displayed marked similarities across multiple dimensions to actual psychedelic states31. A crucial feature of the Hallucination Machine is that the Deep Dream algorithm used to modify the input video is highly parameterizable. Even using a single DCNN trained for a specific categorical image classification task, it is possible with Deep Dream to control the level of abstraction, strength, and category type of the resulting hallucinatory patterns.

We have described a method for simulating altered visual phenomenology similar to visual hallucinations reported in the psychedelic state. Our Hallucination Machine combines panoramic video and audio presented within a head-mounted display, with a modified version of ‘Deep Dream’ algorithm, which is used to visualize the activity and selectivity Chat PG of layers within DCNNs trained for complex visual classification tasks. In two experiments we found that the subjective experiences induced by the Hallucination Machine differed significantly from control (non-‘hallucinogenic’) videos, while bearing phenomenological similarities to the psychedelic state (following administration of psilocybin).

The presentation of panoramic video using a HMD equipped with head-tracking (panoramic VR) allows the individual’s actions (specifically, head movements) to change the viewpoint in the video in a naturalistic manner. This congruency between visual and bodily motion allows participants to experience naturalistic simulated hallucinations in a fully immersive way, which would be impossible to achieve using a standard computer display or conventional CGI VR. We call this combination of techniques the Hallucination Machine. Participants were fitted with a head-mounted display before starting the experiment and exposed, in a counter-balanced manner, to either the Hallucination Machine or the original unaltered (control) video footage. Participants were encouraged to freely investigate the scene in a naturalistic manner. While sitting on a stool they could explore the video footage with 3-degrees of freedom rotational movement.

However, as we found out last month, when the program is used to “dream up” these images of its own, it can get things very wrong. What it creates are uncanny scenes of long-legged slug-monsters, wobbly towers, and flying limbs that look like a Salvador Dalí painting on steroids. PopularAiTools.ai offers a comprehensive collection of AI tools, with a special focus on generative art.

Access it by visiting the website, choosing your image generation mode, entering your prompt, and adjusting the settings to produce your artwork. While there may be premium features or subscriptions for more advanced functionalities, the basic image generation features are generally available without cost. The AI interprets each prompt differently, leading to original and distinct creations every time.

The Struggle To Define What Artificial Intelligence Actually Means

There are some tools that let people with no programming experience try their hand at creating images through DeepDream. To utilize Deep Dream Generator, visit its website, select an image generation mode, input your prompt or concept, and customize settings such as style or quality. Deep Dream Generator’s AI is capable of creating images in a wide range of styles. Users can choose from existing styles or customize settings to explore new artistic expressions. Deep Dream Generator aids in social media growth by allowing users to create unique and captivating images.

Deep Dream Generator offers various features that are available at no cost. However, for additional information regarding any premium features or subscription models, deepdream animator it’s best to visit their website. It specializes in AI animation, offering various pricing tiers and features that are transforming the world of animation.

094,983 stunning art pieces created.

Our setup, by contrast, utilises panoramic recording of real world environments thereby providing a more immersive naturalistic visual experience enabling a much closer approximation to altered states of visual phenomenology. In the present study, these advantages outweigh the drawbacks of current VR systems that utilise real world environments, notably the inability to freely move around or interact with the environment (except via head-movements). We set out to simulate the visual hallucinatory aspects of the psychedelic state using Deep Dream to produce biologically realistic visual hallucinations. To enhance the immersive experiential qualities of these hallucinations, we utilised virtual reality (VR). While previous studies have used computer-generated imagery (CGI) in VR that demonstrate some qualitative similarity to visual hallucinations28,29, we aimed to generate highly naturalistic and dynamic simulated hallucinations. To do so, we presented 360-degree (panoramic) videos of pre-recorded natural scenes within a head-mounted display (HMD), which had been modified using the Deep Dream algorithm.

Examples of the output of Deep Dream used in Experiments 1 and 2 are shown in Fig. We constructed the Hallucination Machine by applying a modified version of the Deep Dream algorithm25 to each frame of a pre-recorded panoramic video (Fig. 1, see also Supplemental Video S1) presented using a HMD. When Google released its DeepDream code for visualizing how computers learn to identify images through the company’s artificial neural networks, trippy images created with the image recognition software began to spring up around the Internet. The Deep Dream Generator analyzes and interprets input (text prompt or image) using AI, applying complex patterns and styles identified by neural networks to generate artistic images based on that input. Deep Dream Generator employs AI algorithms to transform text prompts or conceptual inputs into digital art.

In a similar fashion, for cases in which standard t-tests did not reveal significant differences in subjective ratings between video type we used additional Bayesian t-tests. In brief, the Hallucination Machine was created by applying the Deep Dream algorithm to each frame of a pre-recorded panoramic video presented using a HMD (Fig. 1). Participants could freely explore the virtual environment by moving their head, experiencing highly immersive dynamic hallucination-like visual scenes. The Deep Dream algorithm also uses error backpropagation, but instead of updating the weights between nodes in the DCNN, it fixes the weights between nodes across the entire network and then iteratively updates the input image itself to minimize categorization errors via gradient descent.

However, the AI-powered tools are designed to produce artworks relatively quickly compared to traditional methods. This layer recognizes more complex shapes in the input image and the DeepDream algorithm will therefore produce a more complex image. This layer appears to be recognizing dog-faces and fur which the DeepDream algorithm has therefore added to the image. Bayesian and standard statistical comparisons of ASCQ ratings from Experiment 1 between Hallucination Machine and control video exposure, and between Hallucination Machine and psilocybin administration, data taken from31.

For example, the neural responses induced by a visual stimulus in the human inferior temporal (IT) cortex, widely implicated in object recognition, have been shown to be similar to the activity pattern of higher (deeper) layers of the DCNN22,23. Features selectively detected by lower layers of the same DCNN bear striking similarities to the low-level features processed by the early visual cortices such as V1 and V4. These findings demonstrate that even though DCNNs were not explicitly designed to model the visual system, after training for challenging object recognition tasks they show marked similarities to the functional and hierarchical structure of human visual cortices. In Experiment 1, we compared subjective experiences evoked by the Hallucination Machine with those elicited by both control videos (within subjects) and by pharmacologically induced psychedelic states31 (across studies). A two-factorial repeated measures ANOVA consisting of the factors interval production [1 s, 2 s, 4 s] and video type (control/Hallucination Machine) was used to investigate the effect of video type on interval production.

Every 100 frames (4 seconds) the next layer is targeted until the lowest layer is reached. Integration with Google Photos depends on Deep Dream Generator’s current features. Usually, users download images from Google Photos and then upload them to Deep Dream Generator for processing. Yes, images created using Deep Dream Generator can be used for commercial purposes. This flexibility allows individuals, small businesses, and large corporations to use their creations for various commercial applications, including marketing materials, merchandise, and more. Looking Glass Blocks offers a unique holographic platform for 3D creators.

These images can attract followers and enhance online presence, especially for artists and creatives looking to leverage social media platforms. Krea AI and Fusion Art AI both focus on generative art, enabling users to unlock unique artistic expressions. These tools are ideal for artists and creators who want to explore new realms of creativity. These features make Deep Dream Generator not only a tool for creating art but also a platform for social interaction and artistic exploration. Created the materials and developed the Hallucination Machine system. Layer upon layer begins to transform into even weirder, more frightening images until the computer’s brain looks a bit like a nightmarish acid trip.

• The presentation of panoramic video using a HMD equipped with head-tracking (panoramic VR) allows the individual’s actions (specifically, head movements) to change the viewpoint in the video in a naturalistic manner.
• Samim Winiger took Google’s DeepDream software and created an animation tool that lets anyone take frames from videos and put them through the software to create a video file that shows you what a computer might see.
• Bayesian and standard statistical comparisons of ASCQ ratings from Experiment 1 between Hallucination Machine and control video exposure, and between Hallucination Machine and psilocybin administration, data taken from31.
• However, psychedelic compounds have many systemic physiological effects, not all of which are likely relevant to the generation of altered perceptual phenomenology.

This makes the seams between the tiles invisible in the final DeepDream image. The Inception 5h model has many layers that can be used for Deep Dreaming. But we will be only using 12 most commonly used layers for easy reference. Winiger’s video generator is a natural and exciting evolution of the DeepDream code.

This function is the main optimization-loop for the DeepDream algorithm. It calculates the gradient of the given layer of the Inception model with regard to the input image. The gradient is then added to the input image so the mean value of the layer-tensor is increased. This process is repeated a number of times and amplifies whatever patterns the Inception model sees in the input image. Extract frames from videos, process them with deepdream and then output as new video file.

It allows the conversion of 2D images into holograms, redefining the way digital visualization is approached. The exact size is unclear but maybe 200–300 pixels in each dimension. If we use larger images such as 1920×1080 pixels then the optimize_image() function above will add many small patterns to the image. Neural visualization is computationally intensive and the Caffe/OpenCV/CUDA implementation isn’t designed for real time output of neural visualization. 30fps output seems out of reach – even at lower resolutions, with reduced iteration rates, running on a fast GPU (TITAN X).

In this case we select the entire 3rd layer of the Inception model (layer index 2). It has 192 channels and we will try and maximize the average value across all these channels. However, this may result in visible lines in the final images produced by the DeepDream algorithm. We therefore choose the tiles randomly so the locations of the tiles are always different.

It uses neural networks for pattern recognition, applying these patterns to base images, enabling the creation of unique and intricate artworks. DeepDream is the name of the code that Google published last month for developers to play around with. In order to process and categorize images online, Google Images uses artificial neural networks (ANNs) to look for patterns. Google teaches the program how to do this by showing it tons of pictures of an object so that it knows what that object looks like. For example, after looking at thousands of pictures of a dumbbell, the program would understand a dumbbell to be a metallic cylinder with two large spheres at both ends.

Experiment 1 showed that subjective experiences induced by the Hallucination Machine displayed many similarities to characteristics of the psychedelic state. Based on this finding we next used the Hallucination Machine to investigate another commonly reported aspect of ASC – temporal distortions5,6, by asking twenty-two participants to complete a temporal production task during presentation of Hallucination Machine, or during control videos. A defining feature of the Deep Dream algorithm is the use of backpropagation to alter the input image in order to minimize categorization errors. This process bears intuitive similarities to the influence of perceptual predictions within predictive processing accounts of perception.

This tool is perfect for those looking to bring their static designs to life. Deep Dream Generator not only streamlines artistic creation but also opens new horizons for personal and professional growth. This makes it an invaluable asset for both creative individuals and businesses seeking efficient and innovative ways to produce visual content. This is an example of maximizing only a subset of a layer’s feature-channels using the DeepDream algorithm.

More precisely, the algorithm modifies natural images to reflect the categorical features learnt by the network24,25, with the nature of the modification depending on which layer of the network is clamped (see Fig. 1). What is striking about this process is that the resulting images often have a marked ‘hallucinatory’ quality, bearing intuitive similarities to a wide range of psychedelic visual hallucinations reported in the literature14,26,27 (see Fig. 1). There is a long history of studying altered states of consciousness (ASC) in order to better understand phenomenological properties of conscious perception1,2.

Architect Render is an AI-powered 3D rendering tool that turns designs into photorealistic visuals. This tool is a game-changer for architects and designers, streamlining their design process. If this is not enough I have uploaded one video on YouTube which will further extend your psychedelic experience. First we need a reference to the tensor inside the Inception model which we will maximize in the DeepDream optimization algorithm.

He asks for those that use the program to include the parameters they use in the description of their YouTube videos to help other DeepDream researchers. It would be very helpful for other deepdream researchers, if you could include the used parameters in the description of your youtube videos. Video materials used in the study are available in the supplemental material. The datasets generated in Experiment 1 and 2 are available from the corresponding author upon request. Nordberg’s dive into image recognition is just one of the ways developers are taking advantage of DeepDream. Google trains computers to recognize images by feeding them millions of photos of the same object—for instance, a banana is a yellow, rounded piece of fruit that comes in bunches.

Her work explores new technologies and the way they impact industries, human behavior, and security and privacy. Since leaving the Daily Dot, she’s reported for CNN Money and done technical writing for cybersecurity firm Dragos. Discover how Google’s VLOGGER AI model transforms static images into lifelike video avatars, revolutionizing digital interactions and addressing deepfake concerns. You can foun additiona information about ai customer service and artificial intelligence and NLP. Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the “When inside of” nested selector system. The image is split into tiles and the gradient is calculated for each tile. The tiles are chosen randomly to avoid visible seams / lines in the final DeepDream image.

With each new layer, Google’s software identifies and hones in on a shape or bit of an image it finds familiar. The repeating pattern of layer recognition-enhancement gives us dogs and human eyes very quickly. Each frame is recursively fed back to the network starting with a frame of random noise.

• These findings support the idea that feedforward processing through a DCNN recapitulates at least part of the processing relevant to the formation of visual percepts in human brains.
• In the current study, we chose a relatively higher layer and arbitrary category types (i.e. a category which appeared most similar to the input image was automatically chosen) in order to maximize the chances of creating dramatic, vivid, and complex simulated hallucinations.
• We therefore choose the tiles randomly so the locations of the tiles are always different.
• ASC are not defined by any particular content of consciousness, but cover a wide range of qualitative properties including temporal distortion, disruptions of the self, ego-dissolution, visual distortions and hallucinations, among others4–7.

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In predictive processing theories of visual perception, perceptual content is determined by the reciprocal exchange of (top-down) perceptual predictions and (bottom-up) perceptual predictions errors. The minimisation of perceptual prediction error, across multiple hierarchical layers, approximates a process of Bayesian inference such that perceptual content corresponds to the brain’s “best guess” of the causes of its sensory input. In this framework, hallucinations can be viewed as resulting from imbalances between top-down perceptual predictions (prior expectations or ‘beliefs’) and bottom-up sensory signals. Specifically, excessively strong relative weighting of perceptual priors (perhaps through a pathological reduction of sensory input, see (Abbott, Connor, Artes, & Abadi, 2007; Yacoub & Ferrucci, 2011)) may overwhelm sensory (prediction error) signals leading to hallucinatory perceptions38–43. Studies comparing the internal representational structure of trained DCNNs with primate and human brains performing similar object recognition tasks, have revealed surprising similarities in the representational spaces between these two distinct systems19–21.

The programs can then learn how to discriminate between different objects and recognize a banana from a mango. As the leading directory for AI tools, we prioritize showcasing only the highest quality solutions. Our selection represents the best AI tools and top AI tools that are indispensable for businesses aiming for excellence.

But we also have new fodder for nightmares and artistic renderings alike. The video footage was recorded on the University of Sussex campus using a panoramic video camera (Point Grey, Ladybug 3). The frame rate of the video was 16 fps at a resolution of 4096 × 2048. All video footage was presented using a head mounted display (Oculus Rift, Development Kit 2) using in-house software developed using Unity3D.

Frame blending option is provided, to ensure “stable” dreams across frames. A Bayesian two-factorial repeated measures ANOVA consisting of the factors interval production [1 s, 2 s, 4 s] and video type (control/Hallucination Machine) was used to investigate the effect of video type on interval production. A standard two-factorial repeated measures ANOVA using the same factors as above was also conducted. Thanks to Google’s artificial neural networks, we now have a better understanding of just how computers learn to recognize images.

The content of the visual hallucinations in humans range from coloured shapes or patterns (simple visual hallucinations)7,44, to more well-defined recognizable forms such as faces, objects, and scenes (complex visual hallucinations)45,46. As already mentioned, the output images of Deep Dream are dramatically altered depending on which layer of the network is clamped during the image-alteration process. Conversely, complex visual hallucinations could be explained by the over emphasis of predictions from higher layers of the visual system, with a reduced influence from lower-level input (Fig. 5c). Another key feature of the Hallucination Machine is the use of highly immersive panoramic video of natural scenes presented in virtual reality (VR). Conventional CGI-based VR applications have been developed for analysis or simulation of atypical conscious states including psychosis, sensory hypersensitivity, and visual hallucinations28,29,33–35. However, these previous applications all use of CGI imagery, which while sometimes impressively realistic, is always noticeably distinct from real-world visual input and is therefore suboptimal for investigations of altered visual phenomenology.

In this case it is the layer with index 10 and only its first 3 feature-channels that are maximized. Here comes my favorite part, After educating yourself about the Google Deep Dream, it’s time to switch from a reader mode to a coder mode because from this point onward I’ll only talk about the code which is equally important as knowing the concepts behind any Deep Learning application. Last week hundreds of people morphed images of their own using Zain Shah’s implementation of the DeepDream image generator. A DeepDream twitter bot also makes it easy to spend hours sifting through a feed of these nightmarish images.

Samim Winiger took Google’s DeepDream software and created an animation tool that lets anyone take frames from videos and put them through the software to create a video file that shows you what a computer might see. The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Deep Dream Generator distinguishes itself through its unique features like multiple image generation modes, extensive customization options, and a strong community aspect. Its ability to merge AI technology with artistic creativity in a user-friendly platform sets it apart from other AI art generators. Deep Dream Generator is designed to be user-friendly, making it accessible for individuals with no prior art skills. Its intuitive interface and AI-powered tools enable users to create stunning artworks easily, transforming simple ideas into visual masterpieces without needing technical artistic knowledge.

Specifically, instead of updating network weights via backpropagation to reduce classification error (as in DCNN training), Deep Dream alters the input image (again via backpropagation) while clamping the activity of a pre-selected DCNN layer. Therefore, the result of the Deep Dream process can be intuitively understood as the imposition of a strong perceptual prior on incoming sensory data, establishing a functional (though not computational) parallel with the predictive processing account of perceptual hallucinations given above. Experiment 2 tested whether participants’ perceptual and subjective ratings of the passage of time were influenced during simulated hallucinations, this was motivated by subjective reports of temporal distortion during ASC5,6. In contrast to these earlier findings, neither objective measures (using a temporal production task) nor subjective ratings (retrospective judgements of duration and speed, Q1 and Q2 in Fig. 4) showed significant differences between the simulated hallucination and control conditions. This suggests that experiencing hallucination-like phenomenology is not sufficient to induce temporal distortions, raising the possibility that temporal distortions reported in pharmacologically induced ASC may depend on more general systemic effects of psychedelic compounds.

From a performance perspective, there would appear to be quite a bit of headroom available. My CPU rarely goes above 20%, and the GPU Load remains under 70%. Many aspects of this technology are a black box to me, so perhaps further optimizations are possible. Selena Larson is a technology reporter based in San Francisco who writes about the intersection of technology and culture.

Altered states are defined as a qualitative alteration in the overall pattern of mental functioning, such that the experiencer feels their consciousness is radically different from “normal”1–3, and are typically considered distinct from common global alterations of consciousness such as dreaming. ASC are not defined by any particular content of consciousness, but cover a wide range of qualitative properties including temporal distortion, disruptions of the self, ego-dissolution, visual distortions and hallucinations, among others4–7. Causes of ASC include psychedelic drugs (e.g., LSD, psilocybin) as well as pathological or psychiatric conditions such as epilepsy or psychosis8–10. In recent years, there has been a resurgence in research investigating altered states induced by psychedelic drugs. These studies attempt to understand the neural underpinnings that cause altered conscious experience11–13 as well as investigating the potential psychotherapeutic applications of these drugs4,12,14. However, psychedelic compounds have many systemic physiological effects, not all of which are likely relevant to the generation of altered perceptual phenomenology.

Besides having potential for non-pharmacological simulation of hallucinogenic phenomenology, the Hallucination Machine may shed new light on the neural mechanisms underlying physiologically-induced hallucinogenic states. As Google and others realized, these neural networks that identify images can also make some creepy and stunning bits of art. You might have seen the photos of flower dogs or fish with human eyeballs making their way around the Web, thanks to creative minds messing with DeepDream. Deep Dream Generator is an AI-powered online platform designed for digital art creation. It merges AI technology with artistic creativity, allowing users to generate unique images from textual or conceptual inputs. The time taken to generate an image on Deep Dream Generator varies based on the complexity of the prompt and the chosen settings.