Labeled data is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of it with informative tags called judgments. For example, a data label might indicate whether a photo contains a horse or a cow, which words were uttered in an audio recording, what type of action is being performed in a video, what the topic of a news article is, what the overall sentiment of a tweet is, or whether a dot in an X-ray is a tumor. Labels can be obtained by having humans make judgments about a given piece of unlabeled data. Labeled data is significantly more expensive to obtain than the raw unlabeled data. The quality of labeled data directly influences the performance of supervised machine learning models in operation, as these models learn from the provided labels. == Crowdsourced labeled data == In 2006, Fei-Fei Li, the co-director of the Stanford Human-Centered AI Institute, initiated research to improve the artificial intelligence models and algorithms for image recognition by significantly enlarging the training data. The researchers downloaded millions of images from the World Wide Web and a team of undergraduates started to apply labels for objects to each image. In 2007, Li outsourced the data labeling work on Amazon Mechanical Turk, an online marketplace for digital piece work. The 3.2 million images that were labeled by more than 49,000 workers formed the basis for ImageNet, one of the largest hand-labeled database for outline of object recognition. == Automated data labelling == After obtaining a labeled dataset, machine learning models can be applied to the data so that new unlabeled data can be presented to the model and a likely label can be guessed or predicted for that piece of unlabeled data. == Challenges == === Data-driven bias === Algorithmic decision-making is subject to programmer-driven bias as well as data-driven bias. Training data that relies on bias labeled data will result in prejudices and omissions in a predictive model, despite the machine learning algorithm being legitimate. The labeled data used to train a specific machine learning algorithm needs to be a statistically representative sample to not bias the results. For example, in facial recognition systems underrepresented groups are subsequently often misclassified if the labeled data available to train has not been representative of the population,. In 2018, a study by Joy Buolamwini and Timnit Gebru demonstrated that two facial analysis datasets that have been used to train facial recognition algorithms, IJB-A and Adience, are composed of 79.6% and 86.2% lighter skinned humans respectively. === Human error and inconsistency === Human annotators are prone to errors and biases when labeling data. This can lead to inconsistent labels and affect the quality of the data set. The inconsistency can affect the machine learning model's ability to generalize well. === Domain expertise === Certain fields, such as legal document analysis or medical imaging, require annotators with specialized domain knowledge. Without the expertise, the annotations or labeled data may be inaccurate, negatively impacting the machine learning model's performance in a real-world scenario.
Flutter (software)
Flutter is an open-source UI software development kit created by Google. It can be used to develop cross platform applications from a single codebase for the web, Fuchsia, Android, iOS, Linux, macOS, and Windows. First described in 2015, Flutter was released in May 2017. Flutter is used internally by Google in apps such as Google Pay and Google Earth as well as by other software developers including ByteDance and Alibaba. Flutter ships applications with its own rendering engine which directly outputs pixel data to the screen. This is in contrast to many other UI frameworks that rely on the target platform to provide a rendering engine, such as native Android apps which rely on the device-level Android SDK or iOS SDK which use the target platform's built-in UI stack. Flutter's control of its rendering pipeline simplifies multi-platform support as identical UI code can be used for all target platforms.One of Flutter’s key features is hot reload, which allows developers to see code changes instantly without restarting the application. == Architecture == The basic component in a Flutter program is a "widget", which can in turn consist of other widgets. A widget describes the logic, interaction, and design of a UI element with an implementation similar to React. Unlike other cross-platform toolkits such as React Native and Xamarin which draw widgets using native platform components, Flutter renders widgets itself on a per-pixel basis. Flutter has two types of widgets: stateless and stateful. Stateless widgets only update if their inputs change, meaning they otherwise won't need to be rebuilt when other elements of the screen change, while stateful widgets can call the setState() method to update an internal state and redraw. Although widgets are the primary method of constructing Flutter applications, they can also be bypassed in favor of directly drawing on a canvas. This feature has been occasionally used to implement game engines in Flutter. The Flutter framework contains two sets of widgets that conform to specific design languages: Material Design widgets implement Google's design language of the same name, and Cupertino widgets implement Apple's iOS Human interface guidelines. Flutter allows the developer to use either set of widgets on either platform. Developers can use Cupertino widgets on Android. Flutter apps are written in the Dart language. Release versions of Flutter apps on all platforms use ahead-of-time (AOT) compilation except for on the Web where code is transpiled to JavaScript or WebAssembly. Flutter inherits Dart's Pub package manager and software repository, which allows users to publish and use custom packages as well as Flutter-specific plugins. The Foundation library, written in Dart, provides basic classes and functions that are used to construct applications using Flutter, such as APIs to communicate with the engine. Flutter's engine, written primarily in C++, provides low-level rendering support using either Google's Skia graphics library or the custom "Impeller" graphics layer, which is enabled by default on iOS and Android API 29 and higher. The engine interfaces with platform-specific SDKs such as those provided by Android and iOS to implement features like accessibility, file and network I/O, native plugin support, etc. == History == The first version of Flutter was known as "Sky" and ran on the Android operating system. It was unveiled at the 2015 Dart developer summit with the stated intent of being able to render consistently at 120 frames per second. On December 4, 2018, Flutter 1.0 was released at the Flutter conference in London. On May 6, 2020, the Dart software development kit (SDK) version 2.8 and Flutter 1.17.0 were released, adding support for the Metal API. On March 3, 2021, Google released Flutter 2 during an online Flutter Engage event. It added a Canvas-based renderer for web in addition to the HTML-based renderer and early-access desktop application support for Windows, macOS, and Linux. It also shipped with Dart 2.0 which included support for null-safety. Null safety was initially optional as it was a breaking change and was made mandatory in Dart 3 released in 2023. On May 12, 2022, Flutter 3 and Dart 2.17 were released with support for all desktop platforms as stable. On October 27, 2024, a number of Flutter community developers announced Flock, a fork of Flutter intended to be easier to contribute to while still keeping in sync with all changes made in the upstream code base. In 2025, Google continued Flutter's evolution with enhanced modular architecture, foldable device support, and ARM IoT optimizations as outlined in the updated roadmap. === Major releases in Flutter === Prior to the Flutter 2.0 release in March of 2021, the Flutter framework was centered on mobile development. The developers of Flutter were primarily focused on the two main platforms, IOS and Android. Specifically, they wanted to deliver strong performance and improve access to native API and platform features and expand the widget system. With the release of Flutter 2.0, the framework moved beyond mobile and introduced support for the web platform. This marked a shift into a broader cross platform development environment. With this release, developers could produce applications for Web, Android and IOS from the same codebase. This release also brought the desktop platform closer to stable. There have been a number of improvements since then that have broadened platform support. They introduced enhancements to performance and workflow, redefined the developer’s toolkit, and added an improved rendering engine. "Flutter 2.10.0 release notes". docs.flutter.dev. Retrieved 2025-11-11.
AI Snake Oil
AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference is a 2024 non-fiction book written by scholars Arvind Narayanan and Sayash Kapoor. It is a critique of the tech industry's overly inflated promises and capabilities of artificial intelligence (AI) as well as a debunking of the flawed science fueling AI hype while attempting to outline both the potential positives and negatives that come with different modes of the technology. == Contents == === Publication === The book was published in September 2024 by the Princeton University Press. AI Snake Oil consists of 360 pages and features eight chapters, and sections for acknowledgements, references, and an index. An updated edition with a new preface and epilogue by the authors was published in September 2025. The authors use the term "AI snake oil" derived from the U.S. idiom for a fraudulent remedy, to describe overhyped AI systems. === Chapter one: Introduction === Narayanan and Kapoor argue that many individuals do not yet have the literacy to detect functioning aspects of AI compared to potential snake oil, which they identify as "AI that does not and cannot work as advertised". Some of the major examples utilized by the authors include Allstate's 2013 use of predictive AI, as well as the concern surrounding actors and AI attempting to replicate or use their likeness. Important discussions regarding discrimination are brought up and explored in the first chapter, including the false arrests of six Black individuals due to errors with AI facial recognition tools. The chapter concludes with a comparison to the Industrial Revolution, where Narayanan and Kapoor highlight the extensive human labour that is necessary for artificial intelligence technologies to function. === Chapter two: How Predictive AI Goes Wrong === Chapter two focuses on predictive artificial intelligence, and criticizes the overestimation of the capabilities of the technology. === Chapter three: Why Can't AI Predict the Future? === Chapter three works to inform the reader about the history of early computational prediction attempts, with examples from companies like Simulatics. === Chapter four: The Long Road to Generative AI === The fourth chapter goes in more in-depth in explorations of generative AI. Generative AI software examples include ChatGPT, Midjourney, and DALL-E. The section begins with a positive example of generative AI. As the chapter progresses, the authors begin to provide examples of harm produced by generative AI, including the suicide of a Belgian man after connecting with Chai, a generative chatbot. Issues of deepfakes and preservation of artistic property are also discussed. The use of generative AI to create non-consensual pornographic deepfake content is discussed in relation to female celebrities. === Chapter five: Is Advanced AI an Existential Threat? === The fifth chapter draws attention the AGI, or Artificial General Intelligence. The authors describe AGI as "AI that can perform most or economically relevant tasks as effectively as any human". They summarize that many contributors to the field of artificial intelligence believe AGI to be an impending threat that demands attention. However, they argue that the perceived threat of AGI would only exist if the technology continually functioned reliably. In order to better illustrate the hype surrounding AGI, Narayanan and Kapoor use the Ladder of Generality, which is described as a visual tool in which "each rung represents a way of computing that is more flexible, and more general, than the previous one". They note that we are not yet aware of the next rungs on the ladder, or if the ladder will eventually result in a dead end. The rungs that have been identified so far are as follows: (0, or floor) special purpose hardware, (1) programmable computers, (2) stored program computers, (3) machine learning, (4) deep learning, (5) pretrained models, and, finally, (6) instruction-tuned models. The potential for future rungs and what those rungs might be are currently undetermined. The chapter also discusses the ELIZA effect, which Lawrence Switzky discusses in his article "ELIZA Effects". Switzky attributes the coined term ELIZA Effect to Sherry Turke, who defined it as "our more general tendency to treat responsive computer programs as more intelligent than they really are". === Chapter six: Why Can't AI Fix Social Media? === The sixth chapter focuses on content moderation, why it is important, and how it has been and could be affected by artificial automation. The first issue raised in regard to AI-driven content moderation is the inability for computers and machines to understand context and nuance, resulting in potential for discriminatory moderation and shadow banning. While they note that there are issues with automating content moderation, Narayanan and Kapoor also highlight the psychological impact on human content moderators and their labour. They indicate the hidden labour behind moderation, which is often outsourced to less developed countries, where labourers sort through potentially traumatizing content for pay. However, the discussion focuses more heavily on why automated moderation can be problematic, including discriminatory algorithms and lack of nuance. To balance their argument, issues of discrimination and bias are also discussed in relation the human content moderators. To automate moderation, there are two types of AI used, which are fingerprint matching and machine learning. === Chapter seven: Why Do Myths about AI Persist? === The seventh chapter outlines possible factors that contribute to hype surrounding AI. Narayanan and Kapoor explain how companies often promote their new AI models without properly disclosing how the model works, and what it is learning from. They attribute hype to several different groups, including journalists, researchers, and companies. They explain the impact of companies and the misplaced hype that they spread can be attributed to greed and a desire to grow corporate funds. For journalists, one of the stated sources of hype, they argue that news media has a tendency to prioritize financial incentives over validity and quality of writing. As well, Narayanan and Kapoor point out the emergence of company statement regurgitation in news media, leading to clickbait. Hype from researchers is potentially linked to lack of reproducibility in studies as well as leakage, which occurs when AI models are tested on their training data. === Chapter eight: Where do we go from here? === The final chapter, chapter eight, turns its attention to the future. The authors express their ideas and predictions for how the technology will evolve and be utilized in the upcoming years. == Authors == Author Narayanan is a computer science professor at Princeton University. Kapoor is a doctoral candidate at the same university, and both scholars are located at the Center for Information Technology at Princeton. In 2023, Narayanan and Kapoor appeared on the TIME100 Artificial Intelligence list, which features influential figures in the field. == Reception == Nature, a science and technology peer-reviewed journal, released an article highlighting the top "10 essential reads from the past year", listing Arvind Narayanan and Sayash Kapoor's AI Snake Oil. The article states the that text is "one of the best on this controversial subject". Elizabeth Quill, in her review of the text in Science News, writes that the authors "squarely achieve their stated goal: to empower people to distinguish AI that works well from AI snake oil". Joshua Rothman of The New Yorker writes that "compared with many technologists, Narayanan, Kapoor, and Vallor [Shannon Vallor, University of Edinburgh], are deeply skeptical about today's A.I. technology and what it can achieve. Perhaps they shouldn't be". Rothman argues, following an interview with prominent computer scientist Geoffrey Hinton of University of Toronto, that the potential for AI to replicate complexity is already here and continues to be heavily funded, enhancing the prospective capabilities of the technology. However, he does praise the author's ability to address questions regarding the existential human experience. Alexya Martinez discusses the text in a book review for Journalism and Mass Communication Quarterly, critiquing AI Snake Oil for its extensive focus on the West. Martinez writes that Narayanan and Kapoor "do not fully explore how AI impacts other countries", and suggests more focus on countries outside of the United States to enhance their argument.
Midjourney
Midjourney is a generative artificial intelligence program and service created and hosted by the San Francisco–based "independent research lab" Midjourney, Inc. Midjourney generates images from natural language descriptions, called prompts, similar to OpenAI's DALL-E and Stability AI's Stable Diffusion. It is one of the technologies of the AI boom. The tool was launched into open beta on July 12, 2022. The Midjourney team is led by David Holz, who co-founded Leap Motion. Holz told The Register in August 2022 that the company was already profitable. Users generate images with Midjourney using Discord bot commands or the official website. == History == Midjourney, Inc. was founded in San Francisco, California, by David Holz, previously a co-founder of Leap Motion. The Midjourney image generation platform entered open beta on July 12, 2022. On March 14, 2022, the Midjourney Discord server launched with a request to post high-quality photographs to Twitter and Reddit for systems training. === Model versions === The company has been working on improving its algorithms, releasing new model versions every few months. Version 2 of their algorithm was launched in April 2022, and version 3 on July 25. On November 5, 2022, the alpha iteration of version 4 was released to users. Starting from the 4th version, MJ models were trained on Google TPUs. On March 15, 2023, the alpha iteration of version 5 was released. The 5.1 model is more opinionated than version 5, applying more of its own stylization to images, while the 5.1 RAW model adds improvements while working better with more literal prompts. The version 5.2 included a new "aesthetics system", and the ability to "zoom out" by generating surroundings to an existing image. On December 21, 2023, the alpha iteration of version 6 was released. The model was trained from scratch over a nine month period. Support was added for better text rendition and a more literal interpretation of prompts. == Functionality == Midjourney is accessible through a Discord bot or by accessing their website. Users can use Midjourney through Discord either through their official Discord server, by directly messaging the bot, or by inviting the bot to a third-party server. To generate images, users use the /imagine command and type in a prompt; the bot then returns a set of four images, which users are given the option to upscale. To generate images on the website, users initially needed to have generated at least 1,000 images through the bot; this limitation has since been removed. === Vary (Region) + remix feature === Midjourney released a Vary (Region) feature on September 5, 2023, as part of MidJourney V5.2. This feature allows users to select a specific area of an image and apply variations only to that region while keeping the rest of the image unchanged. === Midjourney web interface === Midjourney introduced its web interface to make its tools more accessible, moving beyond its initial reliance on Discord. This web-based platform was launched in August 2024 alongside the release of Midjourney version 6.1. The web editor consolidates tools such as image editing, panning, zooming, region variation, and inpainting into a single interface. The introduction of the web interface also syncs conversations between Midjourney's Discord channels and web rooms, further enhancing collaboration across both platforms. This shift was in response to growing competition from other AI image generation platforms like Adobe Firefly and Google’s Imagen, which had already launched as native web apps with integration into popular design tools. === Image Weight === This feature lets users control how much influence an uploaded image has on the final output. By adjusting the "image weight" parameter, users can prioritize either the content of the prompt or the characteristics of the image. For instance, setting a higher weight will ensure that the generated result closely follows the image's structure and details, while a lower weight allows the text prompt to have more influence over the final output. === Style Reference === With Style Reference, users can upload an image to use as a stylistic guide for their creation. This tool enables MidJourney to extract the style—whether it is the color palette, texture, or overall atmosphere—from the reference image and apply it to a newly generated image. The feature allows users to fine-tune the aesthetics of their creations by integrating specific artistic styles or moods. === Character Reference === The Character Reference feature allows for a more targeted approach in defining characters. Users can upload an image of a character, and the system uses that image as a reference to generate similar characters in the output. This feature is particularly useful in maintaining consistency in appearance for characters across different images. == Uses == Midjourney's founder, David Holz, told The Register that artists use Midjourney for rapid prototyping of artistic concepts to show to clients before starting work themselves. The advertising industry quickly adopted AI tools such as Midjourney, DALL-E, and Stable Diffusion to create original content and brainstorm ideas. Architects have described using the software to generate mood boards for the early stages of projects, as an alternative to searching Google Images. === Notable usage and controversy === The program was used by the British magazine The Economist to create the front cover for an issue in June 2022. In Italy, the leading newspaper Corriere della Sera published a comic created with Midjourney by writer Vanni Santoni in August 2022. Charlie Warzel used Midjourney to generate two images of Alex Jones for Warzel's newsletter in The Atlantic. The use of an AI-generated cover was criticised by people who felt it was taking jobs from artists. Warzel called his action a mistake in an article about his decision to use generated images. Last Week Tonight with John Oliver included a 10-minute segment on Midjourney in an episode broadcast in August 2022. A Midjourney image called Théâtre D'opéra Spatial won first place in the digital art competition at the 2022 Colorado State Fair. Jason Allen, who wrote the prompt that led Midjourney to generate the image, printed the image onto a canvas and entered it into the competition using the name Jason M. Allen via Midjourney. Other digital artists were upset by the news. Allen was unapologetic, insisting that he followed the competition's rules. The two category judges were unaware that Midjourney used AI to generate images, although they later said that had they known this, they would have awarded Allen the top prize anyway. In December 2022, Midjourney was used to generate the images for an AI-generated children's book that was created over a weekend. Titled Alice and Sparkle, the book features a young girl who builds a robot that becomes self-aware. The creator, Ammaar Reeshi, used Midjourney to generate a large number of images, from which he chose 13 for the book. Both the product and process drew criticism. One artist wrote that "the main problem... is that it was trained off of artists' work. It's our creations, our distinct styles that we created, that we did not consent to being used." In 2023, the realism of AI-based text-to-image generators, such as Midjourney, DALL-E, or Stable Diffusion, reached such a high level that it led to a significant wave of viral AI-generated photos. Widespread attention was gained by a Midjourney-generated photo of Pope Francis wearing a white puffer coat, the fictional arrest of Donald Trump, and a hoax of an attack on the Pentagon, as well as the usage in professional creative arts. Research has suggested that the images Midjourney generates can be biased. For example, even neutral prompts in one study returned unequal results on the aspects of gender, skin color, and location. A study by researchers at the nonprofit group Center for Countering Digital Hate found the tool to be easy to use to generate racist and conspiratorial images. In October 2023, Rest of World reported that Midjourney tends to generate images based on national stereotypes. In 2024, a Frontiers journal published a paper which contained gibberish figures generated with Midjourney, one of which was a diagram of a rat with large testicles and a large penis towering over himself. The paper was retracted a day after the images went viral on Twitter. ==== Content moderation and censorship in Midjourney ==== Prior to May 2023, Midjourney implemented a moderation mechanism predicated on a banned word system. This method prohibited the use of language associated with explicit content, such as sexual or pornographic themes, as well as extreme violence. Moreover, the system also banned certain individual words, including those of religious and political figures, such as Allah or General Secretary of the Chinese Communist Party Xi Jinping. This practice occasionally stirred controversy due to perceiv
Federal Virtual World Challenge
The Federal Virtual Challenge, formerly The Federal Virtual Worlds Challenge is a competition led by the Simulation and Training Technology Center (United States Army Research, Development and Engineering Command). The event is conducted in order to reach a global development community that will create innovative and interactive training and analysis services in virtual worlds. The inaugural event began in 2009 with the awards being conducted during March 2010 GameTech conference in Orlando, Florida. == Description == The focus of the challenge is training or analysis capability conducted wholly in a virtual environment. The training and analysis audience includes all United States Federal Agencies including, Department of Defense, Department of Homeland Security, Department of Transportation, and Department of Health and Human Services, NASA, DOT, and many more.
GazoPa
GazoPa was an image search engine that used features from an image to search for and identify similar images which closed in 2011. GazoPa began in TechCrunch50 in 2008 before launching into a state of open beta in 2009. GazoPa branched out and released a flower photo community site called "GazoPa Bloom" in 2010. This site was for exploring flower images and, if users need help identifying a flower, uploading images for other people try to identify them. Both sites closed to the public in 2011 when the company decided to focus on other areas of their business.
Computer-automated design
Design Automation usually refers to electronic design automation, or Design Automation which is a Product Configurator. Extending Computer-Aided Design (CAD), automated design and Computer-Automated Design (CAutoD) are more concerned with a broader range of applications, such as automotive engineering, civil engineering, composite material design, control engineering, dynamic system identification and optimization, financial systems, industrial equipment, mechatronic systems, steel construction, structural optimisation, and the invention of novel systems. The concept of CAutoD perhaps first appeared in 1963, in the IBM Journal of Research and Development, where a computer program was written. to search for logic circuits having certain constraints on hardware design to evaluate these logics in terms of their discriminating ability over samples of the character set they are expected to recognize. More recently, traditional CAD simulation is seen to be transformed to CAutoD by biologically-inspired machine learning, including heuristic search techniques such as evolutionary computation, and swarm intelligence algorithms. == Guiding designs by performance improvements == To meet the ever-growing demand of quality and competitiveness, iterative physical prototyping is now often replaced by 'digital prototyping' of a 'good design', which aims to meet multiple objectives such as maximised output, energy efficiency, highest speed and cost-effectiveness. The design problem concerns both finding the best design within a known range (i.e., through 'learning' or 'optimisation') and finding a new and better design beyond the existing ones (i.e., through creation and invention). This is equivalent to a search problem in an almost certainly, multidimensional (multivariate), multi-modal space with a single (or weighted) objective or multiple objectives. == Normalized objective function: cost vs. fitness == Using single-objective CAutoD as an example, if the objective function, either as a cost function J ∈ [ 0 , ∞ ) {\displaystyle J\in [0,\infty )} , or inversely, as a fitness function f ∈ ( 0 , 1 ] {\displaystyle f\in (0,1]} , where f = J 1 + J {\displaystyle f={\tfrac {J}{1+J}}} , is differentiable under practical constraints in the multidimensional space, the design problem may be solved analytically. Finding the parameter sets that result in a zero first-order derivative and that satisfy the second-order derivative conditions would reveal all local optima. Then comparing the values of the performance index of all the local optima, together with those of all boundary parameter sets, would lead to the global optimum, whose corresponding 'parameter' set will thus represent the best design. However, in practice, the optimization usually involves multiple objectives and the matters involving derivatives are a lot more complex. == Dealing with practical objectives == In practice, the objective value may be noisy or even non-numerical, and hence its gradient information may be unreliable or unavailable. This is particularly true when the problem is multi-objective. At present, many designs and refinements are mainly made through a manual trial-and-error process with the help of a CAD simulation package. Usually, such a posteriori learning or adjustments need to be repeated many times until a ‘satisfactory’ or ‘optimal’ design emerges. == Exhaustive search == In theory, this adjustment process can be automated by computerised search, such as exhaustive search. As this is an exponential algorithm, it may not deliver solutions in practice within a limited period of time. == Search in polynomial time == One approach to virtual engineering and automated design is evolutionary computation such as evolutionary algorithms. === Evolutionary algorithms === To reduce the search time, the biologically-inspired evolutionary algorithm (EA) can be used instead, which is a (non-deterministic) polynomial algorithm. The EA based multi-objective "search team" can be interfaced with an existing CAD simulation package in a batch mode. The EA encodes the design parameters (encoding being necessary if some parameters are non-numerical) to refine multiple candidates through parallel and interactive search. In the search process, 'selection' is performed using 'survival of the fittest' a posteriori learning. To obtain the next 'generation' of possible solutions, some parameter values are exchanged between two candidates (by an operation called 'crossover') and new values introduced (by an operation called 'mutation'). This way, the evolutionary technique makes use of past trial information in a similarly intelligent manner to the human designer. The EA based optimal designs can start from the designer's existing design database, or from an initial generation of candidate designs obtained randomly. A number of finely evolved top-performing candidates will represent several automatically optimized digital prototypes. There are websites that demonstrate interactive evolutionary algorithms for design. allows you to evolve 3D objects online and have them 3D printed. allows you to do the same for 2D images.