List of artificial intelligence projects

Specialized_projects Blue Brain Project, an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level.  Google Brain, a deep learning project part of Google X attempting to have intelligence similar or equal to human-level.  Human Brain Project, ten-year scientific research project, based on exascale supercomputers.4CAPS, developed at Carnegie Mellon University under Marcel A.  Just ACT-R, developed at Carnegie Mellon University under John R.  Anderson.  AIXI, Universal Artificial Intelligence developed by Marcus Hutter at IDSIA and ANU.  CALO, a DARPA-funded, 25-institution effort to integrate many artificial intelligence approaches (natural language processing, speech recognition, machine vision, probabilistic logic, planning, reasoning, many forms of machine learning) into an AI assistant that learns to help manage your office environment.  CHREST, developed under Fernand Gobet at Brunel University and Peter C.  Lane at the University of Hertfordshire.  CLARION, developed under Ron Sun at Rensselaer Polytechnic Institute and University of Missouri.  CoJACK, an ACT-R inspired extension to the JACK multi-agent system that adds a cognitive architecture to the agents for eliciting more realistic (human-like) behaviors in virtual environments.  Copycat, by Douglas Hofstadter and Melanie Mitchell at the Indiana University.  DUAL, developed at the New Bulgarian University under Boicho Kokinov.  FORR developed by Susan L.  Epstein at The City University of New York.  IDA and LIDA, implementing Global Workspace Theory, developed under Stan Franklin at the University of Memphis.  OpenCog Prime, developed using the OpenCog Framework.  Procedural Reasoning System (PRS), developed by Michael Georgeff and Amy L.  Lansky at SRI International.  Psi-Theory developed under Dietrich D��rner at the Otto-Friedrich University in Bamberg, Germany.  Soar, developed under Allen Newell and John Laird at Carnegie Mellon University and the University of Michigan.  Society of Mind and its successor The Emotion Machine proposed by Marvin Minsky.  Subsumption architectures, developed e. g.  by Rodney Brooks (though it could be argued whether they are cognitive). AlphaGo, software developed by Google that plays the Chinese board game Go.  Chinook, a computer program that plays English draughts; the first to win the world champion title in the competition against humans.  Deep Blue, a chess-playing computer developed by IBM which beat Garry Kasparov in 1997.  Halite, an artificial intelligence programming competition created by Two Sigma in 2016.  Libratus, a poker AI that beat world-class poker players in 2017, intended to be generalisable to other applications.  The Matchbox Educable Noughts and Crosses Engine (sometimes called the Machine Educable Noughts and Crosses Engine or MENACE) was a mechanical computer made from 304 matchboxes designed and built by artificial intelligence researcher Donald Michie in 1961.  Quick, Draw! , an online game developed by Google that challenges players to draw a picture of an object or idea and then uses a neural network to guess what the drawing is.  The Samuel Checkers-playing Program (1959) was among the world's first successful self-learning programs, and as such a very early demonstration of the fundamental concept of artificial intelligence (AI).  Stockfish AI, an open source chess engine currently ranked the highest in many computer chess rankings.


Specialized_projects TD-Gammon, a program that learned to play world-class backgammon partly by playing against itself (temporal difference learning with neural networks). Serenata de Amor, project for the analysis of public expenditures and detect discrepancies. Braina, an intelligent personal assistant application with a voice interface for Windows OS.  Cyc, an attempt to assemble an ontology and database of everyday knowledge, enabling human-like reasoning.  Eurisko, a language by Douglas Lenat for solving problems which consists of heuristics, including some for how to use and change its heuristics.  Google Now, an intelligent personal assistant with a voice interface in Google's Android and Apple Inc. 's iOS, as well as Google Chrome web browser on personal computers.  Holmes a new AI created by Wipro.  Microsoft Cortana, an intelligent personal assistant with a voice interface in Microsoft's various Windows 10 editions.  Mycin, an early medical expert system.  Open Mind Common Sense, a project based at the MIT Media Lab to build a large common sense knowledge base from online contributions.  Siri, an intelligent personal assistant and knowledge navigator with a voice-interface in Apple Inc. 's iOS and macOS.  SNePS, simultaneously a logic-based, frame-based, and network-based knowledge representation, reasoning, and acting system.  Viv (software), a new AI by the creators of Siri.  Wolfram Alpha, an online service that answers queries by computing the answer from structured data. AIBO, the robot pet for the home, grew out of Sony's Computer Science Laboratory (CSL).  Cog, a robot developed by MIT to study theories of cognitive science and artificial intelligence, now discontinued. Melomics, a bioinspired technology for music composition and synthesization of music, where computers develop their own style, rather than mimic musicians. AIML, an XML dialect for creating natural language software agents.  Apache Lucene, a high-performance, full-featured text search engine library written entirely in Java.  Apache OpenNLP, a machine learning based toolkit for the processing of natural language text.  It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking and parsing.  Artificial Linguistic Internet Computer Entity (A. L. I. C. E. ), an award-winning natural language processing chatterbot.  ChatGPT, a chatbot built on top of OpenAI's GPT-3.5 and GPT-4 (paid) family of large language models.  Cleverbot, successor to Jabberwacky, now with 170m lines of conversation, Deep Context, fuzziness and parallel processing.  Cleverbot learns from around 2 million user interactions per month.  ELIZA, a famous 1966 computer program by Joseph Weizenbaum, which parodied person-centered therapy.  FreeHAL, a self-learning conversation simulator (chatterbot) which uses semantic nets to organize its knowledge to imitate a very close human behavior within conversations.  GPT-3, a 2020 language model developed by OpenAI that can produce text difficult to distinguish from that written by a human.  Jabberwacky, a chatbot by Rollo Carpenter, aiming to simulate natural human chat.  LaMDA, a family of conversational neural language models developed by Google.  Mycroft, a free and open-source intelligent personal assistant that uses a natural language user interface.


Specialized_projects PARRY, another early chatterbot, written in 1972 by Kenneth Colby, attempting to simulate a paranoid schizophrenic.  SHRDLU, an early natural language processing computer program developed by Terry Winograd at MIT from 1968 to 1970.  SYSTRAN, a machine translation technology by the company of the same name, used by Yahoo! , AltaVista and Google, among others. CMU Sphinx, a group of speech recognition systems developed at Carnegie Mellon University DeepSpeech, an open-source Speech-To-Text engine based on Baidu's deep speech research paper.  Whisper, an open-source speech recognition system developed at OpenAI15. ai, a real-time artificial intelligence text-to-speech tool developed by an anonymous researcher from MIT Amazon Polly, a speech synthesis software by Amazon Festival Speech Synthesis System, a general multi-lingual speech synthesis system developed at the Centre for Speech Technology Research (CSTR) at the University of Edinburgh WaveNet, a deep neural network for generating raw audio1 the Road, the first novel marketed by an AI.  Synthetic Environment for Analysis and Simulations (SEAS), a model of the real world used by Homeland security and the United States Department of Defense that uses simulation and AI to predict and evaluate future events and courses of action. ��==Multipurpose projects== Apache Mahout, a library of scalable machine learning algorithms.  Deeplearning4j, an open-source, distributed deep learning framework written for the JVM.  Keas, a high level open-source software library for machine learning (works on top of other libraries).  Microsoft Cognitive Toolkit (previously known as CNTK), an open source toolkit for building artificial neural networks.  Open NN, a comprehensive C++ library implementing neural networks.  Python, an open-source Tensor and Dynamic neural network in Python.  TensorFlow, an open-source software library for machine learning.  Theano, a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones. Neural Designer, a commercial deep learning tool for predictive analytics.  Neuroph, a Java neural network framework.  OpenCog, a GPL-licensed framework for artificial intelligence written in C++, Python and Scheme.  PolyAnalyst: A commercial tool for data mining, text mining, and knowledge management.  RapidMiner, an environment for machine learning and data mining, now developed commercially.  Weka, a free implementation of many machine learning algorithms in Java. Data Applied, a web based data mining environment.  Watson, a pilot service by IBM to uncover and share data-driven insights, and to spur cognitive applications.


GitHub Copilot

GitHub Copilot GitHub Copilot (not to be confused with, "Copilot X", the name of GitHub's "vision" for next-gen Copilot features) is a cloud-based artificial intelligence tool developed by GitHub and OpenAI to assist users of Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments (IDEs) by autocompleting code. Currently available by subscription to individual developers and to businesses, the tool was first announced by GitHub on 29 June 2021, and works best for users coding in Python, JavaScript, TypeScript, Ruby, and Go.


GitHub Copilot

Implementation GitHub Copilot is powered by the OpenAI Codex, which is a modified, production version of the Generative Pre-trained Transformer 3 (GPT-3), a language model using deep-learning to produce human-like text. The Codex model is additionally trained on gigabytes of source code in a dozen programming languages. Copilot���s OpenAI Codex is trained on a selection of the English language, public GitHub repositories, and other publicly available source code. This includes a filtered dataset of 159 gigabytes of Python code sourced from 54 million public GitHub repositories. Open AI���s GPT-3 is licensed exclusively to Microsoft, GitHub���s parent company.


GitHub Copilot

See_also Intelligent code completion ChatGPT Generative AI


Artificial intelligence in healthcare

History Research in the 1960s and 1970s produced the first problem-solving program, or expert system, known as Dendral. While it was designed for applications in organic chemistry, it provided the basis for a subsequent system MYCIN, considered one of the most significant early uses of artificial intelligence in medicine. MYCIN and other systems such as INTERNIST-1 and CASNET did not achieve routine use by practitioners, however. The 1980s and 1990s brought the proliferation of the microcomputer and new levels of network connectivity. During this time, there was a recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data and build on the expertise of physicians. Approaches involving fuzzy set theory, Bayesian networks, and artificial neural networks, have been applied to intelligent computing systems in healthcare. Medical and technological advancements occurring over this half-century period that have enabled the growth of healthcare-related applications of AI to include: Improvements in computing power resulting in faster data collection and data processing Growth of genomic sequencing databases Widespread implementation of electronic health record systems Improvements in natural language processing and computer vision, enabling machines to replicate human perceptual processes Enhanced the precision of robot-assisted surgery Increased tree-based machine learning models that allow flexibility in establishing health predictors Improvements in deep learning techniques and data logs in rare diseasesAI algorithms can also be used to analyze large amounts of data through electronic health records for disease prevention and diagnosis. Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center, and the British National Health Service, have developed AI algorithms for their departments. Large technology companies such as IBM and Google, have also developed AI algorithms for healthcare. Additionally, hospitals are looking to AI software to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs. Currently, the United States government is investing billions of dollars to progress the development of AI in healthcare. Companies are developing technologies that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.


Machine translation

Approaches Before the advent of deep learning methods, statistical methods required a lot of rules accompanied by morphological, syntactic, and semantic annotations. The rule-based machine translation approach was used mostly in the creation of dictionaries and grammar programs. Its biggest downfall was that everything had to be made explicit: orthographical variation and erroneous input must be made part of the source language analyser in order to cope with it, and lexical selection rules must be written for all instances of ambiguity. Transfer-based machine translation was similar to interlingual machine translation in that it created a translation from an intermediate representation that simulated the meaning of the original sentence. Unlike interlingual MT, it depended partially on the language pair involved in the translation. Interlingual machine translation was one instance of rule-based machine-translation approaches. In this approach, the source language, i.e. the text to be translated, was transformed into an interlingual language, i.e. a "language neutral" representation that is independent of any language. The target language was then generated out of the interlingua. The only interlingual machine translation system that was made operational at the commercial level was the KANT system (Nyberg and Mitamura, 1992), which was designed to translate Caterpillar Technical English (CTE) into other languages. Machine translation used a method based on dictionary entries, which means that the words were translated as they are by a dictionary. Statistical machine translation tried to generate translations using statistical methods based on bilingual text corpora, such as the Canadian Hansard corpus, the English-French record of the Canadian parliament and EUROPARL, the record of the European Parliament. Where such corpora were available, good results were achieved translating similar texts, but such corpora were rare for many language pairs. The first statistical machine translation software was CANDIDE from IBM. In 2005, Google improved its internal translation capabilities by using approximately 200 billion words from United Nations materials to train their system; translation accuracy improved. SMT's biggest downfall included it being dependent upon huge amounts of parallel texts, its problems with morphology-rich languages (especially with translating into such languages), and its inability to correct singleton errors. A deep learning-based approach to MT, neural machine translation has made rapid progress in recent years. However, current consensus is that the so-called human parity achieved is not real, being based wholly on limited domains, language pairs, and certain test benchmarks i.e., it lacks statistical significance power. Translations by neural MT tools like DeepL Translator, which is thought to usually deliver the best machine translation results as of 2022, typically still need post-editing by a human. Prompt engineering is required in order to steer the GPT-3-generated translations.


Marketing and artificial intelligence

Artificial_intelligence_and_automation_efficiency In terms of marketing, automation uses software to computerize marketing processes that would have otherwise been performed manually. It assists in effectively allowing processes such as customer segmentation, campaign management, and product promotion, to be undertaken at a more efficient rate. Marketing automation is a key component of Customer Relationship Management (CRM). Companies are using systems that employ data-mining algorithms that analyze the customer database, giving further insight into the customer. This information may refer to socio-economic characteristics, earlier interactions with the customer, and information about the purchase history of the customer. Various systems have been designed to give organizations control over their data. Automation tools allow the system to monitor the performance of campaigns, making regular adjustments to the campaigns to improve response rates and to provide campaign performance tracking. Distribution of products requires companies to access accurate data so they are able to respond to fluctuating trends in product demand. Automation processes are able to provide a comprehensive system that improves real-time monitoring and intelligent control. Amazon acquired Kiva Systems, the makers of the warehouse robot for $775 million in 2012. Prior to the purchase of the automated system, human employees would have to walk the enormous warehouse, tracking and retrieving books. The Kiva robots are able to undertake order fulfillment, product replenishment, as well as heavy lifting, thus increasing efficiency for the company.


Machine learning

Machine learning Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines "discover" their "own" algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches. Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine, where it is too costly to develop algorithms to perform the needed tasks. The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods. Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning. ML is known in its application across business problems under the name predictive analytics. Although not all machine learning is statistically based, computational statistics is an important source of the field's methods.


Machine learning

Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.  Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.  In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.  Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.  Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature.  While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research themselves.  Machine learning approaches in particular can suffer from different data biases.  A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data.  When trained on human-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society.  Language models learned from data have been shown to contain human-like biases.  Machine learning systems used for criminal risk assessment have been found to be biased against black people.  In 2015, Google photos would often tag black people as gorillas, and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.  Similar issues with recognizing non-white people have been found in many other systems.  In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.  Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.  Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There's nothing artificial about AI. . . It's inspired by people, it's created by people, and���most importantly���it impacts people.  It is a powerful tool we are only just beginning to understand, and that is a profound responsibility. " Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI.  It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision.  By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively.  XAI may be an implementation of the social right to explanation.


Machine learning

Limitations Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting.  Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data but penalizing the theory in accordance with how complex the theory is.  Learners can also disappoint by "learning the wrong lesson".  A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.  A real-world example is that, unlike humans, current image classifiers often do not primarily make judgments from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects.  Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies.  Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations.  Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification. [citation needed] Machine learning models are often vulnerable to manipulation and/or evasion via adversarial machine learning.  Researchers have demonstrated how backdoors can be placed undetectably into classifying (e. g. , for categories "spam" and well-visible "not spam" of posts) machine learning models which are often developed and/or trained by third parties.  Parties can change the classification of any input, including in cases for which a type of data/software transparency is provided, possibly including white-box access.


Machine learning

Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units. By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI. OpenAI estimated the hardware computing used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months. A physical neural network or Neuromorphic computer is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse. Embedded Machine Learning is a sub-field of machine learning, where the machine learning model is run on embedded systems with limited computing resources such as wearable computers, edge devices and microcontrollers. Running machine learning model in embedded devices removes the need for transferring and storing data on cloud servers for further processing, henceforth, reducing data breaches and privacy leaks happening because of transferring data, and also minimizes theft of intellectual properties, personal data and business secrets. Embedded Machine Learning could be applied through several techniques including hardware acceleration, using approximate computing, optimization of machine learning models and many more.


EleutherAI

EleutherAI EleutherAI (/����lu������r/) is a grass-roots non-profit artificial intelligence (AI) research group. The group, considered an open-source version of OpenAI, was formed in a Discord server in July 2020 to organize a replication of GPT-3. In early 2023, it formally incorporated as the EleutherAI Foundation, a non-profit research institute.


EleutherAI

History EleutherAI began as a Discord server on July 7, 2020 under the tentative name "LibreAI" before rebranding to "EleutherAI" later that month, in reference to eleutheria, an ancient greek term for liberty. On December 30, 2020, EleutherAI released The Pile, a curated dataset of diverse text for training large language models. While the paper referenced the existence of the GPT-Neo models, the models themselves were not released until March 21, 2021. According to a retrospective written several months later, the authors did not anticipate that "people would care so much about our 'small models.'" On June 9, 2021, EleutherAI followed this up with GPT-J-6B, a six billion parameter language model that was again the largest open-source GPT-3-like model in the world. These language models were released under the Apache 2.0 free software license and are considered to have "fueled an entirely new wave of startups". While EleutherAI initially turned down funding offers, preferring to use Google's TPU Research Cloud Program to source their compute, by early 2021 they had accepted funding from CoreWeave (a small cloud computing company) and SpellML (a cloud infrastructure company) in the form of access to powerful GPU clusters that are necessary for large scale machine learning research. On Feb 10, 2022 they released GPT-NeoX-20B, a model similar to their prior work but scaled up thanks to the resources CoreWeave provided. In 2022, many EleutherAI members participated in the BigScience Research Workshop, working on projects including multitask finetuning, training BLOOM, and designing evaluation libraries. Engineers at EleutherAI, Stability AI, and NVIDIA joined forces with biologists lead by Columbia University and Harvard University to train OpenFold, an open-source replication of DeepMind's AlphaFold2. In early 2023, EleutherAI incorporated as a non-profit research institute run by Stella Biderman, Curtis Huebner, and Shivanshu Purohit. This announcement came with the statement that EleutherAI's shift of focus away from training larger language models was part of a deliberate push towards doing work in interpretability, alignment, and scientific research. While EleutherAI is still committed to promoting access to AI technologies, they feel that "there is substantially more interest in training and releasing LLMs than there once was," enabling them to focus on other projects.


EleutherAI

Research According to their website, EleutherAI is a "decentralized grassroots collective of volunteer researchers, engineers, and developers focused on AI alignment, scaling, and open-source AI research". While they do not sell any of their technologies as products, they publish the results of their research in academic venues, write blog posts detailing their ideas and methodologies, and provide trained models for anyone to use for free.[citation needed] The Pile is an 886 GB dataset designed for training large language models. It was originally developed to train EleutherAI's GPT-Neo models but has become widely used to train other models, including Microsoft's Megatron-Turing Natural Language Generation, Meta AI's Open Pre-trained Transformers, LLaMA, and Galactica, Stanford University's BioMedLM 2.7B, the Beijing Academy of Artificial Intelligence's Chinese-Transformer-XL, and Yandex's YaLM 100B. Compared to other datasets, the Pile's main distinguishing features are that it is a curated selection of data chosen by researchers at EleutherAI to contain information they thought language models should learn and that it is the only such dataset that is thoroughly documented by the researchers who developed it. EleutherAI's most prominent research relates to its work to train open-source large language models inspired by OpenAI's GPT-3. EleutherAI's "GPT-Neo" model series has released 125 million, 1.3 billion, 2.7 billion, 6 billion, and 20 billion parameter models. GPT-Neo (125M, 1.3B, 2.7B): released in March 2021, it was the largest open-source GPT-3-style language model in the world at the time of release.GPT-J (6B): released in March 2021, it was the largest open-source GPT-3-style language model in the world at the time of release.GPT-NeoX (20B): released in February 2022, it was the largest open-source language model in the world at the time of release.Pythia (13B): While prior models focused on scaling larger to close the gap with closed-sourced models like GPT-3, the Pythia model suite goes in another direction. The Pythia suite was designed to facilitate scientific research on the capabilities of and learning processes in large language models. Featuring 154 partially trained model checkpoints, fully public training data, and the ability to reproduce the exact training order, Pythia enables research on verifiable training, social biases, memorization, and more.While the overwhelming majority of large language models are[when?] trained in either English or Chinese,[citation needed] EleutherAI also trains language models in other languages, such as the Korean-language Polyglot-Ko. Following the release of DALL-E by OpenAI in January 2021, EleutherAI started working on text-to-image synthesis models. When OpenAI didn't release DALL-E publicly, EleutherAI's Katherine Crowson and digital artist Ryan Murdock developed a technique for using CLIP (another model developed by OpenAI) to convert regular image generation models into text-to-image synthesis ones. Building on ideas dating back to Google's DeepDream, they found their first major success combining CLIP with another publicly available model called VQGAN and the resulting model is called VQGAN-CLIP. Crowson released the technology by tweeting notebooks demonstrating the technique that people could run for free without any special equipment. This work was credited by Stability AI CEO Emad Mostaque as motivating the founding of Stability AI.


EleutherAI

Public_reception EleutherAI's work to democratize GPT-3 won the UNESCO Netexplo Global Innovation Award in 2021, InfoWorld's Best of Open Source Software Award in 2021 and 2022, was nominated for VentureBeat's AI Innovation Award in 2021. Gary Marcus, a cognitive scientist and noted critic of deep learning companies such as OpenAI and DeepMind, has repeatedly praised EleutherAI's dedication to open-source and transparent research. Maximilian Gahntz, a senior policy researcher at the Mozilla Foundation, applauded EleutherAI's efforts to give more researchers the ability to audit and assess AI technology. "If models are open and if data sets are open, that'll enable much more of the critical research that's pointed out many of the flaws and harms associated with generative AI and that's often far too difficult to conduct." Technology journalist Kyle Wiggers has raised concerns about whether EleutherAI is as independent as it claims, or "whether the involvement of commercially motivated ventures like Stability AI and Hugging Face ��� both of which are backed by substantial venture capital ��� might influence EleutherAI's research."


Google Brain

Projects In October 2016, Google Brain designed an experiment to determine that neural networks are capable of learning secure symmetric encryption.  In this experiment, three neural networks were created: Alice, Bob and Eve.  Adhering to the idea of a generative adversarial network (GAN), the goal of the experiment was for Alice to send an encrypted message to Bob that Bob could decrypt, but the adversary, Eve, could not.  Alice and Bob maintained an advantage over Eve, in that they shared a key used for encryption and decryption.  In doing so, Google Brain demonstrated the capability of neural networks to learn secure encryption.  In February 2017, Google Brain determined a probabilistic method for converting pictures with 8x8 resolution to a resolution of 32x32.  The method built upon an already existing probabilistic model called pixelCNN to generate pixel translations.  The proposed software utilizes two neural networks to make approximations for the pixel makeup of translated images.  The first network, known as the "conditioning network," downsizes high-resolution images to 8x8 and attempts to create mappings from the original 8x8 image to these higher-resolution ones.  The other network, known as the "prior network," uses the mappings from the previous network to add more detail to the original image.  The resulting translated image is not the same image in higher resolution, but rather a 32x32 resolution estimation based on other existing high-resolution images.  Google Brain's results indicate the possibility for neural networks to enhance images.  The Google Brain team contributed to the Google Translate project by employing a new deep learning system that combines artificial neural networks with vast databases of multilingual texts.  In September 2016, Google Neural Machine Translation (GNMT) was launched, an end-to-end learning framework, able to learn from a large number of examples.  Previously, Google Translate's Phrase-Based Machine Translation (PBMT) approach would statistically analyze word by word and try to match corresponding words in other languages without considering the surrounding phrases in the sentence.  But rather than choosing a replacement for each individual word in the desired language, GNMT evaluates word segments in the context of the rest of the sentence to choose more accurate replacements.  Compared to older PBMT models, the GNMT model scored a 24% improvement in similarity to human translation, with a 60% reduction in errors.  The GNMT has also shown significant improvement for notoriously difficult translations, like Chinese to English.  While the introduction of the GNMT has increased the quality of Google Translate's translations for the pilot languages, it was very difficult to create such improvements for all of its 103 languages.  Addressing this problem, the Google Brain Team was able to develop a Multilingual GNMT system, which extended the previous one by enabling translations between multiple languages.  Furthermore, it allows for Zero-Shot Translations, which are translations between two languages that the system has never explicitly seen before.  Google announced that Google Translate can now also translate without transcribing, using neural networks.


Google Brain

Projects This means that it is possible to translate speech in one language directly into text in another language, without first transcribing it to text.  According to the Researchers at Google Brain, this intermediate step can be avoided using neural networks.  In order for the system to learn this, they exposed it to many hours of Spanish audio together with the corresponding English text.  The different layers of neural networks, replicating the human brain, were able to link the corresponding parts and subsequently manipulate the audio waveform until it was transformed to English text.  Another drawback of the GNMT model is that it causes the time of translation to increase exponentially with the number of words in the sentence.  This caused the Google Brain Team to add 2000 more processors to ensure the new translation process would still be fast and reliable.  Aiming to improve traditional robotics control algorithms where new skills of a robot need to be hand-programmed, robotics researchers at Google Brain are developing machine learning techniques to allow robots to learn new skills on their own.  They also attempt to develop ways for information sharing between robots so that robots can learn from each other during their learning process, also known as cloud robotics.  As a result, Google has launched the Google Cloud Robotics Platform for developers in 2019, an effort to combine robotics, AI, and the cloud to enable efficient robotic automation through cloud-connected collaborative robots.  Robotics research at Google Brain has focused mostly on improving and applying deep learning algorithms to enable robots to complete tasks by learning from experience, simulation, human demonstrations, and/or visual representations.  For example, Google Brain researchers showed that robots can learn to pick and throw rigid objects into selected boxes by experimenting in an environment without being pre-programmed to do so.  In another research, researchers trained robots to learn behaviors such as pouring liquid from a cup; robots learned from videos of human demonstrations recorded from multiple viewpoints.  Google Brain researchers have collaborated with other companies and academic institutions on robotics research.  In 2016, the Google Brain Team collaborated with researchers at X in a research on learning hand-eye coordination for robotic grasping.  Their method allowed real-time robot control for grasping novel objects with self-correction.  In 2020, researchers from Google Brain, Intel AI Lab, and UC Berkeley created an AI model for robots to learn surgery-related tasks such as suturing from training with surgery videos.  In 2020, Google Brain Team and University of Lille presented a model for automatic speaker recognition which they called Interactive Speaker Recognition.  The ISR module recognizes a speaker from a given list of speakers only by requesting a few user specific words.  The model can be altered to choose speech segments in the context of Text-To-Speech Training.  It can also prevent malicious voice generators from accessing the data.  TensorFlow is an open source software library powered by Google Brain that allows anyone to utilize machine learning by providing the tools to train one's own neural network.


Google Brain

Projects The tool has been used by farmers to reduce the amount of manual labor required to sort their yield, by training it with a data set of human-sorted images.  Magenta is a project that uses Google Brain to create new information in the form of art and music rather than classify and sort existing data.  TensorFlow was updated with a suite of tools for users to guide the neural network to create images and music.  However, the team from Valdosta State University found that the AI struggles to perfectly replicate human intention in artistry, similar to the issues faced in translation.  The image sorting capabilities of Google Brain have been used to help detect certain medical conditions by seeking out patterns that human doctors may not notice to provide an earlier diagnosis.  During screening for breast cancer, this method was found to have one quarter the false positive rate of human pathologists, who require more time to look over each photo and cannot spend their entire focus on this one task.  Due to the neural network's very specific training for a single task, it cannot identify other afflictions present in a photo that a human could easily spot.  Google Brain announced in 2022 that it created two different types of text-to-image models called Imagen and Parti that compete with OpenAI's DALL-E.  Later in 2022, the project was extended to text-to-video.  The Google Brain projects' technology is currently used in various other Google products such as the Android Operating System's speech recognition system, photo search for Google Photos, smart reply in Gmail, and video recommendations in YouTube.


Google Brain

Reception Google Brain has received coverage in Wired, NPR, and Big Think. These articles have contained interviews with key team members Ray Kurzweil and Andrew Ng, and focus on explanations of the project's goals and applications. In December 2020, AI ethicist Timnit Gebru left Google. While the exact nature of her quitting or being fired is disputed, the cause of the departure was her refusal to retract a paper entitled "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" This paper explored potential risks of the growth of AI such as Google Brain, including environmental impact, biases in training data, and the ability to deceive the public. The request to retract the paper was made by Megan Kacholia, vice president of Google Brain. As of April 2021, nearly 7000 current or former Google employees and industry supporters have signed an open letter accusing Google of "research censorship" and condemning Gebru's treatment at the company. In February 2021, Google fired one of the leaders of the company's AI ethics team, Margaret Mitchell. The company's statement alleged that Mitchell had broken company policy by using automated tools to find support for Gebru. In the same month, engineers outside the ethics team began to quit, citing the termination of Gebru as their reason for leaving. In April 2021, Google Brain co-founder Samy Bengio announced his resignation from the company. Despite being Gebru's manager, Bengio was not notified before her termination, and he posted online in support of both her and Mitchell. While Bengio's announcement focused on personal growth as his reason for leaving, anonymous sources indicated to Reuters that the turmoil within the AI ethics team played a role in his considerations. In March 2022, Google fired AI researcher Satrajit Chatterjee after he questioned the findings of a paper published in Nature, by Google's AI team members, Anna Goldie and Azalia Mirhoseini, about their findings on the ability of computers to design computer chip components.


Google DeepMind

History The start-up was founded by Demis Hassabis, Shane Legg and Mustafa Suleyman in September 2010. Hassabis and Legg first met at the Gatsby Computational Neuroscience Unit at University College London (UCL). Demis Hassabis has said that the start-up began working on artificial intelligence technology by teaching it how to play old games from the seventies and eighties, which are relatively primitive compared to the ones that are available today. Some of those games included Breakout, Pong and Space Invaders. AI was introduced to one game at a time, without any prior knowledge of its rules. After spending some time on learning the game, AI would eventually become an expert in it. ���The cognitive processes which the AI goes through are said to be very like those of a human who had never seen the game would use to understand and attempt to master it.��� The goal of the founders is to create a general-purpose AI that can be useful and effective for almost anything. Major venture capital firms Horizons Ventures and Founders Fund invested in the company, as well as entrepreneurs Scott Banister, Peter Thiel, and Elon Musk. Jaan Tallinn was an early investor and an adviser to the company. On January 26, 2014, Google confirmed its acquisition of DeepMind for a price reportedly ranging between $400 million and $650 million. and that it had agreed to take over DeepMind Technologies. The sale to Google took place after Facebook reportedly ended negotiations with DeepMind Technologies in 2013. The company was afterwards renamed Google DeepMind and kept that name for about two years. In 2014, DeepMind received the "Company of the Year" award from Cambridge Computer Laboratory. In September 2015, DeepMind and the Royal Free NHS Trust signed their initial Information Sharing Agreement (ISA) to co-develop a clinical task management app, Streams. After Google's acquisition the company established an artificial intelligence ethics board. The ethics board for AI research remains a mystery, with both Google and DeepMind declining to reveal who sits on the board. DeepMind has opened a new unit called DeepMind Ethics and Society and focused on the ethical and societal questions raised by artificial intelligence featuring prominent philosopher Nick Bostrom as advisor. In October 2017, DeepMind launched a new research team to investigate AI ethics. In December 2019, co-founder Suleyman announced he would be leaving DeepMind to join Google, working in a policy role. In April 2023, DeepMind merged with Google AI's Google Brain division to form Google DeepMind, as part of the company's continued efforts to accelerate work on AI in response to OpenAI's ChatGPT. This marked the end of a years-long struggle from DeepMind executives to secure greater autonomy from Google.


Google DeepMind

Products_and_technologies According to the company's website, DeepMind Technologies' goal is to combine "the best techniques from machine learning and systems neuroscience to build powerful general-purpose learning algorithms".  Google Research released a paper in 2016 regarding AI safety and avoiding undesirable behaviour during the AI learning process.  Deepmind has also released several publications via its website.  In 2017 DeepMind released GridWorld, an open-source testbed for evaluating whether an algorithm learns to disable its kill switch or otherwise exhibits certain undesirable behaviours.  In July 2018, researchers from DeepMind trained one of its systems to play the computer game Quake III Arena.  As of 2020, DeepMind has published over a thousand papers, including thirteen papers that were accepted by Nature or Science. [citation needed] DeepMind received media attention during the AlphaGo period; according to a LexisNexis search, 1842 published news stories mentioned DeepMind in 2016, declining to 1363 in 2019.  As opposed to other AIs, such as IBM's Deep Blue or Watson, which were developed for a pre-defined purpose and only function within that scope, DeepMind claims that its system is not pre-programmed: it learns from experience, using only raw pixels as data input.  Technically it uses deep learning on a convolutional neural network, with a novel form of Q-learning, a form of model-free reinforcement learning.  They test the system on video games, notably early arcade games, such as Space Invaders or Breakout.  Without altering the code, the AI begins to understand how to play the game, and after some time plays, for a few games (most notably Breakout), a more efficient game than any human ever could.  In 2013, DeepMind published research on an AI system that could surpass human abilities in games such as Pong, Breakout and Enduro, while surpassing state of the art performance on Seaquest, Beamrider, and Q*bert.  This work reportedly led to the company's acquisition by Google.  DeepMind's AI had been applied to video games made in the 1970s and 1980s; work was ongoing for more complex 3D games such as Quake, which first appeared in the 1990s.  In 2020, DeepMind published Agent57, an AI Agent which surpasses human level performance on all 57 games of the Atari2600 suite.  In 2014, the company published research on computer systems that are able to play Go.  In October 2015, a computer Go program called AlphaGo, developed by DeepMind, beat the European Go champion Fan Hui, a 2 dan (out of 9 dan possible) professional, five to zero.  This was the first time an artificial intelligence (AI) defeated a professional Go player.  Previously, computers were only known to have played Go at "amateur" level.  Go is considered much more difficult for computers to win compared to other games like chess, due to the much larger number of possibilities, making it prohibitively difficult for traditional AI methods such as brute-force.  In March 2016 it beat Lee Sedol���a 9th dan Go player and one of the highest ranked players in the world���with a score of 4���1 in a five-game match.


Google DeepMind

Products_and_technologies In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie, who at the time continuously held the world No.  1 ranking for two years.  It used a supervised learning protocol, studying large numbers of games played by humans against each other.  In 2017, an improved version, AlphaGo Zero, defeated AlphaGo 100 games to 0.  AlphaGo Zero's strategies were self-taught.  AlphaGo Zero was able to beat its predecessor after just three days with less processing power than AlphaGo; in comparison, the original AlphaGo needed months to learn how to play.  Later that year, AlphaZero, a modified version of AlphaGo Zero but for handling any two-player game of perfect information, gained superhuman abilities at chess and shogi.  Like AlphaGo Zero, AlphaZero learned solely through self-play.  DeepMind researchers published a new model named MuZero that mastered the domains of Go, chess, shogi, and Atari 2600 games without human data, domain knowledge, or known rules.  Researchers applied MuZero to solve the real world challenge of video compression with a set number of bits with respect to Internet traffic on sites such as YouTube, Twitch, and Google Meet.  The goal of MuZero is to optimally compress the video so the quality of the video is maintained with a reduction in data.  The final result using MuZero was a 6.28% average reduction in bitrate.  In October 2022, DeepMind unveiled a new version of AlphaZero, called AlphaTensor, in a paper published in Nature.  The version discovered a faster way to perform matrix multiplication ��� one of the most fundamental tasks in computing ��� using reinforcement learning.  For example, AlphaTensor figured out how to multiply two mod-2 4x4 matrices in only 47 multiplications, unexpectedly beating the 1969 Strassen algorithm record of 49 multiplications.  AlphaGo technology was developed based on the deep reinforcement learning approach.  This makes AlphaGo different from the rest of AI technologies on the market.  With that said, AlphaGo's ���brain��� was introduced to various moves based on historical tournament data.  The number of moves was increased gradually until it eventually processed over 30 million of them.  The aim was to have the system mimic the human player and eventually become better.  It played against itself and learned not only from its own defeats but wins as well; thus, it learned to improve itself over the time and increased its winning rate as a result. [citation needed] AlphaGo used two deep neural networks: a policy network to evaluate move probabilities and a value network to assess positions.  The policy network trained via supervised learning, and was subsequently refined by policy-gradient reinforcement learning.  The value network learned to predict winners of games played by the policy network against itself.  After training, these networks employed a lookahead Monte Carlo tree search (MCTS), using the policy network to identify candidate high-probability moves, while the value network (in conjunction with Monte Carlo rollouts using a fast rollout policy) evaluated tree positions.


Google DeepMind

Products_and_technologies AlphaGo Zero was trained using reinforcement learning in which the system played millions of games against itself.  Its only guide was to increase its win rate.  It did so without learning from games played by humans.  Its only input features are the black and white stones from the board.  It uses a single neural network, rather than separate policy and value networks.  Its simplified tree search relies upon this neural network to evaluate positions and sample moves.  A new reinforcement learning algorithm incorporates lookahead search inside the training loop.  AlphaGo Zero employed around 15 people and millions in computing resources.  Ultimately, it needed much less computing power than AlphaGo, running on four specialized AI processors (Google TPUs), instead of AlphaGo's 48.  In 2016, DeepMind turned its artificial intelligence to protein folding, a long-standing problem in molecular biology.  In December 2018, DeepMind's AlphaFold won the 13th Critical Assessment of Techniques for Protein Structure Prediction (CASP) by successfully predicting the most accurate structure for 25 out of 43 proteins.  ���This is a lighthouse project, our first major investment in terms of people and resources into a fundamental, very important, real-world scientific problem,��� Hassabis said to The Guardian.  In 2020, in the 14th CASP, AlphaFold's predictions achieved an accuracy score regarded as comparable with lab techniques.  Dr Andriy Kryshtafovych, one of the panel of scientific adjudicators, described the achievement as "truly remarkable", and said the problem of predicting how proteins fold had been "largely solved".  In July 2021, the open-source RoseTTAFold and AlphaFold2 were released to allow scientists to run their own versions of the tools.  A week later DeepMind announced that AlphaFold had completed its prediction of nearly all human proteins as well as the entire proteomes of 20 other widely studied organisms.  The structures were released on the AlphaFold Protein Structure Database.  In July 2022, it was announced that the predictions of over 200 million proteins, representing virtually all known proteins, would be released on the AlphaFold database.  In 2016, DeepMind introduced WaveNet, a text-to-speech system.  It was originally too computationally intensive for use in consumer products, but in late 2017 it became ready for use in consumer applications such as Google Assistant.  In 2018 Google launched a commercial text-to-speech product, Cloud Text-to-Speech, based on WaveNet.  In 2018, DeepMind introduced a more efficient model called WaveRNN co-developed with Google AI.  In 2020 WaveNetEQ, a packet loss concealment method based on a WaveRNN architecture, was presented.  In 2019, Google started to roll WaveRNN with WavenetEQ out to Google Duo users.  In 2016, Hassabis discussed the game StarCraft as a future challenge, since it requires strategic thinking and handling imperfect information.  In January 2019, DeepMind introduced AlphaStar, a program playing the real-time strategy game StarCraft II.  AlphaStar used reinforcement learning based on replays from human players, and then played against itself to enhance its skills.  At the time of the presentation, AlphaStar had knowledge equivalent to 200 years of playing time.


Google DeepMind

Products_and_technologies It won 10 consecutive matches against two professional players, although it had the unfair advantage of being able to see the entire field, unlike a human player who has to move the camera manually.  A preliminary version in which that advantage was fixed lost a subsequent match.  In July 2019, AlphaStar began playing against random humans on the public 1v1 European multiplayer ladder.  Unlike the first iteration of AlphaStar, which played only Protoss v.  Protoss, this one played as all of the game's races, and had earlier unfair advantages fixed.  By October 2019, AlphaStar had reached Grandmaster level on the StarCraft II ladder on all three StarCraft races, becoming the first AI to reach the top league of a widely popular esport without any game restrictions.  In 2022, DeepMind unveiled AlphaCode, an AI-powered coding engine that creates computer programs at a rate comparable to that of an average programmer, with the company testing the system against coding challenges created by Codeforces utilized in human competitive programming competitions.  AlphaCode earned a rank equivalent to 54% of the median score on Codeforces after being trained on GitHub data and Codeforce problems and solutions.  The program was required to come up with a unique solution and stopped from duplicating answers.  Gato is a "generalist agent" that learns multiple tasks simultaneously.  Google has stated that DeepMind algorithms have greatly increased the efficiency of cooling its data centers.  In addition, DeepMind (alongside other Alphabet AI researchers) assists Google Play's personalized app recommendations.  DeepMind has also collaborated with the Android team at Google for the creation of two new features which were made available to people with devices running Android Pie, the ninth installment of Google's mobile operating system.  These features, Adaptive Battery and Adaptive Brightness, use machine learning to conserve energy and make devices running the operating system easier to use.  It is the first time DeepMind has used these techniques on such a small scale, with typical machine learning applications requiring orders of magnitude more computing power.  DeepMind researchers have applied machine learning models to the sport of football, often referred to as soccer in North America, modelling the behaviour of football players, including the goalkeeper, defenders, and strikers during different scenarios such as penalty kicks.  The researchers used heat maps and cluster analysis to organize players based on their tendency to behave a certain way during the game when confronted with a decision on how to score or prevent the other team from scoring.  The researchers mention that machine learning models could be used to democratize the football industry by automatically selecting interesting video clips of the game that serve as highlights.  This can be done by searching videos for certain events, which is possible because video analysis is an established field of machine learning.  This is also possible because of extensive sports analytics based on data including annotated passes or shots, sensors that capture data about the players movements many times over the course of a game, and game theory models.


Google DeepMind

Products_and_technologies Google has unveiled a new archaeology document program named Ithaca after the home island of mythical hero Odysseus. [citation needed] The deep neural network helps researchers restore the empty text of damaged documents, identify the place they originated from, and give them a definite accurate date. [citation needed] The work builds on another text analysis network named Pythia.  Ithaca achieves 62% accuracy in restoring damaged texts and 71% location accuracy, and has a dating precision of 30 years. [citation needed] The tool has already been used by historians and ancient Greek archaeologists to make new discoveries in ancient Greek history. [citation needed] The team is working on extending the model to other ancient languages, including Demotic, Akkadian, Hebrew, and Mayan.  Sparrow is an artificial intelligence-powered chatbot developed by DeepMind to build safer machine learning systems by using a mix of human feedback and Google search suggestions.  Chinchilla AI is a language model developed by DeepMind.


Google DeepMind

See_also Anthropic Cohere Glossary of artificial intelligence OpenAI


DeepMind


DALL-E

DALL-E DALL-E (stylized as DALL��E), DALL-E 2, and DALL-E 3 are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions, called "prompts". The original DALL-E was revealed by OpenAI in a blog post in January 5, 2021, and uses a version of GPT-3 modified to generate images. In April 6, 2022, OpenAI announced DALL-E 2, a successor designed to generate more realistic images at higher resolutions that "can combine concepts, attributes, and styles". In September 2023, OpenAI announced their latest image model, DALL-E 3, capable of understanding "significantly more nuance and detail" than previous iterations. On 20 July 2022, DALL-E 2 entered into a beta phase with invitations sent to 1 million waitlisted individuals; users could generate a certain number of images for free every month and may purchase more. Access had previously been restricted to pre-selected users for a research preview due to concerns about ethics and safety. On 28 September 2022, DALL-E 2 was opened to everyone and the waitlist requirement was removed. In early November 2022, OpenAI released DALL-E 2 as an API, allowing developers to integrate the model into their own applications. Microsoft unveiled their implementation of DALL-E 2 in their Designer app and Image Creator tool included in Bing and Microsoft Edge. The API operates on a cost per image basis, with prices varying depending on image resolution. Volume discounts are available to companies working with OpenAI���s enterprise team. DALL-E 3 was released natively into ChatGPT for ChatGPT Plus and ChatGPT Enterprise customers in October 2023, with availability via OpenAI's API and "Labs" platform expected later in the year. Microsoft implemented the model in Bing's Image Creator tool and plans to implement it into their Designer app. The software's name is a portmanteau of the names of animated robot Pixar character WALL-E and the Spanish surrealist artist Salvador Dal��.


DALL-E

Technology The first generative pre-trained transformer (GPT) model was initially developed by OpenAI in 2018, using a Transformer architecture. The first iteration, GPT-1, was scaled up to produce GPT-2 in 2019; in 2020 it was scaled up again to produce GPT-3, with 175 billion parameters. DALL-E's model is a multimodal implementation of GPT-3 with 12 billion parameters which "swaps text for pixels", trained on text-image pairs from the Internet. In detail, the input to the Transformer model is a sequence of tokenized image caption followed by tokenized image patches. The image caption is in English, tokenized by byte pair encoding (vocabulary size 16384), and can be up to 256 tokens long. Each image is a 256x256 RGB image, divided into 32x32 patches of 4x4 each. Each patch is then converted by a discrete VAE to a token (vocabulary size 8192). DALL-E was developed and announced to the public in conjunction with CLIP (Contrastive Language-Image Pre-training). CLIP is a separate model based on zero-shot learning that was trained on 400 million pairs of images with text captions scraped from the Internet. Its role is to "understand and rank" DALL-E's output by predicting which caption from a list of 32,768 captions randomly selected from the dataset (of which one was the correct answer) is most appropriate for an image. This model is used to filter a larger initial list of images generated by DALL-E to select the most appropriate outputs. DALL-E 2 uses 3.5 billion parameters, a smaller number than its predecessor. DALL-E 2 uses a diffusion model conditioned on CLIP image embeddings, which, during inference, are generated from CLIP text embeddings by a prior model.


DALL-E

Capabilities DALL-E can generate imagery in multiple styles, including photorealistic imagery, paintings, and emoji. It can "manipulate and rearrange" objects in its images, and can correctly place design elements in novel compositions without explicit instruction. Thom Dunn writing for BoingBoing remarked that "For example, when asked to draw a daikon radish blowing its nose, sipping a latte, or riding a unicycle, DALL-E often draws the handkerchief, hands, and feet in plausible locations." DALL-E showed the ability to "fill in the blanks" to infer appropriate details without specific prompts, such as adding Christmas imagery to prompts commonly associated with the celebration, and appropriately placed shadows to images that did not mention them. Furthermore, DALL-E exhibits a broad understanding of visual and design trends.[citation needed] DALL-E can produce images for a wide variety of arbitrary descriptions from various viewpoints with only rare failures. Mark Riedl, an associate professor at the Georgia Tech School of Interactive Computing, found that DALL-E could blend concepts (described as a key element of human creativity). Its visual reasoning ability is sufficient to solve Raven's Matrices (visual tests often administered to humans to measure intelligence). DALL-E 3 follows complex prompts with more accuracy and detail than its predecessors, and is able to generate more coherent and accurate text. DALL-E 3 integrates into ChatGPT. It is currently in research preview, and will be available to Plus and Enterprise customers in October 2023, and in Labs later in the year. Given an existing image, DALL-E 2 can produce "variations" of the image as individual outputs based on the original, as well as edit the image to modify or expand upon it. DALL-E 2's "inpainting" and "outpainting" use context from an image to fill in missing areas using a medium consistent with the original, following a given prompt. For example, this can be used to insert a new subject into an image, or expand an image beyond its original borders. According to OpenAI, "Outpainting takes into account the image���s existing visual elements ��� including shadows, reflections, and textures ��� to maintain the context of the original image." DALL-E 2's language understanding has limits. It is sometimes unable to distinguish "A yellow book and a red vase" from "A red book and a yellow vase" or "A panda making latte art" from "Latte art of a panda". It generates images of "an astronaut riding a horse" when presented with the prompt "a horse riding an astronaut". It also fails to generate the correct images in a variety of circumstances. Requesting more than three objects, negation, numbers, and connected sentences may result in mistakes, and object features may appear on the wrong object. Additional limitations include handling text - which, even with legible lettering, almost invariably results in dream-like gibberish - and its limited capacity to address scientific information, such as astronomy or medical imagery.


DALL-E

Ethical_concerns DALL-E 2's reliance on public datasets influences its results and leads to algorithmic bias in some cases, such as generating higher numbers of men than women for requests that do not mention gender. DALL-E 2's training data was filtered to remove violent and sexual imagery, but this was found to increase bias in some cases such as reducing the frequency of women being generated. OpenAI hypothesize that this may be because women were more likely to be sexualized in training data which caused the filter to influence results. In September 2022, OpenAI confirmed to The Verge that DALL-E invisibly inserts phrases into user prompts to address bias in results; for instance, "black man" and "Asian woman" are inserted into prompts that do not specify gender or race. A concern about DALL-E 2 and similar image generation models is that they could be used to propagate deepfakes and other forms of misinformation. As an attempt to mitigate this, the software rejects prompts involving public figures and uploads containing human faces. Prompts containing potentially objectionable content are blocked, and uploaded images are analyzed to detect offensive material. A disadvantage of prompt-based filtering is that it is easy to bypass using alternative phrases that result in a similar output. For example, the word "blood" is filtered, but "ketchup" and "red liquid" are not. Another concern about DALL-E 2 and similar models is that they could cause technological unemployment for artists, photographers, and graphic designers due to their accuracy and popularity.


DALL-E

Reception Most coverage of DALL-E focuses on a small subset of "surreal" or "quirky" outputs. DALL-E's output for "an illustration of a baby daikon radish in a tutu walking a dog" was mentioned in pieces from Input, NBC, Nature, and other publications. Its output for "an armchair in the shape of an avocado" was also widely covered. ExtremeTech stated "you can ask DALL-E for a picture of a phone or vacuum cleaner from a specified period of time, and it understands how those objects have changed". Engadget also noted its unusual capacity for "understanding how telephones and other objects change over time". According to MIT Technology Review, one of OpenAI's objectives was to "give language models a better grasp of the everyday concepts that humans use to make sense of things". Wall Street investors have had a positive reception of DALL-E 2, with some firms thinking it could represent a turning point for a future multi-trillion dollar industry. By mid-2019, OpenAI had already received over $1 billion in funding from Microsoft and Khosla Ventures, and in January 2023, following the launch of DALL-E 2 and ChatGPT, received an additional $10 billion in funding from Microsoft. Japan's anime community has had a negative reaction to DALL-E 2 and similar models. Two arguments are typically presented by artists against the software. The first is that AI art is not art because it is not created by a human with intent. "The juxtaposition of AI-generated images with their own work is degrading and undermines the time and skill that goes into their art. AI-driven image generation tools have been heavily criticized by artists because they are trained on human-made art scraped from the web." The second is the trouble with copyright law and data text-to-image models are trained on. OpenAI has not released information about what dataset(s) were used to train DALL-E 2, inciting concern from some that the work of artists has been used for training without permission. Copyright laws surrounding these topics are inconclusive at the moment. After integrating DALL-E 3 into Bing Chat and ChatGPT, Microsoft and OpenAI faced criticism for excessive content filtering. With critics saying DALL-E had been "lobotomized". With the blocking of specific prompts such as 'man breaks server rack with sledgehammer' cited as evidence. Over the first days of its launch, filtering was reportedly increased to the point where some of Bing's own suggested prompts had become blocked. TechRadar argued that leaning too heavily on the side of caution could limit DALL-E's value as a creative tool.


DALL-E

Open-source_implementations Since OpenAI has not released source code for any of the three models, there have been several attempts to create open-source implementations of DALL-E. Released in 2022 on Hugging Face's Spaces platform, Craiyon (formerly DALL-E Mini until a name change was requested by OpenAI in June 2022) is an AI model based on the original DALL-E that was trained on unfiltered data from the Internet. It attracted substantial media attention in mid-2022, after its release due to its capacity for producing humorous imagery.


DALL-E

See_also Artificial intelligence art DeepDream Imagen (Google Brain) Midjourney Stable Diffusion Prompt engineering


Midjourney

Midjourney Midjourney is a generative artificial intelligence program and service created and hosted by 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. The tool is currently in open beta, which it entered 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 create artwork with Midjourney using Discord bot commands.


Midjourney

Uses Founder David Holz says he sees artists as customers, not competitors of Midjourney.  Holz told The Register that artists use Midjourney for rapid prototyping of artistic concepts to show to clients before starting work themselves.  Some artists have accused Midjourney of devaluing original creative work by using it in the training set; Midjourney's terms of service includes a DMCA takedown policy, allowing artists to request their work to be removed from the set if they believe copyright infringement to be evident.  The advertising industry has been quick to embrace AI tools such as Midjourney, DALL-E, and Stable Diffusion, among others.  The tools, which enable advertisers to create original content and brainstorm ideas quickly are providing new opportunities such as "custom ads created for individuals, a new way to create special effects, or even making e-commerce advertising more efficient", according to Ad Age.  Architects have described using the software to generate mood boards for the early stages of projects, as an alternative to searching Google Images.  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 Th����tre d'Op��ra 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 create the images in an AI-generated children's book in the span of a weekend.  Titled Alice and Sparkle, the book features a young girl who builds a robot that becomes self-aware.  The creator, Ammaar Reeshi, spent hours tweaking Midjourney prompts, rejecting hundreds of generated results to ultimately choose 13 illustrations for the book.  Both the product and process drew criticism: "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," one artist wrote.


Midjourney

Uses 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.  Prior to May 2023, Midjourney implemented a moderation mechanism predicated on a "banned words" 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 religious and political figures such as "Allah" or "Xi Jinping".  This practice occasionally stirred controversy due to perceived instances of censorship within the Midjourney platform.  Commencing in May 2023, with subsequent updates post version 5, Midjourney transitioned to an "AI-powered" content moderation system.  This advanced mechanism allowed for a more nuanced interpretation of user prompts by analyzing them in their entirety.  It consequently facilitated the context-dependent use of words that had previously been prohibited.  For instance, users can now prompt the AI to generate a portrait of "Xi Jinping".  However, the system will prevent the generation of contentious images, such as depictions of global leaders, including Xi Jinping, in situations of arrest.


Midjourney

Litigation On January 13, 2023, three artists ��� Sarah Andersen, Kelly McKernan, and Karla Ortiz ��� filed a copyright infringement lawsuit against Stability AI, Midjourney, and DeviantArt, claiming that these companies have infringed the rights of millions of artists, by training AI tools on five billion images scraped from the web, without the consent of the original artists. The legal action was initiated in San Francisco by attorney Matthew Butterick in partnership with the Joseph Saveri Law Firm, the same team challenging Microsoft, GitHub, and OpenAI (developer of ChatGPT and DALL-E) in court. In July 2023, U.S. District Judge William Orrick inclined to dismiss most of the lawsuit filed by Andersen, McKernan, and Ortiz but allowed them to file a new complaint.


Stable Diffusion

Stable Diffusion Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. It is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text prompt. It was developed by researchers from the CompVis Group at Ludwig Maximilian University of Munich and Runway with a compute donation by Stability AI and training data from non-profit organizations. Stable Diffusion is a latent diffusion model, a kind of deep generative artificial neural network. Its code and model weights have been released publicly, and it can run on most consumer hardware equipped with a modest GPU with at least 8 GB VRAM. This marked a departure from previous proprietary text-to-image models such as DALL-E and Midjourney which were accessible only via cloud services.


Stable Diffusion

Development The development of Stable Diffusion was funded and shaped by the start-up company Stability AI. The technical license for the model was released by the CompVis group at Ludwig Maximilian University of Munich. Development was led by Patrick Esser of Runway and Robin Rombach of CompVis, who were among the researchers who had earlier invented the latent diffusion model architecture used by Stable Diffusion. Stability AI also credited EleutherAI and LAION (a German nonprofit which assembled the dataset on which Stable Diffusion was trained) as supporters of the project. In October 2022, Stability AI raised US$101 million in a round led by Lightspeed Venture Partners and Coatue Management.


Stable Diffusion

Technology Stable Diffusion uses a kind of diffusion model (DM), called a latent diffusion model (LDM) developed by the CompVis group at LMU Munich.  Introduced in 2015, diffusion models are trained with the objective of removing successive applications of Gaussian noise on training images, which can be thought of as a sequence of denoising autoencoders.  Stable Diffusion consists of 3 parts: the variational autoencoder (VAE), U-Net, and an optional text encoder.  The VAE encoder compresses the image from pixel space to a smaller dimensional latent space, capturing a more fundamental semantic meaning of the image.  Gaussian noise is iteratively applied to the compressed latent representation during forward diffusion.  The U-Net block, composed of a ResNet backbone, denoises the output from forward diffusion backwards to obtain a latent representation.  Finally, the VAE decoder generates the final image by converting the representation back into pixel space.  The denoising step can be flexibly conditioned on a string of text, an image, or another modality.  The encoded conditioning data is exposed to denoising U-Nets via a cross-attention mechanism.  For conditioning on text, the fixed, pretrained CLIP ViT-L/14 text encoder is used to transform text prompts to an embedding space.  Researchers point to increased computational efficiency for training and generation as an advantage of LDMs.  With 860 millions of parameters in the U-Net and 123 millions in the text encoder, Stable Diffusion is considered relatively lightweight by 2022 standards, and unlike other diffusion models, it can run on consumer GPUs.  Stable Diffusion was trained on pairs of images and captions taken from LAION-5B, a publicly available dataset derived from Common Crawl data scraped from the web, where 5 billion image-text pairs were classified based on language and filtered into separate datasets by resolution, a predicted likelihood of containing a watermark, and predicted "aesthetic" score (e. g.  subjective visual quality).  The dataset was created by LAION, a German non-profit which receives funding from Stability AI.  The Stable Diffusion model was trained on three subsets of LAION-5B: laion2B-en, laion-high-resolution, and laion-aesthetics v2 5+.  A third-party analysis of the model's training data identified that out of a smaller subset of 12 million images taken from the original wider dataset used, approximately 47% of the sample size of images came from 100 different domains, with Pinterest taking up 8.5% of the subset, followed by websites such as WordPress, Blogspot, Flickr, DeviantArt and Wikimedia Commons.  An investigation by Bayerischer Rundfunk showed that LAION's datasets, hosted on Hugging Face, contain large amounts of private and sensitive data.  The model was initially trained on the laion2B-en and laion-high-resolution subsets, with the last few rounds of training done on LAION-Aesthetics v2 5+, a subset of 600 million captioned images which the LAION-Aesthetics Predictor V2 predicted that humans would, on average, give a score of at least 5 out of 10 when asked to rate how much they liked them.  The LAION-Aesthetics v2 5+ subset also excluded low-resolution images and images which LAION-5B-WatermarkDetection identified as carrying a watermark with greater than 80% probability.


Stable Diffusion

Technology Final rounds of training additionally dropped 10% of text conditioning to improve Classifier-Free Diffusion Guidance.  The model was trained using 256 Nvidia A100 GPUs on Amazon Web Services for a total of 150,000 GPU-hours, at a cost of $600,000.  Stable Diffusion has issues with degradation and inaccuracies in certain scenarios.  Initial releases of the model were trained on a dataset that consists of 512��512 resolution images, meaning that the quality of generated images noticeably degrades when user specifications deviate from its "expected" 512��512 resolution; the version 2.0 update of the Stable Diffusion model later introduced the ability to natively generate images at 768��768 resolution.  Another challenge is in generating human limbs due to poor data quality of limbs in the LAION database.  The model is insufficiently trained to understand human limbs and faces due to the lack of representative features in the database, and prompting the model to generate images of such type can confound the model.  Stable Diffusion XL (SDXL) version 1.0, released in July 2023, introduced native 1024x1024 resolution and improved generation for limbs and text.  Accessibility for individual developers can also be a problem.  In order to customize the model for new use cases that are not included in the dataset, such as generating anime characters ("waifu diffusion"), new data and further training are required.  Fine-tuned adaptations of Stable Diffusion created through additional retraining have been used for a variety of different use-cases, from medical imaging to algorithmically generated music.  However, this fine-tuning process is sensitive to the quality of new data; low resolution images or different resolutions from the original data can not only fail to learn the new task but degrade the overall performance of the model.  Even when the model is additionally trained on high quality images, it is difficult for individuals to run models in consumer electronics.  For example, the training process for waifu-diffusion requires a minimum 30 GB of VRAM, which exceeds the usual resource provided in such consumer GPUs as Nvidia's GeForce 30 series, which has only about 12 GB.  The creators of Stable Diffusion acknowledge the potential for algorithmic bias, as the model was primarily trained on images with English descriptions.  As a result, generated images reinforce social biases and are from a western perspective, as the creators note that the model lacks data from other communities and cultures.  The model gives more accurate results for prompts that are written in English in comparison to those written in other languages, with western or white cultures often being the default representation.  To address the limitations of the model's initial training, end-users may opt to implement additional training to fine-tune generation outputs to match more specific use-cases, a process also referred to as personalization.


Stable Diffusion

Technology There are three methods in which user-accessible fine-tuning can be applied to a Stable Diffusion model checkpoint: An "embedding" can be trained from a collection of user-provided images, and allows the model to generate visually similar images whenever the name of the embedding is used within a generation prompt.  Embeddings are based on the "textual inversion" concept developed by researchers from Tel Aviv University in 2022 with support from Nvidia, where vector representations for specific tokens used by the model's text encoder are linked to new pseudo-words.  Embeddings can be used to reduce biases within the original model, or mimic visual styles.  A "hypernetwork" is a small pretrained neural network that is applied to various points within a larger neural network, and refers to the technique created by NovelAI developer Kurumuz in 2021, originally intended for text-generation transformer models.  Hypernetworks steer results towards a particular direction, allowing Stable Diffusion-based models to imitate the art style of specific artists, even if the artist is not recognised by the original model; they process the image by finding key areas of importance such as hair and eyes, and then patch these areas in secondary latent space.  DreamBooth is a deep learning generation model developed by researchers from Google Research and Boston University in 2022 which can fine-tune the model to generate precise, personalised outputs that depict a specific subject, following training via a set of images which depict the subject.


Stable Diffusion

Capabilities The Stable Diffusion model supports the ability to generate new images from scratch through the use of a text prompt describing elements to be included or omitted from the output.  Existing images can be re-drawn by the model to incorporate new elements described by a text prompt (a process known as "guided image synthesis") through its diffusion-denoising mechanism.  In addition, the model also allows the use of prompts to partially alter existing images via inpainting and outpainting, when used with an appropriate user interface that supports such features, of which numerous different open source implementations exist.  Stable Diffusion is recommended to be run with 10 GB or more VRAM, however users with less VRAM may opt to load the weights in float16 precision instead of the default float32 to tradeoff model performance with lower VRAM usage.  Top: no negative prompt Centre: "green trees" Bottom: "round stones, round rocks"The text to image sampling script within Stable Diffusion, known as "txt2img", consumes a text prompt in addition to assorted option parameters covering sampling types, output image dimensions, and seed values.  The script outputs an image file based on the model's interpretation of the prompt.  Generated images are tagged with an invisible digital watermark to allow users to identify an image as generated by Stable Diffusion, although this watermark loses its efficacy if the image is resized or rotated.  Each txt2img generation will involve a specific seed value which affects the output image.  Users may opt to randomize the seed in order to explore different generated outputs, or use the same seed to obtain the same image output as a previously generated image.  Users are also able to adjust the number of inference steps for the sampler; a higher value takes a longer duration of time, however a smaller value may result in visual defects.  Another configurable option, the classifier-free guidance scale value, allows the user to adjust how closely the output image adheres to the prompt.  More experimentative use cases may opt for a lower scale value, while use cases aiming for more specific outputs may use a higher value.  Additional text2img features are provided by front-end implementations of Stable Diffusion, which allow users to modify the weight given to specific parts of the text prompt.  Emphasis markers allow users to add or reduce emphasis to keywords by enclosing them with brackets.  An alternative method of adjusting weight to parts of the prompt are "negative prompts".  Negative prompts are a feature included in some front-end implementations, including Stability AI's own DreamStudio cloud service, and allow the user to specify prompts which the model should avoid during image generation.  The specified prompts may be undesirable image features that would otherwise be present within image outputs due to the positive prompts provided by the user, or due to how the model was originally trained, with mangled human hands being a common example.


Stable Diffusion

Capabilities Left: Original image created with Stable Diffusion 1.5 Right: Modified image created with Stable Diffusion XL 1.0Stable Diffusion also includes another sampling script, "img2img", which consumes a text prompt, path to an existing image, and strength value between 0.0 and 1.0.  The script outputs a new image based on the original image that also features elements provided within the text prompt.  The strength value denotes the amount of noise added to the output image.  A higher strength value produces more variation within the image but may produce an image that is not semantically consistent with the prompt provided.  The ability of img2img to add noise to the original image makes it potentially useful for data anonymization and data augmentation, in which the visual features of image data are changed and anonymized.  The same process may also be useful for image upscaling, in which the resolution of an image is increased, with more detail potentially being added to the image.  Additionally, Stable Diffusion has been experimented with as a tool for image compression.  Compared to JPEG and WebP, the recent methods used for image compression in Stable Diffusion face limitations in preserving small text and faces.  Additional use-cases for image modification via img2img are offered by numerous front-end implementations of the Stable Diffusion model.  Inpainting involves selectively modifying a portion of an existing image delineated by a user-provided layer mask, which fills the masked space with newly generated content based on the provided prompt.  A dedicated model specifically fine-tuned for inpainting use-cases was created by Stability AI alongside the release of Stable Diffusion 2.0.  Conversely, outpainting extends an image beyond its original dimensions, filling the previously empty space with content generated based on the provided prompt.  A depth-guided model, named "depth2img", was introduced with the release of Stable Diffusion 2.0 on November 24, 2022; this model infers the depth of the provided input image, and generates a new output image based on both the text prompt and the depth information, which allows the coherence and depth of the original input image to be maintained in the generated output.  ControlNet is a neural network architecture designed to manage diffusion models by incorporating additional conditions.  It duplicates the weights of neural network blocks into a "locked" copy and a "trainable" copy.  The "trainable" copy learns the desired condition, while the "locked" copy preserves the original model.  This approach ensures that training with small datasets of image pairs does not compromise the integrity of production-ready diffusion models.  The "zero convolution" is a 1��1 convolution with both weight and bias initialized to zero.  Before training, all zero convolutions produce zero output, preventing any distortion caused by ControlNet.  No layer is trained from scratch; the process is still fine-tuning, keeping the original model secure.  This method enables training on small-scale or even personal devices.


Stable Diffusion

Usage_and_controversy Stable Diffusion claims no rights on generated images and freely gives users the rights of usage to any generated images from the model provided that the image content is not illegal or harmful to individuals. The freedom provided to users over image usage has caused controversy over the ethics of ownership, as Stable Diffusion and other generative models are trained from copyrighted images without the owner���s consent. This also leads to large amounts of private and sensitive information in the training data. As visual styles and compositions are not subject to copyright, it is often interpreted that users of Stable Diffusion who generate images of artworks should not be considered to be infringing upon the copyright of visually similar works. However, individuals depicted in generated images may be protected by personality rights if their likeness is used, and intellectual property such as recognizable brand logos still remain protected by copyright. Nonetheless, visual artists have expressed concern that widespread usage of image synthesis software such as Stable Diffusion may eventually lead to human artists, along with photographers, models, cinematographers, and actors, gradually losing commercial viability against AI-based competitors. Stable Diffusion is notably more permissive in the types of content users may generate, such as violent or sexually explicit imagery, in comparison to other commercial products based on generative AI. Addressing the concerns that the model may be used for abusive purposes, CEO of Stability AI, Emad Mostaque, argues that "[it is] peoples' responsibility as to whether they are ethical, moral, and legal in how they operate this technology", and that putting the capabilities of Stable Diffusion into the hands of the public would result in the technology providing a net benefit, in spite of the potential negative consequences. In addition, Mostaque argues that the intention behind the open availability of Stable Diffusion is to end corporate control and dominance over such technologies, who have previously only developed closed AI systems for image synthesis. This is reflected by the fact that any restrictions Stability AI places on the content that users may generate can easily be bypassed due to the availability of the source code. Controversy around photorealistic sexualized depictions of underage characters have been brought up, due to such images generated by Stable Diffusion being shared on websites such as Pixiv.


Stable Diffusion

License Unlike models like DALL-E, Stable Diffusion makes its source code available, along with the model (pretrained weights). It applies the Creative ML OpenRAIL-M license, a form of Responsible AI License (RAIL), to the model (M). The license prohibits certain use cases, including crime, libel, harassment, doxing, "exploiting ... minors", giving medical advice, automatically creating legal obligations, producing legal evidence, and "discriminating against or harming individuals or groups based on ... social behavior or ... personal or personality characteristics ... [or] legally protected characteristics or categories". The user owns the rights to their generated output images, and is free to use them commercially.


Stable Diffusion

See_also 15.ai Artificial intelligence art Craiyon Hugging Face Imagen (Google Brain)


Artificial intelligence detection software

Text_detection For text, this is usually done to prevent alleged plagiarism, often by detecting repetition of words as telltale signs that a text was AI-generated (including AI hallucinations). They are often used by teachers marking their students, usually on an ad hoc basis. Following the release of ChatGPT and similar AI text generative software, many educational establishments have issued policies against the use of AI by students. AI text detection software is also used by those assessing job applicants, as well as online search engines. Current detectors are unreliable, and have incorrectly marked work by humans as originating from AI while failing to detect AI-generated work in other instances. MIT Technology Review said that the technology "struggled to pick up ChatGPT-generated text that had been slightly rearranged by humans and obfuscated by a paraphrasing tool". AI text detection software has also been shown to discriminate against non-native speakers of English. In May 2023, a professor at Texas A&M University���Commerce used ChatGPT to detect whether his students' content was written by it, which ChatGPT said was the case. As such, he threatened to fail the class, despite ChatGPT not being able to detect AI-generated writing. No students were prevented from graduating because of the issue, and all but one student (who admitted to using the software) were exonerated from having used ChatGPT in their content. In April 2023, Cambridge University and other members of the Russell Group of universities opted out of Turnitin's AI text detection tool, arguing that it was unreliable.