Announced in 2016, Gym is an open-source Python library developed to assist in the development of reinforcement learning algorithms. It aimed to standardize how environments are specified in AI research study, making released research more easily reproducible [24] [144] while offering users with a basic user interface for engaging with these environments. In 2022, brand-new developments of Gym have been moved to the library Gymnasium. [145] [146]
Gym Retro
Released in 2018, Gym Retro is a platform for support learning (RL) research on video games [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing agents to fix single jobs. Gym Retro provides the capability to generalize between video games with similar principles but different appearances.
RoboSumo
Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first lack understanding of how to even stroll, but are provided the goals of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the representatives find out how to adapt to changing conditions. When an agent is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives might produce an intelligence "arms race" that could increase a representative's ability to function even outside the context of the competitors. [148]
OpenAI 5
OpenAI Five is a team of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that find out to play against human gamers at a high ability level completely through experimental algorithms. Before ending up being a group of 5, the first public demonstration happened at The International 2017, the yearly best champion tournament for the video game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for 2 weeks of actual time, and that the knowing software was a step in the direction of creating software that can manage intricate tasks like a cosmetic surgeon. [152] [153] The system utilizes a kind of reinforcement learning, as the bots find out with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an enemy and taking map objectives. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a full group of 5, and they had the ability to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, wiki.snooze-hotelsoftware.de OpenAI Five played in two exhibition matches against professional gamers, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public look came later that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those video games. [165]
OpenAI 5's mechanisms in Dota 2's bot gamer shows the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually demonstrated using deep support learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl
Developed in 2018, Dactyl utilizes maker finding out to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It discovers completely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation issue by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB electronic cameras to allow the robotic to manipulate an arbitrary item by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might resolve a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating progressively more tough environments. ADR varies from manual domain randomization by not needing a human to define randomization varieties. [169]
API
In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new AI models established by OpenAI" to let designers call on it for "any English language AI job". [170] [171]
Text generation
The company has popularized generative pretrained transformers (GPT). [172]
OpenAI's initial GPT design ("GPT-1")
The initial paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his associates, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and process long-range reliances by pre-training on a diverse corpus with long stretches of contiguous text.
GPT-2
Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative variations at first released to the general public. The complete variation of GPT-2 was not right away released due to concern about possible abuse, consisting of applications for writing phony news. [174] Some specialists expressed uncertainty that GPT-2 presented a substantial hazard.
In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural fake news". [175] Other scientists, such as Jeremy Howard, alerted of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language model. [177] Several sites host interactive demonstrations of different instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language designs to be general-purpose students, shown by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not further trained on any task-specific input-output examples).
The corpus it was trained on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain concerns encoding vocabulary with word tokens by using byte pair encoding. This allows representing any string of characters by encoding both specific characters and multiple-character tokens. [181]
GPT-3
First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is an unsupervised transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the complete version of GPT-3 contained 175 billion specifications, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as few as 125 million criteria were likewise trained). [186]
OpenAI specified that GPT-3 prospered at certain "meta-learning" jobs and could generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing between English and Romanian, and between English and German. [184]
GPT-3 drastically improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or coming across the essential ability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the public for concerns of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month totally free private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified solely to Microsoft. [190] [191]
Codex
Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the AI powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can create working code in over a lots programming languages, most successfully in Python. [192]
Several problems with problems, style flaws and security were cited. [195] [196]
GitHub Copilot has actually been implicated of producing copyrighted code, without any author pipewiki.org attribution or license. [197]
OpenAI announced that they would discontinue support for Codex API on March 23, surgiteams.com 2023. [198]
GPT-4
On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the updated innovation passed a simulated law school bar examination with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also check out, examine or produce as much as 25,000 words of text, and compose code in all major programs languages. [200]
Observers reported that the iteration of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal various technical details and data about GPT-4, such as the exact size of the model. [203]
GPT-4o
On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision benchmarks, setting new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized version of GPT-4o changing GPT-3.5 Turbo on the ChatGPT user interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be especially helpful for business, start-ups and designers looking for to automate services with AI agents. [208]
o1
On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been created to take more time to consider their responses, wiki.snooze-hotelsoftware.de resulting in greater accuracy. These models are especially reliable in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3
On December 20, 2024, OpenAI unveiled o3, the successor of the o1 thinking design. OpenAI also unveiled o3-mini, a lighter and much faster variation of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these models. [214] The design is called o3 rather than o2 to avoid confusion with telecoms services service provider O2. [215]
Deep research study
Deep research is a representative developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out substantial web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
Image classification
CLIP
Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to analyze the semantic resemblance between text and images. It can especially be used for image classification. [217]
Text-to-image
DALL-E
Revealed in 2021, DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of a sad capybara") and create corresponding images. It can develop images of reasonable objects ("a stained-glass window with an image of a blue strawberry") along with objects that do not exist in truth ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.
DALL-E 2
In April 2022, OpenAI revealed DALL-E 2, an updated version of the model with more realistic results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new primary system for transforming a text description into a 3-dimensional design. [220]
DALL-E 3
In September 2023, OpenAI announced DALL-E 3, a more powerful model better able to produce images from intricate descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was launched to the general public as a ChatGPT Plus function in October. [222]
Text-to-video
Sora
Sora is a text-to-video design that can create videos based upon brief detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of produced videos is unknown.
Sora's advancement group called it after the Japanese word for "sky", to symbolize its "limitless creative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos licensed for that purpose, but did not expose the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, stating that it could create videos approximately one minute long. It likewise shared a technical report highlighting the techniques utilized to train the design, and the model's capabilities. [225] It acknowledged a few of its imperfections, including struggles simulating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", however noted that they must have been cherry-picked and may not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to create reasonable video from text descriptions, citing its possible to transform storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had decided to pause plans for broadening his Atlanta-based film studio. [227]
Speech-to-text
Whisper
Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a large dataset of varied audio and is likewise a multi-task model that can carry out multilingual speech recognition in addition to speech translation and language identification. [229]
Music generation
MuseNet
Released in 2019, MuseNet is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can produce songs with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to start fairly however then fall into mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet mental thriller Ben Drowned to produce music for gratisafhalen.be the titular character. [232] [233]
Jukebox
Released in 2020, Jukebox is an open-sourced algorithm to generate music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs tune samples. OpenAI specified the tunes "reveal local musical coherence [and] follow traditional chord patterns" but acknowledged that the songs lack "familiar bigger musical structures such as choruses that duplicate" and that "there is a significant gap" in between Jukebox and human-generated music. The Verge specified "It's technically excellent, even if the results sound like mushy variations of songs that may feel familiar", while Business Insider mentioned "surprisingly, some of the resulting tunes are appealing and sound genuine". [234] [235] [236]
Interface
Debate Game
In 2018, OpenAI launched the Debate Game, which teaches devices to discuss toy issues in front of a human judge. The purpose is to research whether such a method may help in auditing AI decisions and in establishing explainable AI. [237] [238]
Microscope
Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of eight neural network designs which are typically studied in interpretability. [240] Microscope was developed to analyze the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, various variations of Inception, and different variations of CLIP Resnet. [241]
ChatGPT
Launched in November 2022, ChatGPT is an expert system tool constructed on top of GPT-3 that supplies a conversational interface that allows users to ask questions in natural language. The system then reacts with an answer within seconds.
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Damian Frazier edited this page 2025-02-07 17:50:37 +08:00