Announced in 2016, Gym is an open-source Python library developed to assist in the development of support knowing algorithms. It aimed to standardize how environments are specified in AI research, making published research more easily reproducible [24] [144] while offering users with a basic user interface for connecting with these environments. In 2022, brand-new advancements of Gym have actually been relocated to the library Gymnasium. [145] [146]
Gym Retro
Released in 2018, Gym Retro is a platform for support knowing (RL) research study on video games [147] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing representatives to solve single jobs. Gym Retro offers the capability to generalize in between games with comparable principles however various looks.
RoboSumo
Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic do not have understanding of how to even walk, however are offered the objectives of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the representatives find out how to adapt to changing conditions. When a representative is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could produce an intelligence "arms race" that might increase an agent's capability to operate even outside the context of the competition. [148]
OpenAI 5
OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that discover to play against human players at a high skill level entirely through trial-and-error algorithms. Before becoming a group of 5, the very first public demonstration happened at The International 2017, the yearly premiere champion competition for the video game, disgaeawiki.info where Dendi, an expert Ukrainian player, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for 2 weeks of actual time, and that the learning software application was an action in the instructions of developing software that can deal with intricate tasks like a surgeon. [152] [153] The system uses a kind of support knowing, as the bots learn gradually 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 ability of the bots broadened to play together as a full team of 5, and they had the ability to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against professional players, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance came later that month, where they played in 42,729 total 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 player reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated the usage of deep reinforcement knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl
Developed in 2018, Dactyl utilizes machine finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical things. [167] It learns totally in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation issue by utilizing domain randomization, disgaeawiki.info a simulation approach which exposes the learner to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having motion tracking cams, also has RGB cams to allow the robot to control an approximate things by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could fix a Rubik's Cube. The robot had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to design. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating progressively harder environments. ADR differs from manual domain randomization by not needing a human to specify randomization ranges. [169]
API
In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new AI designs developed by OpenAI" to let designers contact it for "any English language AI job". [170] [171]
Text generation
The company has actually popularized generative pretrained transformers (GPT). [172]
OpenAI's original GPT design ("GPT-1")
The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and released in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language might obtain world understanding and process long-range dependencies by pre-training on a diverse corpus with long stretches of contiguous text.
GPT-2
Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language model and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative variations at first launched to the public. The full variation of GPT-2 was not instantly launched due to concern about potential misuse, including applications for writing phony news. [174] Some specialists expressed uncertainty that GPT-2 posed a significant hazard.
In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI released the total variation of the GPT-2 language model. [177] Several websites host interactive demonstrations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue unsupervised language designs to be general-purpose students, highlighted 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 a little 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain issues encoding vocabulary with word tokens by utilizing 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 follower to GPT-2. [182] [183] [184] OpenAI specified that the complete version of GPT-3 contained 175 billion criteria, [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 couple of as 125 million specifications were likewise trained). [186]
OpenAI mentioned that GPT-3 succeeded 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 learning between English and Romanian, and in between English and German. [184]
GPT-3 dramatically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or coming across the essential capability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away launched to the general public for issues of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month free personal beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed exclusively to Microsoft. [190] [191]
Codex
Announced in mid-2021, Codex is a descendant of GPT-3 that has additionally 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 private beta. [194] According to OpenAI, the design can develop working code in over a dozen programs languages, the majority of successfully in Python. [192]
Several issues with glitches, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been accused of discharging copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would discontinue support for Codex API on March 23, 2023. [198]
GPT-4
On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They revealed that the updated technology passed a simulated law school bar test with a score around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also check out, analyze or generate up to 25,000 words of text, and write code in all significant programming languages. [200]
Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, higgledy-piggledy.xyz with the caveat that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has declined to reveal different technical details and stats about GPT-4, such as the precise size of the design. [203]
GPT-4o
On May 13, 2024, OpenAI announced and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision criteria, 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 launched GPT-4o mini, a smaller sized variation 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 beneficial for enterprises, startups and developers seeking to automate services with AI agents. [208]
o1
On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been designed to take more time to consider their reactions, causing greater accuracy. These designs are especially reliable in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3
On December 20, 2024, OpenAI unveiled o3, the follower of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and faster variation of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid confusion with telecoms companies O2. [215]
Deep research
Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out substantial web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools made it possible for, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
Image classification
CLIP
Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to analyze the semantic similarity between text and images. It can especially be utilized for image category. [217]
Text-to-image
DALL-E
Revealed in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can develop pictures of sensible items ("a stained-glass window with an image of a blue strawberry") along with objects that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021, no API or wiki.snooze-hotelsoftware.de code is available.
DALL-E 2
In April 2022, OpenAI announced DALL-E 2, an updated variation of the design with more sensible results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new simple system for transforming a text description into a 3-dimensional model. [220]
DALL-E 3
In September 2023, OpenAI announced DALL-E 3, a more powerful model better able to generate images from complex descriptions without manual prompt engineering and render complex details like hands and text. [221] It was released to the general public as a ChatGPT Plus function in October. [222]
Text-to-video
Sora
Sora is a text-to-video model that can produce videos based on brief detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unknown.
Sora's advancement group called it after the Japanese word for "sky", to represent its "endless imaginative capacity". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos accredited for that function, however did not reveal the number or the precise sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, stating that it might produce videos as much as one minute long. It also shared a technical report highlighting the techniques used to train the model, and the model's abilities. [225] It acknowledged some of its drawbacks, consisting of battles replicating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", however kept in mind that they need to have been cherry-picked and may not represent Sora's typical output. [225]
Despite uncertainty from some academic leaders following Sora's public demonstration, significant entertainment-industry figures have revealed considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's capability to generate reasonable video from text descriptions, citing its potential to revolutionize storytelling and material creation. He said that his enjoyment about Sora's possibilities was so strong that he had decided to stop briefly strategies for expanding his Atlanta-based movie studio. [227]
Speech-to-text
Whisper
Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a large dataset of diverse audio and is also a multi-task design that can carry out multilingual speech acknowledgment along with speech translation and language recognition. [229]
Music generation
MuseNet
Released in 2019, MuseNet is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to start fairly but then fall under mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the web psychological thriller Ben Drowned to create music for the titular character. [232] [233]
Jukebox
Released in 2020, Jukebox is an open-sourced algorithm to produce music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and outputs song samples. OpenAI mentioned the tunes "show regional musical coherence [and] follow standard chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that duplicate" which "there is a significant gap" in between Jukebox and human-generated music. The Verge mentioned "It's technically remarkable, even if the results seem like mushy variations of songs that may feel familiar", while Business Insider stated "surprisingly, some of the resulting songs are catchy and sound legitimate". [234] [235] [236]
User interfaces
Debate Game
In 2018, OpenAI introduced the Debate Game, which teaches makers to discuss toy issues in front of a human judge. The function is to research study whether such a technique might help in auditing AI decisions and in developing explainable AI. [237] [238]
Microscope
Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of eight neural network designs which are typically studied in interpretability. [240] Microscope was created to examine the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, different variations of Inception, and different variations of CLIP Resnet. [241]
ChatGPT
Launched in November 2022, ChatGPT is an expert system tool developed on top of GPT-3 that provides a conversational interface that permits users to ask questions in natural language. The system then reacts with a response within seconds.
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shondaleschen6 edited this page 2025-02-17 09:36:22 +08:00