Announced in 2016, Gym is an open-source Python library developed to facilitate the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research study, making released research more easily reproducible [24] [144] while supplying users with a simple user interface for communicating 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 reinforcement knowing (RL) research study on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on enhancing agents to solve single jobs. Gym Retro gives the ability to generalize between video games with comparable ideas however various looks.
RoboSumo
Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially lack understanding of how to even stroll, however are given the goals of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the representatives find out how to adjust to changing conditions. When an agent is then eliminated from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had actually found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives might develop an intelligence "arms race" that could increase a representative's ability to work even outside the context of the competitors. [148]
OpenAI 5
OpenAI Five is a group of five OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that find out to play against human players at a high skill level entirely through experimental algorithms. Before ending up being a group of 5, the first public presentation happened at The International 2017, the yearly premiere champion tournament for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for 2 weeks of real time, and that the learning software was a step in the direction of creating software application that can manage complicated tasks like a cosmetic surgeon. [152] [153] The system uses a type of support learning, as the bots discover over 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 complete team of 5, and they were able to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert gamers, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public appearance came later on that month, where they played in 42,729 overall games in a four-day open online competitors, winning 99.4% of those games. [165]
OpenAI 5's systems in Dota 2's bot gamer shows the difficulties of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually shown using deep support learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl
Developed in 2018, Dactyl uses maker learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical items. [167] It learns entirely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, likewise has RGB cameras to allow the robotic to manipulate an arbitrary object by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might solve a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating gradually more challenging environments. ADR differs from manual domain randomization by not needing a human to specify randomization ranges. [169]
API
In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new AI designs established by OpenAI" to let developers contact it for "any English language AI task". [170] [171]
Text generation
The business has pretrained transformers (GPT). [172]
OpenAI's initial GPT model ("GPT-1")
The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative model of language might obtain world understanding and process long-range dependences 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 design and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative versions at first released to the general public. The full variation of GPT-2 was not immediately launched due to concern about prospective misuse, including applications for writing phony news. [174] Some professionals revealed uncertainty that GPT-2 postured a considerable risk.
In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned of "the technology to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue unsupervised language models to be general-purpose learners, shown by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not more trained on any task-specific input-output examples).
The corpus it was trained on, called WebText, contains somewhat 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both private characters and multiple-character tokens. [181]
GPT-3
First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a not being watched transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the full variation of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were likewise trained). [186]
OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" tasks and could generalize the purpose 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 outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or coming across the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 design. [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 planned to allow 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 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 personal beta. [194] According to OpenAI, the design can develop working code in over a lots programming languages, many successfully in Python. [192]
Several problems with glitches, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been accused of releasing copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would cease 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), capable of accepting text or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar examination 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 likewise check out, examine or generate as much as 25,000 words of text, and write code in all significant shows languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal various technical details and stats about GPT-4, such as the exact size of the model. [203]
GPT-4o
On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced outcomes in voice, multilingual, and vision benchmarks, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller 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 anticipates it to be particularly beneficial for business, startups and developers seeking to automate services with AI representatives. [208]
o1
On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been developed to take more time to think of their responses, leading to greater accuracy. These designs are especially effective in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3
On December 20, 2024, OpenAI revealed o3, the follower of the o1 thinking design. OpenAI likewise unveiled o3-mini, a lighter and quicker variation of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these designs. [214] The design is called o3 instead of o2 to prevent confusion with telecommunications companies O2. [215]
Deep research
Deep research study is a representative established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform extensive web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and links.gtanet.com.br Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
Image category
CLIP
Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to analyze the semantic similarity between text and images. It can especially be utilized for image classification. [217]
Text-to-image
DALL-E
Revealed in 2021, DALL-E is a Transformer model that creates images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can develop pictures of practical objects ("a stained-glass window with a picture of a blue strawberry") in addition to things that do not exist in truth ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.
DALL-E 2
In April 2022, OpenAI announced DALL-E 2, an updated variation of the model with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new primary system for converting a text description into a 3-dimensional design. [220]
DALL-E 3
In September 2023, OpenAI announced DALL-E 3, a more powerful model much better able to generate images from intricate descriptions without manual timely engineering and render complex details like hands and text. [221] It was released to the general public as a ChatGPT Plus feature in October. [222]
Text-to-video
Sora
Sora is a text-to-video model that can produce videos based on brief detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can generate videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.
Sora's development team named it after the Japanese word for "sky", to represent its "unlimited innovative potential". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos certified for that function, but did not expose the number or the specific sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, stating that it might create videos up to one minute long. It also shared a technical report highlighting the techniques used to train the design, and the design's capabilities. [225] It acknowledged some of its imperfections, including battles mimicing intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", but kept in mind that they need to have been cherry-picked and might not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have actually shown substantial interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to create reasonable video from text descriptions, mentioning its prospective to reinvent storytelling and content production. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly strategies for expanding his Atlanta-based film 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 varied audio and is likewise a multi-task design that can perform multilingual speech recognition as well as speech translation and language identification. [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, trademarketclassifieds.com a tune generated by MuseNet tends to start fairly but then fall under turmoil the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized as early as 2020 for the internet mental 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 genre, artist, and a snippet of lyrics and wavedream.wiki outputs song samples. OpenAI mentioned the songs "reveal local musical coherence [and] follow conventional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" and that "there is a significant gap" between Jukebox and human-generated music. The Verge specified "It's technologically excellent, even if the results seem like mushy versions of tunes that might feel familiar", while Business Insider stated "surprisingly, some of the resulting tunes are catchy and sound genuine". [234] [235] [236]
User user interfaces
Debate Game
In 2018, OpenAI released the Debate Game, which teaches machines to debate toy problems in front of a human judge. The function is to research study whether such a technique may assist in auditing AI decisions and in establishing explainable AI. [237] [238]
Microscope
Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of eight neural network designs which are frequently studied in interpretability. [240] Microscope was produced to analyze the features that form inside these neural networks quickly. The models included are AlexNet, VGG-19, various versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT
Launched in November 2022, ChatGPT is an artificial intelligence tool constructed on top of GPT-3 that supplies a conversational interface that permits users to ask questions in natural language. The system then responds with an answer within seconds.
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elizabethsoder edited this page 2025-02-21 09:23:23 +08:00