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>From Wikipedia, the free encyclopedia
Not to be confused with OpenAL.
OpenAIOpenAI Logo.svg
Pioneer Building, San Francisco (2019) -1.jpg
Headquarters at the Pioneer Building in San Francisco
Industry	Artificial intelligence
Founded	December 11, 2015; 7 years ago
Founders	

    Sam Altman
    Trevor Blackwell
    Greg Brockman
    Vicki Cheung
    Reid Hoffman
    Andrej Karpathy
    Durk Kingma
    Jessica Livingston
    Elon Musk
    John Schulman
    Ilya Sutskever
    Peter Thiel
    Pamela Vagata
    Wojciech Zaremba

[1]
Headquarters	Pioneer Building, San Francisco, California, US[2][3]
Key people
	

    Greg Brockman (chairman & president)
    Sam Altman (CEO)
    Ilya Sutskever (chief scientist)

Products	

    DALL-E
    GPT-3
    OpenAI Five
    ChatGPT
    OpenAI Codex

Number of employees
	375 (as of January 2023)[4]
Website	openai.com Edit this at Wikidata

OpenAI is an American artificial intelligence (AI) research laboratory
consisting of the non-profit OpenAI Incorporated (OpenAI Inc.) and its
for-profit subsidiary corporation OpenAI Limited Partnership (OpenAI
LP). OpenAI conducts AI research to promote and develop friendly AI in
a way that benefits all humanity. The organization was founded in San
Francisco in 2015 by Sam Altman, Reid Hoffman, Jessica Livingston,
Elon Musk, Ilya Sutskever, Peter Thiel and others,[5][6][7] who
collectively pledged US$1 billion. Musk resigned from the board in
2018 but remained a donor. Microsoft provided OpenAI LP a $1 billion
investment in 2019 and a second multi-year investment in January 2023,
reported to be $10 billion.[8]
History
Non-profit beginnings

In December 2015, Sam Altman, Elon Musk, Greg Brockman, Reid Hoffman,
Jessica Livingston, Peter Thiel, Amazon Web Services (AWS), Infosys,
and YC Research announced[9] the formation of OpenAI and pledged over
$1 billion to the venture. The organization stated it would "freely
collaborate" with other institutions and researchers by making its
patents and research open to the public.[10][11] OpenAI is
headquartered at the Pioneer Building in Mission District, San
Francisco.[12][3]

According to Wired, Brockman met with Yoshua Bengio, one of the
"founding fathers" of the deep learning movement, and drew up a list
of the "best researchers in the field".[13] Brockman was able to hire
nine of them as the first employees in December 2015.[13] In 2016
OpenAI paid corporate-level (rather than nonprofit-level) salaries,
but did not pay AI researchers salaries comparable to those of
Facebook or Google.[13] (Microsoft's Peter Lee stated that the cost of
a top AI researcher exceeds the cost of a top NFL quarterback
prospect.[13]) Nevertheless, a Google employee stated that he was
willing to leave Google for OpenAI "partly because of the very strong
group of people and, to a very large extent, because of its
mission."[13] Brockman stated that "the best thing that I could
imagine doing was moving humanity closer to building real AI in a safe
way."[13] OpenAI researcher Wojciech Zaremba stated that he turned
down "borderline crazy" offers of two to three times his market value
to join OpenAI instead.[13]

In April 2016, OpenAI released a public beta of "OpenAI Gym", its
platform for reinforcement learning research.[14] In December 2016,
OpenAI released "Universe", a software platform for measuring and
training an AI's general intelligence across the world's supply of
games, websites, and other applications.[15][16][17][18]

In 2017 OpenAI spent $7.9 million, or a quarter of its functional
expenses, on cloud computing alone.[19] In comparison, DeepMind's
total expenses in 2017 were $442 million. In summer 2018, simply
training OpenAI's Dota 2 bots required renting 128,000 CPUs and 256
GPUs from Google for multiple weeks.

In 2018, Musk resigned his board seat, citing "a potential future
conflict (of interest)" with his role as CEO of Tesla due to Tesla's
AI development for self-driving cars, but remained a donor.[20]
Transition to for-profit

In 2019, OpenAI transitioned from non-profit to "capped" for-profit,
with the profit capped at 100 times any investment.[21] According to
OpenAI, the capped-profit model allows OpenAI LP to legally attract
investment from venture funds, and in addition, to grant employees
stakes in the company, the goal being that they can say "I'm going to
Open AI, but in the long term it's not going to be disadvantageous to
us as a family."[22] Many top researchers work for Google Brain,
DeepMind, or Facebook, which offer stock options that a nonprofit
would be unable to.[23] Prior to the transition, public disclosure of
the compensation of top employees at OpenAI was legally required.[24]

The company then distributed equity to its employees and partnered
with Microsoft and Matthew Brown Companies,[25] who announced an
investment package of $1 billion into the company. OpenAI also
announced its intention to commercially license its technologies.[26]
OpenAI plans to spend the $1 billion "within five years, and possibly
much faster".[27] Altman has stated that even a billion dollars may
turn out to be insufficient, and that the lab may ultimately need
"more capital than any non-profit has ever raised" to achieve
artificial general intelligence.[28]

The transition from a nonprofit to a capped-profit company was viewed
with skepticism by Oren Etzioni of the nonprofit Allen Institute for
AI, who agreed that wooing top researchers to a nonprofit is
difficult, but stated "I disagree with the notion that a nonprofit
can't compete" and pointed to successful low-budget projects by OpenAI
and others. "If bigger and better funded was always better, then IBM
would still be number one."

The nonprofit, OpenAI Inc., is the sole controlling shareholder of
OpenAI LP. OpenAI LP, despite being a for-profit company, retains a
formal fiduciary responsibility to OpenAI Inc.'s nonprofit charter. A
majority of OpenAI Inc.'s board is barred from having financial stakes
in OpenAI LP.[22] In addition, minority members with a stake in OpenAI
LP are barred from certain votes due to conflict of interest.[23] Some
researchers have argued that OpenAI LP's switch to for-profit status
is inconsistent with OpenAI's claims to be "democratizing" AI.[29] A
journalist at Vice News wrote that "generally, we've never been able
to rely on venture capitalists to better humanity".[30]
After becoming for-profit

In 2020, OpenAI announced GPT-3, a language model trained on large
internet datasets. GPT-3 is aimed at natural language answering of
questions, but it can also translate between languages and coherently
generate improvised text. It also announced that an associated API,
named simply "the API", would form the heart of its first commercial
product.[31]

In 2021, OpenAI introduced DALL-E, a deep learning model that can
generate digital images from natural language descriptions.[32]

In December 2022, OpenAI received widespread media coverage after
launching a free preview of ChatGPT, its new AI chatbot based on
GPT-3.5. According to OpenAI, the preview received over a million
signups within the first five days.[33] According to anonymous sources
cited by Reuters in December 2022, OpenAI was projecting $200 million
revenue in 2023 and $1 billion revenue in 2024.[34]

As of January 2023, OpenAI was in talks for funding that would value
the company at $29 billion, double the value of the company in
2021.[35] On January 23, 2023, Microsoft announced a new multi-year,
multi-billion dollar (reported to be $10 billion) investment in
OpenAI.[36][37]
Participants

Key employees:

    CEO and co-founder:[38] Sam Altman, former president of the
startup accelerator Y Combinator
    President and co-founder:[39] Greg Brockman, former CTO, 3rd
employee of Stripe[40]
    Chief Scientist and co-founder: Ilya Sutskever, a former Google
expert on machine learning[40]
    Chief Technology Officer:[39] Mira Murati, previously at Leap
Motion and Tesla, Inc.
    Chief Operating Officer:[39] Brad Lightcap, previously at Y
Combinator and JPMorgan Chase

Board of the OpenAI nonprofit:

    Greg Brockman
    Ilya Sutskever
    Sam Altman
    Adam D'Angelo
    Reid Hoffman
    Will Hurd
    Tasha McCauley
    Helen Toner
    Shivon Zilis

Individual investors:[40]

    Reid Hoffman, LinkedIn co-founder[41]
    Peter Thiel, PayPal co-founder[41]
    Jessica Livingston, a founding partner of Y Combinator

Corporate investors:

    Microsoft[42]
    Khosla Ventures[43]
    Infosys[44]

Motives

Some scientists, such as Stephen Hawking and Stuart Russell, have
articulated concerns that if advanced AI someday gains the ability to
re-design itself at an ever-increasing rate, an unstoppable
"intelligence explosion" could lead to human extinction. Musk
characterizes AI as humanity's "biggest existential threat."[45]
OpenAI's founders structured it as a non-profit so that they could
focus its research on making positive long-term contributions to
humanity.[11]

Musk and Altman have stated they are partly motivated by concerns
about AI safety and the existential risk from artificial general
intelligence.[46][47] OpenAI states that "it's hard to fathom how much
human-level AI could benefit society," and that it is equally
difficult to comprehend "how much it could damage society if built or
used incorrectly".[11] Research on safety cannot safely be postponed:
"because of AI's surprising history, it's hard to predict when
human-level AI might come within reach."[48] OpenAI states that AI
"should be an extension of individual human wills and, in the spirit
of liberty, as broadly and evenly distributed as possible...".[11]
Co-chair Sam Altman expects the decades-long project to surpass human
intelligence.[49]

Vishal Sikka, former CEO of Infosys, stated that an "openness" where
the endeavor would "produce results generally in the greater interest
of humanity" was a fundamental requirement for his support, and that
OpenAI "aligns very nicely with our long-held values" and their
"endeavor to do purposeful work".[50] Cade Metz of Wired suggests that
corporations such as Amazon may be motivated by a desire to use
open-source software and data to level the playing field against
corporations such as Google and Facebook that own enormous supplies of
proprietary data. Altman states that Y Combinator companies will share
their data with OpenAI.[49]
Strategy

Musk posed the question: "What is the best thing we can do to ensure
the future is good? We could sit on the sidelines or we can encourage
regulatory oversight, or we could participate with the right structure
with people who care deeply about developing AI in a way that is safe
and is beneficial to humanity." Musk acknowledged that "there is
always some risk that in actually trying to advance (friendly) AI we
may create the thing we are concerned about"; nonetheless, the best
defense is "to empower as many people as possible to have AI. If
everyone has AI powers, then there's not any one person or a small set
of individuals who can have AI superpower."[40]

Musk and Altman's counter-intuitive strategy of trying to reduce the
risk that AI will cause overall harm, by giving AI to everyone, is
controversial among those who are concerned with existential risk from
artificial intelligence. Philosopher Nick Bostrom is skeptical of
Musk's approach: "If you have a button that could do bad things to the
world, you don't want to give it to everyone."[47] During a 2016
conversation about the technological singularity, Altman said that "we
don't plan to release all of our source code" and mentioned a plan to
"allow wide swaths of the world to elect representatives to a new
governance board". Greg Brockman stated that "Our goal right now... is
to do the best thing there is to do. It's a little vague."[51]

Conversely, OpenAI's initial decision to withhold GPT-2 due to a wish
to "err on the side of caution" in the presence of potential misuse,
has been criticized by advocates of openness. Delip Rao, an expert in
text generation, stated "I don't think [OpenAI] spent enough time
proving [GPT-2] was actually dangerous." Other critics argued that
open publication is necessary to replicate the research and to be able
to come up with countermeasures.[52]
Products and applications

OpenAI's research tend to focus on reinforcement learning (RL). OpenAI
is viewed as an important competitor to DeepMind.[53]
Gym

Gym aims to provide an easy to set up, general-intelligence benchmark
with a wide variety of different environments—somewhat akin to, but
broader than, the ImageNet Large Scale Visual Recognition Challenge
used in supervised learning research—and that hopes to standardize the
way in which environments are defined in AI research publications, so
that published research becomes more easily reproducible.[14][54] The
project claims to provide the user with a simple interface. As of June
2017, Gym can only be used with Python.[55] As of September 2017, the
Gym documentation site was not maintained, and active work focused
instead on its GitHub page.[56][non-primary source needed]
RoboSumo

In "RoboSumo", virtual humanoid "metalearning" robots initially lack
knowledge of how to even walk, and are given the goals of learning to
move around, and pushing the opposing agent out of the ring. Through
this adversarial learning process, the agents learn how to adapt to
changing conditions; when an agent is then removed from this virtual
environment and placed in a new virtual environment with high winds,
the agent braces to remain upright, suggesting it had learned how to
balance in a generalized way.[57][58] OpenAI's Igor Mordatch argues
that competition between agents can create an intelligence "arms race"
that can increase an agent's ability to function, even outside the
context of the competition.
Debate Game

In 2018, OpenAI launched the Debate Game, which teaches machines to
debate toy problems in front of a human judge. The purpose is to
research whether such an approach may assist in auditing AI decisions
and in developing explainable AI.[59][60]
Dactyl

Dactyl uses machine learning to train a Shadow Hand, a human-like
robot hand, to manipulate physical objects. It learns entirely in
simulation using the same RL algorithms and training code as OpenAI
Five. OpenAI tackled the object orientation problem by using domain
randomization, a simulation approach which exposes the learner to a
variety of experiences rather than trying to fit to reality. The
set-up for Dactyl, aside from having motion tracking cameras, also has
RGB cameras to allow the robot to manipulate an arbitrary object by
seeing it. In 2018, OpenAI showed that the system was able to
manipulate a cube and an octagonal prism.[61]

In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube.
The robot was able to solve the puzzle 60% of the time. Objects like
the Rubik's Cube introduce complex physics that is harder to model.
OpenAI solved this by improving the robustness of Dactyl to
perturbations; they employed a technique called Automatic Domain
Randomization (ADR), a simulation approach where progressively more
difficult environments are endlessly generated. ADR differs from
manual domain randomization by not needing there to be a human to
specify randomization ranges.[62]
Generative models
GPT
The GPT model

The original paper on generative pre-training (GPT) of a language
model was written by Alec Radford and his colleagues, and published in
preprint on OpenAI's website on June 11, 2018.[63] It showed how a
generative model of language is able to acquire world knowledge and
process long-range dependencies by pre-training on a diverse corpus
with long stretches of contiguous text.
GPT-2
Main article: GPT-2
An instance of GPT-2 writing a paragraph based on a prompt from its
own Wikipedia article in February 2021

Generative Pre-trained Transformer 2, commonly known by its
abbreviated form GPT-2, is an unsupervised transformer language model
and the successor to GPT. GPT-2 was first announced in February 2019,
with only limited demonstrative versions initially released to the
public. The full version of GPT-2 was not immediately released out of
concern over potential misuse, including applications for writing fake
news.[64] Some experts expressed skepticism that GPT-2 posed a
significant threat. The Allen Institute for Artificial Intelligence
responded to GPT-2 with a tool to detect "neural fake news".[65] Other
researchers, such as Jeremy Howard, warned of "the technology to
totally 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".[66] In November 2019, OpenAI released the
complete version of the GPT-2 language model.[67] Several websites
host interactive demonstrations of different instances of GPT-2 and
other transformer models.[68][69][70]

GPT-2's authors argue unsupervised language models to be
general-purpose learners, illustrated by GPT-2 achieving
state-of-the-art accuracy and perplexity on 7 of 8 zero-shot tasks
(i.e. the model was not further trained on any task-specific
input-output examples). The corpus it was trained on, called WebText,
contains slightly over 8 million documents for a total of 40 GB of
text from URLs shared in Reddit submissions with at least 3 upvotes.
It avoids certain issues encoding vocabulary with word tokens by using
byte pair encoding. This allows to represent any string of characters
by encoding both individual characters and multiple-character
tokens.[71]
GPT-3
Main article: GPT-3

Generative Pre-trained[a] Transformer 3, commonly known by its
abbreviated form GPT-3, is an unsupervised transformer language model
and the successor to GPT-2. It was first described in May
2020.[73][74][75] OpenAI stated that full version of GPT-3 contains
175 billion parameters,[75] two orders of magnitude larger than the
1.5 billion parameters[76] in the full version of GPT-2 (although
GPT-3 models with as few as 125 million parameters were also
trained).[77]

OpenAI stated that GPT-3 succeeds at certain "meta-learning" tasks. It
can generalize the purpose of a single input-output pair. The paper
gives an example of translation and cross-linguistic transfer learning
between English and Romanian, and between English and German.[75]

GPT-3 dramatically improved benchmark results over GPT-2. OpenAI
cautioned that such scaling up of language models could be approaching
or encountering the fundamental capability limitations of predictive
language models.[78] Pre-training GPT-3 required several thousand
petaflop/s-days[b] of compute, compared to tens of petaflop/s-days for
the full GPT-2 model.[75] Like that of its predecessor,[64] GPT-3's
fully trained model was not immediately released to the public on the
grounds of possible abuse, though OpenAI planned to allow access
through a paid cloud API after a two-month free private beta that
began in June 2020.[80][81]

On September 23, 2020, GPT-3 was licensed exclusively to Microsoft.[82][83]
ChatGPT
Main article: ChatGPT

ChatGPT is an artificial intelligence tool that provides a
conversational interface that allows you to ask questions in natural
language. The system then responds with an answer within seconds.
ChatGPT was launched in November 2022 and reached 1 million users only
5 days after its initial launch.[84]
Music

OpenAI's MuseNet (2019) is a deep neural net trained to predict
subsequent musical notes in MIDI music files. It can generate songs
with ten different instruments in fifteen different styles. According
to The Verge, a song generated by MuseNet tends to start reasonably
but then fall into chaos the longer it plays.[85][86] In pop culture,
initial applications of this tool were utilized as early as 2020 for
the internet psychological thriller Ben Drowned to create music for
the titular character. [87][88]

OpenAI's Jukebox (2020) is an open-sourced algorithm to generate music
with vocals. After training on 1.2 million samples, the system accepts
a genre, artist, and a snippet of lyrics and outputs song samples.
OpenAI stated the songs "show local musical coherence, follow
traditional chord patterns" but acknowledged that the songs lack
"familiar larger musical structures such as choruses that repeat" and
that "there is a significant gap" between Jukebox and human-generated
music. The Verge stated "It's technologically impressive, even if the
results sound like mushy versions of songs that might feel familiar",
while Business Insider stated "surprisingly, some of the resulting
songs are catchy and sound legitimate".[89][90][91]
Whisper

Whisper is a general-purpose speech recognition model. It is trained
on a large dataset of diverse audio and is also a multi-task model
that can perform multilingual speech recognition as well as speech
translation and language identification.[92]
API

In June 2020, OpenAI announced a multi-purpose API which it said was
"for accessing new AI models developed by OpenAI" to let developers
call on it for "any English language AI task."[80][93]
DALL-E and CLIP
Main article: DALL-E
Images produced by DALL-E when given the text prompt "a professional
high-quality illustration of a giraffe dragon chimera. a giraffe
imitating a dragon. a giraffe made of dragon."

DALL-E is a Transformer model that creates images from textual
descriptions, revealed by OpenAI in January 2021.[94]

CLIP does the opposite: it creates a description for a given
image.[95] DALL-E uses a 12-billion-parameter version of GPT-3 to
interpret 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 create images of realistic
objects ("a stained-glass window with an image of a blue strawberry")
as well as objects that do not exist in reality ("a cube with the
texture of a porcupine"). As of March 2021, no API or code is
available.

In March 2021, OpenAI released a paper titled Multimodal Neurons in
Artificial Neural Networks,[96] where they showed a detailed analysis
of CLIP (and GPT) models and their vulnerabilities. The new type of
attacks on such models was described in this work.

    We refer to these attacks as typographic attacks. We believe
attacks such as those described above are far from simply an academic
concern. By exploiting the model's ability to read text robustly, we
find that even photographs of hand-written text can often fool the
model.
    — Multimodal Neurons in Artificial Neural Networks, OpenAI

In April 2022, OpenAI announced DALL-E 2, an updated version of the
model with more realistic results.[97] In December 2022, OpenAI
published on GitHub software for Point-E, a new rudimentary system for
converting a text description into a 3-dimensional model.[98]
Microscope

OpenAI Microscope[99] is a collection of visualizations of every
significant layer and neuron of eight different neural network models
which are often studied in interpretability. Microscope was created to
analyze the features that form inside these neural networks easily.
The models included are AlexNet, VGG 19, different versions of
Inception, and different versions of CLIP Resnet.[100]
Codex
Main article: OpenAI Codex

OpenAI Codex is a descendant of GPT-3 that has additionally been
trained on code from 54 million GitHub repositories.[101][102] It was
announced in mid-2021 as the AI powering the code autocompletion tool
GitHub Copilot.[102] In August 2021, an API was released in private
beta.[103] According to OpenAI, the model is able to create working
code in over a dozen programming languages, most effectively in
Python.[101]

Several issues with glitches, design flaws, and security
vulnerabilities have been brought up.[104][105]
Video game bots and benchmarks
OpenAI Five
Main article: OpenAI Five

OpenAI Five is the name of a team of five OpenAI-curated bots that are
used in the competitive five-on-five video game Dota 2, who learn to
play against human players at a high skill level entirely through
trial-and-error algorithms. Before becoming a team of five, the first
public demonstration occurred at The International 2017, the annual
premiere championship tournament for the game, where Dendi, a
professional Ukrainian player, lost against a bot in a live 1v1
matchup.[106][107] After the match, CTO Greg Brockman explained that
the bot had learned by playing against itself for two weeks of real
time, and that the learning software was a step in the direction of
creating software that can handle complex tasks like a
surgeon.[108][109] The system uses a form of reinforcement learning,
as the bots learn over time by playing against themselves hundreds of
times a day for months, and are rewarded for actions such as killing
an enemy and taking map objectives.[110][111][112]

By June 2018, the ability of the bots expanded to play together as a
full team of five, and they were able to defeat teams of amateur and
semi-professional players.[113][110][114][115] At The International
2018, OpenAI Five played in two exhibition matches against
professional players, but ended up losing both games.[116][117][118]
In April 2019, OpenAI Five defeated OG, the reigning world champions
of the game at the time, 2:0 in a live exhibition match in San
Francisco.[119][120] 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 games.[121]
GYM Retro

Gym Retro is a platform for RL research on video games. Gym Retro is
used to research RL algorithms and study generalization. Prior
research in RL has focused chiefly on optimizing agents to solve
single tasks. Gym Retro gives the ability to generalize between games
with similar concepts but different appearances.
See also

    DeepMind
    Future of Humanity Institute
    Future of Life Institute
    Machine Intelligence Research Institute

Notes

    The term "pre-training" refers to general language training as
distinct from fine-tuning for specific tasks.[72]
    One petaflop/s-day is approximately equal to 1020 neural net operations.[79]

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location

seems to be based in sfbay area.Mercurywoodrose (talk) 06:10, 12
December 2015 (UTC)[reply]

     Done. Thanks, Gap9551 (talk) 17:49, 12 December 2015 (UTC)[reply]

        thanks i couldnt find a ref, you did.Mercurywoodrose (talk)
19:22, 12 December 2015 (UTC)[reply]

See also itis

many articles like this have too many "see also"s. we shouldnt just
place every related article here. it should be lists that include this
article, and article directly related that have not been able to fit
well into the actual article. other institutes should NOT be listed,
but should be in the body of the article as reliable sources
themselves make the link or connection. its more a style point, too
many see alsos means we are doing the research for the reader on whats
interesting to them. we could put a see also for "luddites" for people
who read this and say "hell no i hate this", or a link to brain
development articles, history of computing, other think tanks in the
bay area, cool AI projects like Watson, the Singularity, roger penrose
who says we cant develop AI, and the movie AI. the list goes on and
on.Mercurywoodrose (talk) 19:30, 12 December 2015 (UTC)[reply]

    I agree. I added just one originally, about the topic Existential
risk from advanced artificial intelligence that closely matches the
aims of the company. There are several institutes with similar goals,
but they can also be found in Category:Existential risk organizations.
I removed Allen Institute for Artificial Intelligence for starters, as
they seem not be specifically aiming to reduce risks associated with
AI, just to develop advanced AI in general. Gap9551 (talk) 00:47, 13
December 2015 (UTC)[reply]

the field and the company

This article is supposed to be about the company, not AIin general,
but I see in the article a great deal of general discussion about the
future prospects for AI. It doesn't belong here. DGG ( talk ) 22:33, 3
February 2016 (UTC)[reply]

    DGG I originally added the content because the mainstream media
coverage of OpenAI talks in great detail about the donors' motivating
beliefs about the future prospects for AI. I know you're a busy admin;
maybe you didn't have time to read the sources? Do you want to discuss
this and the "promotional" tag some more, or would it satisfy your
concerns if I just ask WP:THIRDOPINION for an opinion to avoid taking
up too much of your time? (Of course, if anyone else on this page
shares DGG's concerns and would like to elaborate on possible
concerns, feel free to chime in.) Rolf H Nelson (talk) 20:34, 6
February 2016 (UTC)[reply]

Re deletion of the reference to the sex-bot article

Regarding deletion of the reference to the article "Re: Sex-Bots --
Let Us Look Before We Leap" ( http://www.mdpi.com/2076-0752/7/2/15 ),
several points are in order.

First, the journal in which the article appears is relatively new, but
it is not obscure, having recently published, for example, two
articles by tech industry heavyweights -- "Can Computers Create Art?"
( http://www.mdpi.com/2076-0752/7/2/18 ) and "Art in the Age of
Machine Intelligence" ( http://www.mdpi.com/2076-0752/6/4/18 ) -- and
which have enjoyed between them some 9,400 page views.

And yes, the article in question is an opinion piece -- but at this
point in time, opinion is all we have; i.e., there is no one who can
say with authority where AI is leading us, much less AI-enabled
sex-bots! So if someone -- and that someone, BTW, is yours truly,
although I don't think I've broken any of the WP:SELFCITE guidelines
-- takes the time to express his concerns in a carefully thought-out
and articulated piece, and if that piece is in turn given careful
scrutiny before being published -- and yes, "Let Us Look Before We
Leap" underwent a thorough peer review at Arts, even though published
by them as "Opinion" -- what more could we expect from a source cited
in Wikipedia regarding the quite critical and quite speculative
subject of AI?

And finally, regarding the argument that this Wikipedia article should
be about the company and not AI in general, the fact is that OpenAI
has, by its very charter, captured the subject of the desirability of
requiring that all AI code to which the public is subject be open
source (just as, for example, we now require public disclosure of the
details of all pharmaceuticals), and thus likewise the quite
understandable goal of someone who thinks that this is the correct
approach: he has taken the time to articulate his arguments and have
them published in a reputable journal; and he now wishes in turn to
share them with a larger Wikipedia audience via said article.

Comments, please! I am obviously aiming at a re-instatement of the
deleted content, but can certainly be dissuaded therefrom. Synchronist
(talk) 04:06, 1 August 2018 (UTC)[reply]

    I'll suspend judgement then on whether it's obscure. I removed the
content based on its not meeting WP:RS; I'm happy to solicit other
opinions though. We can always ask WP:DRR/3O for a third opinion if
nobody else on talk has any thoughts on the matter. Rolf H Nelson
(talk) 17:19, 5 August 2018 (UTC)[reply]

    The use is inappropriate: it has no mention or apparent relevance
to OpenAI, we can't put things together like this per WP:SYNTH.
Whether someone wants to share it is irrelevant, this is about things
that are related to OpenAI. Not just the use of the source, but the
commentary "...one juried commentator has asked..." is not
encyclopedic. K.Bog 01:32, 28 August 2018 (UTC)[reply]

Solving Rubik’s Cube with a robot hand

Just want to attract attention to a new article published by OpenAI
October 15th, 2019, about how they made a system that learned to solve
Rubik's Cube all by itself, using only one hand (a Shadow Dexterious
Hand). Maybe someone wants to add a mention of this to the article.
There is a blog post, Solving Rubik’s Cube with a Robot Hand, and a
scientific paper of the same name: Solving Rubik’s Cube with a Robot
Hand. --Jhertel (talk) 17:38, 19 October 2019 (UTC)[reply]
Re: GPT3 "Pre-training GPT-3 required several thousand petaflop/s-days
of compute, compared to tens of petaflop/s-days for the full GPT-2
model."

A while ago I put up a tag saying copy edit was needed, and it was
reverted with a summary stating "[t]his is a correctly used technical
term". I've never seen the term petaflop be used in that particular
way before.

These are the two glaring typographical irregularities that have
gotten me stumped:

    petaflop/s-days: I assume this was supposed to mean either
petaflops/day or petaflop-days, but both nouns are in their plural
forms.
        I have no idea what a petaflop-day is supposed to be.
        petaflops/day means billions of operations per second per day,
which would suggest that pre-training either GPT would require a
computer to perform a certain amount of PFLOPS on one day, and more
PFLOPS than that on the next day, and so on.
    "of compute"; I can't decide if it should be corrected to "of
computation" or "to compute", so I've left that part as-is.

-- MrPersonHumanGuy (talk) 02:04, 30 August 2020 (UTC)[reply]

    Update: I see someone has added something in parentheses to
clarify that several thousand petaflop/s-days are "a unit equivalent
to approximately 1020 neural net operations". A thousand PFLOPS would
be 1018 floating point operations a second. Or, since there's 86,400
seconds in a day, a petaflop would mean 8.64 × 1019 floating-point
operations on a daily basis.

    After some digging through the edit history, I've found the edit
that introduced the odd writing. Below is the prose it replaced, but
I've modified the notes and citations to prevent them from adding a
list to the bottom of the talkspace:

        Lambda Labs estimated that GPT-3 would cost US$4.6M and take
355 GPU years to train using state-of-the-art[b] GPU technology.[64]
Another source lists training costs of US$12M and memory requirement
of 350GB on an undisclosed hardware configuration.[65] Yet another
estimate by Intento calculated that GPT-3 training would take 1 or 2
months[c] and might consume 432 MWh (1,555 GJ) of electricity if run
24/7. [66]

    If the overwriting sentence was supposed to specify how many
PFLOPS and days "of compute [sic]" it took to pre-train GPT-3, then
petaflops and days should be separate words with separate amounts. --
MrPersonHumanGuy (talk) 18:44, 30 August 2020 (UTC)[reply]

        Update 2: To quote the source the clarifier cited;

            A petaflop/s-day (pfs-day) consists of performing 1015
neural net operations per second for one day, or a total of about 1020
operations.

        That is from the second footnote, which is for this sentence:

            The total amount of compute, in petaflop/s-days,[2] used
to train selected results that are relatively well known, used a lot
of compute for their time, and gave enough information to estimate the
compute used.

        I think it's a bit funny how the author(s) of the OpenAI blog
AI and Compute used the word compute in place of computation all over
the place, as if the verb is also a common noun. -- MrPersonHumanGuy
(talk) 12:26, 31 August 2020 (UTC)[reply]

        I'm a little late to the game, so this response is for all
those students, science and non-science. @MrPersonHumanGuy, there is a
reason why your high school science or chemistry teacher emphasized
and stressed always paying attention to the use of units in
calculations.
        It is quite common in technical, and particularly science
fields, to have complex units (qualifiers): foot–pounds vs.
newton–meters. That is a units that are other than simple: inch,
gallon, ton, calorie. So your misapprehension is probably a lack of
exposure.
        Firstly the OpenAI terminology Petaflop/s-day(sic), and
pfs-day(sic). The notation is misleading, the "/" (division) should
have been a dash as in a complex unit, and s-day, the dash should have
been a "/" divisor, i.e. sec/day.
        Pardon the scientific notation. Petaflop is understood to be 1
executed computer op-code with qualifier 10^15 per second, and
s-days(sic) would be 8.64 * 10^4 seconds/day (i.e. 60 sec/min * 60
min/hr * 24 hr/day = 86,400 sec/day ).
        So 1 petaflop–s-day = (1 * 10^15 op/sec) * (8.64 * 10^4
sec/day). Which reduces to 8.64 * 10^19 op/day. Approximately 10^20
op/day. Q.E.D. WurmWoodeT 02:23, 12 January 2022 (UTC)[reply]

Removal of Controversy Section?

The section I added about controversy concerning OpenAI is completely
warranted. I can assure you the creation of OpenAI LP has generated
controversy. Again just last week with the prica announcement of GPT-3
the no longer open company structure of OpenAI is debated. Could you
elaborate your reasons to remove the entire Controversy section? HaeB
Diff here: https://en.wikipedia.org/w/index.php?title=OpenAI&diff=975914486&oldid=975824355

I'd like to add the announced pricing of GPT-3 to the controversy
section as well but before doing that and getting it removed again.
This needs resolving imho. I've seen it in many wikipedia articles
that the controversial things about a subject are being repeated in
that section so imho it doesn't warrant a complete errasure.
Additionally it is true that they are still filing as a non-profit
which is controversial given how non-transparent they have been
lately. — Preceding unsigned comment added by Juliacubed (talk •
contribs) 07:35, 4 September 2020 (UTC)[reply]

    I agree that turning for profit generated a lot of controversy and
deserves a section. See
https://techcrunch.com/2019/03/11/openai-shifts-from-nonprofit-to-capped-profit-to-attract-capital/
and https://www.technologyreview.com/2020/02/17/844721/ai-openai-moonshot-elon-musk-sam-altman-greg-brockman-messy-secretive-reality/
and https://www.wired.com/story/dark-side-big-tech-funding-ai-research/
Yannn11 16:17, 11 July 2021 (UTC)[reply]

There clearly should be a Controversy section. The name "OpenAI" is
deliberately misleading - it suggests that all development is open
source, which is clearly not the case. Removal of the Controversy
section seems to me to have been vandalism. But now the page is
protected so that it's difficult to add it back. Sayitclearly (talk)
11:32, 6 December 2022 (UTC)[reply]
For Profit owned by Non Profit? What?

This is a general encyclopedia for everyone. We need to explain this
corporate/Organisation construct and who can possibly profit or not
profit from this. The current article is bound to confuse, rather than
to clear things up. Can we please get someone who knows about this
legal construct and explain it? Thanks so much. --91.64.59.134 (talk)
20:48, 24 October 2021 (UTC)[reply]
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Unnecessary emphasis on Elon Musk?

I think this page refers to Elon Musk somewhat gratuitously. In
particular, it seems unnecessary to feature a relatively large
portrait of Musk next to a classification of the article as belonging
to a series related to Musk, and linking to a page with his honors and
achievements. Musk was one of several co-founding donors to the openai
project, and no longer has any involvement with it. I think it would
be appropriate to remove the photo of Musk, and link to his honors and
achievements. Nickstudenski (talk) 19:34, 10 June 2022 (UTC)[reply]

    I agree and removed "Elon Musk series." Yannn11 18:30, 11 June
2022 (UTC)[reply]

Greg Brockman page

surely time for a wiki article on him. why not? he's an important
player in OpenAI and thus in AI development.
https://openai.com/blog/authors/greg/
https://www.forbes.com/profile/greg-brockman/
https://csuitespotlight.com/2022/08/23/ivy-league-dropout-greg-brockman-is-leading-the-ai-revolution/
JCJC777 (talk) 13:10, 4 January 2023 (UTC)[reply]
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