User:SkyradBear/sandbox

Contributions after initially editing article
Diff links:

https://en.wikipedia.org/w/index.php?title=Artificial_intelligence&action=history

https://en.wikipedia.org/w/index.php?title=Weak_artificial_intelligence&action=history

Peer review
Peer review:

I think this is a really good start, especially considering that you are drafting a brand new article. I really like the specific, well-divided sections along with the table of contents. Furthermore, your sources look reliable (maybe add a few more if you can?). Although weak artificial intelligence may be a part of civic tech itself, there is still somewhat a lack of a connection between weak and/or strong ai with civic tech. Also, you could really expand on the "impact" section as that is really what the heart of the article could be. Other than that which you probably already had in mind to add on, looks good. Lavarball13 (talk) 03:29, 18 October 2022 (UTC)

Response to peer review:

This article definitely seems to be brand new and I appreciate the comment about it being a good start. There is certainly already a well structured start of an article laid out with specific sections and a table of contents. I will try and add a few more sources if I can, or if it seems relevant and I want to talk more about my topic. I agree that there should be a better connection between weak and strong AI, instead currently with just weak AI. I will certainly talk about this when I talk about the differences and give examples of how each works in the real world. My plan is to really focus on the impact of weak artificial intelligence, and I one hundred percent agree that this should be the main heart of this article. Again, I appreciate the comment about this draft plan looking good, and yes I have already started planning out how to improve the “impact” section.

Topic
Weak AI: I will add more reliable sources including ones from the UC Berkeley library. I also plan on explaining more about the differences between narrow and weak AI, as well as about weak vs. strong AI. Furthermore, I will talk about how weak AI exists in the real world today, and how it can be an issue to our current society. This article does not have an picture with a caption on the right side, so if I can figure out how to do that, I would like to add one.

Link to talk page for article:
Talk:Weak AI

List of possible topics:

 * Behavior selection algorithm - too many different kinds of behavioral modeling, there could be more examples of how it works and a more in-depth description, with more sources
 * Weak AI - could use more reliable sources and explain more on the differences between narrow and weak A.I. and how they're used in the real world today
 * Cultural lag - needs more sources for the information and different (better) sources for different ideas in this article, also could use more examples of problems with cultural lag, especially today
 * Citizen media - does not really talk about what citizen media is like outside the U.S. or could use more modern examples

Contributions Draft
1st general paragraph:

Weak artificial intelligence focuses on mimicking how humans perform basic actions such as remembering things, perceiving things, and solving simple problems. As opposed to strong AI, which uses technology to be able to think and learn on its own. Computers are able to use methods such as algorithms and prior knowledge to develop their own ways of thinking like human beings do. Strong artificial intelligence systems are learning how to run independently of the programmers who programmed them. Weak AI is not able to have a mind of its own, and can only imitate physical behaviors that it can observe.

Narrow section in last paragraph before section:

AI can be classified as being “… limited to a single, narrowly defined task. Most modern AI systems would be classified in this category.” Narrow means the robot or computer is strictly limited to only being able to solve one problem at a time. Strong AI is conversely the opposite. Strong AI is as close to the human brain or mind as possible. This is all believed to be the case by philosopher John Searle. This idea of strong AI is also controversial, and Searle believes that the Turing test (created by Alan Turing during WW2, originally called the Imitation Game, used to test if a machine is as intelligent as a human) is not accurate or appropriate for testing strong AI.

New Examples section:

Some examples of weak AI are self-driving cars, robot systems used in the medical field, and diagnostic doctors. The reason all of these are weak AI systems, self driving cards can cause deadly accidents similarly to how humans normally can. Medicines could be incorrectly sorted and distributed to people. Also medical diagnoses can ultimately have serious and sometimes deadly consequences if the AI is faulty. Another issue with weak artificial intelligence currently, is that behavior that it follows can become inconsistent. Patterns could become difficulty to come up with one consistent system that worked every time.

New Weak vs. strong section:

The differences between weak vs. strong AI is not widely catalogued out there at the moment. Weak AI is commonly associated with basic technology like voice-recognition software such as Siri or Alexa. Whereas strong AI is not fully implemented or testable yet, so it is only really fantasized about in movies or popular culture media. It seems that one approach to AI moving forward is one of an assisting or aiding role to humans. There are some sets of data or numbers that even we humans cannot fully process or understand as quickly as computers can, so this is where AI will play a helping role for us.

Add to Examples section:

Simple artificial intelligence programs have already worked their way into our society and we just might not have noticed it yet. Autocorrection for typing, speech recognition for speech to text programs, and vast expansions in the data science fields are just to name a few. As much as weak and some strong AI is slowly starting to help out societies, they are also starting to hurt it as well. AI had already unfairly put people in jail, discriminated against women in the workplace for hiring, taught some problematic ideas to millions, and even killed people with automatic cars. AI might be a powerful tool that can be used for improving our lives, but it could also be a dangerous technology with the potential for things to get out of hand.

Actual Draft
Weak artificial intelligence focuses on mimicking how humans perform basic actions such as remembering things, perceiving things, and solving simple problems. (1) As opposed to strong AI, which uses technology to be able to think and learn on its own. Computers are able to use methods such as algorithms and prior knowledge to develop their own ways of thinking like human beings do. (1) Strong artificial intelligence systems are learning how to run independently of the programmers who programmed them. Weak AI is not able to have a mind of its own, and can only imitate physical behaviors that it can observe. (2)

AI can be classified as being “… limited to a single, narrowly defined task. Most modern AI systems would be classified in this category.” Narrow means the robot or computer is strictly limited to only being able to solve one problem at a time. Strong AI is conversely the opposite. Strong AI is as close to the human brain or mind as possible. This is all believed to be the case by philosopher John Searle. This idea of strong AI is also controversial, and Searle believes that the Turing test (created by Alan Turing during WW2, originally called the Imitation Game, used to test if a machine is as intelligent as a human) is not accurate or appropriate for testing strong AI.

Some examples of weak AI are self-driving cars, robot systems used in the medical field, and diagnostic doctors. The reason all of these are weak AI systems, self driving  cards  cars can cause deadly accidents similarly to how humans normally can. Medicines could be incorrectly sorted and distributed to people. Also medical diagnoses can ultimately have serious and sometimes deadly consequences if the AI is faulty. 3 Another issue with weak artificial intelligence currently, is that behavior that it follows can become inconsistent. 6 Patterns could become difficulty to come up with one consistent system that worked every time.

The differences between weak vs. strong AI is not widely catalogued out there at the moment. Weak AI is commonly associated with basic technology like voice-recognition software such as Siri or Alexa. Whereas strong AI is not fully implemented or testable yet, so it is only really fantasized about in movies or popular culture media. It seems that one approach to AI moving forward is one of an assisting or aiding role to humans. There are some sets of data or numbers that even we humans cannot fully process or understand as quickly as computers can, so this is where AI will play a helping role for us. (8)

Simple artificial intelligence programs have already worked their way into our society and we just might not have noticed it yet. Autocorrection for typing, speech recognition for speech to text programs, and vast expansions in the data science fields are just to name a few. (9) As much as weak and some strong AI is slowly starting to help out societies, they are also starting to hurt it as well. AI had already unfairly put people in jail, discriminated against women in the workplace for hiring, taught some problematic ideas to millions, and even killed people with automatic cars. (10) AI might be a powerful tool that can be used for improving our lives, but it could also be a dangerous technology with the potential for things to get out of hand.

Actual draft without citations
Weak artificial intelligence focuses on mimicking how humans perform basic actions such as remembering things, perceiving things, and solving simple problems. (1) As opposed to strong AI, which uses technology to be able to think and learn on its own. Computers are able to use methods such as algorithms and prior knowledge to develop their own ways of thinking like human beings do. (1) Strong artificial intelligence systems are learning how to run independently of the programmers who programmed them. Weak AI is not able to have a mind of its own, and can only imitate physical behaviors that it can observe. (2)

Original article sources:

 * 1) Dvorsky, George (1 April 2013). "How Much Longer Before Our First AI Catastrophe?". Gizmodo. Retrieved 27 November 2021.
 * 2) Muehlhauser, Luke (18 October 2013). "Ben Goertzel on AGI as a Field". Machine Intelligence Research Institute. Retrieved 27 November 2021.
 * 3) Chalfen, Mike (15 October 2015). "The Challenges Of Building AI Apps". TechCrunch. Retrieved 27 November 2021.
 * 4) The Cambridge handbook of artificial intelligence. Frankish, Keith., Ramsey, William M., 1960-. Cambridge, UK. 12 June 2014. p. 342. ISBN  978-0-521-87142-6 . OCLC 865297798.
 * 5) Lieto, Antonio (2021). Cognitive Design for Artificial Minds. London, UK: Routledge, Taylor & Francis. ISBN  9781138207929.
 * 6) Goertzel, Ben (6 February 2010). "Siri, the new iPhone "AI personal assistant": Some useful niche applications, not so much AI". The Multiverse According to Ben. Retrieved 27 November 2021.

My sources:

 * 1) Chandler, Daniel (2020). A dictionary of media and communication
 * 2) Colman, Andrew M. (2015). A dictionary of psychology (4th ed.).
 * 3) Bartneck, Christoph; Lütge, Christoph; Wagner, Alan; Welsh, Sean (2021). (4 on talk)
 * 4) Liu, Bin (2021-03-28). ""Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?" (5 on talk)
 * 5) Szocik, Konrad; Jurkowska-Gomułka, Agata (2021-12-16). "Ethical, Legal and Political Challenges of Artificial Intelligence: Law as a Response to AI-Related Threats and Hopes" (3 on talk)
 * 6) Kuleshov, Andrey; Prokhorov, Sergei (September 2019). "Domain Dependence of Definitions Required to Standardize and Compare Performance Characteristics of Weak AI Systems"
 * 7) Kerns, Jeff (February 15, 2017). "What's the Difference Between Weak and Strong AI?"
 * 8) LaPlante, Alice; Maliha, Balala (2018). Solving Quality and Maintenance Problems with AI.
 * 9) Earley, Seth (2017). "The Problem With AI". IT Professional. 19 (4): 63–67.
 * 10) Anirudh, Koul; Siddha, Ganju; Meher, Kasam (2019). Practical Deep Learning for Cloud, Mobile, and Edge

Article Evaluation Quick Link
https://en.wikipedia.org/wiki/Talk:Design_studies#Add_dates_to_all_name

(the rest of the assignment stuff is at the bottom of the sandbox)

= Sandbox zone: =

This is a test. bold Emphasis (typography)

'' Sorry everything is so messy and disorganized right now! Still getting the hang of editing in Wikipedia! ;) ''

Paragraph: Set the style of your text. For example, make a header or plain paragraph text. You can also use it to offset block quotes.
A : Highlight your text, then click here to format it with bold, italics, etc. T he “More” options allows you to underline (U), cross-out text (S) , , change language keyboards (Aあ) , and clear all formatting.

Links: Highlight text and push this button to make it a link. The Visual Editor will automatically suggest related Wikipedia articles for that word or phrase. This is a great way to connect your article to more Wikipedia content. You only have to link important words once, usually during the first time they appear. If you want to link to pages outside of Wikipedia (for an “external links” section, for example) click on the “External link” tab.

Cite: The citation tool in the Visual Editor helps format your citations. You can simply paste a DOI or URL, and the Visual Editor will try to sort out all of the fields you need. Be sure to review it, however, and apply missing fields manually (if you know them). You can also add books, journals, news, and websites manually. That opens up a quick guide for inputting your citations. Once you've added a source, you can click the “re-use” tab to cite it again.


 * Bullets: To add bullet points


 * 1) or a numbered list, click here.



Ω: This tab allows you to add special characters, such as those found in non-English words, scientific notation, and a handful of language extensions.

Article evaluation
{| class="wikitable" Complete your article evaluation below. Here are the key aspects to consider:
 * Evaluate an article

Lead section
A good lead section defines the topic and provides a concise overview. A reader who just wants to identify the topic can read the first sentence. A reader who wants a very brief overview of the most important things about it can read the first paragraph. A reader who wants a quick overview can read the whole lead section.


 * Does the lead include an introductory sentence that concisely and clearly describes the article's topic?
 * Does the lead include a brief description of the article's major sections?
 * Does the lead include information that is not present in the article? (It shouldn't.)
 * Is the lead concise or is it overly detailed?

Content
A good Wikipedia article should cover all the important aspects of a topic, without putting too much weight on one part while neglecting another.


 * Is the article's content relevant to the topic?
 * Is the content up-to-date?
 * Is there content that is missing or content that does not belong?
 * Does the article deal with one of Wikipedia's equity gaps? Does it address topics related to historically underrepresented populations or topics?

Tone and Balance
Wikipedia articles should be written from a neutral point of view; if there are substantial differences of interpretation or controversies among published, reliable sources, those views should be described as fairly as possible.


 * Is the article neutral?
 * Are there any claims that appear heavily biased toward a particular position?
 * Are there viewpoints that are overrepresented, or underrepresented?
 * Are minority or fringe viewpoints accurately described as such?
 * Does the article attempt to persuade the reader in favor of one position or away from another?

Sources and References
A Wikipedia article should be based on the best sources available for the topic at hand. When possible, this means academic and peer-reviewed publications or scholarly books.


 * Are all facts in the article backed up by a reliable secondary source of information?
 * Are the sources thorough - i.e. Do they reflect the available literature on the topic?
 * Are the sources current?
 * Are the sources written by a diverse spectrum of authors? Do they include historically marginalized individuals where possible?
 * Are there better sources available, such as peer-reviewed articles in place of news coverage or random websites? (You may need to do some digging to answer this.)
 * Check a few links. Do they work?

Organization and writing quality
The writing should be clear and professional, the content should be organized sensibly into sections.


 * Is the article well-written - i.e. Is it concise, clear, and easy to read?
 * Does the article have any grammatical or spelling errors?
 * Is the article well-organized - i.e. broken down into sections that reflect the major points of the topic?

Images and Media

 * Does the article include images that enhance understanding of the topic?
 * Are images well-captioned?
 * Do all images adhere to Wikipedia's copyright regulations?
 * Are the images laid out in a visually appealing way?

Talk page discussion
The article's talk page — and any discussions among other Wikipedia editors that have been taking place there — can be a useful window into the state of an article, and might help you focus on important aspects that you didn't think of.


 * What kinds of conversations, if any, are going on behind the scenes about how to represent this topic?
 * How is the article rated? Is it a part of any WikiProjects?
 * How does the way Wikipedia discusses this topic differ from the way we've talked about it in class?

Overall impressions

 * What is the article's overall status?
 * What are the article's strengths?
 * How can the article be improved?
 * How would you assess the article's completeness - i.e. Is the article well-developed? Is it underdeveloped or poorly developed?

Examples of good feedback
A good article evaluation can take a number of forms. The most essential things are to clearly identify the biggest shortcomings, and provide specific guidance on how the article can be improved.


 * Peer review of this article about a famous painting
 * }

Which article are you evaluating?
Design studies

Why you have chosen this article to evaluate?
I am still figuring out what I want to study in college and design is a topic that interests me. I also choose this specific article about design studies because it was under the category of academic disciplines, so was ok for the assignment. After reading it I think it is important to have an article devoted to this topic of design studies because it's an important field for many people and an important distinction from just the field of design. I thought this article was pretty well put together and organized with a good amount of sources for most of the paragraphs.

Evaluate the article

 * Is everything in the article relevant to the article topic? Is there anything that distracted you?
 * yes, no
 * Is any information out of date? Is anything missing that could be added?
 * birth dates of some of the foundational figures
 * Can you identify any notable equity gaps? Does the article underrepresent or misrepresent historically marginalized populations?
 * no
 * What else could be improved?

better sources


 * Is the article neutral? Are there any claims that appear heavily biased toward a particular position?
 * yes, no
 * Are there viewpoints that are overrepresented, or underrepresented?

no


 * Check a few citations. Do the links work? Does the source support the claims in the article?
 * yes, yes
 * Is each fact referenced with an appropriate, reliable reference? Where does the information come from? Are these neutral sources? If biased, is that bias noted?
 * mostly, academic/scholarly sources for the most part, neutral
 * Do the sources come from a diverse array of authors and publications?
 * yes


 * What kinds of conversations, if any, are going on behind the scenes about how to represent this topic?
 * Wikipedia being too conservative or orthodox, not enough citations, some issues with the links, too much information, too many lists, etc.
 * How is the article rated? Is it a part of any WikiProjects?
 * C-class, Architecture, Graphic design, Industrial design, and Systems
 * How does the way Wikipedia discusses this topic differ from the way we've talked about it in class?
 * more formal with sources for everything

For the talk page:

Add dates to all names

This article does a good job covering the history and information about the academic design discipline of design studies, but the Foundational figures section could be improved. A simple fix could be to add the birth dates and/or death dates of the people listed in having to do with design studies. Only some have dates next to their names and this article could be a little bit better if all the names had accurate information of how old these people are and if they are still alive or not.

Final result:

https://en.wikipedia.org/wiki/Talk:Design_studies#Add_dates_to_all_names