2012 Ap Computer Science Free Response Answers

[Elia Khensamphanh]

Whatis the reasoning for creating the others, and are there clear guidelines for which kinds of questions should be posted where? I can see a large amount of potential overlap and many cases of people not getting a good answer to their question, because the person who has the answer isn't browsing that particular site at the moment.

I understand that they were probably created for organizational purposes, but wouldn't it almost make more sense to just have them as categories under Stack Overflow, keep them separated but still connected, instead of making people have to create multiple "account"s, one for each site?

I am sure there was a good reason to break them up, but as someone who is new to SE, it can be somewhat intimidating to decide which one to post to ensure you get a good answer. For example, if I am a computer science student, my first instinct might to be to post in the computer science site, until I see that it literally has 1% of the users as the Stack Overflow site, which still seems to be for programming/computer science related questions.

So my second instinct would then to be to post it into the Stack Overflow site on the basis that I have a significantly higher chance of my question being seen and getting a good answer. Then I notice that there is also a "programmers" site, and I don't even begin to know where that fits in. I, and I am assuming most people, will probably just post on Stack Overflow to be safe.

My question is, other than the short little description blurb of each one, is there a clear-cut set of guidelines which what each site is intended for, what kinds of questions should go to each one, and is anything being done to encourage people to post in these newer, smaller (more specialized?) sites as opposed to just posting in big daddy Stack Overflow?

Now again, this is for computer science/programming/"why isn't this code doing what I want?" related questions, I am not saying that if I had a question about Linux or WordPress or something I would have the same confusion.

Questions seeking debugging help ("why isn't this code working?") must include the desired behavior, a specific problem or error and the shortest code necessary to reproduce it in the question itself. See: How to create a Minimal, Complete, and Verifiable example

If you are creating a new programming language for others to use, and you are looking for advice on what aspects will make it easy to use and/or implement, then this is probably the right site for you.

On Code Review, you share working code from a project that you own or maintain for peer review. The right time for a code review is after you are satisfied that you have done your best, that the code is ready for publication/release, that all the features are in, and that all the tests pass. It is the right place if you want a critique of your code that addresses issues such as:

If your code is not yet producing the output you require then the code is not ready for review. If you need help getting the code to a completed state and you have specific questions about how to do that, then Stack Overflow is the right place to ask.

For questions about computer science, as in the academic discipline. As a rule of thumb, if your question depends on real-life languages/code/hardware/..., ask on Stack Overflow; if your question calls for abstract/mathematical models and reasoning, ask on Computer Science. Algorithms expressed in pseudocode straddle the border.

For questions about education within the context of computer science. The typical site user teaches computer science. Self-learning questions about designing a course of study or an approach to a topic are also possible here, but this site is not meant to teach students about CS directly.

SQA focuses on software testing questions, which run the gamut from technical queries about implementation of your automated tests, to organisational questions like planning training for your test team, or even how you go about persuading your manager to actually hire some professional testers instead of just crossing his/her fingers and hoping. It's aimed at professional software testers, and other related roles (programmers, business analysts) who perform software testing as part of their profession.

Is it some form of coding contest, with a scoring criterion? If so, and only if it has a scoring criterion, then it's on topic for Code Golf. The site is for challenges and contests that involve coding, where answers are a submission to that given contest, almost always in code. Challenges must have an objective scoring criterion, generally code-golf, and clear specifications, and questions asking for programming help are entirely off-topic, unless asking for help with shortening their code. Additionally, it is highly recommended, even more so for new users to the site, that you post your draft into their Sandbox to get feedback before posting it to main.

Many questions about Geographic Information Systems involve programming using libraries such as ArcPy, PyQGIS, ArcGIS API for JavaScript, OpenLayers, Leaflet, ArcObjects, etc. and should be asked at the GIS Stack Exchange. If they are about the underlying programming language such as Python, JavaScript, C#, etc., then they should be researched at Stack Overflow instead.

Open Data is a Q&A site for developers, researchers, and anyone else interested in open data. Open data, as defined by the Open Definition, is any kind of data that can be freely used, modified, and shared by anyone for any purpose. When Computer Scientists and Programmers seek any kind of open data this site can help you to find it.

Web Applications is a question and answer site for power users of web applications. With your help, we're working together to build a library of detailed answers to every question about using web applications including:

Operations Research is the development and use of analytical methods to describe, analyze, plan, design, manage and integrate the operations of systems and enterprises that involve complex interactions among people, processes, materials, equipment, information, organizations and facilities to provide services and produce goods for society.

Employing techniques from other mathematical sciences, such as mathematical modeling, statistical analysis, and mathematical optimization, operations research arrives at optimal or near-optimal solutions to complex decision-making problems. Because of its emphasis on human-technology interaction and because of its focus on practical applications, operations research has overlap with other disciplines, notably industrial engineering and operations management, and draws on psychology and organization science. Operations research is often concerned with determining the extreme values of some real-world objective: the maximum (of profit, performance, or yield) or minimum (of loss, risk, or cost).

Recent developments in artificial intelligence technologies are forcing us to reimagine how we engage with the world around us. Experts in digital technologies and data privacy have been keeping up with the latest AI developments and AI limitations, and many, like Latanya Sweeney, have noticed that the introduction of ChatGPT may signal a major shift in how we engage with the internet, each other, and the world.

Terms like generative AI, machine learning, ChatGPT, and natural language processing are often used interchangeably, but in order to understand the impacts of these technologies, we first have to define the terminology.

Artificial Intelligence (AI) is an umbrella term for any theory, computer system, or software that is developed to allow machines to perform tasks that normally require human intelligence. The virtual assistant software on your smartphone is an example of artificial intelligence.

Machine Learning is a field that develops and uses algorithms and statistical models to allow computer systems to learn and adapt without needing to follow specific instructions. Asking the GPS on your phone to calculate the estimated time of arrival to your next destination is an example of machine learning playing out in your everyday life.

ChatGPT is a chatbot developed by OpenAI that uses generative AI and natural language processing to simulate human-like conversations in a chat window where the user can ask the bot to help with a variety of tasks, including drafting emails, essays, code, and more.

We are just learning all of the ways technologies like ChatGPT can impact how we learn, work, and interact with each other in society. Through this work, we are also better understanding the limits of artificial intelligence as it stands today. Harvard leaders like Bharat Anand, Vice Provost for Advances in Learning at Harvard University, have enjoyed experimenting with the technology. In the clip below, Anand shares one of his experiences and surprising findings:

What are the broader applications of this technology in industry? Imagine being able to ask a chatbot for legal advice. While the advice may not be entirely trustworthy today, this type of service provides some insight on the implications of ChatGPT across industries and workforces.

How will ChatGPT impact how we engage with the world around us and each other? Even before ChatGPT, machine learning has been helping us make decisions for better and for worse. Latanya Sweeney explains:

In a world where computers are enabled with the ability to make decisions big and small, Sweeney is most concerned with maintaining control over our lives and society even as these technologies change the way we live, work, and play.

Can we accurately predict our future alongside generative AI technologies considering the rapid development of these technologies? How can we address AI limitations and improve upon these areas? Latanya Sweeney offers some final thoughts:

All quotes and video clips in this post were excerpted from The Future of Generative AI: Transforming Education, Work, and Society, a webinar coordinated through the Office of the Vice Provost for Advances in Learning (VPAL) featuring panelists from across Harvard University.

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