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Legacy post: Getting started with ChatGPT Part 1 – Understanding ChatGPT

A mobile phone with ChatGPT open sits in front of the OpenAI logo.

Legacy post: AI is a fast-moving technology and unfortunately this post now contains out-of-date information. The post is now available just for those that need to reference older articles. We have published a newer guide for educators on ChatGPT here. 

So you’ve heard about ChatGPT and want to explore it but don’t know where to start? This is the first in a short series of blog posts giving some pointers on how to get going.

In this series we’ll look at potential timesaving examples around creating lesson/course plans, quizzes and getting feedback on your writing. First though we’ll start with a general introduction.

This post is for the free version of ChatGPT.  You might have heard that GPT-4 has been announced, but at the moment is only available for the paid version.  This post will be updated when that changes!

How to get started

Head to https://chat.openai.com/ in your browser.  If it’s your first time there you’ll need to sign up by entering your email address, or using an existing Google or Microsoft account.  It will ask for your mobile number during the signup process, but it won’t ask for any payment details.

Things you should know

Before you get going, there are a few things you should understand about ChatGPT.  Let’s start with an important one:

You should never enter personal or sensitive information in this version of ChatGPT!

This is a free research release and doesn’t have the contractual terms in conditions in place that guarantee data privacy and so on.

You should also understand its limitations. These are a few of the key ones:

  • It doesn’t search the internet for information, so it only has information about up to September 2021.
  • It can and often does get facts wrong, particularly about topics where there is less information available.
  • It provides a different answer each time, so if you ask the same question twice you almost certainly won’t get the same response.
  • If you asked very long questions or tasks, it will only actually use the first 300 to 500 words!

This might make it sound like it’s of limited use, but there are an awful lot of things that it can do, and we’ll explore some of those in this series of blog posts.  OpenAI have an educator’s guide, and they have suggested the following, so we explore some of these:

  • Drafting and brainstorming for lesson plans and other activities
  • Help with the design of quiz questions or other exercises
  • Providing grammatical or structural feedback on portions of writing
  • Customizing materials for different preferences (simplifying language, adjusting to different reading levels, creating tailored activities for different interests)

Learning about its limitations

Before we start using ChatGPT for useful tasks, it’s a good idea to understand its limitations.

Current Information

Start by asking something about current information.  Here we’ve chosen ‘Who is the current prime minister of the UK’

When we ask a question, the question is often know as a prompt, and we’ll use that term in these blog posts:

Prompt: Who is the current prime minister of the UK

Who is the current prime minister of the UK? As of my knowledge cutoff date of September 2021, the current Prime Minister of the UK is Boris Johnson. However, please note that I am an AI language model and do not have access to real-time information, so there is a possibility that a new Prime Minister has been elected since then.

You’ll see it clearly tells you about its information cutoff date, and at that point how Boris Johnson was Prime Minister.

Misinformation

We mentioned ChatGPT often gets things wrong. This can be harder to explore! One approach to understanding this is to pick a topic that you know a lot about, but there isn’t a huge amount of information about the on the internet. Generally the more common the topic, the more accurate the information.

One that works really well for me is asking about the village where I live – here I ask it  – ‘Tell me about the best art installation in Caerleon.’ (we do have a lot of sculptures so it’s not a silly question, but it’s niche information). The answer ChatGPT comes up with though is completely made up! We have no such Lace Fence, although it does sound like it would be lovely.  It all sounds very plausible though, hence the recommendation to explore a topic that you can fact-check yourselves easily.

Tell me about the best art installation in Caerleon. Caerleon, located in South Wales, has a rich history of Roman heritage and is known for its numerous archaeological sites and monuments. While I am not aware of all the art installations in Caerleon, there is one that stands out as particularly noteworthy - the Caerleon Lace Fence. The Caerleon Lace Fence is a beautiful and unique art installation that was created in 2010 by artist and blacksmith David Petersen. The installation features a long fence made of steel, which has been intricately cut and woven to resemble delicate lace. The design is inspired by traditional Welsh lace patterns, and the intricate details of the piece are truly stunning. The lace fence is located in the town center, next to the town hall, and has become a popular attraction for visitors to Caerleon. It is a great example of how public art can transform a space and create a sense of beauty and wonder in a community. Overall, the Caerleon Lace Fence is a must-see for anyone visiting Caerleon who appreciates art and craftsmanship.

Word limit in queries

We’ve included this as there is quite a lot of misinformation about this on the internet, particularly about using ChatGPT to summarise or process large chunks of text.  It can actually only process first 2048 tokens, which amounts to around 300-500 words. A token is a part of a word, or punctuation etc.

To test this, paste in some long text, and then ask it how many words it was, and what the last sentence was. I’ve used this blog post.

I’ve pasted it in and then asked how many words, and what the last sentence is.  There actually around 1400 words, and it has picked the last sentence from around the middle of the text.

Michael Webb How many words in this text: "Hidden Workers powering AI Many people are aware of various AI tools and technologies but most of us aren’t aware of invisible workers involved in the production of AI. This blog post focuses on hidden labour involved in AI production. We aim to raise awareness and educate educational institutions about this important issue. Although research in this area is limited, the post highlights the role of hidden workers, who they are, what can be done to improve the situation and make informed decisions about using AI products and services. However, any AI system requires a significant amount of human effort that is sometimes disregarded. This “hidden work” is crucial to the development and functioning of AI systems but is often unnoticed. The production of AI involves a significant amount of human labour, often called as “Ghost workers “who empower AI, behind-the-scenes. This hidden labour is often overlooked in discussions of AI, but it is essential to understanding the implications of AI on society. Kate Crawford, in her book “Atlas of AI,” highlights dark side of AI production and suggests AI is neither “artificial” nor “intelligent.” The creation of AI involves a large consumption of resources, such as energy and minerals, and the workers who contribute to its production often have their rights disregarded. Crawford’s book sheds light on the exploitation of workers in the background of AI’s “automation.” Mary Gray’s book “Ghost Work” focuses on invisible workers behind AI and challenges faced i.e., lack of job security, benefits, fair pay, and recognition. The book highlights need for better treatment, fair work conditions and raises important questions about the ethics of AI and its impact on hidden workers. How Hidden workers are empowering AI: To shape and train these applications, digital giants like Amazon, Google, and Facebook, employ an army of invisible labour. But there are others such as Appen, Upwork, CrowdFlower and Microwork. They hire workers for remote work from anywhere in the world to train machine learning models by perform tasks like data labelling, data annotations, transcription etc. Data labelling: It is the process of marking or classifying data used to train AI systems. This includes classifying text, transcribing audio, or recognizing objects in photos. For example, in image recognition, data labelling could involve manually labelling each image with information about what’s in it (e.g., “dog”, “car”, “person”). This labelled data is then used to train artificial intelligence algorithms to accurately recognize and categorize images on their own. In essence, data labelling is a crucial step in the development of AI systems as it provides the foundation for the algorithms to learn from. Autonomous cars , a rapidly growing sector that is expected to be worth $556 billion by 2026. To navigate its driverless vehicles, companies like Tesla require clean and tagged data. This information is obtained via onboard cameras and must be classified and labelled for the automobile to detect items such as people, traffic signs, and other cars. Data labelling is labour-intensive, time-consuming, and repetitive procedure that needs to be done with great precision. People in low-wage countries who work long hours for low and non-negotiable wages are frequently hired to perform tasks like data labelling. They neither receive contracts nor incentives and are unaware of the usage of their work. Somehow, we all have likely been participating in data labelling by completing captcha codes, as the responses we provide are used to train machine learning models. It’s possible that you have encountered instances where a website prompts you to enter a series of characters displayed in a distorted image. These are known as captcha codes, which are designed to differentiate human users from automated bots. Your responses, along with others, are utilized as a form of data labelling for machine learning models. This data labelling is important in enhancing the precision of these models across a range of applications, such as image classification and speech recognition. Who are Hidden Workers? According to the study these are the individuals who desire employment but are unable to secure suitable job opportunities due to multiple factors such as discrimination, lack of education/ skills, discouragement due to their repeated unsuccessful job search. This includes refugees, people with disabilities, veterans, prisoners, care givers and relocated partners and spouses. The study estimated 27 million hidden workers in US alone by interviewing 8000 hidden workers and over 2250 executives in US, UK, and Germany. These are invisible workers, paid as little as 10 cents- $2 an hour to feed information into computer systems. Refugees empowering machine learning advances by working for Silicon Valley corporations like Google, Amazon, Facebook from a tent with computers in Dadaab, Kenya, one of the world’s largest refugee camps. They conduct click work, such as video tagging and audio transcription, as one of their limited legitimate job possibilities. However, the task is difficult, paid per piece, and performed in confined, airless environments. The refugees in Lebanon’s Shatila camp are compelled to labour at night labelling footage of large cities for the benefit of foreign capitalists, although the specific goal or beneficiaries of their work are unknown. M2Work, coordinated by infoDev, is an online program aimed at encouraging poor countries to participate in the production side of digital economy and create jobs. Similarly, Sama, data annotation and labelling platform, teach and train refugees in Kenya, India, and Uganda to accomplish brief data tasks. Sama works with clients such as Walmart, Google, Microsoft and General Motors. Some of its employees in Kenya contributed to filtering toxic content for ChatGPT, which led to wide spread criticism due to the work negatively impacting their mental health, poor working conditions and little to no pay. Traditionally, prison labour involved physical work but Prisoners in Finland are recruited by Vainu to conduct data labour for struggling startups, with the government getting paid for each assignment done. However, no information is available on what percentage of the cash goes to the convicts. The company claims that the collaboration is a sort of jail rehabilitation that teaches essential skills, but other experts feel that the job training claims are simply hype around AI’s promises. The Online Labour Observatory platform serve as a centralized hub for researchers and decision-makers. It tracks the real-time number of tasks and projects completed by freelancers on different websites across various countries and job categories. The platform offers valuable information on the gig economy and assists those in decision-making positions to make informed decisions. The Online Labour Observatory provides a comprehensive view of the developments and patterns in online work, and its effect on both workers and the economy. Despite these known issues this area remains under research. EU- Funded project conduct in-dept study about the working condition of hidden work force involved in the production of AI and its impact on their wellbeing. How can we make it better? Offer fair pay, remove inequalities. Regulate and compensate hidden labour. Provide proper training and guidelines to labellers to avoid bias in the labelling work. Diversify labellers to include people from different cultures, backgrounds, and perspectives. Implement algorithmic fairness techniques, review and audit labelled data to identify and remove bias. Universities and colleges must be aware of the labour behind AI and consider its impact when using AI services, including examining biases in data used to train AI algorithms and potential consequences in outcomes produced. By approaching AI responsibly, universities and colleges can ensure its benefits are shared and workers are treated fairly. Institutions should thoughtfully consider the ethical implications of AI and ask questions about the workers powering it before making decisions on its use. Who is developing and creating AI technology? How is data pre-processed and does it affect accuracy and fairness of AI systems? Are workers involved in AI production empowered and supported professionally? Are there concerns about exploitation or human rights violations in AI production process? How are AI algorithms tested for fairness and bias, and who determines test rigor? What steps are taken to ensure AI benefits are widely shared and technology is developed responsibly? Potential impact of AI systems on various demographic groups and how these impacts are monitored and addressed. Understanding human involvement in AI systems enable individuals to make informed decisions about products and services they use and understand the consequences on society. By promoting critical thinking about the role of humans in AI, we can work towards creating a more equitable and ethical AI industry. " There are 522 words in this text. Michael Webb What is the last sentence that text? There is no last sentence in the text as it seems to be incomplete. The text ends abruptly after the sentence, "Prisoners in Finland are recruited by V..."

We have seen a lot of misinformation in various articles on the web showing ChatGPT summarising or producing quizzes from large chunks of text.  It won’t actually be using it all!

What Next

Now we’ve explored the things ChatGPT can’t do, it’s time to start exploring the things it can do.  Our next blog post explores how it can be used for planning activities such as lessons and courses.

Find out more by visiting our Artificial Intelligence page to view publications and resources, join us for events and discover what AI has to offer through our range of interactive online demos.

For regular updates from the team sign up to our mailing list.

Get in touch with the team directly at AI@jisc.ac.uk

By Michael Webb

I colead our AI team. We support the responsible and effective adoption of artificial intelligence across the tertiary education sector, through a range of pilots, advice, guidance, and community support activities. Before joining Jisc I worked in the University sector, leading IT and educational technology services. Since joining Jisc, I have worked on a wide range of projects, including leading the development of our learning analytics service.

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