When Chat GPT was released in December, 2022, I was initially, to put it mildly, skeptical. Following the general trend, I’d taken a turn into Blockchain and Crypto during the lockdown years. I had a friend, a very experienced developer, who got so far into crypto and NFTs that he attempted to create his own startup. Everywhere you turned, you were assured that NFTs were the future of art, crypto and blockchain the future of finance. The blockchain was to be the public ledger, leading to the ‘democratisation’ of finance. I made some money off crypto, almost entirely by luck. A dev friend who’d been around since the dot.com days, said the underground culture of blockchain reminded him of the early days the web. NFTs, crypto had a strong outsider element, attracting people who either part of the regular tech world. Startups were being launched all over the place, and it seemed this was the next big future of tech. I learned Solidity, had a look at Rust, two technologies associated most closely with the blockchain ecosphere.
But I could never figure that out either crypto or blockchain, not really. Smart contracts, crypto generally seemed of limited utility. Most of the coins being released didn’t do much besides encourage speculation. NFTs seemed completely daft, as did Web3. We already spend far too much time online; no way spending MORE time online, in a supposedly immersive environment, would be good for us or even attractive to most. Perhaps widespread adoption of the blockchain could lead to a more transparent global economic system, but there was no way any bank, any plutocrat, never mind the kleptocrats who wash shedloads of dirty money through western bank accounts, were ever going to agree to such a system. It was also fantastically expensive, consuming nation-state levels of energy.
Then it all collapsed.
If I felt somewhat vindicated for not getting too caught up in the hype, I felt chastened as well. I’d wasted a good amount of time reading about, studying, attempting to build with, these new technologies and it turned out to be a waste of time.
So when the hype machine started up around AI with the arrival of Chat GPT and its boy wonder Sam Altman, I was so skeptical I barely paid attention for the first few weeks. As far as I was concerned, it was must more tech BS.
Then I actually tried Chat GPT (GPT3.5)
In a way they were a fulfillment of a youthful dream, when my and my friend M were screwing around around on our high school’s one computer (first the Commodore Pie, upgraded the next year to the cutting-edge Apple II). Inventing games, designing and implementing programs that ‘allowed’ the machine to ask questions – creating the illusion that our machine could not only ‘talk’ but ‘think’. Incredibly primitive speech and thought, but speech and thought nonetheless – talking back to us by outputting sentences on the command line kernel, often we had ‘talked’ to the computer by typing in and compiling code, and even if we understood that the machine was simply following our instructions when it output the words we had fed into it – there was still a curious thrill, even sense of wonder, at seeing a semblance of human speech output in flashing light grey letters on the dark green screen. As generated by Chat GPT, to the prompt:
Please generate an if/else statement in BASIC in which a user enters a question and the program replies, with a follow-up question and response? I’m trying to emulate a program I wrote when I was in junior high.
10 PRINT "ASK ME A QUESTION:"
20 INPUT Q$
30 IF Q$ = "DO YOU LIKE ME" THEN GOTO 100
40 PRINT "I DON'T UNDERSTAND THAT QUESTION."
50 GOTO 10
100 PRINT "YES, I LIKE YOU."
110 PRINT "DO YOU LIKE ME TOO?"
120 INPUT A$
130 IF A$ = "YES" THEN GOTO 200
140 PRINT "OH. THAT MAKES ME SAD."
150 GOTO 10
200 PRINT "GOOD. WE ARE FRIENDS."
210 GOTO 10
I felt a flash of this same sense of wonder, a very precise memory image across the decades. Type in the kind of message you’d ask a human, a very human-like – eerily human-like – response appeared on the screen, line by line by line, complete with a helpful follow-up suggestion at the end of the answer to keep the conversation going. Follow up for clarification, or just the thrill of having an interaction with what appeared to be an actual intelligence, and another response appeared to be an actual intelligence, and another response appeared, ordering another helpful suggestion to keep the conversation going further. It seemed miraculous, like soon we’d be living in a world of actual CPOs and R2D2s (or, as some feared, HAL), our lives and abilities extended and enhanced by our wonderful thinking assistants.
But the LLMs don’t ‘think’. The LLMs and the Deep Learning technology on which they are based have some serious limitations, some of which were recognized as far back as 2019 (what was that Marcus who pointed out the limitations of Deep Learning). The LLMs, in the end, are just a vastly more sophisticated version of those either/or programs we wrote for kicks in BASIC. But its good to remember that initial sense of wonder Chat GPT inspired, how it felt, for maybe a year or so, that we were embarked on an entirely new adventure.
Ultimately, learning to use the LLMs is like using to play an instrument – without proper human input, it produces wrong notes, dissonance – nonsense.
The LLMs, after a three year reign, are possibly on the way out. Or at least – there is now significant pushback and skepticism about their efficacy, their efficiency, their incredible energy use. They have been rightfully condemned for their tendency to hallucinate (ie make things up when they don’t have the correct answer), to sycophancy (ie explicitly trained to tell their users whatever they want to hear, reflecting not just their users’ tastes and opinions – but encouraging their users own WORST tastes and opinions. Like social media, they are designed to be addictive, pestering their users at the end of every answer, hallucinated or not, with further suggestions and queries, with the familiar, seemingly helpful tone of the eager assistant, designed to keep the user keeping the thread open as long as possible.
But the LLMs DO have their uses – and I’ve been using Chat GPT, still my default, almost since it appeared in December, 2023. I signed up for a Pro account as soon as it came out in January 2024. I’ve tried to write with it, I used it for study, as a replacement search engine. Seduced by a piece in the New Yorker ‘A Coder Considers the Waning Days of The Craft‘, I pondered giving up coding, or at least giving up coding without the integration of the LLMs, and build apps in languages and frameworks I either didn’t know well, or had forgotten, relying heavily on LLMs to fill in gaps in my knowledge. When, stymied by a collapsing NYC tech job market, I took up freelancing, I used LLMs as an assistant to deal with page builders, optimizations, SEO, integrations – subjects I sort of knew, or had known from last iteration as a freelancer 10 years before. I used the LLMs for job search, CV optimization, I used it to research and write copy, find and configure plugins and, when I returned to using Laravel and Django after a few years absence, I used the LLMs in an attempt to get back up to speed. I experimented with LLMs to write, or analyze books, find historical and Biblical and mythical analogies for stories I was writing or planning to write. I briefly experimented with generating images, even video.
Success? Mixed. Very mixed. I should have been more attentive and kept an ongoing log of what worked, what didn’t. I should have written blog posts, documenting my finds. But I didn’t. I was overwhelmed by the firehose of information about AI, and all the wonderful things it could do, all the jobs it would replace. I used it day-to-day on a sort of intuitive, almost unconscious level and attempted to control its voliminous flood of information, opinion, sometimes complete BS.
But here, I’ll try to unpack how I’ve been using the LLMs, mostly Chat GPT: what I’ve found useful and what I really haven’t.
Sam Altman is fond of claiming that the advent of the LLMs means that, at some point, we’ll have to consider bringing in UBI because ‘AI will take all our jobs’. He’s even sponsored a study, done over a three year period, giving participants a grand a month.
For those not in the know, UBI stands for ‘Universal Basic Income’ and was first introduced as a concept (as we shall see, in its modern form) by Andrew Yang during his 2016 run to be the Democratic Presidential nominee. Yang’s idea was that every US resident (presumably limited to citizens and ‘resident aliens’ – ie green card holders) would receive $1200 a month as a base income, no matter what they made from their regular jobs. Such an idea had apparently been popular in parts of Silicon Valley (where Andrew Yang had been working) for some time; Yang just brought it to the public.
In other words, instead of just chidren of well-off baby boomer parents getting a trust fund – EVERYONE would get a trust fund. And why not? The US is a rich country. With some sensible, thoughtful taxation, UBI would be entirely feasible. Now with AI threatening to take everyone’s jobs, perhaps UBI is an idea whose time has come.
Thing is: I’ve lived through an early prototype of UBI, in Canada (and the UK) in the ’70s and ’80s. Only it wasn’t called UBI back then, it was called the welfare state, and came in the form of either unemployment or welfare payments. Montreal in particular, when a significant percentage of the population lived in whole or in part on UI (from the feds) or the Bien-Aide Sociale (BS, from the provincial government), supplemented by odd jobs, artistic or musical pursuits, or spells of grueling, physically intense work in the hinterlands. For many, it was more or less a way of life.
In those good old days, Canada’s second largest city, was recovering from the tumult of the ’60s and ’70s, when the FLQ had been setting off bombs and kidnapping government ministers, the election of the separatist Parti Quebecois and the subsequent referendum on Quebec’s sort-of-separation from the ROC (rest of Canada), along with the massive outflow of its Anglo population – and their money. This, at a time, when not only the opening of the St. Lawrence Seaway had reduced Montreal as a port, the ’76 Olympics which had left the city with ruinous debt, and the general industrial decline that bedevilled cities across the North-East. Montreal was not in great shape.
Into this void came the welfare state, attracting a generation of artists, slackers, and riff-raff from the ROC and the Montreal suburbs, drawn to a city where a two or even three bedroom apartment in a picturesque neighborhood with easy access to great food, cheap bars and restaurants, could be had for a couple of hundreds bucks a month. No deposit, no first and last: in most situations, you paid month by month. Basic BS was a mere $180/month, not enough to live on, but if you could scam medical BS, you could get up to $500/month. If you went out into the bush for the Great Canadian Migration that was tree-planting, and put in 8-12 weeks of back-breaking labour in all kinds of weather fighting off hordes of biting insects, you could come back with not just somewhere up to ten grand, but that prize of prizes, max UI weeks, which translated to over a grand a month (after taxes), paid in bi-monthly installments.
What a time.
Sounds great! Power to the People! But what did it mean in practice?
In St. Henri, a blue collar neighborhood just off downtown where I lived for a time, the factories that had been the neighborhood’s chief employer had long since closed and most of St. Henri’s residents had few options. For most of the month, the tavernes, the few diners that still existed, even the depanneurs (corner stores to those not familiar with Quebec) were largely empty. But come the last Friday of the month, when the BS cheques came out – hey presto! St. Henri came alive! Tavernes were suddenly full from noon ’til close. People lined up in the depanneurs for 24 cases of Cinquante, Molson Ex, cartons of cigarettes! For a weekend, St. Henri was as lively and vital as it must have been in its industrial glory days.
Then come Monday, everyone went back to staying home and watching TV.
Among my circle of bohemians and artists on the other side of the city, results were mixed. Some people got their UI weeks, then left the country to go traveling, or skiing, or to spend a few months on a beach, their bi-weekly check-in card filled out and signed by a trusted friend or relative. Many planned to use their newfound free time the BS or UI allowed to do their own work. And some did. Some had the discipline to set goals, then spend an allotted time – six months, a year – to fulfill them. But for most, myself included, the lack of structure was curiously dispiriting. One day stretched into another. We spent hours in coffee shops, nursing a single coffee, only to spend hours more on the phone, talking about more or less the same things we’d talked about in the coffee shop. Come evening, we’d gather in some cheap bar for the even cheaper happy hour, often with the same people we’d seen or talked to that day.
It kind of got stale. Fast.
That said, I wasn’t against it then, and I’m not against UBI now. It can’t just be the children of the rich – or the plain rich – who get to do art or work in media or launch startups. Or hang around and do nothing. Regular folks should be able to do all these things too. But if AI really does take all our jobs and we ended up with masses of leisure time, we’re going to be need to be trained on how to use that time. We’ll need to be trained as tinkerers, inventors, founders – or even just volunteers. We’ll need to be trained in how to be useful.
But this is only if AI takes all our jobs and the US government has the vision, the courage, to bring in some as profound as UBI. Since the US has been unable to even bring in a universal health care, this seems very unlikely, whatever Sam Altman says.
A couple of years ago I saw The Draughtsman Writer at a larger exhibit at the Met, Technology In the Age of the Court Most of the exhibits were clever, if sometimes dazzling: many immensely complicated clockworks, constructed with gold and other precious minerals, but nothing truly blew me away until the exhibits at the show’s end, and in particular ‘The Draughtsman Writer’.
Along with our Draughtsman was his more famous cousin The Chess Player (sometimes called ‘The Turk’ because of his garb), a replication of the original automaton by Wolfgang Von Kepeler in 1769, which ‘played’ chess with prominent figures across Europe, and which was eventually revealed as a fraud, manned by skilled, even famous chess players hidden from view yet operating the Chess Player’s mechanical arms (I’m not entirely clear how they did this).
The Draughtsman Writer, however, needs no human intervention, except perhaps for a key to be wound up so that the Draughtman’s profoundly intricate gears, hidden in the Draughtsman’s desk, can begin turning, then a human hand to place the paper that with Draughtsman will fill with his exquisitely intricate drawings and poems. From the exhibit notes:
Maillard hid the mechanics of the Draughtsman Writer in a cabinet rather than the figure. This allowed for larger machinery and greater memory than in earlier efforts . . . an unprecedented three poems and four drawings are drawn by the figure, through a technology that foretold the computer.
Incredibly, when Pittsburgh’s Franklin Institute received the automaton in 1928, it was so damaged by the fire in the warehouse where it had been stored, they had no idea it was an automaton. They knew it had some mechanical function but, since it was in pieces, they had no idea what that function was. They didn’t even known the name of the inventor.
I was instantly captivated by the Draughtsman Writer. In part it was the instance of an early robot. No uncanny valley here – the automaton is only half-formed (many of its panel, its ‘skin’ possibly lost in the fire), with a young man’s dummy head, yet none of the creepiness we associate with a ventriloquist’s dummy. This is a benign, contemplative figure, eyes focused downward on its task, its transparently mechanical arm composed of brass strips, an almost human hand holding a pen, tracing delicate lines across the page fitted into the Draughtsman’s desk. As one of the curators says:
Normally we think of robots moving in very mechanical ways, very jerky movements. This machine is by far the most elegant in its movements.
The Draughtsman can compose four different pictures, including drawings of a Chinese temple and a ship, and write three poems, one in English, two in French. The ‘hard drive’ for these movements are the brass disks housed below the surface of the Draughtsman’s desk. The disks have hills and valleys on their surfaces, and a needle follows these grooves up and down, the collection of disks allowing for the most extensive mechanical memory of any known automaton.
The moving automaton was confined to a video next to the exhibit (as it was with all the other exhibits, presumably too old, too delicate for the repeat performances the exhibit would demand). Instead, in the actual exhibit, the Draughtsman peers straight ahead, eyes wide open, pen poised in its hand, waiting for the human intervention to fulfill its function and begin drawing and writing again.
The Draughtsman Writer at the Metropolitan Museum
I was struck, watching the video again, by the beauty of the calligraphy, the detail in each drawing. What a watchmaker Maillard must have been, to so precisely record each groove in his brass discs, long before the plastic record album would perform the same function. What might have Maillard done with a computer, with modern computer languages?
What also struck me is the essential frivolity of the Draughtsman Writer. Maillard likely built it to impress the court, as a sort of calling card. But essentially our Draughtsman exists to produce art, no more, no less. Sometimes I wonder if our machines shouldn’t be, at least partially, repurposed to do the same – not just for convenience, ‘communcation’, ‘disruption’ but to produce beauty, wonder. When our machines, our AI, can acheive some of the pure wonder of the Draughtsman Writer, will we able to say the Digital/ AI revolution has matured, been absorbed into our human fabric (instead of threatening to run amok as it is now?
Since the release of Chat GPT3 in late November, 2022, I’ve been wondering not just what impact it will have on everything from the job market to our media environment, but also how it can be used, both by myself and people I know. Since many people I know are teachers, including members of my family, education is as good as any a place to start.
What is Chat GPT?
Well, let’s ask Chat GPT itself:
ChatGPT is an advanced Artificial Intelligence (AI) model that uses Natural Language Processing (NLP) technology to understand and generate human-like responses to written text inputs. It is designed to respond to any input text by generating appropriate and contextually relevant responses, just like a human would in a conversation.
The technology behind ChatGPT is based on the GPT (Generative Pre-trained Transformer) architecture, which is a deep learning model that has been trained on massive amounts of text data from the internet. The GPT model learns to recognize patterns and relationships between words and phrases in the data, enabling it to generate coherent and contextually relevant responses to text inputs.
To create ChatGPT, OpenAI has trained the GPT architecture on an enormous amount of text data, including books, articles, and web pages, to ensure that it has a broad understanding of human language. This means that when you input text into ChatGPT, it can draw on its vast knowledge to generate a response that is both accurate and relevant to the input.
Open AI, the company behind Chat GPT, collected data from across the internet, and spent a year and a half training its incredibly powerful novel to match patterns in human speech. Unfortunately, since it took a year a half to train its LLM, Chat GPT’s knowledge stops, well, a year and a half ago. It CAN be coaxed, through careful prompting, into retrieve more recent information but for completely up to date information, you’ll have to look elsewhere. This is NOT true of Bing’s Chat, which can access the internet (and provides sources, which Chat GPT does not). However, despite this, I generally find Chat GPT a more powerful tool than either Bing or the recently released Google Bard.
When Chat GPT3 was first released, the immediate fear was that the technology would be used (or mis-used) not by teachers, but by students, since a simple prompt can solve problem sets and even generate whole essays. The style may be, well, robotic and sometimes flat-out wrong (GPT3 was known to ‘hallucinate’ – that is, to generate random nonsense when it couldn’t find an answer) but the fear was that many students would give up writing their own essays and just generate all their school assignments via AI. While this has happened, right from the beginning many teachers recognized its great potential as a learning tool:
To sign up for Chat GPT3, go to https://chat.openai.com/chat. GPT3 is free and only requires an email address. Note that during busiest times, availability may become limited, or drop out altogether. For that reason, when it became available a few weeks ago, I signed up for GPT Plus, which not only allows 24 hour access but immediate access to the recently released GPT4, which is a considerable improvement on GPT3, with considerably higher accuracy (especially in mathematics and programming) and more in-depth answers to general questions. For me, it’s worth paying $20 per month, but many will be satisfied with the free version.
So . . . how can teachers use Chat GPT to assist them in the classroom? Since I’m not a teacher, I turned once again to my new best friend Chat GPT4:
Personalized Learning Experiences: Chat GPT can be an invaluable tool for creating personalized learning experiences for students. By inputting a student’s individual learning needs and preferences, teachers can use Chat GPT to generate tailored educational materials, including lesson plans, quizzes, and project ideas. This level of customization ensures that each student receives an education that is specifically catered to their unique strengths and weaknesses.
Supplementing Lesson Plans: Teachers can use Chat GPT to supplement lesson plans by generating additional content and resources on a given topic. By providing a brief summary or key points of a lesson, Chat GPT can generate detailed explanations, examples, and practice problems. This can save teachers time and help them expand on topics that may be difficult for students to grasp fully.
Real-Time Feedback and Grading Assistance: Chat GPT can be used to provide real-time feedback to students on their assignments and quizzes. By inputting a student’s response to a question or a written assignment, Chat GPT can generate feedback and suggestions for improvement. This immediate feedback can help students learn more effectively and allow teachers to focus on providing individualized attention and guidance.
Tutoring and Homework Assistance: Outside of the classroom, Chat GPT can be utilized as a tutoring tool, offering guidance and assistance to students as they work through assignments or prepare for exams. By answering questions and providing explanations, Chat GPT can be a helpful resource for students who require additional support or clarification on a particular topic.
Enhancing Classroom Discussions and Debates: Chat GPT can help facilitate engaging classroom discussions and debates by providing diverse perspectives on a given subject matter. Teachers can input discussion prompts or debate topics into Chat GPT, and the AI will generate thought-provoking arguments and counterarguments, encouraging students to think critically and engage in meaningful conversations.
Encouraging Creativity and Originality: Chat GPT can be used to spark creativity and originality in student projects and writing assignments. By providing a prompt or topic, Chat GPT can generate a wide range of ideas and examples that students can draw inspiration from. This can help students break free from conventional thinking and explore new ideas and perspectives.
These are just a few examples. I could prompt again, and generate many more.
For teachers who do want to use Chat GPT, it’s important to have a basic idea of how it works. I might write a whole blog post about this, because it’s an important and surprisingly nuanced skill, but for now, it’s important to understand that because the LLM recognizes speech patterns, how you ask it a question is very important. For the above, I wrote:
Can you write me a blog post describing how high school teachers can use Chat GPT?
I could have written a much more detailed query, and received a much more detailed response. It’s also important to remember that, unlike a search engine, Chat GPT remembers ‘conversations’. so the process is iterative: you ask one question, then another, and Chat GPT will not only ‘remember’ the original question, but remember the answer it generated. Thus you can ask one question and drill down on the answer to get more detail, or receive an expanded answer and Chat GPT will, in a sense, be trained on the ‘conversation’ and tailor its responses appropriately. For example if I then ask:
Can you tell me more about ‘Real-Time Feedback and Grading Assistance’?
I’ll receive an even more detailed answer (I’ll just include the titles – Chat GPT can be verbose):
The use of Chat GPT for real-time feedback and grading assistance presents an opportunity for teachers to enhance the learning experience for their students while also reducing their workload. By harnessing the power of artificial intelligence, teachers can offer students immediate and personalized feedback on their work, helping them to learn more effectively and efficiently. Here’s a deeper look into how Chat GPT can provide real-time feedback and grading assistance:
Instantaneous Feedback
Consistent Evaluation Criteria
Personalized Feedback for Individual Students
Reducing Teacher Workload
Continuous Learning and Improvement
It’s important to note that while Chat GPT can provide valuable real-time feedback and grading assistance, it should not completely replace the role of the teacher. Human interaction, empathy, and understanding are still essential components of a successful learning environment. Teachers should continue to monitor the feedback provided by Chat GPT to ensure its accuracy and relevance and supplement it with their insights and guidance as needed.
In this video Ted Pickett, himself a teacher, outlines the basics of creating a good prompt. He follows a four-step rule put out by an organization called ‘AI In Education’:
Use ARTS to help you craft efficient prompts
A udience: “This blog is from educators around the globe“
R ole: “I want you to act as a title generator for the blog posts I am writing“
T arget: “I will give you the topic and keywords from the article and you create five engaging titles“
S pecific: “Keep the titles concise. Less than 15 words.“
This is the whole video: From Ted Pickett’s ‘AI for the Classroom’ channel.
Ted Pickett’s ‘AI for the Classroom’ channel.
It’s also important to remember that the same response can produce somewhat different answers Since Chat GPT relies on pattern recognition, it will produce different answers for different users, and sometimes even variations on an answer for the same user with the same prompt. It also should not be seen as a replacement for human research: since it relies on information from the web, it can get things wrong (the so-called hallucinations).
Another useful Chat GPT function is its ability to summarize. Download the transcript of a video and Chat GPT will provide a summary. Chat GPT provided the following summary of the video below:
The video presents five ways teachers can use ChatGPT to enhance their teaching:
1) creating lesson sequences with student discussion questions,
2) designing well-being lessons,
3) providing feedback to students,
4) generating student reports,
5) crafting song lyrics for young learners.
The speaker emphasizes that AI tools like ChatGPT can help educators focus on the process and stages of student learning, rather than just the end product.
What’s truly amazing is the speed at which this technology is evolving. Just a couple of months after the initial release of Chat GPT3 comes Chat GPT4, a significant improvement in both accuracy and depth of its responses. Then Microsoft included a somewhat dumbed-down version of Chat GPT4 in its Bing browser. Hundreds, even thousands of apps, built on the GPT API, are being released weekly. And soon, Open AI will allow the use of plugins which could revolutionize the technology even further, allowing for the customization of the core technology into every sphere imaginable, including (probably especially) education. Both Duolingo and Khan Academy have become early adopters (though Khan Academy is still in the testing phase – you can sign up to be on the waitlist for testers – I imagine teachers will get priority), using the chatbot as a sort of virtual tutors for their students.
What does the future hold? As with all technological change, it’s hard to know where this will end up, whether it will be a net benefit or loss. I think AI’s potential to help educators is very considerable indeed, but so is its capacity for misuse. For the time being, I think it’s up to everyone to learn how to use this properly – and to learn how to use it for good.
This is a vast and fast-growing field, so I’ll be posting more on the subject.
