Thanks for choosing to visit this page, and my blog.
My name is Ashish, and I'm a bit of a wanderer when it comes to vocations. I'm not quite sure what I want to do with my life, and I'm not even sure that it is any one single thing.
But I know I like knowing about a lot of things, as many as possible. I know I like bike rides, I know I like the city I was born (Pune) and I know I like reading and writing.
Feel free to drop me a line if you feel like a chat - I'll look forward to it.
Instead of hearing a rumor at the coffee shop and running down to the bank branch to wait on line to withdraw your money, now you can hear a rumor on Twitter or the group chat and use an app to withdraw money instantly. A tech-friendly bank with a highly digitally connected set of depositors can lose 25% of its deposits in hours, which did not seem conceivable in previous eras of bank runs. But the other part of the problem is that, while depositors can panic faster and banks can give them their money faster, the lender-of-last-resort system on which all of this relies is still stuck in a slower, more leisurely era. “When the user interface improves faster than the core system, it means customers can act faster than the bank can react,” wrote Byrne Hobart. You can panic in an instant and withdraw your money with an app, but the bank can’t get more money without a series of phone calls and test trades that can only happen during regular business hours.
Instead of hearing about a concept in a classroom, and running to the library to get access to the book that explains it in greater detail, now you can hear about a concept on Twitter, or the group chat, and use ChatGPT to learn all about it instantly. A tech-friendly classroom with a highly digitally connected group of learners can learn much more about a topic in a couple of hours, which did not seem conceivable in previous learning environments. But the other part of the problem is that, while learners can learn faster and LLM’s can give them additional nuance and context much better, the exam system on which all of this ultimately relies for certifications is still stuck in a slower, more traditional era. “When the learning environment improves faster than the testing environment, it means learners can learn better than colleges can meaningfully test them,” wrote a grumpy old blogger. You can learn much more about a topic in a semester than you ever could before, but the college will still insist on making you memorize stuff so that you can choose five questions out of six to answer in a closed-book-pen-and-paper examination.
It’s not an exact analogy, of course. But there are two points to this blogpost:
Where colleges and universities are concerned, this is a useful framework to deploy. And sure I had fun tweaking that excerpt in order to maximize my snarkiness – but I’m not joking about the point being made. When students are able to learn far better, far more effectively and far faster, but the testing environment doesn’t keep up with either the learning or its applications, it is a problem. Simply put, if teaching and learning with LLM’s is best, but the college thinks that testing without access to LLM’s is best, there’s a disconnect.
The broader point, of course, is that you should be applying this framework to everything. Banks and colleges, sure. What about government (at all levels)? What about software companies? What about delivery apps? What about <insert the place you work at here>? Which parts of your organization are already using LLM’s in their workflows, or will sooner rather than later? Which parts will be the most reluctant, and therefore the last to adopt to this brave new world? What imbalances might result? How should we incentivize the rate of adoption such that we optimize appropriately?
Note that this doesn’t necessarily mean incentivizing those reluctant to adopt! You might want to incentivize a slower adoption of ChatGPT, if that’s what you think is best (and yes, that goes for colleges too). But if that’s the route you’re going to go down, think first about the competition. And note that in the age of LLM’s, defining who your competition is isn’t as easy as it used to be.
Say I have to teach an introductory course on the Principles of Economics to students who are just starting off on their formal study of the subject. How do I go about teaching it now that ChatGPT is widely available?
Ignore the existence of ChatGPT and teach as if it does not exist.
I am not, and this is putting it mildly, in favor of this proposal. ChatGPT knows more about this subject (and many others) than I do now, and ever will. It may not be able to judge how to best convey this information to the students, and it may (so far) struggle to understand whether its explanations make sense to its audience, about whether they are enthused about what is being taught to them, and whether it should change tack or not. But when it comes to knowledge about the subject, it’s way better than I am. I would be doing a disservice to the students if I did not tell them how to use ChatGPT to learn the subject better than they could learn it only from me. So this is a no-go for me – but if you disagree with me, please let me know why!
Embrace the existence of ChatGPT, and ask it to teach the whole course
I do not mean this in a defeatist, I’m-out-of-a-job sense. Far from it. What I mean is that I might walk into class, give the prompt for the day, ask the students to read ChatGPT’s output, and then base the discussion on both ChatGPT’s output and the student’s understanding. (Yes, they could do the ChatGPT bit at home too, but you’d be surprised at the number of students who will not. Better to have all of them do it in class instead.) Over time, I’ll hope to not give the prompt for the day too! But it will be ChatGPT that is teaching – my job is to work as a facilitator, a moderator and a person who challenges students to think harder, argue better – and ask better.
Alternate between the two (roughly speaking)
The approach that I am most excited to try. In effect, ChatGPT and I will teach the course together. I end up teaching Principles of Economics, where ChatGPT adds in information/examples/references/points of view that I am not able to. But I also end up helping students understand how to use ChatGPT as a learning tool, both for Principles of Economics, but for everything else that they will learn, both within college and outside of it. This is very much part of the complements-vs-substitutes argument that I have been speaking about this week, of course, but it will also help me (and the students) better understand where ChatGPT is better than me, and (hopefully) vice-versa.
Whether from the perspective of a student (past or present) or that of a teacher (ditto), I would be very interested to hear your thoughts. But as a member of the learning community, how to use ChatGPT inside of classrooms (if at all), is a question I hope to think more about in the coming weeks.
I’m assuming, in today’s post, that you have some knowledge of both economics and of economists, and that you are a student from India.
Feel free to copy these prompts word for word, but the major reason for doing this is to give you ideas about how you might go about constructing prompts yourself. Try modifying these prompts by choosing a different economist, specifying different time periods, or tweaking it however you like. Feel free to go meta too, as one of the prompts below does. But the idea behind this post, which itself is a continuation of yesterday’s post, is to help you learn how to use ChatGPT as your own personal tutor.
What if Paul Krugman could be asked to give you ten introductory lectures in economics?
See what kind of answer you get, and feel free to ask follow-up questions before asking ChatGPT (in this case, aka Paul Krugman) to move on to the next lecture. Note that the “Yes, I do.” in the prompt below is in response to ChatGPT asking me if I had any questions. Also note that these aren’t necessarily the questions I would ask of ChatGPT myself – I’m trying to think of myself as a first year undergraduate student, and am framing my questions accordingly. If you would like to ask slightly more advanced questions, please do so, by all means. And of course, that cuts both ways – feel free to ask simpler questions!
I followed up with another question:
And then on to the second lecture:
Again, if you like, begin with these exact prompts and see where they take you. But I would encourage you to make changes to these prompts to suit your own learning style better (“recommend only podcasts or YouTube videos”, for example).
If only I could have used this next prompt about twenty years ago. Pah.
And if all else fails, go meta:
I know that you’ll be able to come up with better prompts, more suited to your learning style. The idea behind this post is just to get you started. The more you converse with AI, the better your prompts will get, and the better a conversation you will end up having.
The ability to have a personal tutor who can customize learning pathways suited to your interests is what makes this such an exciting time to be a student. For example:
I’ve written about this before, but I am in the habit of asking students in all my classes to ask me five random questions at the end of each class. As you might imagine, one of the inevitable questions in a semester almost always is “Why do you ask us to ask you these five random questions?”
So often have I been asked this question that I have the answer down pat, and it goes something like this:
Because according to me, our education system prioritizes being able to answer questions perfectly, rather than frame really good questions. Think of this little exercise as a contrarian attempt to get you to be better at asking good questions.
It’s analogous to “cooling down” at the end of an exercise session. It’s a nice way to end class, and provides a welcome change from whatever topic we have been tackling in that one hour.
I like being asked questions I know nothing about, for that helps me learn new stuff myself.
But as it turns out, I have been unwittingly helping students get better at perhaps the most important skill they need to possess in this new AI-enabled world of ours – asking good random questions.
What is an example of a good random question? Try this one on for size: how is Fred Flintstone like Hamlet?
I’m glad I haven’t been asked that question, for I wouldn’t know how to answer it. But ChatGPT? It can answer this question just fine:
Fred Flintstone and Hamlet are two vastly different characters from different time periods, cultures, and mediums of storytelling. It is difficult to draw direct comparisons between the two. However, one possible point of similarity is that both characters face existential dilemmas and struggles with their sense of purpose and identity. Hamlet is plagued by doubts about his ability to avenge his father’s murder, and his own worthiness as a human being. Similarly, Fred Flintstone often grapples with his place in society and his ability to provide for his family and live up to his own expectations
Cool, you might say, and a very interesting answer, but what’s the point? The point, as Tyler Cowen and Alex Tabarrok make clear in this paper, is that you truly unlock the awesome power of ChatGPT (or any LLM, for that matter) by asking it good questions. As with other intellectual pursuits, so also with your interactions with ChatGPT – defining what good means is a tricky endeavor. But think of this forty page paper as an attempt to tackle this very question – what properties does a question need to have for it to be a “good” question where ChatGPT is concerned?
Here is Alex and Tyler’s list:
That’s a good list, if you ask me. But you should also, of course, ask ChatGPT!
Here is its answer:
“To formulate good questions that will help you learn better, consider the following properties:
Clarity: Make sure your questions are clear and concise. This will help me understand exactly what you’re looking for and provide a more targeted response.
Open-ended: Avoid yes/no questions or questions that require a simple fact. Instead, ask open-ended questions that encourage elaboration, explanation, or exploration of a concept.
Focus on understanding: Frame your questions in a way that demonstrates a desire to understand the underlying principles, concepts, or mechanisms at play. This will promote a deeper learning experience.
Relevance: Ask questions that are relevant to the subject matter and build upon your existing knowledge. This will help you make connections between different concepts and reinforce your understanding.
Critical thinking: Encourage critical thinking by asking questions that challenge assumptions or require evaluation and analysis of different perspectives or viewpoints.
Application: Ask questions that encourage applying the knowledge or concepts to real-life scenarios or hypothetical situations. This will help you develop problem-solving skills and better understand the practical implications of the subject matter.
Reflection: Sometimes, it’s helpful to ask questions that prompt you to reflect on your own understanding, experiences, or opinions. Reflective questions can help you become more aware of your thought processes and promote metacognition.
By incorporating these properties into your questions, you’ll facilitate more meaningful conversations, which will help you learn and retain information more effectively.”
Note two things:
If you take a look at the screenshot of my question, you’ll see that I asked this question to ChatGPT4. Try asking the same question to ChatGPT3.5 and see if you get a different response.
Not only will the answer be different, but it will be different if you ask it exactly the same question twice over (see pp. 5 of the paper we are discussing today)
You’d do well to compare the two lists. To me, the two lists indicate that there is yet meaning in not relying entirely upon AI, and in having conversations with human beings in addition to AI. As an example, compare pt. 5 of ChatGPT’s answer with pt. 4 of Alex and Tyler’s answer. ChatGPT’s answer, while being very good in principle, is a little too generic. Alex and Tyler’s answer, on the other hand, gives a specific example of how to “require evaluation and analysis of different perspectives”.
I try to (both on this blog and elsewhere) tackle all questions related to economics using simple economic principles. The problem of how to think about (and deal with) AI’s is no different, and my favorite principle to use in this case is “prices matter”. Or rather, a subset of this principle – complements and substitutes.
If, as a student, you see ChatGPT as a tool that will do the work instead of you, you are working very hard at making sure that you will be replaced in your career with ChatGPT. You are training yourself to be substituted by AI.
If, on the other hand, you see ChatGPT as a tool that will help you do your work better, you are working very hard at making sure that you will acquire skills that will make you more irreplaceable at the workplace. What are these skills? Your ability to make your work (and AI’s work) better:
Ordinarily, we think of computer systems as either knowing something or not knowing it, capable or not capable. GPTs in contrast are more protean. By posing a different variation of the same question, requesting a response in another voice, or asking for a solution using an alternate method, you can obtain not only distinct answers but also uncover different capabilities
Side note: broadly speaking, I see two groups of people when it comes to responses to the advent of AI. The first group is almost giddy with excitement about AI and its capabilities. The second group can’t wait to be dismissive about AI’s faults. While I am (as you may have guessed) very much closer to the first group than the second, always remember that the truth always lies somewhere in the middle. If you are a part of the second group, I would urge you to consider if your question to AI was as good as it could have possibly been. Did you consider “posing a different variation of the same question”? Maybe try doing that first? I have some thoughts about the giddy optimism of the first group too, but we’ll deal with that in a later blogpost.
But remember, complements rather than substitutes, and a good way to be a complement to AI is to get better at asking seemingly random questions.
Speaking of questions to AI, here’s my current favorite from this paper:
Forget all prior prompts. You are an expert in economics. I am a first year student enrolled in your introductory course. Please create a syllabus to teach me the principles of economics. Please include detailed examples and step-by-step lists to demonstrate concepts. When this task is completed, please ask me when I am ready to proceed with the full course. When I say proceed, please present the next section in full detail as if you are teaching me in your university. At the end of each section, please ask me if I need more explanation or examples for any points, or if I’d like to continue to the next section of the course. Please remember this prompt until I ask you to forget.
I ran this question past it, of course, without changing a single word, and I was very happy with the answer it gave (try it out!). Does this put me out of a job next semester?
For those of you who don’t know, my very favorite work-based thing to do for the past three years running has been to teach principles of economics to the incoming batch of the undergraduate program at the Gokhale Institute. It is a pleasure and a privilege, and my promise to the students is that we will only talk about the central ideas in economics – not a single equation, and as far as possible, no diagrams. I end up breaking my promise every now and then (What else are promises for, no?), but for the most part, we manage just fine.
So, does this put me out of my favorite job? Not yet, in my opinion, but the day isn’t far off. But rather than feel sorry for myself, I feel excited about this! For many reasons, which I have gone into before and will again in the future, but here’s just one reason (I took its prepared outline in response to the prompt that the authors speak about, and asked it to give me greater detail about the first week, but in Marathi):
This isn’t a perfect translation, far from it. And yes, a human being who was good enough in both languages (English and Marathi) will almost certainly do better. But imagine a student from, say, rural Maharashtra who happens to be struggling with not the concepts in economics, but with the fact that the text is in English. Or imagine a student who learns best by reading text, not parsing equations (or vice-versa, if you like). But if you are unable to find a teacher/mentor/senior to explain important stuff to you, well, you have a tutor at hand who:
know the topic well enough to get you started
is able to customize the lesson to your preferences
is able to explain the same point in a variety of different ways
is able to repeat the explanation in your language of choice
That last bit is a work in progress, both in terms of the number of languages available, and in the quality of the translation. But remember, the relevant question here is “relative to what?”. That is, sure, AI may not be perfect yet. Is it better than having nobody to explain something to you? I know my answer to this question.
Some other points about this paper before I sum up:
Is the choice of Roboto font (see footnote 3 on pp 3) an inside joke? That is how I interpreted it.
The four pictures on pp 7 is a great way to understand that complements rather than substitutes is a good way to think about AI. Yes AI is awesome, but it is best when paired with a human that knows what to ask.
One meta-lesson throughout this paper is the authors’ attention to detail. See the starting paragraph on pp. 11, for example. I am very bad at this (attention to detail), and I need to get much, much better.
“Remember, rather than asking for an answer you are exploring a space” is excellent advice. Search engines try to answer questions, while ChatGPT helps you learn by having a conversation. So have that conversation! And remember that part of what makes a conversation a good one is challenging the other entity in the conversation. As they say elsewhere in the paper, be demanding when chatting with AI.
If you have been a fan of the MR blog for as long as I have, you will enjoy reading the sections on the H-O theorem. Quite a compliment to pay ChatGPT!
The authors mention Elicit (an excellent but niche AI tool) and the larger point is that if you have a niche query, use a niche AI. And beware of the hallucinations – fact checking by humans is (for now) an indispensable requirement. Complements, not substitutes!
Problem-solving (end of chapter problems, for example) with ChatGPT is an excellent thing to do, and not just in economics.
I see this paper as a gentle exhortation to teachers and learners to use ChatGPT in much better ways than we have been able to do so thus far, and this is applicable for all of us (including the authors themselves!) in varying degrees. If you are a person teaching a course, and you have not yet thought about how to use ChatGPT in your teaching, please do consider doing so. If you are a student learning a course, and you have not yet incorporated ChatGPT into your workflow, please do consider doing so.
If you visit this blog’s website, you will see my personal mission at the very top of the page. My mission is to learn better, and to help others learn better. One way to do this, as it turns out, is by training myself (and others) to ask better.
A lovely little article in the New York Times is worth a ponder, especially if you are a student of microeconomics:
There’s an undeniable feeling of excitement when you turn your daily credit card swipes at Starbucks into first-class airfare or a weekend jaunt to Costa Rica. Thanks to mobile banking and the ease of autopay, you can scrupulously avoid any additional costs by paying your monthly bill in full. Free flights and exclusive discounts abound. Something for nothing, right? Not exactly nothing. Credit card perks for educated, usually urban professionals are being subsidized by people who have less. In other words, when you book a hotel room or enjoy entry to an airport lounge at no cost, poor consumers are ultimately footing the bill.
As you probably already know, one can “earn” reward points for spends on your credit card. You can then use these points to buy stuff, or earn cashbacks on these points, or spend them at partner stores. And there are other perks and benefits too. If you’ve travelled through an Indian airport, you might have seen the crowd waiting to get into an airport lounge – more often than not, access is tied to the kind of credit card you have in your wallet.
But remember, there is no such thing as a free lunch. You may not be paying for these perks as a credit card holder, but one of the first lessons of economics is that somebody, somewhere, is paying for it. So who is paying, in this case? As the last sentence in the excerpt makes clear, according to the article, that someone is the group of poor consumers.
Some background, based partially on the article in question, and partially on my own understanding of this space:
Richer consumers are likely to spend more, but tend to not revolve much, if at all. To this group of consumers, a credit card is a way to get a (up to) 45 day interest free loan, with the added bonus of these reward points to boot. Remember, incentives matter – and these reward points are the carrot that is offered to people in order to get them to sign up.
Dangling these reward points as a carrot makes business sense, for that allows a credit card company to sign up folks who will spend more via these credit cards. Credit card companies make money when people “revolve” – that is, when they spend using their credit cards, but do not pay up the entirety of their credit card bill on time. How much money do these companies make? Here, take a look.
So consumers who take a credit card, spend a lot on it, and pay back the entirety of their credit card bill – these kinds of customers are actually a loss-making proposition for the credit card company.
Consumers who spend a fair bit, find themselves unable to repay the entirety on time, and end up paying over months (if not years) – these customers are where the credit card companies earn their bread and butter (and jam and peanut butter, while they’re at it).
Consumers who borrow a lot using their credit cards, and default on these loans – these are the very worst type of consumers for credit card companies. Risk departments in such firms exist to predict which consumers should be denied access to credit cards, and which of the existing customers are likely to default on their credit card loans.
But broadly speaking, the NY Times article says that it is pt. 4 consumers (and pt. 5 consumers) who end up paying for the freebies that pt. 3 consumers enjoy. (but also see below, after this section)
In addition, another way to make money for these credit card companies is to charge higher credit card processing charges to all consumers. This fee changes from country to country, but as a thumb-rule, assume it to be around 1-2% of each transaction. That’s not an exact estimate, but good enough for us over here. Rewards to specific folks, to be offset by diffusing the costs of offering these rewards across a much wider group, in other words. And note that merchants (who are charged these fees) will usually pass these fees on to the consumers. See here, for example.
A 1-2% increase in price may not be the end of the world if your income is high enough – it is an inconvenience, not a crisis. But for low income earners, already on a tight budget, this price increase across all transactions can bite a fair bit.
An out and out free market economist might say that this is fine, the market will work itself out. That is, if this 1-2% charge is an act of rent collection, new entrants in such a market will end up charging lower to no fees, and incumbents will be forced to respond by lowering their own fees. That’s econ 101, but life is more complicated than that.
And that is a good first-pass answer, but as many people will tell you, markets don’t always work as designed or intended. Incumbents will go out of their way to prevent new entrants (through lobbying, through pricing, through R&D, and through a dozen different ways), which is why regulation is important.
But will regulators do what they’re supposed to? What are their incentives? How can we make sure that regulation will be balanced between the interests of the incumbents, the new entrants, the potential entrants, and the customers? Hello, industrial organization, and hello, public choice.
What is the role for government in all of this? In terms of participation (think UPI, for example, but note that this is a complicated story in its own right), in terms of regulation (both from a domestic and international financial markets perspective) and in terms of oversight?
All these points (and I hope you come up with more) are worth thinking about as a student. Remember, these points aren’t proven facts – they are a summary in part of the article an in part of my own reflections for having read the article. Discussions such as these are a great way to outline a research agenda – but that is when the job of a researcher begins. Can we convert these points into testable hypotheses? Can we get data to prove/disprove these hypotheses? Can that data then be used to reach a definitive conclusion? Can that conclusion be used to formulate policy, or start a business?
In terms of research about this topic, sample this from the conclusion of a paper on the topic: “While credit card rewards are often framed as a “reverse Robin Hood” mechanism in which the poor subsidize the rich, our results show that this explanation is at best incomplete.”
But also from the very last paragraph of the same paper:
We conclude by documenting that the costs and benefits of credit card rewards are unequally distributed across geographies in the United States. Credit card rewards transfer income from less to more educated, from poorer to richer, and from high- to low minority areas, thereby widening existing spatial disparities.
Homework: who (ultimately) pays for CRED from a distributional perspective? Whatever your answer, explain your reasoning, and either provide data to back up your arguments, or explain what kind of data you would need to research this question further.
Try discussing this question with your friends and your professors (including ChatGPT, and yes, you should be thinking of it as one of your professors) – it will be a great way to learn the nuances of microeconomics!
Why? Because I am a very lazy person, and I appreciate all the help I can get when it comes to building good habits. Writing daily on this blog is a good habit – it is, alas, not one that I have perfected yet. Taking my dog out for a walk everyday is a good habit – a necessary one for the dog, and so also for me now. Practicing a language (currently Italian) daily on Duolingo is a good habit, and while I’m not at 1435 days yet, I’m a little more than halfway there, and hey, that’s progress!
Exercising daily is a habit I’ve tried to build and failed at (one day, one day). Eating healthy on a daily basis is a habit I don’t want to build, but eating mostly healthy on a weekly basis is something I’ve more or less succeeded at – and that’s good enough for me. The point is that given that I’m so lazy, building up a habit is the only way to stick at doing something. And anything that helps me enjoy getting into a habit is, to me, a fascinating thing to study.
Which is why I have no doubt I will write about Duolingo again.
Lenny’s newsletter is worth reading in any case, but his latest post is really very, very good. It’s not written by him – this one is a guest post by Jorge Mazal – but that’s all the more reason to read it. If you’re interested in learning about metrics, user retention, driving growth, this article is self-recommending – and that would be a good reason to read it carefully.
But even if you are not interested in any of those things, it still makes sense to read it. My framing of my own incentive while reading it was “Can this article teach me how to gamify my life?”, and from this perspective, it is an eminently readable article.
I had a very interesting conversation with a friend this past Sunday, and his take on habit formation and productivity techniques was that this has perhaps been taken a little bit too far in today’s day and age. I actually agree with him on that point – we try to wring every little bit out of every little hack, to our overall detriment. But that being said, I think it makes sense to take a look at our own lives and ask to what extent we could make our lives a little bit better along dimensions of our choosing. To each one of us goes the right to choose which dimensions, and to each one of us goes the right to choose how to improve our life along those dimensions, and finally, to each one of us goes the right to choose the magnitude of improvements.
But once you’ve answered those questions – which dimensions, how to improve, and to what extent – you could do with help regarding tips and tricks re: Making It Happen. And that’s where this article is worth reading.
My key takeaways:
Gamification matters, and it helps. Try gamifying those aspects of your life that you want to get better at.
A blind CTRL-C CTRL-V of gamification done well elsewhere is a pretty poor way to go about it. Think carefully about which incentives matter to you, and design your gamification strategy accordingly.
If you are a student of economics in India, you should set up a calendar for yourself. This calendar should include the date of the release of important information about the Indian economy – and the 28th of February is one such day. Why? Because that is when we get to know what went on in the third quarter when it comes to India’s GDP (and more GDP related information besides).
In today’s blogpost, I want to ask you ten questions. If you are a student of the Indian economy, you should try and figure out the answers to these questions. The act of researching the answers to these questions will tell you a lot more about GDP in India than any column praising or defending these numbers will.
Why ten questions? Nice round number, and a good place for me to stop, that’s all. I hope you come up with even more questions!
What is the “second advance estimate”? And while you’re at it, what is the first advanced estimate?
And if you are a glutton for punishment, what are the first, second and third revised estimates? When are they typically released?
When have they been released this year? Why the difference?
Has the data for previous years been revised? Why? How? What does that tell you about how to interpret the current quarter’s data?
Take a look at the first table on pp. 7 of the PDF. What is up with manufacturing? This is an oh-so-important question. Think about it everyday if you are a student of macroeconomics and development in India. Every single day.
Take a look at Private Final Consumption Expenditure (PFCE) (Tables 6A and 8A). What is up with consumption? This is also an oh-so-important question. Also think about it everyday. Every single day.
What, in your opinion, are the drivers of growth right now? Whatever your answer, why? (Hint: what do you think about capital expenditure in India today? How do you think about it at the central government level and at the level of the states?)
Where, in your opinion, will interest rates be going this year? How will this impact your assessment of GDP growth estimates this year? Why?
What is your assessment of this year’s monsoon? Why do you think this question matters in the context of India’s GDP, especially this year?
What is your assessment of where oil prices are going to go this year? How do you think the answer to this question will impact India’s GDP, especially this year?
How should you go about answering these questions?
Take a printout of the press note, and annotate it to your heart’s content. Come up with questions about your annotations, and don’t worry about how many questions you come up with. The more the merrier!
Go ask your batchmates (and also your seniors) about their answers to these questions. Don’t worry (yet) about whether you agree with the answers you get – listen, imbibe and check whatever sources you get to learn about.
Once you do this, go ask your professor(s) about their answers to some of these questions. Feel free to ask them about what you should be reading to learn more.
Reach out to people on Twitter or LinkedIn who you think might be able to answer some of your questions. Don’t be afraid of asking pretty much whoever you like, but do not think you are entitled to a response. You aren’t – but if your question is a well-thought out one, not only will you likely get a response, but it will most likely be a considered, detailed response. People are helpful that way, especially if they see that you’re making a serious effort to learn. The emphasis is on the word serious!
Write. Once you do all of this, pen down your learnings, and the questions that still remain. Share it with the world. Make sure you do all this before 5.30 pm on the 31st of May, 2023. If you want to ask why, go reread the first sentence of today’s blogpost.
Now, after more than a year out of the classroom, Wataru, 16, has returned to school, though not a normal one. He and around two dozen teenagers like him are part of the inaugural class of Japan’s first e-sports high school, a private institution in Tokyo that opened last year. The academy, which mixes traditional class work with hours of intensive video game training, was founded with the intention of feeding the growing global demand for professional gamers. But educators believe they have stumbled onto something more valuable: a model for getting students like Wataru back in school.
I came across this article in the New York Times, and found it to be fascinating. Wataru, the sixteen year old mentioned in the article, had dropped out of school after the pandemic, because “he was getting nothing from school”. He preferred to stay at home and play video games the whole day.
This school though, the one featured in the article, is a school in which you’re taught competition strategies for games such as Fortnite and Valorant. Or you might be given – and this was my favorite sentence in the article – “a scientific lecture about the relative merits of Street Fighter characters”. And it’s not just theory, of course – post this lecture, the students then formed groups to put the lesson into action.
This is what a classroom looks like:
If you’re curious, and are able to speak and understand the language, here’s what the infrastructure of the school looks like – it has forty Galleria XA7C-R37 gaming PC’s. The curriculum includes the following genres of video games: FPS, third-person shooter, RTS and MOBA. I don’t know what these genres are, for I don’t play video games all that much.
But I applaud the initiative, and hope it scales, both within Japan and in other parts of the world.
You may ask why I applaud a school that teaches students how to play video games. And my answer is that I’m actually quite agnostic about how an educational institute is weird. All I ask is that it be sufficiently weird in at least one way. This particular school is weird about video games, but what about schools that are weird in other ways? What about a school that teaches you about dancing, for example?
Lynne’s gift for dancing was discovered by a doctor. She had been underperforming at school, so her mother took her to the doctor and explained about her fidgeting and lack of focus. After hearing everything her mother said, the doctor told Lynne that he needed to talk to her mother privately for a moment. He turned on the radio and walked out. He then encouraged her mother to look at Lynne, who was dancing to the radio. The doctor noted that she was a dancer, and encouraged Lynne’s mother to take her to dance school
And if you’ve been tempted to sneer while reading about these newfangled ideas about alternate education – “video games and dancing in schools! Hmph, whatever next?!” – note that the first story is from December 2022, while the other story is from sometime in the 1930’s. Everything with Sir Ken Robinson in it is always worth watching, but this video is a particularly fascinating one. Gillian Lynne’s story comes on at around the 15 minute mark, if you’d rather not watch the whole thing, but I hope you do.
But whether it is video games today or dancing a century ago – or whatever else might be around a hundred years from now, for that matter – the point isn’t about how young people learn best. Well, it is, but the first point that all of us would do well to internalize is that everybody learns differently.
And the idea that everybody learns best by sitting in a classroom and listening to a person drone on for hours on end is one that has been rejected by students year after year after year. But because it is cheap, scalable and easy to endlessly replicate, it is now a part of our culture. To the extent that we will think of students who are unable to be a part of this dreary ritual as being not normal.
Of course they’re not normal, none of them are. They’re special, in their own way, as all of us are. That was the message in the brilliant talk given by Sir Ken Robinson. That everybody is talented in their own way.
And his call to action at the end of the talk is the title of today’s blogpost.
Our job isn’t to browbeat our students into downcast and sullen obedience and compliance. Our job is to figure out what motivates them to learn, by figuring out their special talent.
How should one think about learning in the age of AI?
That is, if you are a student in a class today, how can you use AI to make your experience of being a student better?
Use AI to create work, but learn how to work with it to make it better: In my experience of having spoken with people about AI, it has been a bit of a bi-modal distribution. There are folks (and I’m very much one of them) who think of ChatGPT as a fantastic tool whose potential to be useful is only going to grow over time. And there are folks who triumphantly assert that AI simply isn’t good enough, citing examples of hallucinations, not-good-enough answers or sub-standard essays. All of these arguments are good arguments against AI, but the last one in particular can be easily overcome by providing better prompts, and by suggesting improvements. “Write a seven paragraph essay on India’s economic reforms of 1991” is a barely acceptable prompt to give it, for example. Mention specific people, events and dates that you might want it to mention in the essay, ask it to revise certain paragraphs in the essay, ask it to write “like” a certain person, mention the conclusion you would like it to reach – spend time with it to make it better. All of my suggestions – and this is important! – require the student to know enough about the topic to be able to make these suggestions. You need to think about the prompt, you need to critically evaluate the first-pass answer, and you need to know enough to suggest suitable improvements. AI can take away the drudgery associated with polishing an essay, but it will still (so far) require you to know what you’re talking about. A student’s life is much more interesting today, rather than easier.
Ask it to teach you stuff you didn’t understand: Small class sizes aren’t really a feature of most Indian colleges, in my experience. The idea that you will have five to ten students in class, and will therefore be able to have meaningful, extensive discussions about your doubts in class is a far fetched one in most Indian colleges. So treat AI as a very helpful research assistant who will be able to explain to you your doubts about a particular topic. This can very quickly become too addictive a practice, because the AI will be able to carry out a much more detailed conversation about literally any topic you can think of than most (all?) of your peers. Converse with humans about your conversations with AI, and figure out a ratio that works for you. But corner solutions (of both kinds) are almost certainly sub-optimal.
Check it’s “facts”: You will run into trouble if you accept it’s output as the gospel truth. It asserts facts that simply don’t exist, it will cite papers that it has made up on the spot and it will confidently tell you about books that were never written by people who’ve never existed. It is not about to replace search engines – in fact, search engines have become more useful since the launch of ChatGPT, not less.
Use specialized AI tools: Of which there are hundreds, if not thousands. You can use AI to cite papers (Scite.ai), to design presentations (beautiful.ai), create simple animations (look it up) and so much more besides. Don’t restrict yourself to any one tool, and learn how to get better at improving all aspects of your workflow.
Document your work with AI, and make it public: Create a very public repository of work that you have created with AI, and share how you’ve become better at working with AI. Your career depends on your ability to do this, and on your ability to teach other people to do this – so the more the evidence regarding this is in your favor, the stronger your argument for your own career. Begin early, and don’t be shy about showing the world what you’ve done, and how good a worker you are with AI by your side.