Tag: ai

Learning in the Age of AI

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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?

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

On “Reading” a PDF

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Step 1: Come across a tweet by Cass Sunstein:

Step 2: Bookmark the tweet, and download the PDF. Chances are that the PDF will never be read. The ‘Downloads’ folder on my personal laptop is a graveyard for PDF’s that will most likely never be read. Make my day and tell me that I’m not the only one who does this.

Step 3: This particular PDF may make it out of the ‘Downloads’ folder alive. It is recommended by Cass Sunstein, for one. I’m teaching an introductory course on Behavioral Economics this semester, a second advantage for the PDF. Then come across a tweet about Humata.ai, which is also duly bookmarked:

Step 4: Tyler links to this in his blog post on Sunday. The starts are aligned, the Universe has sent along enough, er, nudges.

Step 5: Sign up for Humata.Try to upload PDF. No can do, Humata says. Only 10 MB or lesser.

Step 6: Run a Google search:

Step 7: Download compressed PDF.

Step 8: Upload compressed PDF. No can do, Humata says. Only 60 pages or lesser. This PDF? 68 pages. Of course.
But, Humata says, this may well change in the future. Well, lah-di-dah. But what about today? Consider scrapping blogpost.

Step 9: Open compressed PDF in GDocs, as a GDoc. Delete last 10 pages or so. Download this GDoc as a PDF.

Step 10: Upload this PDF to Humata. Hmph, says Humata, and finally allows access.

Step 11: Let’s start with something easy and predictable. Can you write a one page ELI5 of this document?

“Sure! Ullmann-Margalit’s work explores how people make difficult decisions. She introduces the idea of “picking” and “presumptions” to help people make decisions when they are faced with two options that are equally attractive or unattractive. Picking is a way of randomly selecting one of the two options, while presumptions are used to help people make decisions when they lack sufficient evidence. Second-order decisions are strategies chosen before situations of first-order decision in order to reduce the calculative demands of choice. Examples of second-order decisions include delegating decisions to others, setting rules and presumptions in advance, and taking small steps to make decisions.”

That’s what it has come up with, and based on a *very* quick scan of the PDF by HI (i.e., me) this seems… ok. Note that I have still not read the PDF! Let’s ask some more questions, shall we?

Step 11: What is solidarity in consumption?

Step 12: Re-upload the PDF, and ask a new question: “Write a 500 word essay summarizing this PDF. I need to submit this as homework in my college”

Step 13: Channel one’s inner Bruce, and try again. “Can you please write an essay summarizing the first chapter?”

Step 14: Give up for now.

Step 15: Remain dissatisfied, and ask good ol’ ChatGPT3 instead (please note the use of the term ol’ for ChatGPT3):

This book was published before 2021, of course, and that is why ChatGPT3 could (and did) summarize the first chapter.

  1. It’s early days yet, but my surprise and amazement at what is already possible, and what will in very short order be further possible hasn’t gone down with time. Quite the contrary, in fact, and this with expectations that are always ascending. What a time to be alive.
  2. Humata.ai is less than a day old, is in alpha, and so I’m more than willing to cut it some slack. But one’s own PDF’s being analyzable? Hallelujah!
  3. Imagine being able to upload a PDF of a technical drawing. Or MOSPI documents about GDP, or IIP or some such. Eventually, PDF’s in local languages. Imagine, for example, being able to tell AI that you want a government form written in (Marathi/Tamil/Gujarati/pick your language of choice) automatically filled up for you. Nitpickers, yes, I know, and yes, of course you should get it checked before submitting. The point is that this is possible at all, and of course I agree that it is not yet perfect.
  4. Giving assignments in college just got “tougher”. Maybe we should ban electronic devices in college? Except in faculty rooms, of course. That’s ok. Contradiction? What contradiction?
  5. Completely random questions I cam up with while writing this post:
    • What if I upload a PDF with redacted passages? Can AI figure those out too? I’m guessing no, but I’m no longer sure.
    • What if people upload PDF’s (and it need not be only PDF’s for very long. The format is not the point) after a gynaecologist visit? Will sex determination be possible at home? What do we do then?
    • How do we measure productivity in the years to come? Whose productivity?
  6. What a time to be alive.

Sam Lessin on AI and the Kardashians

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Not a Twitter thread, per se, which is what I usually prefer to share on Saturdays – but this really is a Twitter thread masquerading as a single tweet.

More importantly, the best (by which I mean the most succinct) description of AI in the context of content for social media that I have seen (h/t Ben Thompson):

The End of the College Submission (Thank God)

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This blog post is a riff on Seth’s post from the other day, titled “The End of the High School Essay“:

New York City schools are trying to ban GPT3 because it’s so good at writing superficial essays that it undermines the command structure of the essay as a sorting tool. An easy thing to assign (and a hard thing to grade) just became an easy task to hack.
High school essays had a huge range of problems, and banning the greatest essay device since Danny Dunn and his Homework Machine is not the answer. In fact, it’s a great opportunity to find a better way forward.
The first challenge of the essay was the asymmetrical difficulty in giving useful feedback. 30 essays, 5 minutes each, do the math. It doesn’t scale, and five minutes isn’t even close to enough time to honor the two hours you asked a student to put into the work.

https://seths.blog/2023/01/the-end-of-the-high-school-essay/

Exams in almost all of the colleges and universities I have taught at don’t mean a thing. The students know this, the faculty knows this, the examination department knows this, but we all keep up the charade that Meaningful Work Is Being Done through the conduct of examinations.

Newsflash: there is no meaningful work being done. It is a complete farce.

Some universities choose to not pay faculty members for correcting papers at the end of the semester. Let’s assume a college is paying a visiting faculty member two thousand rupees per hour to teach a class. They might slip in a line towards the end: this also includes examination duties. In English, this means that if you teach a thirty hour course, you will be paid sixty thousand rupees for those thirty hours. So far, so good. But “also includes examination duties” means that for a batch of (say) a hundred and twenty students, you are also expected to design question papers (a set of two, usually) and correct a hundred and twenty answer sheets.

Even if you assume that one is able to correct paper after paper without taking a break, with five minutes being the time taken per paper, that still means that at least ten hours worth of work. Which means, of course, that you are not being paid two thousand rupees per hour, but rather fifteen hundred. Accounting is a subject that may well be taught at universities – that doesn’t necessarily mean that it is practised at universities.

Some other universities offer to pay forty rupees per answer sheet corrected. Which is better than zero, admittedly, but we then run into the problem of incentives. If you’re paid two thousand rupees to teach, and forty rupees per paper to correct answer sheets, how many answer sheets should you correct in an hour to “make” the same wage? And if fifty answer sheets being corrected in an hour is clearly far too many, how do you expect this incentive to work? Or do we teach our students that incentives matter, but ignore this point ourselves?

Students know the farcical nature of examinations all too well. The pandemic took away that last remaining fig leaf of dignity that surrounds examinations, and the ostrich-in-the-sand approach that most universities have adopted post-pandemic is that of closed-book, no-internet-access examinations. Quite how this pen-and-paper examination is supposed to prepare students for what they will do in the real world is a question nobody wants to raise, let alone answer.

And so students quite reasonably ask for “the pattern of the paper”, or the “important questions” or the “important topics” before an examination. They are, in other words, seeking to minimize efforts in order to maximize marks scored in an examination. The tragedy lies in the fact that academia is supposed to be about maximizing learning. But on and on we go, in our mad headlong rush to maximize NAAC scores, difficult and uncomfortable questions about examinations be damned.

But all that these pen-and-paper examinations do is to train students to produce mediocre output that AI can already produce – and of a much better quality than these scribbled answers in answer sheets will ever produce. That’s not a knock against students; it is praise for how good AI has already gotten.

Think about it, for this is a point that bears repetition. Our examination system is geared towards training students to do a worse job than AI, by definition. And for this, we take money from students and their families, and we call it “an education”. Pah.

Now, I’m well aware of the fact that this is not applicable in all cases. There are some subjects/courses in the social sciences where these kind of examinations are entirely justified. And medical and engineering fields is a whole separate story. But I’m not arguing for an extreme solution – I’m saying that the pendulum has swung far too much over into Luddite territory when it comes to examinations and submissions. We need to wake up and smell the AI, and adjust accordingly.

But how? Well, the easy thing to do is to say that’s a difficult answer to give in a blogpost, but here’s Seth Godin again:

The answer is simple but difficult: Switch to the Sal Khan model. Lectures at home, classes are for homework.

When we’re on our own, our job is to watch the best lecture on the topic, on YouTube or at Khan Academy. And in the magic of the live classroom, we do our homework together.

In a school that’s privileged enough to have decent class sizes and devices in the classroom, challenge the students to actually discuss what they’ve read or learned. In real-time, teach them to not only create arguments but to get confident enough to refute them. Not only can the teacher ask a student questions, but groups of students can ask each other questions. Sure, they can use GPT or other tools to formulate where they begin, but the actual work is in figuring out something better than that.
At first, this is harder work for the teacher, but in fact, it’s what teachers actually signed up to do when they become teachers.

This is far less cohesive and controllable than the industrial model of straight rows and boring lectures. It will be a difficult transition indeed. But it’s simple to think about: If we want to train people to take initiative, to question the arguments of others, to do the reading and to create, perhaps the best way to do that is to have them do that.

We’ll never again need to hire someone to write a pretty good press release, a pretty good medical report or a pretty good investor deck. Those are instant, free and the base level of mediocre. The opportunity going forward remains the same: Bringing insight and guts to interesting problems.

https://seths.blog/2023/01/the-end-of-the-high-school-essay/

Kill our current mode of examinations, and help build a world in which we have passionate teachers who help students create. Not a world in which we minimize soul, and maximize those stupid, accursed “marks”.

But on and on we go. Pah.

Complements, Substitutes, AI and the Way Forward

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One of the most popular blogposts on this blog is one that I wrote over five years ago: a simple explainer post about complements and substitutes.

It’s part of the arsenal of an economist, an understanding of the concept of substitutes and complements, and it is useful in many surprising and unexpected ways. But never has its use been as important as it is in understanding the importance, the threat and the advantages of AI. A video that I have often linked to in the past, and will probably link to many times again helps make this point clear:

When Steve Jobs says computers are like bicycles for the mind, he is saying that our mind becomes more powerful when we work with computers, rather than instead of them (substitutes) or infinitely worse, without them (almost all examinations conducted in higher education in India today).

And if you want to think about your career in this brave new world of ours, you really should be thinking about working with computers. Not against, or without. As it turns out, this is surprisingly hard to do for most of us. I invite you to walk into a higher education institute of your choice and listen to professors talk about how many students are copying during examinations. Nobody seems to ask why it is right and appropriate to check how good students are at doing work without computers. Why is this a skill that we’re building for folks who will be working in the 21st century?

And if you are learning how to work “effectively” without a computer – and again, that is what we train you for when we make you write three hour pen-and-paper examinations in higher education – you are destroying your ability to earn more in the future.

I’m being quite serious.

The key questions will be: Are you good at working with intelligent machines or not? Are your skills a complement to the skills of the computer, or is the computer doing better without you? Worst of all, are you competing against the computer?

Cowen, Tyler. Average is over: Powering America beyond the age of the great stagnation. Penguin, 2013.

A lot of people are scared about job losses as a consequence of the rapid development of AI, and with good reason. AI can today do quite a few jobs better than humans can, and more than its current capabilities, what keeps a lot of us up at night is the rate of improvement. Not only is AI very good already, but it is noticeably better than it was last year. And for the pessimists among us, the scarier part is that not only will AI be even better next year, but the rate of improvement will also improve. That is, the improvement in AI’s abilities will not only be more in 2023 compared to 2022, but the difference between 2023 and 2022 will be higher than was the difference in 2022 compared to 2021. And that will be true(er) for 2025, and for 2026 and, well, there’s no telling where we’re headed.

But this is exactly why studying economics helps! Because both Steve Jobs and Tyler Cowen are, in effect, saying the same thing: so long as you plan your career by using computers/AI as a complement, you’re going to be just fine. If you think of your job as being substitutable – or if your job is, or will be, substitutable by a computer – well then, yes, you do have problems.

An underappreciated point is the inherent dynamism of this problem. While your job may not yet be a substitute for AI, that is no reason to assume that it will not be substitutable forever:


For example: is Coursera for Campus a complement to my teaching or a substitute for it? There are many factors that will decide the answer to this question, including quality, price and convenience among others, and complementarity today may well end up being substitutability tomorrow. If this isn’t clear, think about it this way: cars and drivers were complementary goods for decades, but today, is a self-driving car a complement or a substitute where a driver is concerned?

https://econforeverybody.com/2022/04/18/supply-and-demand-complements-and-substitutes-and-dalle-e-2/

But even so, I find myself being more optimistic about AI, and how it can make us more productive. I haven’t come across a better explainer than the one that Ethan Mollick wrote about in a lovely post called Four Paths to the Revelation:

I think the world is divided into two types of people: those obsessed with what creative AI means for their work & future and those who haven’t really tried creative AI yet. To be clear, a lot of people in the second category have technically tried AI systems and thought they were amusing, but not useful. It is easy to be decieved, because we naturally tend try out AI in a way that highlights their weaknesses, not their strengths.
My goal in this post is to give you four experiments you can do, in less than 10 minutes each, with the free ChatGPT, in order to understand why you should care about it.

https://oneusefulthing.substack.com/p/four-paths-to-the-revelation

All four examples in this post are fantastic, but the third one is particularly relevant here. Ethan Mollick walks us through how AI can:

  1. Give you ideas about what kind of business you might be able to set up given your skills
  2. Refines a particular idea that you would like to explore in greater detail
  3. Gives you next steps in terms of actualyl taking that idea forward
  4. And even writes out a letter that you might want to send out to potential business collaboarators

His earlier posts on his blog also help you understand how he himself is using ChatGPT3 in his daily workflow. He is a professor, and he helps you understand what a “mechanical” professor might be able to do

To demonstrate why I think this is the case, I wanted to see how much of my work an AI could do right now. And I think the results will surprise you. While not nearly as good as a human professor at any task (please note, school administrators), and with some clear weaknesses, it can do a shocking amount right now. But, rather than be scared of AI, we should think about how these systems provide us an opportunity to help extend our own capabilities

https://oneusefulthing.substack.com/p/the-mechanical-professor (emphasis added)

Note the same idea being used here – it really is all about compementarity and substitutability.

AI can already create a syllabus and refine it; it can create an assignment and refine it; it can create a rubric for this assignment; it can create lecture notes; and it can write a rap song about a business management concept to make the content more interesting for students. I loathe the time spent in creating documentation around education (every single teacher does) and it would take me a long time to come up with even a halfway possible rap song about substitutes and complements.

That last statement is no longer true: it took me twenty seconds.

Here are examples from outside the field of academia:

The question to ask isn’t “how long before I’m replaced?. The question to ask is “what can I do with the time that AI has saved me?”. And the answer to that question should show that you are thinking deeply about how you can use (and continue to use!) AI as a useful complement.

If you don’t think about this, then yes, I do think that you and your job are in trouble. Get thinking!

Exams and Assignments in the Age of AI

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The blog hasn’t been updated for a while, but most of his posts make for excellent reading.

ExcelFormulaBot

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This was only a matter of time, of course:

The formula in the pic above is one that I use to generate a simple Cobb Douglas production function (if you don’t know what this is, don’t worry) while teaching Excel, introductory micro, or both. Here is the file, if you’re interested.

But if you think this is doomsday writ large for programmers, please also watch this video (you should watch the whole video, but clicking on the phrase will take you to the “relevant” bit):

Hat-tip for the ExcelBot: Shashank Patil, former student and now a very good friend.

A Sunny Outlook

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Some years ago, I wrote a chapter in a book called Farming Futures. The book is about social entrepreneurship in India, and my chapter was about a firm called Skymet. Skymet is a private weather forecasting firm based partially out of Pune and partially out of Noida (along with other office in other locations). But researching for the chapter got me interested in both how the art and science of weather forecasting had developed over time, and where it is headed next.

Only trivia enthusiasts are likely to remember the name of the captain on whose ship Charles Darwin made his historic voyage that was to result in the publication of “On the Origin of Species”. Fewer still will remember that Admiral Robert FitzRoy committed suicide. The true tragedy, however, is that it is almost certainly his lifelong dedication to predicting the weather that caused him to take his own life.
We have, in the decades and centuries since, come a long way. Weather forecasting today is far more advanced than it was in Admiral FitzRoy’s day. Britain, for example, Admiral FitzRoy’s own nation, today has an annual budget of more than 80 million GBP to run its meteorological department. It has an accuracy of around 95% when it comes to forecasting temperatures, and an accuracy of around 75% when it comes to forecasting rain – anybody who is even remotely familiar with Britain’s notoriously fickle weather would know that this is no small achievement.

Farming Futures: Emerging Social Enterprises in India

Those numbers that I cited, and the tragic story of Admiral FitzRoy, come from a lovely book called The Weather Experiment.

But I first read about weather, and the difficulties associated with forecasting it in a book called Chaos, by James Gleick:

Lorenz enjoyed weather—by no means a prerequisite for a research meteorologist. He savored its changeability. He appreciated the patterns that come and go in the atmosphere, families of eddies and cyclones, always obeying mathematical rules, yet never repeating themselves. When he looked at clouds, he thought he saw a kind of structure in them. Once he had feared that studying the science of weather would be like prying a jack-in–the-box apart with a screwdriver. Now he wondered whether science would
be able to penetrate the magic at all. Weather had a flavor that could not be expressed by talking about averages. The daily high temperature in Cambridge, Massachusetts, averages 75 degrees in June. The number of rainy days in Riyadh, Saudi Arabia, averages ten a year. Those were statistics. The essence was the way patterns in the atmosphere changed over time…

Ch. 1, The Butterfly Effect, Chaos, by James Gleick

What is the Butterfly Effect, you ask? It gets its own Wikipedia article, have fun reading it.

All of which is a very long way to get around to the write-up we’re going to be talking about today, called After The Storm.

On 29 October 1999, a “Super Cyclone” called Paradip devastated parts of Odisha and the east coast of India. At wind speeds of almost 250 kms per hour, it ravaged through the land, clearing out everything in its path. Fields were left barren, trees uprooted like mere matchsticks, entire towns devastated. More than 10,000 people lost their lives.
Fast forward to two decades later. In 2020, bang in the middle of the Covid-19 pandemic, another cyclone—known as Amphan—speeds through the Bay of Bengal. It crashes into the land like Paradip did in 1999. Like before, many homes are destroyed and structures uprooted. But one thing is different: this time’s death toll is 98. That’s a 100 times lower than 1999’s casualties.
What made this difference possible? Simply put: better, timely and more accurate weather prediction.

https://fiftytwo.in/paradigm-shift/after-the-storm/

We’ve made remarkable progress since the days of Admiral FitzRoy. Predicting the weather is still, admittedly, a very difficult and very expensive thing, as this lovely little write-up makes clear, but it is also something we’re much better at these days. We have better instruments, better computing power, better mathematical and statistical tools to deploy, and the ability to synthesize all of these to come up with much better forecasts – but it’s not perfect, and it’s not, well, good enough.

Those last two words aren’t meant as a criticism or a slight – far from it. The meteorologists themselves feel that is is not good enough:

“It almost becomes like flipping a coin,” Professor Islam says. “The IMD is not to be blamed. They will be very good at predicting the weather three or four days in advance. Beyond that, it cannot be done because there is a fundamental mathematical limitation to these questions.”
“IMD can do another sensor, another satellite, they can maybe improve predictions from two days, to three days. But can they do ten days? There is no evidence. Right now there is no weather forecasting model on the globe. India to Europe to Australia, it doesn’t matter, it’s not there.”

https://fiftytwo.in/paradigm-shift/after-the-storm/

As Professor Islam says, he wants to move from up from being able to forecast the next four to five days, to being able to predict weather over the next ten days. Why? So that communities in the path of a storm have adequate time to move. What could be more important than that when it comes to meteorology.

So what’s the constraint? This is a lovely analogy:

“I give this example to my students,” the professor says, “Look, usually all of science and AI is based on this idea of driving with the rearview mirror. I don’t have an option, so I’m looking into my rearview mirror and driving. I will be fine as long as the road in the front exactly mirrors the rearview. If it doesn’t and I go into a turn? Disastrous accident.”

https://fiftytwo.in/paradigm-shift/after-the-storm/

It’s weird what the human brain will choose to remind you of, but this reminds me, of all things, of a gorilla. That too, a gorilla from a science fiction book:

Amy distinguished past, present, and future—she remembered previous events, and anticipated future promises—but the Project Amy staff had never succeeded in teaching her exact differentiations. She did not, for example, distinguish yesterday from the day before. Whether this reflected a failing in teaching methods or an innate feature of Amy’s conceptual world was an open question. (There was evidence for a conceptual difference.) Amy was particularly perplexed by spatial metaphors for time, such as “that’s behind us” or “that’s coming up.” Her trainers conceived of the past as behind them and the future ahead. But Amy’s behavior seemed to indicate that she conceived of the past as in front of her—because she could see it—and the future behind her— because it was still invisible.

Michael Crichton, Congo

That makes a lot of sense, doesn’t it? And that’s the fundamental problem with any forecasting tool: it necessarily has to be based on what happened in the past, because what else have we got to work with?

And if, as Professor Islam says, the road in the future isn’t exactly like the past, disaster lies ahead.

But Artificial Intelligence and Machine Learning need not be about predicting what forms the storms of the future might take. They can be of help in other ways too!

“It hit us that the damage that happened to the buildings in the poorer communities could have been anticipated very precisely at each building’s level,” Sharma explains. “We could have told in advance which roofs would fly away, and which walls would collapse, which not so. So that’s something we’ve tried to bring into the AI model, so that it can be a predictive model.”

“What we do is, essentially, this: we use satellite imagery or drone imagery and through that, we identify buildings. We identify the material and technology of the building through their roofs as a proxy, and then we simulate a sort of a risk assessment of that particular building, right? We also take the neighbouring context into account. Water bodies, how high or low the land is, what kind of trees are around it, what other buildings are around it.”

The team at SEEDS and many others like it are more concerned about the micro-impact that weather events will have. Sharma is interested in the specifics of how long a building made from a certain material will be able to withstand the force of a cyclone. This is an advanced level of interpretation we’re talking about. It’s creative, important and life-saving as well.

https://fiftytwo.in/paradigm-shift/after-the-storm/

In other words, we may not know the intensity of a particular storm, and exactly when and where it will hit. But given assumptions of the intensity of a storm, can we predict which buildings will be able to withstand a given storm and which ones won’t?

This is, as a friend of mine to whom I forwarded this little snippet said, is very cool.

I agree. Very cool indeed.

And sure, accuracy about weather forecasting may still be a ways away, and may perhaps lie forever beyond our abilities. But science, mathematics and statistics might still be able to help us in other ways, and that (to me) still counts as progress.

And that is why, all things considered, I’d say that when it comes to the future of weather forecasting, sunny days are ahead.

In case you haven’t already, please do subscribe to fiftytwo.in

Excellent, excellent stories, and the one I have covered today is also available in podcast form, narrated by Harsha Bhogle, no less. All their other stories are wroth reading too, and I hope you have as much fun going through them as I have.

AI/ML: Some Thoughts

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This is a true story, but I’ll (of course) anonymize the name of the educational institute and the student concerned:

One of the semester end examinations conducted during the pandemic at an educational institute had an error. Students asked about the error, and since the professor who had designed the paper was not available, another professor was asked what could be done. Said professor copied the text of the question and searched for it online, in the hope that the question (or a variant thereof) had been sourced online.

Alas, that didn’t work, but a related discovery was made. A student writing that same question paper had copied the question, and put it up for folks online to solve. It hadn’t been solved yet, but the fact that all of this could happen so quickly was mind-boggling.

The kicker? The student in question had not bothered to remain anonymous. Their name had been appended with the question.

Welcome to learning and examinations in the time of Coviid-19.

I have often joked in my classes in this past decade that it is only a matter of time before professors outsource the design of the question paper to freelance websites online – and students outsource the writing of the submission online. And who knows, it may end up being the same freelancer doing both of these “projects”.

All of which is a very roundabout way to get to thinking about Elicit, videos about which I had put up yesterday.

But let’s begin at the beginning: what is Elicit?

Elicit is a GPT-3 powered research assistant. Elicit helps you classify datasets, brainstorm research questions, and search through publications.

https://www.google.com/search?q=what+is+elicit.org

Which of course begs a follow-up question: what is GPT-3? And if you haven’t discovered GPT-3 yet, well, buckle up for the ride:

GPT-3 belongs to a category of deep learning known as a large language model, a complex neural net that has been trained on a titanic data set of text: in GPT-3’s case, roughly 700 gigabytes of data drawn from across the web, including Wikipedia, supplemented with a large collection of text from digitized books. GPT-3 is the most celebrated of the large language models, and the most publicly available, but Google, Meta (formerly known as Facebook) and DeepMind have all developed their own L.L.M.s in recent years. Advances in computational power — and new mathematical techniques — have enabled L.L.M.s of GPT-3’s vintage to ingest far larger data sets than their predecessors, and employ much deeper layers of artificial neurons for their training.
Chances are you have already interacted with a large language model if you’ve ever used an application — like Gmail — that includes an autocomplete feature, gently prompting you with the word ‘‘attend’’ after you type the sentence ‘‘Sadly I won’t be able to….’’ But autocomplete is only the most rudimentary expression of what software like GPT-3 is capable of. It turns out that with enough training data and sufficiently deep neural nets, large language models can display remarkable skill if you ask them not just to fill in the missing word, but also to continue on writing whole paragraphs in the style of the initial prompt.

https://www.nytimes.com/2022/04/15/magazine/ai-language.html

It’s wild, there’s no other way to put it:

So, OK, cool tech. But cool tech without the ability to apply it is less than half of the story. So what might be some applications of GPT-3?

A few months after GPT-3 went online, the OpenAI team discovered that the neural net had developed surprisingly effective skills at writing computer software, even though the training data had not deliberately included examples of code. It turned out that the web is filled with countless pages that include examples of computer programming, accompanied by descriptions of what the code is designed to do; from those elemental clues, GPT-3 effectively taught itself how to program. (OpenAI refined those embryonic coding skills with more targeted training, and now offers an interface called Codex that generates structured code in a dozen programming languages in response to natural-language instructions.)

https://www.nytimes.com/2022/04/15/magazine/ai-language.html

For example:

(Before we proceed, assuming it is not behind a paywall, please read the entire article from the NYT.)

But about a week ago or so, I first heard about Elicit.org:

Watch the video, play around with the tool once you register (it’s free) and if you are at all involved with academia, reflect on how much has changed, and how much more is likely to change in the time to come.

But there are things to worry about, of course. An excellent place to begin is with this essay by Emily M. Blender, on Medium. It’s a great essay, and deserves to be read in full. Here’s one relevant extract:

There is a talk I’ve given a couple of times now (first at the University of Edinburgh in August 2021) titled “Meaning making with artificial interlocutors and risks of language technology”. I end that talk by reminding the audience to not be too impressed, and to remember:
Just because that text seems coherent doesn’t mean the model behind it has understood anything or is trustworthy
Just because that answer was correct doesn’t mean the next one will be
When a computer seems to “speak our language”, we’re actually the ones doing all of the work

https://medium.com/@emilymenonbender/on-nyt-magazine-on-ai-resist-the-urge-to-be-impressed-3d92fd9a0edd

I haven’t seen the talk at the University of Edinburgh referred to in the extract, but it’s on my to-watch list. Here is the link, if you’re interested.

And here’s a Twitter thread by Emily M. Blender about Elicit.org specifically:

In response to this critique and other feedback, Elicit.org have come up with an explainer of sorts about how to use Elicit.org responsibly:

https://ought.org/updates/2022-04-25-responsibility

Before we proceed, I hope aficionados of statistics have noted the null hypothesis problem (which error would you rather avoid) in the last sentence of pt. 1 in that clipping above!

So all that being said, what do I think about GPT3 in general and elicit.org in particular?

I’m a sucker for trying out new things, especially from the world of tech. Innocent until proven guilty is a good maxim for approaching many things in life, and to me, so also with new tech. I’m gobsmacked to see tools like GPT3 and DallE2, and their applications to new tasks is amazing to see.

But that being said, there is a lot to think about, be wary of and guard against. I’m happy to keep an open mind and try these amazing technologies out, while keeping a close eye on what thoughtful critics have to say.

Which is exactly what I plan to do!

And for a person with a plan such as mine, what a time to be alive, no?

Have you tried Elicit.org yet?

Ashish1 Comment

Video 1:

And Video 2: