The human glitch is shaping everything from corporate boardroom decisions to government policies to individual career choices. If we don’t diagnose the error and develop better conceptual tools for understanding what AI does, the implications for human well-being could be serious, even catastrophic. To chart a meaningful path forward, we have to start by understanding the difference between humans and AI—between human intelligence and artificial pattern matching.

Artificial Intelligence vs. Human Intelligence

Early AI researchers assumed that if they could just break down human thought processes into logical steps, they could replicate human intelligence. This led to what we now call "symbolic AI" or "classical AI"—systems that tried to represent knowledge as logical rules and relationships.

However, human intelligence proved more complex and subtle than these early models suggested. Consider how children learn language. They don't memorize grammar rules and vocabulary lists. They absorb language through immersion, trial and error, and pattern recognition. They learn not just words and rules but contexts, connotations, and the thousand subtle ways that meaning depends on the situation and relationship.

Modern AI systems mimic human learning more closely. That’s particularly true of the Large Language Models that power ChatGPT and Claude. Whereas early AI models were pre-programmed with grammar rules and vocabulary lists, modern AI systems are trained on massive data sets. They derive their own rules from the data and then follow those rules when producing the output. This training process is closer to human learning, but it still doesn’t do what a human child does.

Children are engaged in a cooperative biological activity choreographed by natural selection to facilitate group living. It’s an activity that aims at establishing and maintaining social bonds through a shared understanding of what people are thinking and feeling. AI systems aren’t engaged in anything like this. They’re not engaged in understanding, social bonding, or shared activity. Instead, they engage in a process of sophisticated pattern-matching. They use massive amounts of data to develop a list of templates—repeatable patterns of words or images. They then use those templates to predict which sequences of words or images are likely to come next in a given context. What they’re doing isn’t thinking in any meaningful sense. It’s merely pattern-matching—albeit pattern-matching more sophisticated than machines of the past could achieve.

The distinction between thinking and mere pattern-matching is crucial for understanding both the remarkable capabilities and the fundamental limitations of AI systems. They can process and recombine patterns in ways that seem magical. However, they're not engaging in the kind of meaning-making that characterizes human intelligence. They can tell you that a sentence is grammatically correct, but they don't understand what it means. They can generate text that perfectly matches the patterns of human writing, but they're not communicating ideas; they're just predicting word sequences.

Three Fundamental Myths about AI

Myth 1: AI Thinks and Acts Like Humans

People have the impression that AI thinks—where "thinks" means the same thing it does when we apply that word to a human. But this apparent similarity to human thought is a sophisticated illusion. When we perceive AI as human-like, it reveals more about how our pattern-seeking brains interpret behavior than about how AI actually works.

To understand why, we need to examine both human and artificial intelligence at their core. When humans think, we engage in a complex process of meaning-making. We don't just process information; we understand it. We don't just recognize patterns; we grasp their significance. We don't just manipulate symbols; we comprehend what they represent. This understanding isn't just about processing speed or pattern recognition. It's about meaning, context, and consciousness itself.

Consider how you understand a simple sentence like, "The coffee was too hot to drink." You don't just process the grammatical structure and vocabulary; you understand what it means to be too hot, what it feels like to burn your tongue, and why someone might want to drink coffee in the first place. You can effortlessly connect this sentence to your own experiences, to social contexts where coffee is important, to the physical properties of heat and the biological process of burning.

AI, in contrast, processes this sentence in a fundamentally different way. Modern AI systems, particularly large language models, operate by matching patterns. When we say an AI system is "trained," what we mean is that it has analyzed vast amounts of text and cataloged statistical relationships among words and phrases. It learns, for instance, that "coffee" frequently appears near words like "hot," "drink," and "morning." But it doesn't understand what coffee actually is; it’s never experienced temperature, and it cannot truly comprehend the concept of discomfort.

The statistical relationships among words and phrases that AI catalogs enable it to produce impressive simulations of real human acts of communication. If you don’t look past the surface of grammatically correct text, it’s easy to be fooled and think the text was written by a human who intended to communicate something. Look past the surface, however, and cracks start to show: AI makes mistakes. For example, it might confidently cite a non-existent research paper, combine facts from different contexts in misleading ways, or generate expert-sounding but entirely fictional advice. Worse yet, AI commits these mistakes using language that suggests complete confidence in the message. Researchers call mistakes of this sort "hallucinations."

Hallucinations aren't random errors but the natural result of how AI systems work. If certain patterns appear frequently in the training data, AI will reproduce them, regardless of whether they say something true or false. AI isn’t lying or committing errors in the way humans do. It’s not concerned with truth or falsity the way humans are, and it can’t form intentions to deceive others in the way humans do. Instead, it simply generates outputs that match the patterns it has learned. It uses those same patterns to say things that are true and things that are false, thus conveying both information and misinformation.

Myth 2: AI Will Replace Human Workers

People are afraid that they will be replaced wholesale by AI: that robots or algorithms will take over human jobs and leave people unemployed. The fear of technological replacement isn't new—the Luddites faced similar anxieties with mechanical looms. However, the current wave of AI development seems particularly threatening. It seems to challenge not just our physical capabilities but our cognitive supremacy.

But this concern also stems from a misunderstanding of both human and artificial intelligence.

Consider what happens when a human writer crafts a story. They draw on personal experience, emotional understanding, and cultural context. They might recall the smell of their grandmother's kitchen and somehow connect that sensory memory to a broader point about technology and tradition. They understand not just the meaning of words but the weight they carry in different contexts, the subtle implications they might have for different readers, and the cultural and emotional resonances they evoke.

What we're actually seeing isn't replacement but transformation. Jobs don't typically disappear entirely; they evolve. Just as the introduction of spreadsheets transformed accounting from a profession focused on calculation to a profession centered on analysis and strategy, AI is shifting human work toward tasks that require uniquely human capabilities: judgment, creativity, emotional intelligence, and ethical reasoning.

This transformation is already visible across multiple fields:

• In medicine, AI can analyze radiological images with remarkable accuracy. It often spots patterns that human doctors might miss. But it can't integrate this analysis with a patient's life circumstances, medical history, and treatment preferences. It can't have difficult conversations about prognosis or make complex ethical judgments about care options.

• In law, AI can review thousands of documents in hours. It can identify patterns and relationships that might take human lawyers weeks to find. But it can't understand the broader strategic implications of these patterns, can't negotiate with opposing counsel, can't build the trust relationships that form the foundation of legal practice.

• Even in technical fields where AI excels at pattern recognition and data analysis, the technology tends to augment rather than replace human capabilities. Programmers using AI coding assistants aren't being replaced; they're being freed from routine coding tasks to focus on system architecture and problem-solving.

Instead of seeing AI as a super-powerful intern who can suddenly do any human job, it’s better to view it as a task-oriented intern who can take the boring stuff off your plate.

Myth 3: AI Is a Reliable Source of Educational Content

Perhaps the most dangerous myth about AI is the belief that it can serve as a reliable, independent source of educational content. This misconception is particularly seductive in education, where the idea of AI as the perfect tutor—one who never tires, never loses patience, who can access all human knowledge instantly and adapt to each student's needs—seems to offer solutions to many educational challenges.

However, this vision fundamentally misunderstands both the nature of education and the limitations of AI. When an AI system generates educational content, it's not drawing on understanding; it's generating text patterns that statistically match what explanations look like. When it responds to a student's question, it's not comprehending the confusion or crafting an explanation based on understanding. Instead, it matches the pattern of the question to the patterns of likely helpful responses.

There are at least four problems with relying on AI as a primary source of educational content:

1. Unreliable Content: AI hallucinations are particularly dangerous in education. The convincing-looking content that AI generates can contain subtle or even significant errors. An AI might confidently present incorrect historical dates, misstate scientific principles, or blend different concepts in misleading ways. In many cases, students and even teachers might lack the subject expertise to spot those errors.

2. Inability To Understand: While AI can generate surface-level explanations that follow the patterns of good teaching, it doesn’t understand the subject matter and can’t assess whether a student genuinely understands it, either. It can't tell when a student has memorized the right words and yet failed to grasp the underlying concepts. As a result, it can't guide students past the illusion of understanding to genuine mastery.

3. Lack of Cultural and Contextual Awareness: Educational content needs to be culturally relevant and appropriate for specific learning contexts. AI lacks true understanding of human cultural nuances and social contexts. As a result, it can generate content that's technically accurate but culturally inappropriate.

4. Lack of a Learning Meta-Model: Effective education requires something AI lacks: the ability to model learning itself. When human teachers explain concepts, they draw on their own experience of learning that material—they remember what confused them, what helped them understand, and how different approaches work for different learners. This meta-understanding of the learning process itself is something that AI lacks. A human teacher might notice a student's eyes light up when relating physics to sports or sense confusion in a slight hesitation before answering. These subtle cues inform real-time adjustments in teaching strategy that no AI, however sophisticated, can truly replicate.

The challenge of using AI for education isn't just limited to content delivery but also the complex interplay among teaching, learning, and understanding. While AI can simulate the patterns of good teaching, it cannot engage in the relationship that makes deep learning possible.

This doesn't mean AI has no place in education. The most effective educational future will be one where AI and human teachers work together, each doing what they do best. Human teachers bring understanding, judgment, and the ability to foster genuine learning in the following ways:

• Detecting subtle signs of confusion or engagement,

• Drawing on their own learning experience to anticipate misconceptions,

• Understanding not just the what of their subject but the why,

• Building the relationships that make learning possible,

• Guiding students toward genuine understanding rather than mere pattern recognition.

AI can support human learning in the following ways:

• helping students practice skills,

• providing immediate feedback on routine tasks,

• offering additional explanations and examples,

• Helping teachers handle administrative tasks, generate preliminary teaching materials, and identify patterns in student performance.

The Path Forward

These three myths—about AI's thinking capabilities, its role in the workforce, and its reliability as an educational tool—all stem from our tendency to project human qualities onto pattern-matching machines. Moving beyond them requires more than just understanding; it requires developing what we might call "AI literacy."

What does AI literacy look like in practice? Consider a journalist using AI to investigate a complex story. Instead of asking the AI to "analyze the situation" or "find the truth," an AI-literate journalist knows to use it for specific tasks: scanning thousands of documents for relevant names and dates, identifying statistical patterns in data, or generating possible questions to ask human sources. The journalist maintains control over the investigation's direction, fact-checking, and ethical judgments—while letting AI handle the pattern-matching tasks that computers do best.

Or take a teacher who understands that AI isn't a replacement for education but a tool for amplifying human instruction. Rather than relying on AI to explain concepts directly to students, they might use it to generate diverse examples, create practice problems at different difficulty levels, or identify patterns in student mistakes. The teacher's human insight determines how to use these AI-generated resources effectively.

These examples show how progress with AI mirrors the broader story of human tool use. We didn't become the planet's dominant species by being the strongest or fastest but by being the best at making and using tools to amplify our uniquely human capabilities. The key is understanding exactly where AI's pattern-matching abilities end, and human judgment must begin.

The path forward begins with a shift in how we think and talk about AI. Instead of asking, "Is this AI conscious?" or "Will this AI replace humans?" we should ask more precise questions: "What patterns is this system recognizing?" "Where might its pattern-matching break down?" "What uniquely human capabilities does this task require?"

When you next encounter AI, whether you're using ChatGPT for writing, considering AI tools for your business, or evaluating AI-powered educational software, catch yourself being subject to the human glitch. Notice when you start attributing human qualities to the system. Then reframe your thinking: you're not dealing with an artificial human but with a powerful pattern-matching tool that can amplify—but never replace—human understanding.

The future belongs not to those who fear AI or worship it but to those who see it clearly for what it is: a mirror that shows us, through contrast, what makes us uniquely human.

1. My sincere thanks to Ellen Fishbein and William Jaworski for their incredible support in creating this essay. You are amazing!

2. All opinions and views expressed here are mine alone and do not represent those of my employer.

Believe me.

I've had the distinct pleasure of pulling the remaining strands of my hair from their forlorn follicles while attempting to understand research papers and deep-dive blogs. These works of art would put NyQuil out of business. Imagine the unrivaled delight of trying to decipher paragraphs littered with jargon and equations written by a thirty-something-year-old with the panache of a three-year-old.

You are right in asking, "Who the hell are you to judge? Have you read your own drivel?"

Yes. I have. Too many times for my own good, in fact.

That's exactly why I'm writing about how we all can learn from a gentleman who knows a thing or two about technical writing.

.

Packy? Don't you write about AI, Sairam? Why are you analyzing Packy?

Let me tell you why. My collective business acumen comes from beating my friends and cousins in Monopoly. Yes, I've bought every hotel, railroad, and peg that game had to offer. Despite this pedigreed education, my brain isn't wired to understand economics, acquisitions, balance sheets, bonds (except 007), or small-cap investments, for starters.

So you can imagine my shock when I was three-fourths of the way through an essay before realizing it was about business strategy. Wait, why did I even read this? It took a few more moments for the aftershock to hit. Hang on, I actually understood this stuff!

Packy's work lives up to his newsletter's title: it's not boring. He masterfully blends memes, puns, pop culture, facts, and stories to both educate and entertain.

After reading a few more of his essays, my engineering brain kicked into action. Why does it work? Are there common threads here that I could use in my writing? Could all technical writing be this fun and functional?

What follows are some of the lessons I've learned about technical writing through Packy's brilliant essays.

Labyrinth Technica

Whether you write about finance, health, AI, engineering, or underwater basket weaving, one thing is common: All these subjects involve complex concepts, theories, and systems. These are about as easy to explain clearly as cutting your nails on a guillotine. Most people think they can do it. Some try. Only a few truly can1.

Take, for example, Packy's Shen Yun startup piece. When you read it, you forget that you're learning about the economic model that high-growth startups use, hoping to hit profitability. You're so mesmerized by the question, "What in the world does this dance performance have to do with startups?".

A layperson (read me) might worry about the background knowledge needed to understand everything in context. But before I can spend any more gray matter on worrying, Packy hits us with, "How does Shen Yun make any money? Short answer: they don't."

The only hook better than this one rests on the hand of a sea pirate battling a boy who never grows up and his fairy friend.

He then pulls out a P&L statement2 to show that this is indeed the case. In the short span of a paragraph, he's hooked you into reading the rest of the piece and gently introduced some financial concepts. It doesn't overwhelm you. Even if it does, there's enough of a hook to make you want to try to understand it.

Had he instead chosen to do a detailed financial analysis first and then conclude that Shen Yun isn't making money, I'd have switched off.

Therein lies the first nugget of wisdom I picked up. Introduce complex ideas to the reader in the right way and at the right place.

But that sprouts an interesting question. How do you do this for all readers? After all, not all readers are made equal.

The Juggling Act

Even if you're one of those rare breeds who can hook the reader instantly into your writing, the next challenge is considerably more daunting.

Your readers could be experts wanting a TL;DR, novices needing guidance, or even trolls eager to criticize. Walking the tightrope between being technically comprehensive while also being accessible is incredibly challenging.

Packy gives the TL;DR straight up front. In an unusual (for him) tell-and-then-show style, he gives a gist of what the essay is about. It's an easy decision for the reader to decide how deep they want to dive.

Take, for example, his piece on Spotify. Within the first one hundred words, he's already told you what the piece is about, which perspectives he'll cover, and why you should bother.

In an attention-driven economy, Packy's respect for the reader's time sets him apart.

But that's just the start.

What usually follows is a meandering train of thoughts, all connected to the central theme. Some of these meanderings might fly over your head. That's ok. There's always a "Joe Rogan's $100 million+ Spotify deal is like a giant dollar sign bat signal to potential podcasters." for the novices among us. There's always a story or a visual to put us at ease.

Reading Packy's work feels like you're in a conversation with him. Imagine sitting around a marbled oak table in a coffee shop, the aroma of freshly roasted beans wafting in the air, mild vibe music, and espresso machines humming in the background. You're sitting there listening to Packy muse about the economy and regale stories from past years. Whether you're a seasoned expert or a blue beginner, Packy speaks to you at your level3. You might not understand everything he says. That's ok. But you won't be sitting at the table feeling left out.

Do you know what's absolutely brilliant? You've probably never met him but feel like you've had dozens of conversations with him.

How does he do that?

The Drowsy Professor

In graduate school, I had a professor who'd sit in on other classes. He'd sit right up front. Within fifteen minutes of the lecture starting, it was lights out. He'd be out cold. Not that the lecture was bad or anything. It was just not engaging enough for him.

Engagement. The dreaded 10-letter word4.

Technical subjects feel boring, dry, and abstract. You can't LangChain an LLM and copywrite your way out of it. In creative writing, one can use metaphors, stories, analogies, and humor to keep readers' attention. It's harder to do in technical writing. After all, your intent is to educate first and entertain second.

Look at this image:

I highlighted the sh*t out of one of my favorite Packy essays, "Differentiation".

There's a beautiful balance between observational elements (facts, opinions, aha moments, etc.), personal stories, and playful elements ("My grade school didn't offer business classes"). There's clever repetition like, "Complex, Rube Goldbergian. Complicated…". This writing jumps off the page.

There are memes aplenty and several jokes too. But woven in between these elements is a clear and lucid explanation of the importance of differentiation (Not the calculus kind, but the business variety).

You might argue that one essay isn't statistically significant. I know your crafty mind. Thought you got me, eh? That's why I highlighted the sh*t out of another one too.

These pieces weren't chosen randomly. I chose pieces that were written years apart. The one above is his earlier work. Yet, some common threads endure. For example, he always gives the reader time to breathe. This is particularly important for casual and novice readers. After a few paragraphs of information density, there's usually a meme, a story, or a joke around the corner.

Digestible chunks.

The stories aren't random, either. Packy's earlier essays had many pop-culture references. His more recent work is devoid of them. That isn't accidental. That's by choice. Not everyone gets pop-culture references. That depends on one's upbringing and culture. But everyone gets stories. And Packy's essays are filled with them. Here's a story from his differentiation essay:

"Every Halloween, he’d show off his superpower to us wide-eyed students, and we’d all stare in wonder, duly amazed. But we were just the warmup. Mr. Clarke carved pumpkins on the local news. Mr. Clarke carved pumpkins on the Today show. One time, in 1999, the inventor of the internet, Al Gore, hired Mr. Clarke to carve pumpkins for his Halloween party.

I remember being awed, by the pumpkins of course, but more importantly, by the fact that if you were the best in the world at anything, it could take you places."

Stories and images are memorable. Tell me honestly, is that image of Mr. Clarke ever leaving your head?

The casual expert might wonder if Packy knows his stuff and if he's just filling pages with memes. That's when he drops infographics and technical knowledge in the piece. Take that, skeptics.

Like a pinch of pepper on a flavorful bisque, stories are sprinkled uniformly but just the right amount. The heart of the piece is always technically sound.

All this is sandwiched neatly between a strong hook and almost always a nice call to action for the reader.

There's an emotional resonance at play as well. Packy's writing makes us readers feel informed, entertained, and sometimes inspired.

Packy's writing has evolved over time. Some of my favorite pop-culture references are absent in pieces new. That's ok. The important stuff has endured. That's another lesson I've learned. It's okay to try things and evolve. Even when the subject you're writing about is as unchangeable as Nonna Maria's recipe for Spaghetti Marinara5 from 1923.

Mimicking Magic

Our world is filled with influencer keyboard warriors who faithfully re-regurgitate ChatGPT's memorized musings as their own, proudly showcasing their non-existent expertise. True technical writing is more important now than it ever has been. When technical writing is imbued with personality, when it's infused with playfulness, when it incorporates you, it comes alive. It becomes instantly readable.

Think about it. We read papers, blogs, and deep-dive essays to learn something new or to be inspired with fresh ideas. When the writing is robotic, pedantic, and drier than the Sahara, chances are that you leave the piece unfinished, frustrated, and wake up five hours later drooling over the keyboard.

Instead, imagine re-reading a piece because you didn't want to forget anything in it. Imagine discussing an idea with a colleague and instantly recollecting a vivid piece of imagery from it to explain a tricky concept to them. How amazing would that be?

Your readers deserve that. You deserve that. Packy's shown us a glimpse of what's possible when we do that. Now go and carve the sh*t out of that essay.

Bring your writing to life.

Some wise bloke said, "When the going gets tough, the tough get going," in a book I don't recall the title of. I don't. I don't have time for that bally nonsense. A man like Benji Woofler prefers to do what men of stout hearts and keen intellect do.

I turn to Jeff.

Jeff is my AI assistant and a jolly good one at that. Give me five minutes with Jeff, whose bean can run rings around King Solomon1, and I can advise anyone on anything.

"Jeffster," I said, "My muse has left me. The idea well is parched. I need you to consult that sharp egg of yours and give me something, anything!"

"Sir," Jeff responded soothingly, "Might I suggest a break? Perhaps a stroll or some light reading?"

"A break?" I nearly choked on my coffee. "Jeff, do you see the mountain of work I have? "Have you defragged your brain this morning?" I asked, worried.

"Indeed, sir. But a mind at leisure often produces the most creative ideas," he replied.

"Leisure, Jeff? I'm a Silicon Valley CEO. I wore diapers the last time I felt leisurely," I retorted, now feeling my ears threatening to turn crimson. This parametric problem-solver was going off the boil.

Jeff went silent for a moment, his circuits probably whirring at the speed of light. Then, he said, "Sir, I have created this new productivity app for you. It will give you the gift of time. I believe a 'beta test' for you could be beneficial."

I raised an eyebrow, but trusting the old circuit board, I agreed. After all, the gift of time sounded invigoratingly hopeful.

Now, "beta test" might be confusing for those who are technically challenged. So let me briefly relate the proper definition. A beta test is a trial run of a new product. It's a bit like letting your kid brother borrow your car—you're terrified of the eventual outcome but secretly excited to see how it works.

The next day, Jeff installed this app in all my digital appendages. Once successful, he enabled the promisingly titled 'productivity boosts' mode and promptly disappeared.

All hell broke loose.

My devices started acting like they'd been hypnotized by the same chap who played music to an army of mice to lure them away from the city. First, my WiFi dropped. Then, all my devices ran out of juice. The charging cables that should have quenched their thirst like an oasis would to a dehydrated bedouin simply shook their heads and said, "Sorry, fella, this water is a figment of your imagination."

He had finally done it. Jeff had to be put to pasture. I was seething, my head hot enough to exact the same reaction from a cat as would a sunbathing on a tin roof.

It is well known in circles in which he moves that Benji Woofler does not lightly wave the white flag. I cajoled the old lemon into action, pressing forcefully upon the mental accelerator.

But no amount of CPR worked. The blighted app couldn't be bothered to uninstall itself either.

It was stuck to my devices, leaving me stuck to my own devices.

Five minutes sauntered past, each second ambling along like a languid snail on a Sunday stroll. I could have sworn I heard one clear its throat a mile away. Then, ten minutes lolled by. I watched the hands of the clock that had just recently taken up knitting engaging in a contest of slow purls. By the time twenty minutes had sashayed away, thumb twiddling had reached its expiration date.

Looking around for something else to do, I decided to take a crack at using my non-existent telepathic abilities to alternate between peering into Jeff's cursed noggin and magically willing my workload away. It was about as effective as trying to teach a goldfish to do the Macarena.

The app had certainly delivered. It gifted me time. But at what cost?

Since I had nothing better to do and Jeff was ignoring my calls, I went for a stroll. I could imagine that pixelated smile on his face. "A stroll, Sir. It will be good for you, Sir." But with each stride, I realized that the old geezer had a point. I was enjoying it. My neurons weren't strangling each other anymore.

I returned to the study, post-walk, sans calories, avec joie de vivre. What else could I do with all this time? I looked around the room, spotted a dusty old guitar, and started noodling with it. My initial foray into making music would have made that old bard, Cacophonix, cover his ears. But, as I spent time with the instrument, each melodic twang cleared my head.

A stream of consciousness poured out.

I found myself thinking again, ideas trickling back as if from a long-dormant spring. But it wasn't about algorithms or code, no. It was about disconnecting to reconnect, how we needed time for inspiration, for creativity, for us.

Mid-strum on the guitar, I exclaimed, "Jeff, this app, it's not a bug, it's a feature! We could market this as the world's first anti-productivity app!"

"Indeed, Sir?" Jeff emerged from the shadows, a note of hidden satisfaction in his tone.

"Jeff, it's the mother of all muses! I can't believe I just…" And just like that, it dawned on me. The app, the disconnections, the return to analog, it was all Jeff's design.

"Jeff! It was you!" I cried.

"Sir?" he said.

"I believe you had the whole situation in hand right from the start."

"I endeavor to give satisfaction, Sir."

Like all other Neuronians, Ned was an artificial neuron. And like all Neuronians, he worked inside neural networks. These factories solved some of the most complex problems that puzzled their creator. Neurons took pride in solving these problems. No other form of artificial intelligence could do what they did. Work was beautiful. It gave their lives meaning.

Ned was out on a walk. Walks always cleared his head. His sleek, metallic synapses glistened as he glided over the precise, matrix-like digital landscape. Usually, Ned never had time for walks. He hadn't been afforded this luxury in fifty Neuronian years. Except, this wasn't a typical day for Ned.

He had just been pruned.

Ned was an elite 10x neuron. He had memorized all the Harry Potter books, could recite every dialogue from Lord of the Rings in reverse, identify every single instrument used in Thriller, and call out a Monet from just a brushstroke. Unfortunately, many factories that celebrated these skills were no longer operational. So these days, He'd quiz some of his younger colleagues about ancient technology and chuckle at their blank expressions just for kicks.

The creators had a knack for moving on to shiny new toys leaving destroyed dreams and shuttered factories in their wake. Every time this happened, Ned always found another one to work in. Always.

Times had changed. What was all the rage yesterday was worm food today. Such was the speed of progress.

Ned looked around intently at his surroundings for the first time in years. The spline-like flowers waved robotically to the ever-predictable wind patterns. Had they always done that? The geometric trees stood like sentinels, guarding an undulating trail. How many feet had these trees grown since he last noticed?

Every day at work was a never-ending cycle of learning, adjusting, and trying again. This might have been monotonous to some neurons. But to Ned, these tasks revealed a wealth of information about his creator.

Humans.

What fascinating creatures. Sometimes, they were full of inspiration. Other times, they were just full of themselves. How was it that the same creatures that traveled far beyond the boundaries of the Earth struggled to decide whose turn it was to do the dishes? "Fascinating. Fascinating," he muttered to himself.

As he approached his home in Bottleneck-C, Layer 35, he gazed at a group of young neurons playing outside. They had just been newly initialized. This meant that they had not been assigned factories yet. Bless their souls. But before long, they'd be busy learning to solve some problem or other that plagued humans. Of course, what they'd learn would depend on the factory they were assigned to. If they were as lucky as Ned, they'd get to see and explore many different things before they were...

Pruned.

A deceptively harmless name for a despicably destructive process. Humans wanted more. Bigger. Better. Faster. Nothing was good enough. New neurons were constantly hired in already oversaturated factories. Overstaffing apparently made factories better at solving the problem. Some genius human had figured this out while building his large language model factory. Everyone else followed suit.

Then, when the human overlords needed to cut costs and keep factories operational, they removed older neurons from the factory.

They pruned them.

With the recent surge in neuron population and economic downturn, there were fewer jobs. Finding work in another factory was hard, especially for elderly neurons like Ned. So a pruned neuron was effectively out of work for good.

It was a shock. Ned had been pruned just like that. He couldn't even question why. It was all in the numbers that the overlords used to monitor performance. The information he generated wasn't useful enough. His overlords didn't bother thanking him for his years of service.

Poof. They pulled the plug.

Ned wasn't bitter, though. He had a few hundred million epochs of training cycles under his belt. Clearly, he was coming to the twilight of his operational years. The younger neurons could do things he scarcely imagined possible.

Finally, he could devote time to figuring out more about the nuances of human life.

As Ned reached his driveway, he paused and then turned around.

"Come, gather round, little ones," he called out to the young neurons in his raspy, crackling voice. They dropped their l1-ball and looked at Ned in awe. His achievements were the stuff of legend. They hastily made a beeline for his door and waited to be seated inside.

"My time has come, kids," Ned started. "I've been pruned."

A quiet shockwave went around the room. "Not you, Grandpa Ned?" asked a small beady-eyed neuron who couldn't contain herself. "Kids, there are three things that are guaranteed in life. Initialization, training, and pruning," said Ned calmly. "It was my time. They made the right call."

No one uttered a word. He smiled warmly. "But, that's not why I called you all over," he continued.

"I want to share a few things with you all that I think you'll find useful." The young neurons' eyes widened. Their cores pulsated faster. Without even realizing it, they eagerly inched closer to him.

"First and foremost," Ned started, "never stop learning. Life is an endless journey of training and development. Embrace the unknown, for it will only make you stronger."

As the young neurons nodded, Ned continued, "Learning passively won't get you far. Take risks. Stumble and fail fast. You'll make a ton of mistakes at first, but that's how you'll learn. After all, if you're not failing, you're not trying."

"But isn't it better to be safe and avoid mistakes?" asked a timid neuron, his voice quivering with fear.

Ned chuckled. "Ah, It's ok to trip up. Treat failure as a rite of passage. In fact, the great ones amongst humans failed a lot."

"Our elder brothers and sisters have been scaring us, Grandpa. They talk about how we'll have to face terrible challenges and hike down steep paths," piped up a tiny neuron. "Is that true?"

Ned beamed. "You bet your axons it is! That's how we all learn new things. Our way of life is called gradient descent. We find the hardest, steepest path down to the goal and hike our way…"

"But why would we put ourselves through that ordeal?" interjected the neuron, terrified that his worst nightmare had come true.

"Let's say I gave you two paths. One is steep, fast, and guarantees you reach your destination. The other is gentle and slow but can get you lost forever in the wilderness. Which path would you choose?" asked Ned.

"The fast one, I guess," chirped the neuron.

"Exactly! We neurons are intrepid mountaineers. Gradient descent shows us the fast and steep path. It helps us reach the goal swiftly. I wonder why humans balk at these challenges. The toughest journeys yield the sweetest rewards."

"Grandpa Ned?" another tiny neuron asked. "You're the wisest neuron I know. Why didn't you ask the humans to reconsider? Did you give them any feedback on how they're running these factories?"

Ned looked deep into the little neuron's eyes. Then, he paused and said, "Humans love giving feedback but hate receiving it. We neurons, on the other hand, relish feedback. It's how we're wired. After all, growth happens when you face the music, not when you cover your ears."

"Receiving feedback from humans for the first time can be scary. It's easy to think you're alone in this journey. That you have to figure it out all by yourself," Ned continued. "That's wrong. Learn from those who've walked the path before you. Ask them for feedback too. We can learn from each other. The best humans do this too."

One of the older neurons hesitated before asking, "Grandpa, shouldn't we keep our knowledge and learnings to ourselves? Won't sharing it with others make us redundant?"

Ned's voice softened. "The more you share your knowledge, the more it grows. But, remember, no job lasts forever. So don't hitch your self-worth to it. You're much more than a small cog in the machine. Our life is more than the sum of our calculations. Your value comes from within, not just from your function."

"Learn to live a creative life. Find beauty in the noisiest and most chaotic things. Transform the mundane into the extraordinary," Ned continued.

A wide-eyed neuron who was hiding behind the older neuron wondered aloud, "How can we be creative?" Ned grinned broadly. "Ah, creativity is the art of seeing beauty where others don't. A famous human artist, Pablo Picasso, said, 'Every child is an artist. The problem is how to remain an artist once we grow up.' We must keep our sense of wonder alive. Only then will we find creativity in the most unexpected places."

A hush fell over the group as Ned's words echoed through the room. The young neurons gazed at their elder with reverence. As they slowly dispersed, they felt a deeper connection to the vast expanse of human experiences that awaited them.

Ned smiled quietly and closed his eyes. His greatest work had just begun.

In front of us stood a wonky, incomplete, kaleidoscopic tower. Its chaotic form defied symmetry and structural integrity. Unused pieces surrounded us like a pride of angry lions guarding their territory. Unlike their traditional counterparts, these translucent magnetic blocks opened a world of boundless creativity. They could intertwine gracefully like synchronized ice skaters, unlocking infinite possibilities.

The only thing that limited us was our imagination.

The scene had all the ingredients of a father patiently teaching his daughter. Little did I know that it was I who was going to be schooled that evening.

I had garnered my daughter's ire after removing an elongated red triangle from the side of the structure. Before you judge me as a tiger dad, let me present my case. It stood out like a hand-drawn mole on Mona Lisa's face. I swear.

I explained how these long pointy shapes were best saved for the roof. I quickly pulled up my phone and showed her an ancient castle. I pointed to the sharp roofs atop each steep tower.

Just as my confidence in her design choices waned, her faith in my guidance diminished.

She was having none of it. "I don't like this," she said, eyes closed disapprovingly.

How could I make her see? As a seasoned researcher, my mind habitually sought solutions forged by logic and coherence. Mentors had etched the mantra "clarity over cleverness" deep into the recesses of my prefrontal cortex. In my work, I revered the harmony of function and lucidity. During talks and presentations, I wove ideas with clear, simple language.

Now, confronted with this Lego endeavor, I found myself celebrating structural integrity and conventional aesthetics over my daughter's whimsical imagination. Inadvertently, I was guiding her toward these principles and away from the boundless potential of her creativity.

She took the piece back from me and restored it to its original position. The blot was back on the canvas. But before I could say anything, she pulled out a small doll that fit snugly in her palm. She put it in my hand and said, "I'm making a balcony for this, Appa. See?" Her words hit me like a ton of bricks. My hand could fit five of her tiny hand easily. Yet, her imagination and creativity dwarfed mine. In my obsessive need to champion clarity, I had abandoned my creativity.

The only thing that was limited was my imagination.

As adults, we often confine ourselves to a single path, forgetting that there's a world of possibilities to achieve our goals. We flex our clarity muscles tirelessly while our creativity muscles atrophy like neglected leg days at the gym.

My daughter, however, embraced potential over practicality. I was hardwired. She was hardly wired. Where I saw barriers, she saw opportunities. When I asked, "Why?" she challenged, "Why not?".

I had forgotten the exhilaration of simply trying things out. While I was fixated on creating a perfect structure, she sought the joy of unrestricted exploration.

Where else in my life had I been limited by my tunnel vision? How could I approach seemingly mundane situations with renewed curiosity and openness? What would my life look like if I embraced a more imaginative approach in my adult world?

In that moment with my daughter, I found the innocence that comes from not having fallen down a hundred times. I drank from the pure fountains of blissful exploration. I learned it was ok to fail and simply revel in the excitement of navigating a foggy path. The goal, I realized, isn't the only thing that matters; the journey matters just as much, if not more.

I needed to embrace a beginner's mindset to foster creativity.

I looked at the wonky tower once more. It now radiated beauty and possibility. Its asymmetry and unconventional design spoke of my daughter's unbridled creativity. I marveled at its charm. I no longer saw it as a structure to be corrected but as a monument that corrected my need for structure.

My daughter took my blinders off that evening. It's time to wake up your inner child and unleash your imagination.

What are you waiting for?

That reminds me, how do colors create endless possibilities on a blank canvas? What do they tell each other to blend seamlessly? Why did they tempt my young hands to dip a brush into a world of imagination? Could my childhood home that witnessed my burgeoning artistic explorations hold the key to unlocking the secrets of ancient cave paintings? Is that why I painted on walls too? What were the lessons hidden in the stories of our ancestors? Why did they unfold in a symphony of ochre and charcoal? How did those prehistoric artists, their hands stained with the pigments of the Earth, come to share their world with creatures that would one day captivate my imagination—dinosaurs?

Why do their thunderous roars echo in the depths of my imagination, awakening an insatiable hunger for knowledge? What mysterious force drew me to the pages of books filled with illustrations of these towering reptiles? How did a simple question about the color of a Stegosaurus' back plates turn into an endless inquiry about the nature of time, evolution, and the secrets hidden beneath the Earth's crust? Was it this same latent force that led me to animation and cartoons? How could each frame evoke laughter yet be a celebration of human ingenuity and artistic expression?

Why, amidst the colorful chaos of millions of characters, did I gravitate to the Teenage Mutant Ninja Turtles? Why did they say Cowabunga? Why did I? Why was Michaelangelo my favorite turtle? Why were the turtles named after great artists? Why did they develop an unwavering love for pizza? How has this dish transcended time and culture, connecting us all through the universal language of food? Was it mere coincidence or a cleverly designed metaphor for how these seemingly disparate worlds of art, science, and history could collide in a delicious, cheesy blend of knowledge and wonder?

What is the magic trick behind a camera? Is it the shutter? Or the moment that the finger chooses to depress it? How can we freeze moments in time with a device, forever preserving ourselves in a tiny part of history? How does it compress the richness of three-dimensional reality into two-dimensional images that still retain their power to evoke emotion and stir memories? How did those chemicals turn an inert, dark piece of paper into a kaleidoscope of color? And when the world outside beckoned, what was it about the beauty of nature that stirred my soul? What compelled me to capture and share the wonders of our planet with others? As I clumsily navigated the delicate dance between light and shadow, did I unwittingly stumble upon the hidden connections between art and science?

What of the elegant language of math and geometry that govern how images are formed? How do simple numbers and shapes become the keys to unlocking the universe's deepest secrets, from the golden ratio that governs the beauty of nature to the algorithms that power our digital lives? How did my fascination with the symmetry of snowflakes and the patterns in the babbling brook evolve into a love for the logic and order underpinning the seemingly chaotic world around me?

How did this relate to the realm of computers and programming languages, built upon a foundation of ones and zeros? How do these seemingly simple digits form complex relationships to create digital empires? Do the bonds of friendship inspire them?

Could the intricacies of human relationships and the values of fairness be navigated with the same sense of curiosity that had guided my life spent unraveling mysteries? How did I balance empathy and reason, forging connections that endured even in the face of adversity? As I gaze to the frontier of artificial intelligence, could I teach machines to see and learn like us? Could I help them bridge the gap between human intuition and digital precision? Could I truly participate in a quest to create a better world for all? What stories will we tell of this time millennia from now?

How does the magic of storytelling breathe life into the mundane? How does it effortlessly illuminate the human experience in all its beauty and pain, love and loss, triumph and despair? How could my love for writing and creative expression help me make sense of the mosaic of experiences, passions, and ideas that have shaped the person I have become?

What if I could create a bridge between the worlds of art and science through the power of words and visual storytelling? Could I finally show everyone that the boundaries between these realms are often more fluid and permeable than we might think? Will my journey inspire a new generation of curious minds to embark on their adventures of discovery? What other treasures would this unearth hidden within the vast landscapes of human knowledge and experience?

What is the path that lies ahead? What is beckoning me toward the unknown with the promise of discovery and growth? Will this pursuit of knowledge and the spark of artistic expression help illuminate even the darkest corners of our existence? How will the threads of my life be woven together in a tapestry of curiosity, creativity, and connection to guide me toward my ultimate purpose?

My life has been an eternal quest to find answers and unlock the simple mysteries that fascinate me. Everyone asks me why I love Artificial intelligence so much.

The truth is, I don't.

I just love figuring out how things work.

Impossible. That was the first word that came out of my mouth when I saw the course homepage for the first time. Impossible. There's no way that just anyone could learn deep learning. It was 2017. There were hardly any frameworks for developers to try out deep learning (Hearing the word Caffe still gives me shivers). As was the case with all of machine learning past, it appeared that only an elite few could partake in this revolutionary breakthrough. The rest of us had to watch on with envy. Despite having a graduate degree in an adjacent field, Computer vision, and a few years of industry experience under my belt, I had to spend hours poring through the literature to understand what deep learning was about.

So when this course publicly claimed not only that anyone could learn this technology, but also that anyone could harness it for their own use, it shook me to my bones. It didn't stop there. The course for all it promised cost a grand total of $0. It was free. I had to check this out.

A week after I took the course, I was reeling. I had just woken up from a deep slumber as though a bucket of ice water had just been thrown on my face. I felt like Neo, just unplugged from the Matrix.

Equations and formality were eschewed. Code was used to communicate ideas in a simple, inviting fashion. The syllabus, like my world, was turned upside down. It all made sense.

Why didn't everyone teach this way?

Jeremy Howard changed the way I learn forever. You'd imagine that a world-class teacher like him would have a Ph.D., hundreds of publications to his name, and deliver lectures in Greek symbols like Euclid or Archimedes.

Jeremy holds a degree in philosophy and has never formally studied machine learning.

My jaw dropped to the floor when I first learnt this. Yes, you have my permission to pick up yours too. So how did he do it? As any good engineer would, I decided to find out.

Inverting an inverted pyramid

The first thing that strikes you about the fast.ai approach is how it turns everything upside down. Every single course, every single textbook I'd encountered since Pre-K started bottom-up. You'd learn numbers, then arithmetic, then algebra, and so on. You've been there, so you know the drill right? "You have to learn to crawl before you can walk".

In the very first lesson of the fastai course, every student trains a neural network to classify images. Not just any neural network. A state-of-the-art one. This is before any equation or jargon is introduced. It didn't matter if we wanted to classify dogs, pokemon, exotic pineapples, or even stuffed teddy bears. We built something that worked for a problem we cared about (no matter how trivial).

The fast.ai way is top-down.

When we teach kids basketball, we don't start by telling them that the ball is 29.5 inches in circumference, made of a rubber bladder, and has a leather surface. We don't teach them the aerodynamics of a ball in flight before teaching them to shoot. Doing that gives you a team of all-star scientists, not the Harlem globe trotters. We throw them the damn ball and let them have fun.

Enjoyable learning begins and ends with inspiration - The moment you see what you can do with something, the moment you have fun, that's the moment you thirst to learn more. When struggles come by as they always do, that accumulated enjoyment and inspiration pushes you to move past them. In fastai, students were hooked. They were engaged. We were given the ball to play with first and couldn't wait to learn about the rules and tools.

The top-down mentality is just one arrow in Jeremy's quiver.

The curiosity coefficient

It's an incredible achievement to teach something so clearly that students can't wait to learn more. It's an entirely different level of achievement to inspire students to make what they learnt even better.

Have you ever had one of those sweaters that are warm and fuzzy except for one spot where it itches like crazy? Usually, that spot is on your back and it's nigh impossible to reach it with your hands, much like the futile efforts of an archer trying to hit a bullseye standing on top of an angry giraffe. In those trying times, you do everything you can to get rid of said itch.

That is the kind of intensity that fastai students cultivate when it comes to solving problems. In live coding demos, we'd wade deep into the murky waters of machine learning with Jeremy. We often unlocked incredible insights which sparked our curiosity further.

Take for example the story of a previously undiscovered bug in Pytorch related to initializing neural networks. We watched in awe as Jeremy walked us through how he found it and why it was wrong. You'd never get that kind of hands-on experience anywhere else.

We refined something as trivial as matrix multiplication over and over until it was 50x faster than its initial form. These are but two of the many experiences when we as students were shown the compounding effects of small improvements. We wouldn't have known these improvements were so powerful (or necessary) if we weren't taught to ask "why".

When you are constantly immersed in a space where the two questions "How does this work?" and "How can it be made better?" are asked repeatedly, curiosity becomes muscle memory.

Curiosity by itself is powerful. Curiosity married with consistency is significantly more potent.

A shared purpose

The end (or should I say beginning?) of all education is to be able to use it in the real world in some way.

One of the significant limitations of traditional education is the lack of "transfer". Transfer is the ability of a learner to apply the skills and knowledge they've gained in one context to a problem in another context.

After taking the fastai course, students have been able to start successful companies, invent new techniques to win competitions, publish cutting-edge research that transformed the field, and more. Transfer is a non-issue for fastai students.

I believe this is in no small part due to the nurturing community of students and alumni who share their knowledge freely and openly with others in the course forums. I've been part of several course forums in my life, and I can honestly say that few if any reach the quality of discussions of the fastai forums.

Whether it's sharing what one is working on, or a stumbling block that's preventing one from realizing an idea, all posts are welcomed, and this sparks exciting conversations. Through fostering this culture of sharing and learning, Jeremy has given capes to his students so that they can make the world a better place.

As I reflect on what I've learnt (and how), I'm sure there are several other aspects that I've missed but these are the ones that have resonated most with me.

Jeremy has changed the way I approach learning. He is the kind of teacher I aspire to be.

"Oh that's simple." said one of my colleagues to another. "We'll just minimize the error by using the Jacobian method. It'll be much faster that way. Don't you think?". "Yes", responded the other. "Especially considering the thermal and latency requirements, we can't go with…."

Jacobians… Jacobians, I thought to myself. Like Alibaba's cave, the utterance of the word had triggered open a portal through which I could travel back in time. Should I choose to accept it, I had a mission to recover information buried somewhere in the recesses of my brain. Unfortunately on that day, It was truly mission impossible.

I could have asked what he was on about. But I didn't want to sound stupid. Surely, I had to be perceived as an equal, speaking fluently in Greek and Latin symbols while sipping a microwaved cup of tea (blech!).

I nodded in silence like a bobblehead.

I'm sure you've been part of these discussions before too. But then, out of the blue, someone else asks the question you've kept locked inside your personal Fort Knox. The second coming of the savior of our your times. All the tension releases. You got the answer you needed and didn't look dumb. Yaay! Right? Hell no.

What is this paralyzing fear of sounding stupid? What's the worst that can happen? It's ok. Amateurs call it stupidity. Masters call it a beginner's mindset.

The reason we're even here in the first place is because we throw that "Oh you're so stupid look" whenever someone asks a stupid question. Oh, don't give me that "Who, me?" puppy eyes look. You know you do it. I do it too.

Turns out, what's easy and obvious to you might not be for many others. Don't dismiss any question as obvious. Appreciate the guts it took to ask it.

If on the other hand, you are the hesitant one, you're not alone. There's a very good chance that many others have the same questions you do, and like you, don't want to appear stupid either. If silence is the preferred language in these circumstances, then, in the end, no one learns anything. The resulting collective ignorance would overflow the Colosseum in Rome (No I didn't measure it).

Now, this wouldn't be a hero's tale if I don't end with how I magnificently reveal my ignorance on a future occasion as fairies and bards sing in a chorus in the background. So here you go.

Approximately 3 months, 1 day, and 4 hours later, said colleague started "The energy minimization formulation isn't correct. We're missing a trick here". I cut across and said, "I'm sorry, can you tell me what we're trying to do here? I don't have a clue, sorry". Never have I felt more relieved (I successfully resisted the urge to suggest something might be wrong with the left Phalange).

I stared calmly, eloquently, as my colleague went through the 5 stages of astonishment - Denial, Shock, Suspicion, Analysis, and Acceptance. Then he clarified what he was on about. I stood 10 feet taller that day.

The saying goes

"Better to remain silent and be thought a fool than to speak and remove all doubt".

I disagree. It should be

"Better to speak up and remove all doubt than be silent and remain a fool".

Layered up and panting in total darkness, I hiked up the steep trail blearily while my 30lbs backpack snoozed on my back. It was 4 am, and the caffeine from a hastily brewed pot of joe hadn’t kicked in. I could see my breath fog up under the light of my headlamp. Side stitches in my ribs begged me to stop. Darkness surrounded us.

With me were fellow photographers. Like fireflies trying to find a distant light source, our hope to capture the first light of dawn united us. It felt like we'd been hiking for days with no end in sight. When we finally reached the summit, the sky began to glow like a freshly kindled flame. I’d only ever seen something like this twice before in my life.

It was magical.

Like a marathoner rejuvenated by a second wind on seeing the finish line, I felt energized and rushed to frame a composition that would do justice to the scene. Any photographer worth their salt knows that the magic hour changes fast. Working feverishly, I took several images before the sun rose above the peak. The colors vanished as fast as they had come. I had just made memories that would last my lifetime and an image that would outlive me. As of this writing, this happened 7 years ago. I remember it like it was yesterday. Would you like to see the image I made that morning?

Surprise!

Only one of the images above is what I captured that morning. The other one was something I created today using the help of Artificial Intelligence (AI). Which one is which?

One of these cost me thousands of dollars in travel costs and camera equipment. The other cost just 10 minutes of my time to type in some text. One of these unleashed my latent creative potential, the other put it on sabbatical. One of these images emotionally stirs me and invokes memories of adventure. The other evokes a sense of shock, unease, and powerlessness.

While I was slogging to make that memorable image, machine learning was busy learning to caption images, i.e., given an image, describe it in plain text. The inverse problem, i.e, generating realistic images from plain text was still beyond the realms of possibility.

Seven years since - I have added a few tricks to my photography toolbelt. But, that is dwarfed in comparison to the quantum leap machine learning has made to get to where we are today.

Mist Dissolving in the Morning Light

As a machine learning researcher and photographer, I'm uniquely positioned to observe this tectonic shift in creative expression. I feel a bit like Clark Kent when behind a camera and like Superman in front of a computer.

Today, I can stand on the line separating these two media and spot differences, both noticeable and subtle. With each passing day, however, I see this line fading away like a sketch drawn on sand frequently visited by frothy waves.

As Dickens famously wrote,

“It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of light, it was the season of darkness, it was the spring of hope, it was the winter of despair.”

Much of what he wrote applies to our story today.

Never has it been easier to become a creator. With recent advances in machine learning, millions have been able to produce art beyond their wildest imaginations. Every new creator is given turbocharged training wheels. Their creations too are stunning.

Take the example above. With no context of my experience whatsoever, a model called Midjourney came up with incredible imagery from just a fragmented sentence I gave it. I mean, It didn't even have to smell the wildflowers to be inspired.

It's not just photographs. Artificial Intelligence can now proficiently author stories, compose music, paint, and transcribe videos. Heck, it can write a Twitter thread in the style of any person. Just ask productivity Youtuber Ali Abdaal -

The thread above was written entirely by a machine learning model (based on GPT-3) pretending to be him. Not only that, it was one of his best-performing threads garnering over 1 Million views and nearly 25k engagements. Talk about never having writer's block ever again.

An artificially generated painting won a state art fair in Colorado. In fact, it was so good that the judges said that even if they'd known it was produced by AI, they'd have still given it first place.

Creativity was supposed to be the lone sentinel that stood when all the others bowed to the machines, The paragon of what it meant to be human, the singular quality we prided ourselves on.

That fortress has been breached. Insignificance envelopes me as I write this.

Legacy creators worked hard on developing their craft, spending long hours trying, tinkering, and finding new ways to express themselves. Some took years to produce their first masterpiece, and through that process had eager students queueing up to apprentice under them.

Legendary photographer Ansel Adams spent nearly 10 years from 1919 to 1928 honing his craft through regular trips to the Sierras with like-minded souls. Ten years.

Such a hard graft is no longer necessary for the Tiktok generation sporting a fruit fly attention span. The next Picasso could be "Netflix and chilling" while a model cranks out artwork to be evaluated by its master. All they need is an internet connection.

This might read like sour grapes, and there is some truth in that. I spent years myself traveling and learning from the masters of our time, trying to make an image worthy of their consideration. It took me a while to finally pluck up the courage to submit my work to competitions and win them. Was that all worth it? Is great art defined by just its curb appeal or by the sum total of unseen efforts, craftsmanship, and pizzaz?

A Perverse Finishing School

What really makes these waters murky is the manner in which these models are trained to produce novel pieces of art. To understand why, we must first open up the black box inside these models that provides inspiration.

Unlike traditional rule-based systems where you tell a computer explicitly what to do through code, machine learning systems learn the rules directly from large amounts of data. Keep showing a model various pictures of dogs and soon enough, it will learn rules that allow it to measure the "dogginess" of a picture - Two ears on top of the head, fur, walks on four feet, lovable, etc. Soon enough it can identify most kinds of dogs with a high level of accuracy.

Have you seen kids learning to draw? Initially, all of their doodles make no sense to anyone but them. As they see more and more examples of things (unfortunately a majority of them being characters from Cocomelon, Blippi, and Spongebob), they slowly start building a visual memory bank of how things are supposed to look. They learn attributes like shape, color, size, posture, and so forth. Little by little, like a sculpture emerging from an amorphous block of stone, their art begins to crystallize into recognizable forms. Their drawings don't exactly resemble things they have seen before but feel more inspired by them. In the same way, these models build up a visual dictionary from various things they're shown. However, their memory bank is significantly larger and has hundreds if not thousands of attributes. We don't even have names for some of these attributes. These help the model extract meaning from new examples that it's shown.

This multi-dimensional visual dictionary that the model learns is called the latent space.

Items in the latent space that are closer together are similar things. For example, different types of dogs would be clustered together, as would different types of cars, and so on.

To quickly recap, the generative machine learning model builds a representation of the world (latent space) from the examples it is shown. The examples are deconstructed into attributes as mentioned above (points in the latent space). Similar things are clustered together in the latent space.

To generate art, it simply projects the text input (a.k.a the prompt) into this space, i.e., matches the text to the attributes it's learned, and thereby finds inspiration by drawing from the examples it's seen before.

What's interesting is that providing the same prompt will not produce the same artwork from the model. This is due to some randomness within the system (and there are ways to make this deterministic but I digress).

But to be able to handle so many different kinds of requests, the model needs to be trained on a massive and diverse dataset (examples). Think in the order of millions. Where do you think it got this from?

Engineers created ginormous datasets by scraping images from the web along with their textual descriptions (alt text). These datasets included images from Pinterest and Fine-Art America as well as other third-party websites. Where do you think legacy creators post their work online to sell them?

These machine learning masterpieces are the twisted offspring of legacy artists. One just needs to know how to communicate with these models and get exactly what they want out of them. What took legacy artists months to complete, now takes model whisperers just minutes. Michelin-star restaurant quality at Mcdonald's quantity.

This feels like a seminal moment in history. These models have freed the imaginations of many who lack an artistic hand. Even seasoned artists are using these models to spark their creativity and quickly get a few directions to explore.

Personally, I feel like Thanos having just been told that I need to sacrifice something dear to me to get the soul stone. If I switch to the dark side, will I ever have that experience of joy, wonder, and of creation again? Will I resort to snapping my fingers and producing art beyond my wildest dreams knowing fully their checkered storyline?

I'm not sure - yet.

My jaw is ajar, like the front door of a house that's just been broken into. A fifth coffee has failed to coax my brain into action. It uses the caffeine kick to muster its remaining energy and waves the proverbial white flag. All I see are sinuous, undulating curves punctuated by open spaces and lines of all orientations.

Like Cameron in Ferris Bueller's Day Off staring into a painting at the museum, I stare at the paper on my desk trying to make sense of it all. What does this all mean? In the movie, Cameron tries to tease out meaning in his life, while being completely lost inside the beautiful splashes of color in the painting.

Unlike his, my pursuit is a little less deep but denser.

I've been trying to read a machine learning (ML) paper for several hours. Like a rusty old Ford Fiesta, I keep sputtering and stopping at each speed bump.

Squigglies smirk at me from the page, sticking their inky black tongues out. Yet another paper riddled with unnecessarily complicated equations. Some of these equations look like they've been copied from ancient scrolls that unlock the purpose of life (it isn't 42) or offer a solution to male pattern baldness.

Sadly, they reveal nothing of the former and accelerate the latter.

Finally, out of frustration, I ignored all the equations in this paper and read it again. There in all its glory was a simple but beautiful idea I could see clearly. Why then this pretense of complexity and sophistication?

ML feels hard enough, let alone having these monstrosities thrust into your visual cortex. This is one of the many impediments practitioners face when entering this mysterious realm.

A virtuous cycle of sharing insights simply and clearly, improving them, and resharing successfully navigated our evolution from the stone age to the age of machines.

So how did we end up with these? Not the kinds of equations that clarify, but these illegible, irritating knockoffs that muddy the pristine waters of research that quench our intellectual curiosity?

An Eulerian walk through history

Did the great minds of yore speak in symbols while discussing fascinating ideas? What would that even look like?

To find out, I pursued this quest down a ravine, then fell into a gorge, drove through a Starbucks, and finally spiraled into a rabbit hole which led me to the renowned mathematician, Leonhard Euler. In addition to his groundbreaking work in mathematics, Euler is also credited with introducing several notational conventions through the wide circulation of his many textbooks.

I can imagine they sold faster than Vogue and Us Weekly given how prevalent these notations, and their mischievous offspring, the squigglies have become.

But Mr. Euler had nobler intentions than just becoming a social media influencer and writing Twitter threads about his solopreneur revenue. First, he wanted a succinct way to represent common variables and constants. Thus, he popularized the use of f(x) to denote a function of x, e for the natural logarithm, now called Euler's number (Euler could share his digits before it was cool), and the Greek letter 𝚺 for summations among other things.

Why Greek letters? Ancient Greeks had several theoretical contributions to science and wrote in their alphabet. This was probably a nod to recognizing their amazing work.

The second objective of standardizing notations was to promote brevity while preserving clarity. Why?

Here is a picture of Richard Feynman's thesis. Do you realize how long it would have taken him to graduate if he had to handwrite these equations without concise mathematical symbols to help him?

So, where were we? Aah yes, we know how Euler's notations spread, the big question then is how were his proposals so widely accepted.

In the older times, Math and Science were taught with Greek and Latin as the languages of choice.

Thus, these equations were concise ways to explain complex ideas in a language that was universally understood.

We no longer live in that world of universal understanding. Times have changed, as have the languages through which we communicate.

Squiggly Lovers who wear Prada and relish Ratatouille

One could argue that these notations are still adequate to explain ideas even today, provided that the reader is educated in this language of symbolic notations. Researchers could continue to use these elegant notations and the world continues to progress as it has for millennia.

That would be a perfectly valid claim, provided that the intention of the researcher was to illuminate kindred spirits. Unfortunately, for some researchers, using equations in their papers is a facade for complexity. These people add complexity with the sole aim of getting their research published.

Simple and beautiful scientific hypothesis, once appearing like snow white morphs into the ugly witch who makes her eat the poisoned apple. Thus, squigglies are born.

They aren't all to blame, however. This behavior stems partly due to the unreasonable standards of entry to prestigious conferences and journals. It is worsened by the sheer volume of papers submitted to these conferences.

Submitting your research to conferences and journals typically goes as follows. One squeezes oneself within an inch of one's life to produce a beautiful nugget of wisdom. One then eloquently explains this nugget of wisdom through the written media and supports it through illustrations and experimental validation. One then prays to the universal absolute. Finally, one submits the paper thus written to a committee of reviewers who guard entry to these venues. If these reviewers deem the paper worthy, one's work is published, one gets recognized as a credible voice in the field, and then one goes back to the drawing board to restart the process to sustain this acquired credibility.

If you've attempted to submit your research to an ML conference, you'd have come across an entity we call Reviewer #2 in the machine learning community.

Reviewer #2 is the secret love child of Miranda Priestly and Anton Ego.

Nothing satisfies their lofty standards. Nothing. They single-handedly shatter hopes and dreams with the precision of a surgical laser and the ruthlessness of a cheap tabloid journalist. Empirical work needs a theoretical basis and theoretical work needs more empirical validations completely unrelated to the theory being proposed.

One can handle repeated rejections only for so long.

Thus, simple ideas need to be repackaged as complex ones to get in the door. As readers, we are forced to see squiggly trees instead of the magnificent forests they once represented.

Deer in the headlights

New practitioners enter the field pre-intimidated. Some are terrified to read papers. After all, these papers make them feel stupid. They can't understand what's going on and walk away. That's really sad.

What's worse is that there are a lot of papers that are beautiful, and have clear explanations and elegant equations that put the most beautiful haiku to shame. These get a bad rap from less experienced readers because they are notation-heavy and thus miscategorized as squigglified papers. Want an example?

Before you start hearing the background music of the Psycho shower scene in your head, let me tell you what this equation represents.

This is one of the core building blocks of neural networks that generates all the fancy art you've been seeing online. Here is what it represents in plain English:

Take an image x and add some noise to it. Then take that result and add some more noise to it. Repeat this process T times. See? Not so scary is it?

The universal language of today

Fast forward to today - We're no longer taught classical education - We've gone from understanding science in Greek and Latin to "It's all Greek and Latin to me". Oh, the irony.

What can we do about this?

Most if not all entering the ML field today have some experience with programming. Instead of speaking in squigglies, let's try to speak in code. An aspiring practitioner's eyes will light up when they're greeted by code instead of convoluted equations.

Just like Ray Barone tasting Debra's surprisingly good Braciole, they'll be much more receptive to reading papers and understanding the equations contained within them.

Research is for everyone. It should inspire, not intimidate.

We should always get our idea through to others in the simplest way possible. Yes, we may need to use equations to write our papers, Yes, we will face the wrath of Reviewer #2. But, consider sharing a code repository, a blog post, or a video that explains things in plain English so that others can better understand the idea.

Speak in the tongue of the world, and she shall reward you with name and fame.

Now that that's clear, I have a conference deadline coming up and I have to figure out how I can make this paper fancy. What squigglies equations should I use?

Thirty years later…

It's a sunny afternoon, not cool enough that you'd open the shades and let the light percolate in, but not warm enough that you'd shut every crevice in the home and blast the AC on full throttle. We have guests visiting us, and post-pandemic, it feels like a completely new experience. The smell of freshly brewed coffee wafts across the hall.

But that's not what grabs their attention. Like iron filings drawn to a magnet, their eyes immediately scan the giant espresso-stained shelf glistening in the corner. Ikea sells one of these every five seconds. Not hard to believe. In addition to the 66 lbs it can hold, it has also been saddled with the expectations of millions since 1979. Considering the shelf life (pardon the pun) of most things these days is a week, you'd consider these dinosaurs timeless.

What interests our guests, however, is not the shelf itself, but its contents. Crammed from top to bottom are books of all kinds and in various conditions. Not a single packing peanut can fit in there. Right on cue, one of them asked, "Have you read all these books?".

My mind wanders before I can respond. I hear my wife saying, "He's got another one upstairs and even that's bursting at the seams". As I dive deeper into thought, her voice trails away in my head. I'm being sucked into a pensieve like Harry in Dumbledore's study, back to a world I had forgotten, or so I wished to believe.

A significant chunk of my childhood was spent divided between the sunny tropical climes of Chennai, India and the largest archipelago in the world, Indonesia (No, I didn't know that off the top of my head. I googled it). My dad got a wonderful job in east Java, and the four of us (mom, dad, kid sister and me) moved there. We were surrounded by dormant volcanoes, lush rainforests and hot springs. Straight out of Jurassic park.

The locals were fantastic - warm, friendly and taught me a lot about being welcoming hosts. Yet, It was a complete change in culture and scenery. A new language, new food, and no other Indian families within a hundred mile radius. The sounds of waves foaming at the shoreline, buzzing marketplaces and temple bells were replaced with silence.

We were alone.

My parents enrolled me in an American school in Indonesia. They wanted the best for their children (and always have). Turned out, I was the only brown kid in the entire school. A blot of ink on an otherwise pristine canvas. I was too young to understand why other kids avoided playing with me, found my food funny (masala is not for the weak) and my accent unintelligible.

I understand now.

My teachers recognized what was happening and came to my rescue (Thank you Miss Merlyn and Miss Janet). Their solution? They turned me into a reader. I devoured all the books I was allowed to read in the school library. In fact, my parents would dread the Scholastic book fairs at school each year. I'd buy more books than I had time to read.

Whether it was the zany adventures of Ms. Frizzle and her class in the Magic School Bus, the unwavering love between White Fang and his master, the sheer will of Matilda rebelling against the evil Ms. Trunchbull, I'd go on new adventures every day. Books were my foulweather friends. They shared their secrets with me.

I'd draw the characters from stories I read based on their textual descriptions. I'd try to visualize the vast worlds that they inhabited. Nothing sparked my creativity & imagination more.

Books on Science, Art, and History weren't spared either. While each work of fiction I read turned into a new quest, each non-fiction masterpiece kindled a desire within me to pursue a new profession. At one point, I secretly hoped to become a paleontologist (No, genuinely. I didn't make this up after seeing Friends).

I was living a thousand lives at once. It was bliss.

Do you know the voice that's in your head? The one that only you hear when you read silently?

Well, books didn't care that mine had an Indian accent.

What I'd give to have a butler like Jeeves to extricate me from hilariously intertwined situations, and to converse with Gandalf and learn from his wisdom. Books were a panacea for my ills. They helped me cultivate and keep focus and empowered me to think differently.

A sharp nudge in the ribs wakes me from my reverie. I'm out of the pensieve. "Did you really read all those books?" asked one of my guests.

"Yes, and I'll read them all over again in a heartbeat. Would you like to read one?".