We’ve been taking a walk after dinner of late.
We’ve been noticing the stars more as a result, and realized it has been many years since we’ve noticed stars.
That sounds crazy. But we just didn’t step out most nights. And, even if we did, we didn’t look up to appreciate the gorgeous night sky.
It made me think of the many wonderful things around me that I take for granted.
All we need is to make space for us to look around… and notice.
“Innovation happens not within but between brains.” | Matt Ridley, How Innovation Works
Matt Ridley’s thorough analysis of innovations in over multiple millennia make it clear that the “great man” theory is one we’ve created out of convenience and due (more recently) to intellectual property law.
Innovation has consistently arrived because of humans who chose to build on the work of their rivals and predecessors and combine existing ideas in interesting ways.
Not within but between brains.
There’s something about the term “deliberate practice” that feels… boring. Steph Curry’s coach has a saying “There’s no boring in our workouts.”
His approach is to replace drills with all sorts of games. For example, they might play a game where Steph is not allowed to make a particular move or where he has to play 1 vs. 3. There could also be games along the lines of 2 minutes for 21 points and you have to sprint to the center of the court each time.
His view is that deliberate practice can lead to burnouts or “bore-outs.” Ergo deliberate play.
It resonated.
H/T: Adam Grant’s Hidden Potential
I asked someone for something the other day. They said (with a lot of confidence) – “No, we don’t have that anywhere close by.”
I walked a bit further and found what I was looking for.
The truth is that I’ve done the same on occasion in the past. I’ve said a no just because it was easier. Laziness is a powerful motivator.
Two reflections.
First, it is important to reflect on whether we’re saying a no with confidence to someone simply because we don’t want to do the work. Rather than dissuade them, we should choose honesty – “I don’t know” is a far better answer.
Second, don’t take every “no” we get too seriously. Trust but verify.
Below is a chart about how couples meet in the US. It is always fascinating to see a chart like this. It is wild to think that one in three couples used to meet each other through friends. Now, it is less than 1 in 10.
Similarly, 1 in 5 couples used to meet at a bar. That’s dipped closer to 1 in 20. 1 in 4 met through family. That’s also closer to 1 in 20.
It made me wonder about the breadth and depth of our relationships.
It also made me wonder about the ripple effects of this. For example, below is an excerpt from a paper about the impact of assortative mating and income inequality.
“We find that the observed increase in assortativeness accounts for approximately half of the increase in household income inequality between 1980 and 2020,” a paper from the Federal Reserve Bank of St. Louis states. “The most important factors contributing to household income inequality through mate selection are selection on education (35%) and skill (30%), with selection on income (15%) and age (15%) trailing significantly, while selection by race (5%) plays a relatively inconsequential role.” (ref)
There are many such unknowns that I’m sure will be studied in time. The impacts, as always, with big societal changes, will be far reaching.
Actor Matt Damon had a great story of the vacation he promised his wife after a series of movies.
Except they had to cancel their long-planned vacation because Chrisopher Nolan called to offer him a role in Oppenheimer. In Damon’s words –
“I had — not to get too personal — negotiated extensively with my wife that I was taking time off. I had been in Interstellar, and then Chris put me on ice for a couple of movies, so I wasn’t in the rotation, but I actually negotiated in couples therapy — this is a true story — the one caveat to my taking time off was if Chris Nolan called. This is without knowing whether or not he was working on anything, because he never tells you. He just calls you out of the blue. And so, it was a moment in my household.”
He had an agreement with his wife that he’d hold the vacation sacred. The only exception was to answer a Christopher Nolan call.
His other colleagues agreed. The Nolan call was always special.
I thought it was such a lovely testament to Nolan’s leadership and impact on his team.
Inspirational.
I spent many years in awe of Roger Federer’s artistry and Rafael Nadal’s grit. As a Roger Federer fan, I used to hate Roger-Rafa games. He had a way of making Roger, even in his prime, seem human.
He did so with the kind of tenacity that I thought made him the greatest sporting role model I know.
Seeing Roger’s note to Rafa on his retirement brought all those memories and emotions back. As is the case with any exchange between these two, it oozed class. (H/T: Dan’s blog for sharing)
As you get ready to graduate from tennis, I’ve got a few things to share before I maybe get emotional.
Let’s start with the obvious: you beat me—a lot. More than I managed to beat you. You challenged me in ways no one else could. On clay, it felt like I was stepping into your backyard, and you made me work harder than I ever thought I could just to hold my ground. You made me reimagine my game—even going so far as to change the size of my racquet head, hoping for any edge.
I’m not a very superstitious person, but you took it to the next level. Your whole process. All those rituals. Assembling your water bottles like toy soldiers in formation, fixing your hair, adjusting your underwear… All of it with the highest intensity. Secretly, I kind of loved the whole thing. Because it was so unique—it was so you.
And you know what, Rafa, you made me enjoy the game even more.
OK, maybe not at first. After the 2004 Australian Open, I achieved the #1 ranking for the first time. I thought I was on top of the world. And I was—until two months later, when you walked on the court in Miami in your red sleeveless shirt, showing off those biceps, and you beat me convincingly. All that buzz I’d been hearing about you—about this amazing young player from Mallorca, a generational talent, probably going to win a major someday—it wasn’t just hype.
We were both at the start of our journey and it’s one we ended up taking together. Twenty years later, Rafa, I have to say: What an incredible run you’ve had. Including 14 French Opens—historic! You made Spain proud… you made the whole tennis world proud.
I keep thinking about the memories we’ve shared. Promoting the sport together. Playing that match on half-grass, half-clay. Breaking the all-time attendance record by playing in front of more than 50,000 fans in Cape Town, South Africa. Always cracking each other up. Wearing each other out on the court and then, sometimes, almost literally having to hold each other up during trophy ceremonies.
I’m still grateful you invited me to Mallorca to help launch the Rafa Nadal Academy in 2016. Actually, I kind of invited myself. I knew you were too polite to insist on me being there, but I didn’t want to miss it. You have always been a role model for kids around the world, and Mirka and I are so glad that our children have all trained at your academies. They had a blast and learned so much—like thousands of other young players. Although I always worried my kids would come home playing tennis as lefties.
And then there was London—the Laver Cup in 2022. My final match. It meant everything to me that you were there by my side—not as my rival but as my doubles partner. Sharing the court with you that night, and sharing those tears, will forever be one of the most special moments of my career.
Rafa, I know you’re focused on the last stretch of your epic career. We will talk when it’s done. For now, I just want to congratulate your family and team, who all played a massive role in your success. And I want you to know that your old friend is always cheering for you, and will be cheering just as loud for everything you do next.
Rafa that!
Best always, your fan,
Roger
Prof Ethan Mollick has a helpful post on “Thinking like an AI.” 3 salient notes:
(1) Large Language Models do “next token” prediction. They predict the next word that follows our prompt (and so on). That doesn’t mean they’re dumb autocomplete systems. But it does help develop the intuition for why the prompt matters. The pattern of words in a prompt
For example, if I write “The best type of pet is a” the LLM predicts that the most likely tokens to come next, based on its model of human language, are either “dog”, “personal,” “subjective,” or “cat.”
If I change the word “type” to “kind” in the sentence, the probabilities of all the top tokens drop and I am much more likely to get an exotic answer like “calm” or “bunny.” If I add an extra space after the word “pet,” then “dog” isn’t even in the top three predicted tokens!
(2) LLMs make predictions based on their training data. So, the better the training data, the better the predictions.
Contrary to some people’s beliefs, the AI is rarely producing substantial text from its training data verbatim. The sentences the AI provides are usually entirely novel, extrapolated from the language patterns it learned. Occasionally, the model might reproduce a specific fact or phrase it memorized from its training data, but more often, it’s generalizing from learned patterns to produce new content.
(3) LLMs have a limited memory.
Prof Mollick ends his post with a useful caveat.
Understanding token prediction, training data, and memory constraints gives us a peek behind the curtain, but it doesn’t fully explain the magic happening on stage. That said, this knowledge can help you push AI in more interesting directions. Want more original outputs? Try prompts that veer into less common territory in the training data. Stuck in a conversational rut? Remember the context window and start fresh.
But the real way to understand AI is to use it. A lot. For about 10 hours, just do stuff with AI that you do for work or fun. Poke it, prod it, ask it weird questions. See where it shines and where it stumbles. Your hands-on experience will teach you more than any article ever could (even this long one). You’ll figure out a remarkable amount about how to use AI effectively, and you might even surprise yourself with what you discover.
While true, I think his post is an excellent starting point.
When I first heard the term “metabolic syndrome”, my reaction was that there was no way I have that.
Casey Means, in her book, Good Energy explained that Metabolic syndrome means our cells are struggling to get their jobs done because of problems in their energy production system. It is clinically defined as having three or more of the following traits:
3 and 4 are definitely suspect from the last time I did my blood work.
This realization was part of what inspired so much action after reading her book.
This blog has been a vehicle to read and share what I learn about interesting technology. Over the years, I’ve gravitated toward energy – following the exponential changes in solar and wind adoption has been incredible to watch. One area I haven’t written enough (or at all) about is autonomous cars.
On reflection, I think the reason for this reluctance was largely an acceptance of the challenges involved coupled with a lack of imagination. Every time I thought about autonomy, I found myself wondering whether autonomous cards would ever work in a country like India. The answer to that felt self-evident. So I moved on.
2 recent experiences changed this –
(1) I took a Waymo x Uber taxi. It was a smooth ride without any of the pressure to make conversation (which I appreciate).
(2) I tried Tesla’s FSD or Full Self Driving capability. It has gotten very good.
Both of these provide interesting visions of the future of transportation. They’ve inspired me to spend more time thinking about it.
A Learning a Day 