Wow, what a year 2022 was. For the SuperDataScience Podcast in particular, it was our best year yet:
We had incredible guests. I’m proud of the conversations and content covered in every single episode this year and I appreciate the feedback you’ve provided and questions you’ve had for guests in order to make that happen.
The SuperDataScience Podcast team has grown, allowing us to produce even slicker episodes with more meat on the bones. Specifically, we added three folks to the team:
The brilliant data scientist Serg Masís as our researcher. He digs into guests’ backgrounds super thoroughly and comes up with amazing topics to discuss with them — bridging guests’ backgrounds in order to come up with questions that they might be the only person on the planet that can answer them.
In addition to Sylvia Ogweng who’s already been writing for us, we added Dr. Zara Karschay as a second writer on the show, enabling us to produce exquisitely professional episode summaries, show notes, and social media posts for you.
And the indefatigable Natalie Ziajski joined me full-time as my Operations Manager to keep all my plates spinning across the podcast and my other professional commitments. For the podcast, for example, this has allowed us to build up a much much deeper guest and episode pipeline than ever before and also to increase the richness of our offering and engagement across platforms like YouTube, Twitter, LinkedIn, and even a developing TikTok channel.
With incredible guests and incredible new team members, it’s perhaps unsurprising that the show has continued to enjoy tremendous growth: If you compare the most recent quarter with the same quarter a year earlier, the number of downloads of the show has grown by 90% — that is, it has very nearly doubled. Individual guest episodes now conservatively garner at least 35,000 listens. So thank you for listening, for watching, for engaging, and for letting your friends and colleagues know about the SuperDataScience Podcast. We put our heart and soul into all 104 episodes each year and we do it for you — it means a ton to us that you commit some of your valuable attention to us. Thanks again, it’s truly the great honor of my life so far to serve you.
To cap 2022 off, like I did to cap 2021 off, for today’s episode I’ll cover the five big lessons that I learned over the course of the year. Namely:
I Can't Do Everything at Once
Orders of Magnitude More Parameters produce Unbelievable A.I. Models
The 24-Hour News Cycle is Exhausting and Unsatisfying
Working In-Person is way more Fun
Logging Nutrition is Effective and Paradoxically Liberating
So we’ll go through those five one by one, starting with the first one:
1. I Can't Do Everything at Once
In addition to the tremendous success we’ve had with the SuperDataScience Podcast, SDS success, my machine learning company, Nebula also launched its first product into a private beta in the autumn. And that's a lot, but there's lots of other things that I hoped to do this year. One of the things that I regret the most is that I wasn't able to keep up this year with my weekly updates to my YouTube channel and the corresponding Udemy course. I haven't been able to update the course since the Northern Hemisphere spring which is disappointing because I get lots of students reaching out, lots of people commenting on YouTube videos, and lots of people adding me on LinkedIn asking when there’s going to be new content. Another thing that I haven't been able to make progress on is the corresponding book. I signed a contract with the publisher Pearson to write my second book, the Mathematical Foundations of Machine Learning. I can't wait to be able to get around to writing that book. But again, just something I haven't been able to make much progress on in 2022. I'm already heavily overweight on how much time I spend working, and my life is out of balance related to that. So just accepting that I can't do everything at once has been the big first lesson for me in 2022
2. Orders of Magnitude More Parameters produce Unbelievable A.I. Models
We started to already get a taste of this in the last couple of years. Models like GPT-3 that have orders of magnitude, more model parameters than its predecessor GPT-2. It had lots of emergent properties that the authors of that algorithm, so people like Melanie Subbiah who was on this program in episode #559, was one of the first authors on the GPT-3 paper. It's mind blowing the breadth and often human level capability that these generative models have. Leveraging what we call foundational large language models, like GPT-3, with orders of magnitude and more parameters, those foundational models have been augmented with other models appended to them, new training data sets, and we witnessed a big bang in the emergence of generative models with staggering expert human level creative capacity on tasks as diverse as artwork (e.g., DALL-E 2), long-form conversational text (e.g., ChatGPT), and language-based gameplay negotiation (i.e., CICERO). I just got email confirmation from one of the engineering managers working on CICERO that he'll come and do an episode in early 2023 to break down this huge achievement. But in the meantime, if you can't wait to hear about that huge achievement, you can go back and check out episode #569 with Dr. Noam Brown. He talks about lots of different gameplay AI algorithms. I did not anticipate anything like this at the beginning of 2022. And so I'm holding my breath for what will happen in 2023 with the release of next generation large language models like GPT-4, which is rumored to be released early in the year, maybe February. And so with that, you can expect existing human-level generative capabilities that we saw this year to become markedly more refined and even more realistic and human-like. We've got more coming up on that in the very next episode, episode #641 when we go into 2023 data science trends with the brilliant Sadie St. Lawrence.
3. The 24-Hour News Cycle is Exhausting and Unsatisfying
When the COVID pandemic hit, I went from being somebody who only read the news in the physical Economist newspaper that would arrive at my doorstep once a week, to being glued to the news on my phone and looking at rates of COVID transmission and testing and just trying to understand how this was going to change the world and the economy and my businesses and how I needed to adapt. And just as the pandemic was coming to an end, Russia invades Ukraine. And again, I was just glued to the news cycle. The COVID pandemic right into war left me constantly exhausted. When I have a break or I'm on the subway my default is to go check the news; and I think that isn't good for my mood, it leaves me feeling fatigued. So in 2023, I hope to be able to shake my addiction to the 24-hour news cycle and get back to what I was doing before just reading the physical, the paper Economist that comes once a week. So hopefully I'll go dig into a great book, which is something, since the pandemic hit, I've barely done, and that that's another huge joy in my life.
4. Working In-Person is way more Fun
This plays on one of my lessons from last year, which is that remote working works. I'm still blown away at how efficient my machine learning company is largely being remote. The SuperDataScience Podcast also, except for Natalie and me working together most days, is remote all around the world. At my machine learning company, for example, this year with so many people being vaccinated and COVID being less of a risk, we've been having offsites for the first time, because prior to the pandemic, we were an all in-person company. So we're having offsites for the first time in our now remote-first company. And so that includes things like social activities with other data scientists on my team. We went on a big trip to Cabo in Mexico this summer, Northern hemisphere summer, and it’s so great to spend time with all those people. Sometimes I just have meals with data scientists on my team who live with me here in New York. And as I just mentioned, Natalie, my operations manager, who is that full-time hire I mentioned at the top of the episode is in-person most days. And that is such a treat. Being able to have a bit of banter and laughter doesn't have the same positive impact on my mood over Zoom. Another thing that happened this year is I was able to go in-person to conferences again, like ODSC West, and we recorded a whole bunch of episodes at ODSC West in San Francisco this year, episode number 628, 630, 632, 633, and 634. All of those were recorded at ODSC West, and it was so much fun. I also recorded a bunch of episodes in-person at my apartment or on stage at other conferences this year. The Noam Brown episode that I mentioned earlier, that was episode #569. We recorded that at ML conf in New York, and we also did an amazing episode with Hillary Mason, episode #589 at the R Conference in New York. I’m looking for more of it in 2023, and hopefully seeing more of you listeners out there at conferences as well. It was fun getting to meet a bunch of you this year.
5. Logging Nutrition is Effective and Paradoxically Liberating
I started logging macros. So the grams of fat, the grams of carbs, and the grams of protein, of every single thing that I eat about five or six days a week. I've discovered by doing it consistently, it paradoxically saves me time. So specifically, I use the MyFitnessPal app. There's this paradox because while obviously it takes time to enter the macros as I eat everything over the course of the day, it saves me mental time because otherwise I spend this time wondering, oh, have I eaten enough today? Am I just hungry because I'm bored or lonely? And when I'm logging everything, I can see if I have a calorie budget for the day and I'm hungry, I know that this hunger is real. But the other really cool thing about logging macros is that I never have really felt hungry, but I also never feel stuffed either. So my energy is more consistent, and it's also led to consistent results. Also, my body fat percentage peaked above 17% in November 2020, and now a little over two years later, I'm down below 11%. So having gone from over 17% to under 11% in two years is a big difference. You can see tremendous results slowly.
As a relatively big person, mobility can be an issue and can be liable to injuries. But I've been using an app called Pliability at least six days a week to just stretch out my whole body. Between consistently lifting, going to the gym five to six days a week, eating well, and having this commitment to mobility, and then also just in the last few months, getting involved in Team CrossFit competitions has been a lot. But, being in competitions with a team, wanting to be ready for those competitions, and then working out with teammates, has made working out so much more fun, and. easier for me to stay consistent, and push yourself. So as a result of all those factors, nutrition, weightlifting consistency, mobility commitment, and working together with other people at weightlifting, I've had a crazy, crazy year for personal records:
Power Lifts:
Deadlift
Back squat
Front squat
Bench
Olympic Lifts:
Clean
Jerk
Snatch
Classic CrossFit workouts:
Grace
Fran
400m run
It's all about being consistent and everybody knows that it's hard to be consistent. But I do also have frameworks to help out with that. So you can check back to episode #538 when I talk about habit tracking. And so that can be a key to developing the habits that you want
What I didn’t learn:
In 2021, one of my five lessons was all work, no play makes Jon a dull boy. And I'm still pretty dull, not a lot of play. So despite getting way better at delegating in 2022, I didn't really learn how to work less. Demands at my machine learning company, Nebula, as well as at the SuperDataScience Podcast scaled up dramatically. And so while I've had a tiny, tiny, tiny amount of travel and time with family and friends this year, it's not been nearly enough. This is no way to live. It isn't a life. It's productive. And I absolutely love seeing the results, the fruits of these labors, and being able to create a podcast in particular for all of you folks. But this is no way to live a life. There was little time in 2022 for personal development, playing musical instruments which I love, enjoying the arts, and reading for leisure. Since the pandemic hit, I've read one book. Specifically, that book is 4,000 weeks. You can hear me talk about that book in episode #606 this year. I also did a follow-up episode a little bit later on episode #618, which also discusses topics from that book, 4,000 Weeks. And 4,000 Weeks, it's how many weeks you have in your life. And I read it because it seems so perfect. It's about, you have a limited lifespan, you're mortal. And so you can't just work all the time. Productivity techniques are a trap. They just lead to more and more and more work. And life isn't just about work. Life is also about just enjoying being alive, the simple things and connection with people in a personal way. So read the book in hopes that I'd find some way to get out of this trap of all work and no play makes Jon a dull boy. But I haven't figured that out yet.
SDS Pod in 2023
And so in all likelihood, 2023 will be even more work, but it'll even be a bigger year for this podcast, which is good news for you all. We'll be stretching ourselves to find even bigger names than ever before in data science and machine learning and AI, and cover even more mind-blowing topics and have even more deeply practical conversations for you. I'll continue to have more experts join me on Friday episodes, which is something we just started experimenting with regularly in 2022. Expect to see more of that in 2023 with guests coming on Fridays and digging into fascinating topics outside the narrow confines of the field of data science, but nevertheless, topics that I am confident will inspire and support you through your career and your life. So combine those things with perhaps 2023 being the biggest year ever in terms of AI innovations and applications we're surely in for an exciting year.
Thanks to everyone who works on the podcast. I mentioned everyone that we hired newly this year already at the top of the show, so Serg Masis our researcher; our new writer, Dr. Zara Karschay; as well as Natalie Ziajski, my operations manager. But in addition, I'd like to take a moment here to thank the people who have been working on the show all year bound. So I already alluded to Ivana Zibert, our podcast manager. She's just incredible. So all year round, a 104 episodes a year, she's on the ball with every aspect of the production of all these episodes. And they're always, in my view, at least tremendously high quality about as good as podcasts get out there. So amazing to have Ivana captaining the podcast and making sure that everything happens to an extremely high standard. So thanks to Ivana. Mario Pombo, he's doing all of the audio and video editing for all 104 episodes all year round, and he just does an incredible job. He's so thoughtful about ways that we can be improving the way that we do things all the time, and being extremely consistent in delivering exquisitely professional high quality all year round on those 104 episodes. In addition to Zara Karschay whom we added this year, we already had Sylvia Ogweng, who's been working with us for a couple of years. She does an amazing job writing and creating podcast pages, show notes, social media posts that are awesome, so easy to read and give great summaries of what's going on in each episode. And then finally, thanks, of course, to Kirill Eremenko. Kirill founded the show and hosted the show up until I took over two years ago. He has a big influence on the direction of the show, and it's always such a joy to talk to him. We typically book hour-long meetings and end up talking for several hours, and despite one being either early morning or late evening for one of us, because he's based in Australia and I'm in New York, but we make it happen. And, of course, everyone at Nebula for supporting me, giving me the time to taking time out of my day there to be making this program for all you out there.
Last year I ended the episode by playing a song, and playing a song on the guitar and singing along. And I intended on having that be something that I would do every year. But sometimes things happen beyond our control. And I had a freak accident putting away a barbell recently and broke the end of my right index finger, which you use for holding a guitar pick. And so I can't play guitar right now. I've got a splint on that finger. So I recently came across this story, this legend from Hinduism that I thought was really inspiring. So I thought I would end the year with that instead of a song this year:
There's two characters in this really short legend. One of them is Rama. Rama is a major Hindu deity, and then the other character is Hanuman. Hanuman is part monkey, part man, and he's a devotee of the deity Rama. And so Rama asks Hanuman, what are you? And Hanuman says, when I don't know who I am, I serve you, when I do know who I am, I am you.
I found that really beautiful. It connects me to you and to everything else. And it makes me think about how when we're not aware of that connection that we all have with each other, we can at least focus on service to each other and to the greater good.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.