Why online meetings work but not online conferences

Sitting through a “slip fielding meeting” this morning, I had an epiphany – on why office work and meetings have adjusted fairly well to online formats, but not conferences. It has to do with backchannel conversations.

In meetings where everyone is in the same room, there is naturally just one conversation. Everyone is speaking to everyone else at the same time. Unless the meeting is humongously large, it is considered rude for people to “cross talk” in the meeting, and hence there is just one conversation. Of course, in the last decade or so, people have taken to texting at meetings and stuff, but that is still small.

The advantage with moving this kind of a meeting online is that now crosstalk is fully legit, as long as you are doing it using text only. Anyway, everyone is sitting with their computers. All it takes is one simple alt-tab or command-tab, and you can chat away with others present in the meeting. In fact, this makes online meetings MORE efficient by increasing the information flow (since the main channel of large meetings are usually low throughput).

It is the other way round with conferences and events. In conferences and events, the whole point is backchannel conversation. Pretty much nobody is there to listen to the lectures or panel discussions anyways – all that most attendees want to do is to meet other attendees.

And off-line conferences are conveniently structured to enable such interaction. By having multiple parallel sessions, for example, it becomes legit to just stay out and talk to others. There is always a buzz in the corridors (one conference which was single-session-at-a-time only turned out to be bloody boring).

The other thing is that most backchannel and side channel conversations at conferences are between people who don’t yet know each other, and who are there for discovery. So you need to physically bump into someone to talk to them – you can’t randomly start a conversation with someone.

And this translates horribly to online. Online is great for backchannel and side channel conversations with people you already know well – like colleagues. When you don’t know most other people, side channel conversation is awkward. And the main channel content in conferences is largely useless anyway.

This is why it is important that conferences and seminars and other such events move to an offline format asap. For large work meetings we can continue online even after we’re all back at office.

PS: I’m firmly in the DJ D-Sol camp in terms of calling people back to work, at least to make them live in or close to the “home locations”. This way, you have the optionality to meet at short notice without planning, something that fully remote work makes it really hard.

Modelling for accuracy

Recently I’ve been remembering the first assignment of my “quantitative methods 2” course at IIMB back in 2004. In the first part of that course, we were learning regression. And so this assignment involved a regression problem. Not too hard at first sight – maybe 3 explanatory variables.

We had been randomly divided into teams of four. I remember working on it in the Computer Centre, in close proximity to some other teams. I remember trying to “do gymnastics” – combining variables, transforming them, all in the hope of trying to get the “best possible R square”. From what I remember, most of the groups went “R square hunting” that day. The assignment had been cleverly chosen such that for an academic exercise, the R Square wasn’t very high.

As an aside – one thing a lot of people take a long time to come to terms with is that in “real life” (industry problems) R squares aren’t usually that high. Forecast accuracy isn’t that high. And that the elegant methods they had learnt back in school / academia may not be as elegant any more in industry. I think I’ve written about this, but I can’t find the link now.

Anyway, back to QM2. I remember the professor telling us that three groups would be chosen at random on the day of the assignment submission, and from each of these three groups one person would be chosen at random who would have to present the group’s solution to the class. I remember that the other three people in my group all decided to bunk class that day! In any case, our group wasn’t called to present.

The whole point of this massive build up is – our approach (and the approach of most other groups) had been all wrong. We had just gone in a mad hunt for R square, not bothering to figure out whether the wild transformations and combinations that we were making made any business sense. Moreover, in our mad hunt for R square, we had all forgotten to consider whether a particular variable was significant, and if the regression itself was significant.

What we learnt was that while R square matters, it is not everything. The “model needs to be good”. The variables need to make sense. In statistics you can’t just go about optimising for one metric – there are several others. And this lesson has stuck with me. And guides how I approach all kinds of data modelling work. And I realise that is in conflict with the way data science is widely practiced nowadays.

The way data science is largely practiced in the wild nowadays is precisely a mad hunt for R Square (or area under ROC curve, if you’re doing a classification problem). Whether the variables used make sense doesn’t matter. Whether the transformations are sound doesn’t matter. It doesn’t matter at all whether the model is “good”, or appropriate – the only measure of goodness of the model seems to be the R square!

In a way, contests such as Kaggle have exacerbated this trend. In contests, typically, there is a precise metric (such as R Square) that you are supposed to maximise. With contests being evaluated algorithmically, it is difficult to evaluate on multiple parameters – especially not whether “the model is good”. And since nowadays a lot of data scientists hone their skills by participating in contests such as on Kaggle, they are tuned to simply go R square hunting.

Also, the big difference between Kaggle and real life is that in Kaggle, the model that you build doesn’t matter. It’s just a combination. You get the best R square. You win. You take the prize. You go home.

You don’t need to worry about how the data for the model was collected. The model doesn’t have to be implemented. No business decisions need to be made based on the model. Contest done, model done.

Obviously that is not how things work in real life. Building the model is only one in a long series of steps in solving the business problem. And when you focus too much on just one thing – the model’s accuracy in the data that you have been given, a lot can be lost in the rest of the chain (including application of the model in future situations).

And in this way, by focussing on just a small portion of the entire data science process (model building), I think Kaggle (and other similar competition platforms) has actually done a massive disservice to data science itself.

Tailpiece

This is completely unrelated to the rest of the post, but too small to merit a post of its own.

Suppose you ask a software engineer to sort a few datasets. He goes about applying bubble sort, heap sort, quick sort, insertion sort and a whole host of other techniques. And then picks the one that sorted the given datasets fastest.

That’s precisely how it seems “data science” is practiced nowadays

Junior Data Scientists

Since this is a work related post, I need to emphasise that all opinions in this are my own, and don’t reflect that of any organisation / organisations I might be affiliated with

The last-released episode of my Data Chatter podcast is with Abdul Majed Raja, a data scientist at Atlassian. We mostly spoke about R and Python, the two programming languages / packages most used for data science, and spoke about their relative merits and demerits.

While we mostly spoke about R and Python, Abdul’s most insightful comment, in my opinion, had to do with neither. While talking about online tutorials and training, he spoke about how most tutorials related to data science are aimed at the entry level, for people wanting to become data scientists, and that there was very little readymade material to help people become better data scientists.

And from my vantage point, as someone who has been heavily trying to recruit data scientists through the course of this year, this is spot on. A lot of profiles I get (most candidates who apply to my team get put through an open ended assignment) seem uncorrelated with the stated years of experience on their CVs. Essentially, a lot of them just appear “very junior”.

This “juniority”, in most cases, comes through in the way that people have done their assignments. A telltale sign, for example, is an excessive focus on necessary but nowhere sufficient things such as data cleaning, variable transformation, etc. Another telltale sign is the simple application of methods without bothering to explain why the method was chosen in the first place.

Apart from the lack of tutorials around, one reason why the quality of data science profiles continues to remain “junior” could be the organisation of teams themselves. To become better at your job, you need interact with people who are better than you at your job. Unfortunately, the rapid rise in demand for data scientists in the last decade has meant that this peer learning is not always there.

Yes – if you are a bunch of data scientists working together, you can pull each other up. However, if many of you have come in through the same process, it is that much more difficult – there is no benchmark for you.

The other thing is the structure of the teams (I’m saying this with very little data, so call me out if I’m bullshitting) – unlike software engineers, data scientists seldom work in large teams. Sometimes they are scattered across the organisation, largely working with tech or business teams. In any case, companies don’t need that many data scientists. So the number is low to start off with as well.

Another reason is the structure of the market – for the last decade the demand for data scientists has far exceeded the available supply. So that has meant that there is no real reason to upskill – you’ll get a job anyway.

Abdul’s solution, in the absence of tutorials, is for data scientists to look at other people’s code. The R community, for example, has a weekly Tidy Tuesday data challenge, and a lot of people who take that challenge put up their code online. I’m pretty certain similar resources exist for Python (on Kaggle, if not anywhere else).

So for someone who wants to see how other data scientists work and learn from them, there is plenty of resources around.

PS: I want to record a podcast episode on the “pile stirring” epidemic in machine learning (where people simply throw methods at a dataset without really understanding why that should work, or understanding the basic math of different methods). So far I’ve been unable to find a suitable guest. Recommendations welcome.

Formal interactions

Over the last couple of years, as the covid-19 pandemic has hit us and people have been asked to work from home, there has been a raging debate on the utility of office, especially for “knowledge work” (where the only “tool” you need is a computer).

Some companies such as Twitter have announced a “remote work in perpetuity”. Others such as Goldman Sachs have declared that remote work is inefficient and people need to return to offices asap. I probably was closer to the twitter position not so long ago, but now I think I’m firmly in the GS camp.

If you look at all the articles on remote work (I think Derek Thompson of The Atlantic has written some interesting pieces on this), one of the main arguments in favour of getting people to office is “informal interactions”, “bumping into colleagues”, “water cooler conversations”, etc. These kind of unstructured interactions can lead to new thoughts, which lead to innovation which lead to growth, goes the saying.

And in response to this, some companies have been trying to replicate these informal interactions in the zoom world. Instead of bumping into a colleague, you are forced to do a random “coffee chat” with a random colleague. There are online events. The hope here is that they will stand in for offline informal interactions.

Whether these events actually work or not, I don’t know. However, as I come close to a year in my job, it is not the informal interactions that I care about when I think of office vs remote. It’s “formal interactions”.

The lightbulb moment occurred earlier this week. I’m working on a fairly challenging problem with two others in my team. Two of the three of us were in office, and started talking about this problem. We drew some stuff on the whiteboard. Did some handwaving. And soon we had a new idea on how to approach this problem.

Now the task at hand was to explain this to the third guy, who is in another city. We opened Google Meet. We opened a “JamBoard” in that. I tried to replicate the whiteboard drawing, but he couldn’t see my handwaving (you realise that in video calls, video and screen share are two disjoint things!). It took a whole lot of effort to get the idea across.

This is not an isolated incident. In terms of collaborative work, I’ve found on multiple occasions that simply sitting together for a short duration of time can achieve so much more than what you can do in online meetings.

Another thing is that I’ve found myself to get exhausted faster in online meetings. Maybe I speak louder. Maybe having to look in one particular direction for the duration of the meeting is stressful. Offline meetings I can keep going and going and going (especially when on methylphenidate). Online, 2-3 meetings and I’m exhausted.

And then you have new colleagues and onboarding. Employees at an early stage require an extremely high degree of collaborative work. You need to “show stuff” to your new colleagues. Sometimes you might just take over their laptop. There are times when they need interventions that in the off-line world take 2 minutes, but online you need to schedule a meeting for.

Notice that none of the stuff I’ve mentioned so far is “informal”. Maybe it’s the nature of the work – involving deep thinking and complicated ideas. Remote work is absolutely brilliant in terms of the ability to shut yourself off without distractions and do deep work. The moment you need to collaborate, though, you need to be in the same physical space as your collaborators.

It’s unlikely I’ll ever want to go back to office full time (as I said, working from home is brilliant for deep work). However, I do look forward to a permanent hybrid model, meeting in office at least once a week. Hopefully the pandemic will allow us to get to this sooner rather than later.

Oh, and informal interactions are only a bonus.

Work is a momentum trade

Last evening, I called it a day at work at 4:30 pm. It was similar on Tuesday as well – I had gone to office, but decided to leave at 4, and go home and continue working. On both these days, the reason I shut shop early is that I wasn’t being productive. My mind was in a rut and I was unable to think.

I might compensate for it by working longer today. I might have already compensated for it by working late into the evening on Monday. I don’t really know.

Basically, the way I like to work is to treat it as a “momentum trade” (as they call it in capital markets). On days when work is going well, I just go on for longer and longer. On days when I’m not doing well, unless there are urgent deadlines, I shut shop early.

And for me, “going well” and “going badly” can be very very different. The amount I can achieve per hour of work when I’m in flow is far more than what I can achieve per hour of work when I’m not in flow. Hence, by working for longer on days when I’m doing well, I basically maximise the amount of work I get done per hour of work.

It is not always like this, and not with everyone. Our modern workday came from the industrial revolution, and factories. In factories, work is tightly defined. Also, assembly lines mean it is impossible for people to work unless people around them are also working (this is one supposed reason for the five day workweek developing in the US – with large numbers of both Christian and Jewish employees, it didn’t make sense for the factory to be operational on either Saturday or Sunday).

And our modern office working hours have developed from this factory working hours, because of which we traditionally have everyone working on a fixed shift. We define a start and end of the work day, and shut shop precisely at 6pm (say) irrespective of how work is going.

In my view, while this works for factories or factory-like “procedural” work,  for knowledge work that is a bad trade. You abruptly cut the wins when the going is good, and just keep going on when the going is bad, and end up taking a much longer time (on average) to achieve the same amount of work.

Then again, I have the flexibility to define my own work hours (as long as I attend the meetings I’ve committed to and finish the work I’m supposed to finish), so I’m able to make this “better momentum trade” for myself. If you are in a “thinking” profession, you should try it too.

The Fragile Charioteer

A few days back, I was thinking of an interesting counterfactual in the Mahabharata. As most people know, the story goes that Arjuna went to battle with his charioteer Krishna, and got jitters looking at all his relatives and elders on the other side, and almost lost the will to fight.

And then Krishna recited to him the Bhagavad Gita, which inspired Arjuna to get back to battle, and with Krishna’s expert charioteering (and occasional advice), Arjuna led the Pandavas to (an ultimately pyrrhic) victory in the war.

A long time back I had introduced my blog readers to the “army of monkeys” framework. In that I had contrasted the war in Ramayana (a seemingly straightforward war fought against a foreign king who had kidnapped the hero’s wife) to the war in the Mahabharata (a more complex war fought between cousins).

Given that the Ramayana war was largely straightforward, with the only trickery being in the form of special weapons, going to war with an army of monkeys was a logical choice. Generals on both sides apart, the army of monkeys helped defeat the Lankan army, and the war (and Sita) was won.

The Mahabharata war was more complex, with lots of “mental trickery” (one of which almost led Arjuna to quit the war) and deception from both sides. While LOTS of soldiers died (the story goes that almost all the Kshatriyas in India died in the war), the war was ultimately won in the mind.

In that sense, the Pandavas’ choice of choosing a clever but non-combatant Krishna rather than his entire army (which fought on the side of the Kauravas) turned out to be prescient.

When I wrote the original post on this topic, I was a consultant, and had gotten mildly annoyed at a prospective client deciding to engage an army rather than my trickery for a problem they were facing. Now, I’m part of a company, and I’m recruiting heavily for my team, and I sometimes look at this question from the other side.

One advantage of an uncorrelated army of monkeys is that not all of them will run away together. Yes, some might run away from time to time, but you keep getting new monkeys, and on a consistent basis you have an army.

On the other hand, if you decide to go with a “clever charioteer”, you run the risk that the charioteer might choose to run away one day. And the problem with clever charioteers is that no two of them are alike, and if one runs away, he is not easy to replace (you might have to buy a new chariot to suit the new charioteer).

Maybe that’s one reason why some companies choose to hire armies of monkeys rather than charioteers?

Then again, I think it depends upon the problem at hand. If the “war” (set of business problems) to be fought is more or less straightforward, an army of monkeys is a superior choice. However, if you are defining the terrain rather than just navigating it, a clever charioteer, however short-lived he might be, might just be a superior choice.

It was this thought of fleeing charioteers that made me think of the counterfactual with which I begin this post. What do you think about this?

PS: I had thought about this post a month or two back, but it is only today that I’m actually getting down to writing it. It is strictly a coincidence that today also happens to be Sri Krishna Janmashtami.

Enjoy your chakli!

The Misfit Job Market

Exactly 15 years ago, I was looking for a job. I had graduated from IIMB four months earlier, taken my first ever full time job 3 months earlier, and was already serving notice. Very quickly on, I had figured that I was not a good fit for the job that I had taken up, and so decided to cut my losses and move on.

The only problem was job hunting was hard. Back then, most people I spoke to seemed suspicious of me because I was getting out of my first job so early. For the longest time (years later), people spoke to me as if there was something wrong with me because I had quit my first job within three months. Finally I ended up taking a 20% pay cut to take another job where I seemed a better fit.

Thinking back, I don’t think I’m alone. The sheer randomness of the campus placement process means that a lot of people end up in jobs that they are ill suited for, purely based on a bit of bad judgment here and a lucky interview there. And most smart people figure out quickly enough that in case they are in jobs they are not a good fit for, it’s better to cut losses and move on. If it is their first ever jobs (applies for undergrad jobs, and for MBAs without prior work experience), the desperation to get out of their misfit jobs will be high.

I think this is a highly underserved market. Companies fall head over heels over themselves to access premium slots in the random process called campus placements, without realising that a significant part of the same pool will (theoretically) be available for a proper interview just a few months hence.

5-6 years back, an old friend of mine had started a company which was essentially a clearinghouse targeted at this precise market – to enable companies hire people in their first years of employment. Unfortunately the company didn’t take off, suggesting that the market design problem is not easy to solve.

Anyway, in case you are a just-graduated student who believes you are a misfit in your first job, and instead want to do analytics, get in touch with me. Having been on the other side, I’m more than happy to fish in this pool, and I know that I’ll get some temporarily undervalued talent here.

Just that I don’t know what sort of market or clearinghouse I need to go to to tap this supply, and so I’m putting out a bid here in the form of this blogpost.

PS: In case you’re a recent reader of my blog, I’ve written a book on market design.

Slip fielding meetings

It’s been nearly six months since I returned to corporate life. As you might imagine, I have participated in lots of meetings in this period. Some of them are 1-on-1s. Some are in slightly larger groups. Some meetings have big groups.

Meetings in big groups are of two types – ones where you do a lot of the talking, and what I have come to call as “slip fielder meetings”.

Basically, participating in these meetings is like fielding at slip in a cricket match. For most of the day, you just stand there doing nothing, but occasionally once in a while a ball will come towards you and you are expected to catch it. That means you need to be alert all the time.

These meetings are the same. For most of the discussion you are not necessarily required, but once in a while there might be some matter that comes up where your opinion is required, and you need to be prepared for that.

I can think of at least two occasions in the last six months where I was rudely awoken from my daydreams (no I wasn’t literally napping) with someone saying “Karthik, what do you think we should do about this?”.

And since then I’ve learnt to anticipate. Anticipate when my presence might be required. Figure out from the broad contours of the conversation on when I might be called upon. And remain alert when called upon (though on one occasion early on in the company my internet decided to give way just when I had started talking in a 20 person meeting).

Yesterday, a colleague gave me a good idea on how to stay alert through these “slip fielder meetings”. “Just turn on the automated captions on Google Meet”, he said. “Occasionally it can be super funny. Like one day ‘inbound docks’ was shown as ‘inborn dogs'”.

I think this is a great idea. By continuously looking at the captions, I can remain sufficiently stimulated and entertained, and also know what exactly is happening in the meeting. I’m going to use this today onwards.

I now wonder what real slip fielders do to stay alert. I’m not sure chatting with the wicketkeeper is entertaining enough.

Fifteen years of professional life

I was supposed to begin my first job on the 1st of May 2006. A week before, I got a call from HR stating that my joining date had been shifted to the 2nd. “1st May is Maharashtra Day, and all Mumbai-based employees have a holiday that day. So you start on the second”, she said.

I was thinking about this particular job (where I lasted all of three months) for a totally different reason last night. We will talk about that sometime in another blogpost (once those thoughts are well formed).

The other day I was thinking about how I have changed since the time I was working. I mean there are a lot of cosmetic changes – I’m older now. I can claim to have “experience”. I have a family. I have a better idea now of what I’m good at and all that.

However, if I think about the biggest change from a professional front that has happened to me, it is in (finally, belatedly) coming to realise that the world (especially, “wealth games”) is positive sum, and not zero sum.

The eight years before I started my first job in 2006 were spent in insanely competitive environments. First there was mugging for IIT JEE, where what mattered was the rank, not the absolute number of marks. Then, in IIT, people targeted “branch position” (relative position in class) rather than absolute CGPA. We even had a term for it – “RG” (for relative grading).

And so it went along. More entrance exams. Another round of RG. And then campus interviews where companies came with a fixed number of open positions. I don’t think I realised this then, but all of my late teens and early twenties spent in ultra competitive environments meant that I entered corporate life also thinking that it was a zero sum thing.

I kept comparing myself to everyone around. It didn’t matter if it was the company’s CEO, or my boss, or some junior, or someone completely unconnected in another part of the firm. The only thing that was constant was that I would instinctively compare myself

“Why do people think this person is good? I’m smarter than him”
“Oh, she seems to be much smarter than me. I should be like her”

And that went on for a while. Somewhere along the way I decided to quit corporate altogether and start my own consulting business. Along the way I met a lot of people. Some were people I was trying to sell to. Others I worked with after having sold to some of their colleagues. I saw companies in action. I saw diverse people get together to get work done.

Along the way something flipped. I don’t exactly know what. And I started seeing how things in the real world are not a zero sum game after all. It didn’t matter who was good at what. It didn’t matter if one person “dominated” another (was good at the latter on all counts). People worked together and got things done.

My own sales process also contributed. I spoke to several people. And every sale I achieved was a win-win. Every assignment came about because I was adding value to them, and because they were adding (monetary) value to me. It was all positive sum. There were no favours involved.

And so by the time I got back to corporate life once again at the end of last year, I had changed completely. I had started seeing everything in a “positive sum” sort of way and not “zero sum” like I used to in my first stint in corporate life. That is possibly one reason why I’m enjoying this corporate stint much better.

PS: If you haven’t already done so, listen to this podcast by Naval Ravikant. It is rather profound (I don’t say that easily). Talks about how wealth is a positive sum game while status is a zero sum game. And to summarise this post, I had spent eight years immediately before I started building wealth by competing for status, in zero sum games.

JEE Rank, branch position, getting the “most coveted job” – they were all games of status. It is interesting (and unfortunate) that it took me so long to change my perspective to what was useful in the wealth business.

PPS: I’ve written this blogpost over nearly two hours, while half-watching an old Rajkumar movie. My apologies if it seems a bit rambling or incoherent or repetitive.

 

 

The Office!

For the first time in nearly ten years, I went to an office where I’m employed to work. I’m not going to start going regularly, yet. This was a one off since I had to meet some people who were visiting. On the evidence of today, though, I think i once again sort of enjoy going to an office, and might actually look forward to when I start going regularly again.

Metro

I had initially thought I’d drive to the office, but white topping work on CMH Road means I didn’t fancy driving. Also, the office being literally a stone’s throw away from the Indiranagar Metro Station meant that taking the Metro was an easy enough decision.

The walk to South End Metro station was uneventful, though I must mention that the footpath close to the metro station works after a very long time! However, they’ve changed the gate that’s kept open to enter the station which means that the escalator wasn’t available.

The first order of business upon entering the station was to show my palm to one reader which took my temperature and let me go past. As someone had instructed me on twitter, I put my phone, wallet and watch in my bag as I got it scanned.

Despite not having taken the metro for at least 11 months, the balance on my card remained, and as I swiped it while entering, I heard announcements of a train to Peenya about to enter the station. I bounded up the stairs, only to see that the train was a little distance away.

In 2019, when I had just moved back to Bangalore from London, I had declared that the air conditioning in the Bangalore Metro is the best ever in the city. Unfortunately post-covid protocols mean that the train is kept at a much warmer temperature than usual. So on the way to the office, I kept sweating like a pig.

The train wasn’t too crowded, though. On the green line (till Majestic), everyone was comfortably seated  (despite every alternate seat having been blocked off). I panicked once, though, when a guy seated two seats away from me sneezed. I felt less worried when I saw he was wearing a mask.

The purple line from Majestic was another story. It felt somewhat silly that every alternate seat remained blcoked off when plenty of people were crowding around standing. I must mention, though, that the crowd was nothing like what it normally is. In any case, most of the train emptied out at Vidhana Soudha, and it was a peaceful ride from there on.

40 minute from door to door. Once office starts regularly, I plan to take the metro every day.

The Office

While the office was thinly populated, it felt good being back there. I was meeting several of my colleagues for the first time ever, and it was good to see them in person. We sat together for lunch (ordered from Thai House), and spoke about random things while eating. There was an office boy who, from time to time, ensured that my water glass and bottle were always filled up.

In the evening, one colleague and I went for coffee to the darshini next door. That the coffee was provided in paper cups meant we could safely socially distance from the little crowd at that restaurant. The coffee at this place is actually good – which again bodes well for my office.

And then some usual office-y things happened. I was in a meeting room doing a call with my team when someone else knocked asking if he could use the room. I got into a constant cycle of “watering and dewatering”, something I always do when I’m in an office. The combination of the thin attendance and the office boy, though, meant that there was no need to crowd around the water cooler.

I guess this is what 2020 has done to us. Normally, going to office to work should be the “most normal and boring thing ever”. However, 2020 means that it is now an event worth blogging about. Then again, I don’t need much persuasion to write about anything, do I?