arbit – Page 23 – Pertinent Observations

Ticking all the boxes

Last month my Kindle gave up. It refused to take charge, only heating up the charging cable (and possibly destroying an Android charger) in the process. This wasn’t the first time this was happening.

In 2012, my first Kindle had given up a few months after I started using it, with its home button refusing to work. Amazon had sent me a new one then (I’d been amazed at the no-questions-asked customer-centric replacement process). My second Kindle (the replacement) developed problems in 2016, which I made worse by trying to pry it open with a knife. After I had sufficiently damaged it, there was no way I could ask Amazon to do anything about it.

Over the last year, I’ve discovered that I read much faster on my Kindle than in print – possibly because it allows me to read in the dark, it’s easy to hold, I can read without distractions (unlike phone/iPad) and it’s easy on the eye. I possibly take half the time to read on a Kindle what I take to read in print. Moreover, I find the note-taking and highlighting feature invaluable (I never made a habit of taking notes on physical books).

So when the kindle stopped working I started wondering if I might have to go back to print books (there was no way I would invest in a new Kindle). Customer care confirmed that my Kindle was out of warranty, and after putting me on hold for a long time, gave me two options. I could either take a voucher that would give me 15% off on a new Kindle, or the customer care executive could “talk to the software engineers” to see if they could send me a replacement (but there was no guarantee).

Since I had no plans of buying a new Kindle, I decided to take a chance. The customer care executive told me he would get back to me “within 24 hours”. It took barely an hour for him to call me back, and a replacement was in my hands in 2 days.

It got me wondering what “software engineers” had to do with the decision to give me a replacement (refurbished) Kindle. Shortly I realised that Amazon possibly has an algorithm to determine whether to give a replacement Kindle for those that have gone kaput out of warranty. I started trying to guess what such an algorithm might look like.

The interesting thing is that among all the factors that I could list out based on which Amazon might make a decision to send me a new Kindle, there was not one that would suggest that I shouldn’t be given a replacement. In no particular order:

I have been an Amazon Prime customer for three years now
I buy a lot of books on the Kindle store. I suspect I’ve purchased books worth more than the cost of the Kindle in the last year.
I read heavily on the Kindle
I don’t read Kindle books on other apps (phone / iPad / computer)
I haven’t bought too many print books from Amazon. Most of the print books I’ve bought have been gifts (I’ve got them wrapped)
My Goodreads activity suggests that I don’t read much outside of what I’ve bought from the Kindle store

In hindsight, I guess I made the correct decision of letting the “software engineers” determine whether I qualify for a new Kindle. I guess Amazon figured that had they not sent me a new Kindle, there was a significant amount of low-marginal-cost sales that they were going to lose!

I duly rewarded them with two book purchases on the Kindle store in the course of the following week!

The Cow-Postman Paradigm

I thought I had written about this already sometime ago, but I can’t seem to find it. Anyways.

When we were in class 6, we were taught to write essays in Hindi. “Taught to write” is a loose phrase there, for we were just given two essays and asked to mug them up. One was on “gaay” (cow) and the other was on “daakiya” (postman). The teacher had reliably informed us that while the exam would have a question requiring us to write an essay, the topic would be either “gaay” or “daakiya”. And it was a given that mugging up the given essay and spitting it out would get full credit.

To my credit (I used to be topper types, you remember?), I mugged up both. This girl who was at my van stop didn’t. She only mugged up “gaay” since that seemed to be the more likely to appear in the exam. Another girl, two years our senior, who had been through this routine, consoled her saying that if the question is for “daakiya”, she can still write about “gaay”, and she would get at least 2 out of 5 marks.

As it happened, the exam required us to write a question on daakiya. The girl in my van stop wrote an essay on gaay. And got 0 for it.

This brings me to a fairly common practice, at least in India, of coming up with a set of answers and spouting them out irrespective of what the question is. And I call this the “cow-postman paradigm” based on the above anecdote.

A popular example of the cow-postman paradigm can be seen at beauty contests, where the beauty queens spout inanities in order to show off their “nobility” and show themselves as being “worthy ambassadors” of the Miss Whatever project. The most famous example of this probably is Priyanka Chopra, who answered “Mother Teresa” when asked who is the “living woman she most admires” (Mother Teresa had died two years before Priyanka Chopra became Miss World).

Politicians and PR agents are also masters of the Cow-Postman paradigm. Irrespective of what interviewers ask, they simply spout their prepared lines in the hope that it will sometimes answer the question. In case the interviewer decides to be snarky, this can be made fun of. In most cases, the “leaders” get away with it.

It is possibly these instances of “getting away with it” (or even “benefitting from it”) that results in propagating the Cow-Postman paradigm. Maybe it is a worthy effort of journalists, possibly at listicle-based publications, to make note of and make a list of such famous cow-postman instances. That’s the only way we can cure it!

When Jayalalithaa Ruined My Birthday

As the Babri Masjid was being brought down, I celebrated.

I had come up with this line a few years ago, and said that whenever I write my autobiography, I’m going to begin it this way. And while I’m not as certain nowadays that I’ll write an autobiography, in case I write one I’ll still use this line to open it.

This line could also be used in a logic class, the kind of lectures I delivered fairly frequently between 2012 and 2016, illustrating logical fallacies. For this one might induce the correlation-is-causation fallacy in your head, and you might think that if I celebrated while the Babri Masjid was being brought down, I must be a Muslim-hating bigot. So here is what will be the second line in my autobiography, whenever I write it:

It was my tenth birthday, and there was a party at home.

There is something special about your birthday falling on Sundays. The first time that happened, in 1987, was also the first time that my parents organised a birthday party for me. I’m too young to know how many people came, but there were a lot of people filling our house that evening. We had professional catering and I got so many gifts that I got to using some of them (such as Enid Blyton story books) several years later.

Maybe I read some of the books around the time my birthday fell on a Sunday once again, which happened in 1992. That also happened to be the next time I had a party at home, and this one was different, with less than ten guests, with all of them being my classmates in school.

My mother had done the cooking that day. We played cricket and hide-and-seek, and some other party games (which I don’t remember now). And then later that evening, news on television told us that the Babri Masjid had been brought down that day and riots had started.

The only thing that registered in my head then was that there would be no school the next day, and I didn’t know when I would distribute the chocolates I had bought for the customary school distribution.

The long term impact, though, was that my birthday got inextricably linked to the Babri Masjid demolition.

So over the years, when people have searched for an anchor to remember my birthday, they’ve inevitably used news of the anniversary of the Babri Masjid demolition. This morning, for example, I got a message that said “Happy birthday. Babri Masjid article came up somewhere 🙂 “. Another friend messaged me to remind me of what I’ve written to being this post.

A couple of years back, a friend messaged me later in December apologising for missing my birthday, adding that he had missed it because there wasn’t much news about the Babri Masjid anniversary. This must have been in 2016, which was among my worst birthdays because beyond close family, hardly anyone wished me that day.

And I blame former Tamil Nadu chief minister Jayalalithaa for that, for after a rather prolonged illness, she had passed away the previous night. And that meant that the news waves in India on the 6th of December 2016 were filled with news of Jayalalithaa’s demise, with any Babri Masjid anniversary stuff being pushed to the backburner.

The situation got rectified last year with it being the 25th anniversary of the Babri Masjid demolition, so the number of people who wished me went back to “normal levels”. And perhaps with elections being round the corner again, and without an important death to distract the news, I’m guessing that Babri Masjid has made enough news today for enough people to remember my birthday!

I must also take this opportunity to thank certain entities who unfailingly wish me on every birthday.

Oh, and I discovered this morning that today is 6/12/18. And my wife helpfully added that I turned 36 today.

Now I feel really old!

What Ails Liverpool

So Liverpool FC has had a mixed season so far. They’re second in the Premier League with 36 points from 14 games (only points dropped being draws against ManCity, Chelsea and Arsenal), but are on the verge of going out of the Champions League, having lost all three away games.

Yesterday’s win over Everton was damn lucky, down to a 96th minute freak goal scored by Divock Origi (I’d forgotten he’s still at the club). Last weekend’s 3-0 against Watford wasn’t as comfortable as the scoreline suggested, the scoreline having been opened only midway through the second half. The 2-0 against Fulham before that was similarly a close-fought game.

Of concern to most Liverpool fans has been the form of the starting front three – Mo Salah, Roberto Firmino and Sadio Mane. The trio has missed a host of chances this season, and the team has looked incredibly ineffective in the away losses in the Champions League (the only shot on target in the 2-1 loss against PSG being the penalty that was scored by Milner).

There are positives, of course. The defence has been tightened considerably compared to last season. Liverpool aren’t leaking goals the way they did last season. There have been quite a few clean sheets so far this season. So far there has been no repeat of last season’s situation where they went 4-1 up against ManCity, only to quickly let in two goals and then set up a tense finish.

So my theory is this – each of the front three of Liverpool has an incredibly low strike rate. I don’t know if the xG stat captures this, but the number of chances required by each of Mane, Salah and Firmino before they can convert is rather low. If the average striker converts one in two chances, all of these guys convert one in four (these numbers are pulled out of thin air. I haven’t looked at the statistics).

And even during the “glory days” of last season when Liverpool was scoring like crazy, this low strike rate remained. Instead, what helped then was a massive increase in the number of chances created. The one game I watched live (against Spurs at Wembley), what struck me was the number of chances Salah kept missing. But as the chances kept getting created, he ultimately scored one (Liverpool lost 4-1).

What I suspect is that as Klopp decided to tighten things up at the back this season, the number of chances being created has dropped. And with the low strike rate of each of the front three, this lower number of chances translates into much lower number of goals being scored. If we want last season’s scoring rate, we might also have to accept last season’s concession rate (though this season’s goalie is much much better).

There ain’t no such thing as a free lunch.

Shouting, Jumping and Peacock Feathers

The daughter has been ill for nearly the last two weeks, struck by one bacterium after one virus, with a short gap in between. Through her first illness (a stomach bug), she had remained cheerful and happy. And when I had taken her to hospital, she had responded by trying to climb up an abacus they had placed there in the children’s urgent care room.

So when the virus passed and she recovered, the transition was a rather smooth one. The day after she recovered I took her to the park where she jumped and ran around and rode the swing and the slide. Within a day or two after that she was eating normally, and we thought she had recovered.

Only for a bacterium to hit her and lay her low with a throat infection and fever. Perhaps being a stronger creature than the earlier virus, or maybe because it was the second illness in the space of a week, this one really laid her low. She quickly became weak, and rather than responding to “how are you?” with her usual cheerful “I’m good!!”, she started responding with a weak “I’m tired”. As the infection grew worse, she stopped eating, which made her weaker and her fever worse. Ultimately, a trip to the doctor and a course of antibiotics was necessary.

It was only yesterday that she started eating without a fuss (evidently, the antibiotic had started to do its work), and when she made a real fuss about eating her curd rice last night, I was deeply sceptical about how she would get on at her nursery today.

As it happened, she was completely fine, and had eaten all her meals at the nursery in full. And when I got her home in the evening, it seemed like she was fully alright.

She is normally a mildly naughty and loud kid, but today she seemed to make an extra effort in monkeying around. She discovered a new game of jumping off the edge of the sofa on to a pillow placed alongside – a sort of dangerous one that kept us on the edge of our seats. And periodically she would run around quickly and scream at the top of her voice.

To me, this was like a peacock’s feathers – by wasting her energy in unnecessary activities such as jumping and screaming, the daughter was (I think) trying to signal that she had completely recovered from her illness, and that she now had excess energy that she could expend in useless activities.

The upside of all this monkeying around was that soon after I had helped her get through 2-3 books post her dinner, she declared that it was “taachi (sleep) time”, and soon enough was fast asleep. This is significant in that the last few days when she spent all the time at home, her sleep schedule had gotten ruined.

Bangalore names are getting shorter

The Bangalore Names Dataset, derived from the Bangalore Voter Rolls (cleaned version here), validates a hypothesis that a lot of people had – that given names in Bangalore are becoming shorter. From an average of 9 letters in the name for a male aged around 80, the length of the name comes down to 6.5 letters for a 20 year old male.

What is interesting from the graph (click through for a larger version) is the difference in lengths of male and female names – notice the crossover around the age 25 or so. At some point in time, men’s names continue to become shorter while women’s names’ lengths stagnate.

So how are names becoming shorter? For one, honorific endings such as -appa, -amma, -anna, -aiah and -akka are becoming increasingly less common. Someone named “Krishnappa” (the most common name with the ‘appa’ suffix) in Bangalore is on average 56 years old, while someone named Krishna (the same name without the suffix) is on average only 44 years old. Similarly, the average age of people named Lakshmamma is 55, while that of everyone named Lakshmi is just 40. while the average Lakshmi (same name no suffix) is just 40.

In fact, if we look at the top 12 male and female names with a honorific ending, the average age of the version without the ending is lower than that of the version with the ending. I’ve even graphed some of the distributions to illustrate this.

In each case, the red line shows the distribution of the longer version of the name, and the blue line the distribution of the shorter version

In one of the posts yesterday, we looked at the most typical names by age in Bangalore. What happens when we flip the question? Can we define what are the “oldest” and “youngest” names? Can we define these based on the average age of people who hold that name? In order to rule out fads, let’s stick to names that are held by at least 10000 people each.

These graphs are candidates for my own Bad Visualisations Tumblr, but I couldn’t think of a better way to represent the data. These graphs show the most popular male and female names, with the average age of a voter with that given name on the X axis, and the number of voters with that name on the Y axis. The information is all in the X axis – the Y axis is there just so that names don’t overlap.

So Karthik is among the youngest names among men, with an average age among voters being about 28 (remember this is not the average age of all Karthiks in Bangalore – those aged below 18 or otherwise not eligible to vote have been excluded). On the women’s side, Divya, Pavithra and Ramya are among the “youngest names”.

At the other end, you can see all the -appas and -ammas. The “oldest male name” is Krishnappa, with an average age 56. And then you have Krishnamurthy and Narayana, which don’t have the -appa suffix but represent an old population anyway (the other -appa names just don’t clear the 10000 people cutoff).

More women’s names with the -amma suffix clear the 10000 names cutoff, and we can see that pretty much all women’s names with an average age of 50 and above have that suffix. And the “oldest female name”, subject to 10000 people having that name, is Muniyamma. And then you have Sarojamma and Jayamma and Lakshmamma. And a lot of other ammas.

What will be the oldest and youngest names we relax the popularity cutoff, and instead look at names with at least 1000 people? The five youngest names are Dhanush, Prajwal, Harshitha, Tejas and Rakshitha, all with an average age (among voters) less than 24. The five oldest names are Papamma, Kannamma, Munivenkatappa, Seethamma and Ramaiah.

This should give another indication of where names are headed in Bangalore!

Smashing the Law of Conservation of H

A decade and half ago, Ravikiran Rao came up with what he called the “law of conservation of H“. The concept has to do with the South Indian practice of adding a “H” to denote a soft consonant, a practice not shared by North Indians (Karthik instead of Kartik for example). This practice, Ravikiran claims, is balanced by the “South Indian” practice of using “S” instead of “Sh”, because of which the number of Hs in a name is conserved.

Ravikiran writes:

The Law of conservation of H states that the total number of H’s in the universe will be conserved. So the extra H’s that are added when Southies have to write names like Sunitha and Savitha are taken from the words Sasi and Sri Sri Ravisankar, thus maintaining a balance in the language.

Using data from the Bangalore first names data set (warning: very large file), it is clear that this theory doesn’t hold water, in Bangalore at least. For what the data shows is that not only do Bangaloreans love the “th” and “dh” for the soft T and D, they also use “sh” to mean “sh” rather than use “s” instead.

The most commonly cited examples of LoCoH are Swetha/Shweta and Sruthi/Shruti. In both cases, the former is the supposed “South Indian” spelling (with th for the soft T, and S instead of sh), while the latter is the “North Indian” spelling. As it turns out, in Bangalore, both these combinations are rather unpopular. Instead, it seems like if Bangaloreans can add a H to their name, they do. This table shows the number of people in Bangalore with different spellings for Shwetha and Shruthi (now I’m using the dominant Bangalorean spellings).

As you can see, Shwetha and Shruthi are miles ahead of any of the alternate ways in which the names can be spelt. And this heavy usage of H can be attributed to the way Kannada incorporates both Sanskrit and Dravidian history.

Kannada has a pretty large vocabulary of consonants. Every consonant has both the aspirated and unaspirated version, and voiced and unvoiced. There are three different S sounds (compared to Tamil which has none) and two Ls. And we need a way to transliterate each of them when writing in English. And while capitalising letters in the middle of a word (as per Harvard Kyoto convention) is not common practice, standard transliteration tries to differentiate as much as possible.

And so, since aspirated Tha and Dha aren’t that common in Kannada (except in the “Tha-Tha” symbols used by non-Kannadigas to show raised eyes), th and dh are used for the dental letters. And since Sh exists (and in two forms), there is no reason to substitute it with S (unlike Tamil). And so we have H everywhere.

Now, lest you were to think that I’m using just two names (Shwetha and Shruthi) to make my point, I dug through the names dataset to see how often names with interchangeable T and Th, and names with interchangeable S and Sh, appear in the Bangalore dataset. Here is a sample of both:

There are 13002 Karthiks registered to vote in Bangalore, but only 213 Kartiks. There are a hundred times as many Lathas as Latas. Shobha is far more common than Sobha, and Chandrashekhar much more common than Chandrasekhar.

So while other South Indians might conserve H, by not using them with S to compensate for using it with T and D, it doesn’t apply to Bangalore. Thinking about it, I wonder how a Kannadiga (Ravikiran) came up with this theory. Perhaps the fact that he has never lived in Karnataka explains it.

Why Real Estate Prices are High

World over, high housing prices seem to be a problem. They’ve always been an issue in India. They are an issue in the US, where millennials are not able to afford houses to live in. In the UK as well, rising housing prices mean that today’s young are unable to buy up houses. The global phenomenon that is driving all this is the drive towards increasingly large cities.

Going by first principles, there are two major components that determine the cost of a house (note that I said cost and not price) – the cost of the land and the cost of construction. It can be safely assumed that the latter hasn’t increased at a rate dramatically higher than inflation over the years.

Yes, there are bubbles and busts in prices of commodities such as steel and cement. Houses nowadays are being built largely to better specifications and quality than earlier homes. In places like the US, modern houses are bigger. But all this is balanced by technological innovation which makes stuff cheaper. So on an average, the increase in construction costs over the years is not dramatic.

That implies that the massive increase in price of housing the world over is driven by increasing costs of land. Some scaremongers will try to tell you that this is due to there being too many human beings in the world, and we are soon headed for a Malthusian collapse. However, the land needed for housing is small, compared to say agriculture, so regular transfer of land from agriculture to housing should take care of this. So why are land prices increasing so much?

It has to do with the distribution. During most of the 20th century, manufacturing being the base of the economy meant that a lot of smaller cities and towns flourished. These cities and towns were either located conveniently enough to tap raw materials or markets for industrial goods, or were helped by the fact that land requirements for industries meant that big cities would get expensive very soon for industries, driving development to smaller cities and towns.

As the share of populations in manufacturing falls, and more people move into services, the larger cities gain at the expense of smaller cities and towns. This means the distribution of demand has changed massively over the last 30 years or so. Rather than demand being more or less uniform over cities, nowadays most of the housing demand is spread over a few small cities.

And these cities aren’t able to keep up. Supply in some cities such as San Francisco and Mumbai, are constrained by regulations on how much can be built. Other cities such as Bangalore or Houston have expanded radially, but housing in the far suburbs is much less attractive than closer to town (due to increased transport costs), and there is only so much supply in “convenient areas” of towns.

This changing pattern of urbanisation is leading to rapid increase in the prices of housing in places that people want to live in. And so millennials are being priced out, unable to buy homes. The distribution of jobs across cities means they don’t have the luxury of “settling down” in smaller cities and towns where housing is still affordable. And until the larger cities hit their limits of growth and businesses start moving to smaller cities (thus creating newer hubs), this housing shortage will exist.

Randomness and sample size

I have had a strange relationship with volleyball, as I’ve documented here. Unlike in most other sports I’ve played, I was a rather defensive volleyball player, excelling in backline defence, setting and blocking, rather than spiking.

The one aspect of my game which was out of line with the rest of my volleyball, but in line with my play in most other sports I’ve played competitively, was my serve. I had a big booming serve, which at school level was mostly unreturnable.

The downside of having an unreturnable serve, though, is that you are likely to miss your serve more often than the rest – it might mean hitting it too long, or into the net, or wide. And like in one of the examples I’ve quoted in my earlier post, it might mean not getting a chance to serve at all, as the warm up serve gets returned or goes into the net.

So I was discussing my volleyball non-career with a friend who is now heavily involved in the game, and he thought that I had possibly been extremely unlucky. My own take on this is that given how little I played, it’s quite likely that things would have gone spectacularly wrong.

Changing domains a little bit, there was a time when I was building strategies for algorithmic trading, in a class known as “statistical arbitrage”. The deal there is that you have a small “edge” on each trade, but if you do a large enough number of trades, you will make money. As it happened, the guy I was working for then got spooked out after the first couple of trades went bad and shut down the strategy at a heavy loss.

Changing domains a little less this time, this is also the reason why you shouldn’t check your portfolio too often if you’re investing for the long term – in the short run, when there have been “fewer plays”, the chances of having a negative return are higher even if you’re in a mostly safe strategy, as I had illustrated in this blog post in 2008 (using the Livejournal URL since the table didn’t port well to wordpress).

And changing domains once again, the sheer number of “samples” is possibly one reason that the whole idea of quantification of sport and “SABRmetrics” first took hold in baseball. The Major League Baseball season is typically 162 games long (and this is before the playoffs), which means that any small edge will translate into results in the course of the league. A smaller league would mean fewer games and thus more randomness, and a higher chance that a “better play” wouldn’t work out.

This also explains why when “Moneyball” took off with the Oakland A’s in the 1990s, they focussed mainly on league performance and not performance in the playoffs – in the latter, there are simply not enough “samples” for a marginal advantage in team strength to necessarily have the impact in terms of results.

And this is the problem with newly appointed managers of elite football clubs in Europe “targeting the Champions League” – a knockout tournament of that format means that the best team need not always win. Targeting a national league, played out over at least 34 games in the season is a much better bet.

Finally, there is also the issue of variance. A higher variance in performance means that observations of a few instances of bad performance is not sufficient to conclude that the player is a bad performer – a great performance need not be too far away. For a player with less randomness in performance – a more steady player, if you will – a few bad performances will tell you that they are unlikely to come good. High risk high return players, on the other hand, need to be given a longer rope.

I’d put this in a different way in a blog a few years back, about Mitchell Johnson.

Human, Animal and Machine Intelligence

Earlier this week I started watching this series on Netflix called “Terrorism Close Calls“. Each episode is about an instance of attempted terrorism that has been foiled in the last 2 decades. For example, there is one example of the plot to bomb a set of transatlantic flights from London to North America in 2006 (a consequence of which is that liquids still aren’t allowed on board flights).

So the first episode of the series involves this Afghani guy who drives all the way from Colorado to New York to place a series of bombs in the latter’s subways (metro train system). He is under surveillance through the length of his journey, and just as he is about to enter New York, he is stopped for what seems like a “routine drugs test”.

As the episode explains, “a set of dogs went around his car sniffing”, but “rather than being trained to sniff drugs” (as is routine in such a stop), “these dogs had been trained to sniff explosives”.

This little snippet got me thinking about how machines are “trained” to “learn”. At the most basic level, machine learning involves showing a large number of “positive cases” and “negative cases” based on which the program “learns” the differences between the positive and negative cases, and thus to identify the positive cases.

So if you want to built a system to identify cats in an image, you feed the machine a large number of images with cats in them, and a large(r) number of images without cats in them, each appropriately “labelled” (“cat” or “no cat”) and based on the differences, the system learns to identify cats.

Similarly, if you want to teach a system to detect cancers based on MRIs, you show it a set of MRIs that show malignant tumours, and another set of MRIs without malignant tumours, and sure enough the machine learns to distinguish between the two sets (you might have come across claims of “AI can cure cancer”. This is how it does it).

However, AI can sometimes go wrong by learning the wrong things. For example, an algorithm trained to recognise sheep started classifying grass as “sheep” (since most of the positive training samples had sheep in meadows). Another system went crazy in its labelling when an unexpected object (an elephant in a drawing room) was present in the picture.

While machines learn through lots of positive and negative examples, that is not how humans learn, as I’ve been observing as my daughter grows up. When she was very little, we got her a book with one photo each of 100 different animals. And we would sit with her every day pointing at each picture and telling her what each was.

Soon enough, she could recognise cats and dogs and elephants and tigers. All by means of being “trained on” one image of each such animal. Soon enough, she could recognise hitherto unseen pictures of cats and dogs (and elephants and tigers). And then recognise dogs (as dogs) as they passed her on the street. What absolutely astounded me was that she managed to correctly recognise a cartoon cat, when all she had seen thus far were “real cats”.

So where do animals stand, in this spectrum of human to machine learning? Do they recognise from positive examples only (like humans do)? Or do they learn from a combination of positive and negative examples (like machines)? One thing that limits the positive-only learning for animals is the limited range of their communication.

What drives my curiosity is that they get trained for specific things – that you have dogs to identify drugs and dogs to identify explosives. You don’t usually have dogs that can recognise both (specialisation is for insects, as they say – or maybe it’s for all non-human animals).

My suspicion (having never had a pet) is that the way animals learn is closer to how humans learn – based on a large number of positive examples, rather than as the difference between positive and negative examples. Just that the limit of the animal’s communication being limited means that it is hard to train them for more than one thing (or maybe there’s something to do with their mental bandwidth as well. I don’t know).

What do you think? Interestingly enough, there is a recent paper that talks about how many machine learning systems have “animal-like abilities” rather than coming close to human intelligence.

For millions of years, mankind lived, just like the animals.
And then something happened that unleashed the power of our imagination. We learned to talk
– Stephen Hawking, in the opening of a Roger Waters-less Pink Floyd’s Keep Talking