Math and Science in daily life – Part 1


In my job, I spend a lot of time explaining technology topics to my clients in simple english. Off late, a lot of such conversations are about AI, Data science, Quantum Computing etc . Those topics are rooted in maths and physics (amongst other things), and I often find there is some fear about math and science that exists in the minds of some people listening to me, and it gets in the way of their appreciation of technology.

I am also the dad of a teenage daughter who loves math and science. From my daughter and her friends and teachers – I got the feeling that many a time students get education in math and science in quite an abstract way, and it leads them to think “Why should I really learn any of this? I am not going to use this in real life”. In a few weeks, I am going to give a talk to high schoolers on how the math they learn in school manifests in real life in ways they may not have realized .

So I am going to try a couple of posts here to see if I can explain how simple math leads to powerful and beautiful things we see and use in our daily lives. I would really love your feedback on each topic including alternate/better explanations, and also your suggestions on what else would make good examples. If anyone wants to post a guest blog – we can consider that too.

Let me ease into it with simple math and see how it goes . We can build up from there . Here we go !

Pythagorean theorem

Lets start with the very simple and quite powerful Pythagorean theorem . It states that the square of the hypotenuse (the side opposite the right angle) is equal to the sum of the squares of the other two sides. We remember it via this elegant equation

 A2  + B2 = C2 Screen Shot 2018-03-02 at 4.33.04 PM

This theorem has been proved in several ways , so we will skip that part and get to the fun aspects . If there are three positive numbers A, B and C where A2  + B2 = C2    is true, then it means that there exists a right angled triangle with A and B as the short sides and C as hypotenuse. Some of the fun is based on this converse property of this simple theorem.

How to make square edges on your garden bed

Any three numbers which make true is called a pythagorean triple. The numbers 3,4,5 form one such triple ( 3 squared is 9, 4 squared is 16. the sum of 9 and 16 is 25 which also happens to be the square of 5 ).

Here is a simple problem – lets say you are making a garden bed to plant vegetables.  How do we make the edges square if all you have is a tape measure ? Easy – just make a a triangle with sides 3, 4 and 5 units using the tape and mark the end points on the ground. Voila – You have edges at perfect square !

How big of a ladder do you need ?

An extension of this is also true for finding the length of a ladder required to paint a wall – probably a question kids get in school exams. If the point where the ladder needs to touch the wall is 4 meters, and the bottom of the ladder is 3 meters away from the wall on the floor – the length of the ladder needed is 5 meters !

Or let’s say you want to convert stairs into a ramp on your back porch – the same concept applies .

Real engineers use this information frequently – the one I remember the most from my college days is in surveying land, finding length of trusses etc .

Planning your next painting project

Lets say we draw three similar figures using the sides of the right angled triangle forming one side of those figures. Similar figures just mean the lengths of sides have same ratios, the included angle between sides are the same.  Easy way to think about it is zooming a picture on your phone . All parts increase or decrease in size – but their ratio to each other stays the same . It does not matter what the figure is – it could be a square, a pentagon or another triangle. The area of the largest figure (the one using hypotenuse as one of its sides) is ALWAYS the same as the sum of areas of the two smaller figures. How cool is that ! See this picture from wikipedia to see what I mean

520px-Pythagorean.svg

Lets say these three squares are walls you need to paint . For the same quantity of paint you will need to paint the largest square – you can paint both the smaller squares !

How much marinara do you need ?

It’s not just about squares either – this area computation via Pythagorean triples also applies to circles . So for the the quantity of marinara you need for a 5 inch pizza can be used to coat both a 3 inch and 4 inch pizza ! If your mom is like mine – I wouldn’t advice trying this math on her while she is making you pizza . Theory of “mom is always right” over rules every other rule 🙂

If you are a social media marketer

You probably are a fan of Metcalfe’s law which says the value of a network is proportional to the square of the number of connected users in it . Let’s say you are given access to three potential networks . A has 3 connections , B has 4 connections and C has 5 connections . Since The squares of A and B add to the square of C – you get as much out of a network with 5 connections as you would get from A and B which together add to 7 connections . Spend your time and money wisely !

PS : I have my own misgivings on this – but would like to hear your feedback anyway . I didn’t come up with this myself and can’t remember anymore who told me this originally . But I have teased many a marketer friend with this at social events 🙂

What’s next

I think I would like to example the wonderful pi next . There is pi in pie !

Looking ahead – what jobs will technology take away ?


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As with a lot of things like politics, religion and so on – the world is sharply divided between people who believe AI and Robots (or automation in general) will take more jobs away than it creates.  I was drawn into this debate yet again by a few friends couple of weeks ago – so let me jot down while I still have it fresh in my mind. My crystal ball is not any more effective than yours when it comes to looking into the future – but there are a few scenarios where I do think jobs will be taken away. If your job is in one of these categories, the smart thing to do is to gain additional skills. Just to be clear – I also think there won’t be any net job losses. As always – this is all strictly my personal views on the topic, and not that of my employer.

Another way to look at this is – many companies will automate tasks and eliminate labor where they can to save costs. If you have skills that can make them earn more revenue directly or indirectly, you get to stay employed. Otherwise instead of reinvesting the savings, the company will probably treat it as profit, or keep the cash for future. The job itself might stay in many cases – but there just won’t be a need for as many people to do it. Granted – there will always be exceptions. Technology will also create a bunch of new jobs – which I will write about in another post.

I think there are at least four categories of jobs that will get disrupted soon.

1.If most of what you know is public knowledge

This is especially true for my own profession, which is consulting. In the 90s when I got out of college – there was no google. If I knew something special ( from books , professional magazines, training etc ) – a client would pay some money for me to tell them what I knew that they did not. That does not happen much any more – there is no premium for internet access . Clients and consultants both have access to similar information – so you need to know more than what is available on internet to fetch a premium. It might sound ridiculously obvious – but this is a bigger threat to (especially junior) consultants than almost anything else .

You absolutely need to stay couple of steps ahead of market to add value to a client today. Having a logically defensible point of view, knowing what others in the industry are up to, what disruptions are on the horizon, what untapped opportunities exist are all still things a client will pay a premium for.

Consultants are not the only ones at risk either. As an example – A hotel concierge function could also fall in this category. You don’t need a human to get you a restaurant reservation, check weather, know the local tourist spots and  so on. However, it will be hard to replace a human who can help you score a last minute Hamilton ticket in Broadway, or one who can answer questions from four different customers in parallel and make them all feel special.

2.If your work is all about short tail questions from a customer 

A lot of systems we use were not designed with end users in mind. Thanks to that, a lot of human intervention is still needed for people to use things they bought. A good part of customer service calls are about answering questions like “whats my account balance”, “can you reset my password”, “Can I set up a payment plan”, “Can I use a different credit card” etc. Automation is mature enough already to do those things without human intervention . If that is all your skill is – your job probably will be taken away soon.

But there are lot of things automation cannot do in this scenario  – at least not yet. For example , talking a customer out of canceling a service is not something AI can do very effectively like a trained retention specialist. From a customer’s point of view – an automated way of resetting a password, or making a routine payment would be easier/faster than needing to talk to someone. But when you are upset with poor service , or want to talk through multiple options – there is nothing worse than listening to a machine with a long menu. Also think of this – as tech (and laws) improves all around, in most categories customers will have zero or low switching costs.

So if you are skilled at higher value service – you should be in hot demand. The money an employer saves by automating the short tail responses is what lets them invest more in higher value services. Of course we can also take a cynical view that some companies will just add it to bottom line and not bother re-investing. While that is a short term possibility, I doubt they can do it in long term without risking their whole business.

3.If you are in a job where process trumps thinking 

There are several jobs where the job needs very little original thinking. The critical thinking is done by few people who designed the workflow, and not by people executing. This would include things like preparing fast food, paying invoices, checking totals, scanning documents etc. These jobs are generally at risk given they are easily automated – and probably the only reason they are still around is because of the one time cost of implementing new technology. Given all tech will eventually commoditize, this is only a temporary safety net.

The human intervention will be limited to exception processing in these workflows, especially those that involve safety, brand issues, downtime issues etc like – what if the lettuce delivered is rotten and you need to run to local grocery to buy some ? What happens if the scanner stops working the last day of the fiscal period ? Do you want to harass a customer on collecting $100, when you know in 3 months they are due for a $1000 renewal?

4.If your job is only about answering questions, and not about asking questions

Computers – and all the advances in AI and Quantum computing and whatever comes next – will keep getting better at answering more and more complex questions. There are questions a computer can answer faster and more frequently than humans today – like who was the 44th President of US? What planet is closest to earth in the solar system ? . There are questions that are really hard for computers too, where a human can often answer effortlessly – like who was the quarterback of the super bowl winning team the year the 44th President of US took office ?  But over time, we should expect computers to generally be able to answer most questions we ask.

But humans are way better than computers when it comes to asking questions. At some point, computers probably can interpret a medical image better , and compare it against a million other images faster than any trained human medical expert. However, that is only a starting point – human experts are way better at asking better/unique/complex questions and explore any body of knowledge and expand on it. This is why I think no expert system will eliminate doctors – they will just make the quality of medical service a doctor can provide a lot better, and reduce mistakes. In short – We get to ask the smart questions, and mostly leave finding the answers to machines.

In various forms – this phenomena will play out in every job . People who have access to smart machines that can find better answers get to make decisions faster and cheaper than others, and that is how competitive advantages will be created in the market.

So in a nutshell – differentiation in future will be based on humans who can ask better questions than they can ask today, and machines which can answer better, faster and cheaper than they can today.

Sounds pretty straightforward, but we will of course fight this every step of the way. When horse drawn fire engines were first introduced, humans used to race them on foot to prove their superiority. We know what happened after that. For many reasons – political, legal, social and economic – just because technology can be used to effectively solve a problem does not mean that it will happen fast. So in my view, there is practically very low risk of massive unemployment any time soon. But without a doubt , every job around us will evolve in a way that human value add will become all about asking better questions and technology’s value add will be about giving better answers.

Microservices – What have we learned ?


Yesterday, I shared some of my thoughts on serverless architecture  and ended up getting a lot of feedback and a lot of it went back to SOA and then logically on to Microservices. So I thought it may be worth penning some thoughts on this topic as well. Microservices are not a fad – they get used quite a bit today across big important companies, although perhaps not very consistently. Also, I think we have enough experience by now to calibrate our expectations compared to a few years ago.

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What is microservices architecture?

I am sure there is an official definition out there somewhere. But a simple way to describe it is as a way to design a solution as a collection of services, each of which does one thing quite well independently. Each service is its own thing – own process, implementation, language etc . The collection of such services that solve an end-to-end problem is then orchestrated with a lot of automation capabilities to form an application.

Why is this a good thing compared to a “monolith” architecture ?

Separation of concerns is not a new idea in designing systems. Microservices architecture is built on this principle. The traditional way of building an application includes a front end ( html/js) , a database ( SQL/NoSQL/File/…) and App server to handle logic. When we hear people criticizing “monolith” apps – they are usually referring to the serverside logic built as a logical whole.

Monoliths are not bad per se – they can be designed and implemented in a modular way, and can scale with the help of good design using load balancers etc. Just that when it comes to scaling, testing etc – you have to deal with the whole even though only a small part needs to change. As cloud becomes more and more the default deployment option, the flexibility to scale and change quickly becomes a bigger need than in the past. Microservices is a very good way to deal with it. Many monolith systems will co-exist with the world of Microservices .

How micro is a microservice ?

This is one area where the wisdom from actual projects tend to throw cold water on the theory and philosophy of microservices. The tendency for many engineers is to go super granular in service definition . Almost without exception, everyone I know who started with this approach has regretted it and agreed to start with fewer services and then break them into smaller chunks over time. The operational overhead is quite significant as you play with tens of services – you now have to maintain and monitor several services, and at some point there is a performance penalty for too much communication across a bunch of services that all do one little thing.

Another interesting aspect is whether your system needs to behave more in a synchronous fashion or an asynchronous fashion. When you break the system into smaller chunks, essentially you are favoring asynchronous communication between them. Then if you need to make it work in a synchronous fashion – you may question your granularity decision quickly.

What about the product/project team?

I have seen several ways in which teams are organized , and have spoken to folks who worked in such teams where I had no direct involvement. There are a few consistent themes

  1. The need to communicate frequently and freely is a make or break criteria, way more than traditional approaches. With great flexibility comes great responsibility !
  2. One big advantage that comes with microservices is that each service can be implemented with a different fit for purpose language. And each service might choose a different database for persistence. While that is all great in theory, just because you can should not translate to you should. For large projects – too many technology choices leads to diminishing returns. Choose wisely !
  3. There is practically no good way to hand off to an ops team when dev is over. Microservices forces a DevOps culture – or at least DevOps tooling for sure. Its probably a good idea to get EVERYONE in the team some training in tooling. You need different muscles for this world than dealing with a Tomcat cluster. The promise of CI/CD needs a highly trained, high performing team. I may even venture to say that best practice is to have the same team that builds the team to continue to support and enhance systems built on microservices. There are just too many moving parts to effectively transition to a completely new team.
  4. There is no substitute for experience. There are not enough highly skilled folks around , so the ones you get need to carry the weight of mentoring their less experienced colleagues. Written standards might not be enough to overcome this. A common observation is two services looking at the same business object – like a vendor object that is of interest to an accounts payables service and a compliance service – and interpreting the semantics differently. Only with experience can you catch this early and converge.

Is it truly easy to make changes compared to monoliths ?

If you are a microservices fanatic, you probably are well versed in all backward compatibility tips and tricks, and hence your answer has to be YES. I will just say that there are some cases where you wish you were working in a Monolith, especially when faced with pressing timelines. A good example is the changes many apps will need due to GDPR  . When multiple services need new functionality – you need to wrestle with the best approach to get this done. Would you create a new service that others can call ? Maybe a common library? Maybe change each service and make local changes? Each has obvious penalties. No silver bullets – decisions taken in designing the app will dictate whether you buy aspirin from Walgreens sized box or Costco sized box 🙂

What about monitoring, testing, debugging etc ?

All the overheads on these topics that comes from distributed computing are in full force here. This is one area where the difference is significantly more noticeable than in the monolith world. Many of us are fans of doing Canary releases . You should have some consistent philosophy agreed on upfront for release management. Whether we admit it explicitly or not, lean and fast deployment has a tradeoff with testing effectiveness. Essentially you are relying more on your ability of montiring your app ( via all the services and messaging frameworks and redundancies) and making quick changes vs trusting impeccable test results . This is a significant change management issue for most technology teams and especially their managers.

So is microservices architecture a safe bet for future ?

There are plenty of public success stories today of microservices implementations – and conferences and tech magazine articles and youtube videos and all that. All Major SIs have expertise in implementing them. So in general, I think the answer is YES. However, I am not sure if microservices over time will be any less frustrating than monoliths in how they evolve. I probably will get some heat from my purist friends for saying this – but perhaps one way to smoothen the journey is to start with a monolith as we have done in the past, then as it evolves – perhaps have services that call the monolith’s APIs. And as you learn more, break down the monolith to a full set of services. I am not saying this because I am a non believer – I am basing it strictly on the talent available to do full justice to a pure microservices based architecture in a mainstream way. Just because Netflix did it does not mean everyone can. In any case – the mainstream pattern in large companies any ways is to start with their old monoliths and roughly follow the approach I mentioned.