Resisting the 80 hour week

The “long hours” culture is glorified in many industries, including the tech industry where I work. Rachel Thomas writes clearly about how this is discriminatory and counter-productive, and tweeted:

As a PhD student and in my early career, I often worked long hours. At other points in my career I’ve worked fewer hours, and sometimes not at all for several months. I have never put in anything approaching 80 hours a week for a sustained period of time, and yet I still consider myself to have had a successful career in technology.

I have two young children. Returning from maternity leave the first time round, I knew that I didn’t want to work full-time and so moved into a role which I could do 3 days a week. The role had been advertised as full-time, but I knew the team and they agreed to hire me on a part-time basis. This worked out well as I soon became pregnant for the second time. Combining pregnancy and work with a young toddler at home, I was physically very tired and happy not to be working full-time.

Heading back to work after my second maternity leave was a different story. I chose to go back into full-time work when my second child was a year old, and stayed full-time for 4 years. By the end of the 4 years both my children were in school and I started to experience the difficulties that this brings. Christine Armstrong has written extensively about the particular difficulties of combining parenthood with full-time work. School brought more additional admin and complex logistics than I anticipated – getting the right kit and uniforms, organising after-school clubs and activities, parents evenings, school trips – and in the UK school hours don’t align easily with a working day. It is hard to find childcare past 5:30pm, which is exactly the time that those on the US West Coast begin their day.

I have also taken three significant periods of time out of paid work over the last years. Maternity leave in the UK is generous, you are entitled to take up to 12 months out and return to a similar role (though sadly pregnancy discrimination is rife). I took 11 months off from paid work with my first child in 2011 and 8 months with my second in 2013. More recently, after 4 years working full-time with 2 young kids, I simply needed a break! I took almost 5 months out of work, most of the second half of 2018, to recharge and spend time with my family.

At the beginning of 2019 I began working at Cobalt Speech, again part-time, 4 days a week. As with my previous part-time position, this role came through my network and so I felt able to ask for reduced hours upfront. Working 4 days gives me time to pick up my kids from school at least one day a week, and have extra time in the week to do a bunch of things I don’t have time for when working full-time.

Not everyone is able to work fewer hours or take periods of time out. I am lucky to have experience working in AI/ML where there’s a currently shortage of talent. I was also fortunate to have good health in my early career years so I could gain experience, and to have a supportive husband who has also worked flexibly at different times. Still, I can’t say I always get the balance right!

Being able to work flexibly in different ways at different times has helped me keep my career going. I hope that more employers in the tech industry continue to make flexible and part-time working a reality for those who need it.

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Light-up snowman decoration

I’ve been looking for craft projects to do with kids, and just in time for Christmas made this light-up snowman decoration. I used:

  • A snowman shape with holes cut out for buttons
  • 3x 3mm LEDs (mine are from here)
  • 3x 100 ohm resistors (from here)
  • 3V coin battery
  • Tin foil
  • Sellotape
  • Paint
  • Ribbon

I started with a snowman shape:

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Ready-cut snowman

This one was lasercut at Makespace but you could easily cut one out of thick cardboard. The important thing is that the button holes are big enough to poke the 3mm LEDs through.

The next step was to find a circuit for 3 LEDs that could be powered by a small battery. I chose a 3V coin battery because I could easily stick it to the snowman and avoid wires hanging off. I also wanted a circuit I could put together easily without too much specialist equipment.

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3 parallel LEDs

On the back of the snowman I arranged the circuit with the LEDs poking through the buttonholes. There wasn’t much room and getting the circuit in place was fiddly. Tin foil made tracks down either side of the snowman’s body and the battery was stuck at the base of the snowman. Solder and wire would be a better choice than tin foil, but would also make it less child-friendly.

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The circuit on the snowman

I stuck the whole circuit down with sellotape, painted the front, strung some ribbon through the hat and hung the snowman on the tree:

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Finished article

 

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Explaining AI to primary school kids

Recently I was asked to talk about computers and artificial intelligence to year 5 and 6 primary school kids (i.e. ages 9-11). I’ve spent a lot of time explaining AI and ML to a range of different people, but never to such a young audience! I couldn’t find much inspiration online so came up with my own material. Here’s the outline of my lesson plan which had me spending 30 minutes with each class.

1. Introduction
My day job involves voice recognition, so I start by introducing myself and asking the class how many of them had ever talked to a computer or a device before to get something done – normally I get the majority of the class putting their hand up.

For some context, I include a bit of history. Primary school children have grown up around smartphones and voice computing and often don’t know how fast technology moves. At the time of writing, the Y5 class was born in 2008/2009. For comparison, the iPhone was launched in 2008 and Siri was released on it in 2011.

2. Computer Programming
The first discussion topic is how computers are programmed to do things by writing rules (or programs). It’s possible to write very complicated and seemingly intelligent computer programs this way. By Y5 and 6, the children in this school have had lessons programming using scratch. They understand how you make computer programs from instructions, and how it’s easy to get those instructions wrong so the computer doesn’t do what you meant it to.

3. Rules to identify cats
People like to share photos on the internet, and they share a lot of photos of cats! One thing that might make a computer artificially intelligent is being able to say whether a photo is of a cat or not. I brainstorm with the class for some rules to write on the board that we could use for identifying a cat in a picture. Some of the rules we’ve come up with in previous sessions are:

  • Has a tail
  • Is furry
  • Is cute, makes people go ‘awww’
  • Has whiskers
  • Has eyes
  • Is an animal

These aren’t rules that we can directly implement in a computer, but they get the idea across.

Cat or not?

Next we go through a slide deck of pictures playing ‘cat or not’. My slides have a range of pictures – some are easy to identify as cats or not, some are cats which are obscured or in funny poses, and some are other animals which have some of the same features as cats. They get the children thinking about how to define the task (do big cats count as cats or not?), edge cases (what about the drawing of a cat?) and the kind of rule you really need (how exactly do you distinguish a red panda from a cat?).

4. Intelligent computers
After this, I ask if the kids thought the rules we had were good or not. Most acknowledge that our rules don’t cover all the pictures and so could have been better.

Some tasks, like seeing and hearing, are impossible to write rules for that a computer can follow. We need a different method. Instead of writing rules, we have a computer learn how to do the task from data. To do this we take a lot of data (images, audio, video etc.) and have people label it. For images the people might label each image what object is depicted. From this database of ‘labelled data’, we can train a machine to learn the patterns of what makes a picture of a cat or not. Once the computer has learnt from this training data, we can take the model that the computer builds and use it to identify cats in pictures it’s never seen before.

Identifying cats or other objects in pictures might seem frivolous, but there are lots of ways you can use this technology in the real world. One example which a lot of people are working on is to identify what is in front of you in a self-driving car. Another is helping to save endangered wildlife by identifying animals and counting them in the wild.

5. Discussion and Questions
I finish with a discussion about what kind of smart robots the class would build in the future. Where I know anything about their suggestions, I’ll talk about what’s being already built in that area.

Examples of ideas I’ve heard include:

  • Computers that help blind people: examples include Microsoft’s Seeing AI or text-to-speech technology which can read text aloud
  • Space travel: we have sent men to the moon and rovers to Mars already, but AI can help us explore further e.g. rover navigation or identifying objects in space,
  • Looking after people in hospital: there are many ways computers can help in hospitals from surgery to diagnosis to doing admin and so freeing up the time of doctors and nurses to spend with patients.
  • A robot that can cook: the sort of robotics needed to handle ingredients is tough to build, but there are examples
  • A computer that does homework. I don’t normally point out that voice assistants like Alexa can already answer a lot of questions

When I ask the class teacher, they usually just want an intelligent computer to help them with marking homework!

There are a few other discussion points I add into the discussion:

  • Computers aren’t always right. When computers learn from data, we always know they’ll make some mistakes.
  • Some things are not possible for computers to do, e.g. tell if someone is lying. In general if it’s hard for a person to do, it’s also hard for a machine to learn.
  • There are bad uses of technology as well as good ones. Sometimes people in the class come up with ideas that others find creepy, which helps illustrate the point.

Extra background
For those who are interested to know more, image recognition has a long history and had been well researched over recent years. One database of images, called ImageNet, has been the basis of much academic research on object recognition. This database has several million images in more than 20,000 categories.

In 2012, it took 16000 computers to learn how to identify a cat. In recent years, researchers have also looked at harder tasks like distinguishing between 5000 species of animal, identifying and tracking objects in video or automatically creating in-depth descriptions of what is happening in a photo.

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Reading list: new to management

Over the past years I moved from an individual contributor role into a management one. I had some classroom training from my employer through the transition, but I also found a few books helpful along the way. Here are some of my favourites:

  • One Minute Manager – was recommended to me by my mentor when I began to manage others, and I’ve since recommended them to other new managers too. There’s a whole series of books, and I found them a great starting point to illustrate a few basic ideas.
  • Peopleware – more in-depth than the One Minute Manager, on how to manage people and projects.
  • Crucial Conversations – on having difficult conversations, a part of any management role.
  • The Five Dysfunctions of a team – on forming teams and understanding team dynamics.
  • The Leadership Pipeline and The Manager’s Path – for putting management into context, and describing the different roles of management levels from frontline management up to CEO.
  • Fearless Change – not just for managers but for anyone trying to drive change, a set of helpful ‘design patterns’ to use when trying to make change happen.
  • Never split the difference – on negotiation from a former FBI hostage negotiator.
  • What got you here won’t get you there – dispelling the myth that quietly getting on and doing good work will get you to the next level.

Let me know in the comments if you have other book recommendations for anyone new to management.

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Data science on the command line

There are just a few linux command line tools that I use many times a day!

less for checking the contents of files, verifying you’ve got the right output or input format, quickly examining data

grep for searching within files, especially as you can search for regular expressions

awk is incredibly useful for doing basic operations on text files, simple transformations from one format to another, or getting simple stats

Combine these with a few useful helpers like paste, diffsortwc, head, tail and cut, and you can do some really complex operations on actually quite large files.

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Science and engineering gifts for young kids: 2014 edition

I wrote about science and engineering gifts last year, but now we’re all a year older and wiser it’s time to look around for more ideas.I’ve found that there are plenty of science and construction kits around for older kids, but durable pre-school friendly kit isn’t as common. My daughter has left the toddler years behind, and my son (now 18 months old) occasionally plays with the contents of the box, instead of just the box itself. So I’m covering both the toddler and pre-school ages, roughly 1.5-4 years.

Hence, here’s my 2014 xmas gift idea list; science and engineering gifts for pre-schoolers:

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Best books for pre-school girls

My 3.5 year old daughter loves reading books, but it can sometimes be hard to find great stories. Traditional fairytales are full of wicked stepmothers, spineless fathers and helpless princesses, while other stories are full of brave boys and girls who dream of becoming princesses. We often (at my daughter’s insistence) change the gender of the lead characters, but even better are the books which don’t resort to stereotypes.

So, in roughly age-appropriate order, here are my favourite books for inspiring pre-school girls:

  • That’s not my Robot for young toddlers, this has touchy-feely patches on every page. “That’s not my…” is a huge range, but IMHO this one has the most interesting range of textures of the ones we’ve read.
  • What the Ladybird Heard – many of the Julia Donaldson books are a pleasure to read, even after the 100th repetition! This one in particular proved really popular: a tiny ladybird outwits two thieves intent on stealing the prize cow.
  • The Crunching Munching Caterpillar with sounds! The story of a caterpillar who dreams of flying, but it’s really the sounds in this that my daughter loves.
  • A House in the Woods, Bear, Moose and the two little pigs build a house so they can all live together.
  • Look Inside: Your Body my daughter is fascinated with this book, and it has loads of flaps to look behind. The text is quite advanced, but easy enough to simplify for younger kids. There’s a whole range of ‘Look Inside’ books that cover lots of science topics.
  • Animal Stories for Little Children 5 beautifully illustrated stories from around the world
  • I am Amelia Earhart, about the famous pilot who was the first woman to fly solo across the Atlantic.
  • Rosie Revere, Engineer, we haven’t actually read this one yet, but I put it on the list because any book about a female Engineer has to be great!
  • Alice in Wonderland once upon a time, in desperation as my daughter wouldn’t go to sleep, I began telling her a story based on what I remembered of Alice in Wonderland. And since that day it’s been a popular choice at bedtime, though we’ve now graduated to reading chapters of the original.
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