Computers and People

In Grade 6 you followed the story of computing from the abacus to the phone. Now step back and look at it in large pieces. A stretch of time when one kind of computer was the usual kind, and people used it in one main way, is called an era. Each era began when something new became cheap or easy enough to spread; the eras overlap, because old machines stay in use long after new ones arrive.

1. Gears and punched cards (1640s to 1930s). Machines that added, and later machines that sorted and counted punched cards. They helped people do sums; most computing was still done by human computers, people whose job was to calculate.
2. The electronic giants (1940s to 1970s). Computers worked by electricity alone, filled a room, cost a fortune and were run by experts. One computer served a whole organization; later, many people used one at the same time from terminals, a keyboard and screen joined to it by a cable. These big shared computers are called mainframes.
3. Personal computers (1970s to 1990s). Cheap chips made a computer small and cheap enough for one person’s desk, at home, at school and in the office.
4. The internet and the web (1990s and 2000s). Computers were joined together, and the web made it easy for anyone to publish and read.
5. Mobile (2000s onward). The computer moved into the pocket: first phones for calls and text messages, then, from about 2007, phones with touchscreens and apps.
6. Cloud and AI (2010s onward). Much of the computing now happens in huge data centers far away, buildings full of computers, reached through the phone; and programs that learn from examples (AI) became part of everyday apps.

Who shaped each era

In Grade 6 you saw that individuals, organizations, governments and communities all play a part. Compare which of them led in each era, and you find a pattern.

Era	Who led	Why them
Gears and punched cards	inventors working alone; governments, which needed to count their people	a machine could be built by one person and a workshop; a census is a government’s job
Electronic giants	governments and armies (codebreaking, war, the census), big companies, universities	each computer cost millions; only they could pay
Personal computers	clubs of people who loved electronics, small new companies, then big companies	chips became cheap enough for anyone to build or buy one
Internet and web	a government research network, universities, a physics laboratory that gave the web away, volunteer communities	joining computers needed agreements that everyone shares, and gifts that nobody owns
Mobile	phone companies and a few big technology companies; governments, which sell the right to use radio waves	building towers and making billions of phones needs huge companies
Cloud and AI	a few giant companies; governments, which write the rules	a large data center costs as much as a big hydropower plant, or more

So the pattern is: when computing is very expensive, the few who can pay (governments and giant companies) lead. When it becomes cheap, individuals and communities can join in and invent. Look at the newest era: a giant data center is again something only a few can afford.

Nepal’s own eras

Nepal’s story follows the same eras, often a little later, and shows the same pattern of who led:

• Government first. In 1971, for the census, the government rented Nepal’s first computer (Grade 6). For the 1981 census it brought in a bigger British mainframe that 64 people could use at once from their own terminals.
• Companies and scientists join. In 1993 and 1994, the Royal Nepal Academy of Science and Technology and a private company, Mercantile Office Systems, set up Nepal’s first email links; by 1995 the company offered internet service. In May 1999 Nepal’s first mobile phone network, run by the government’s telecom company, began.
• People and communities fill the gaps. Where no company found it worth building, people built for themselves, as you will see in the story of Nangi below.

Every technology is made for a purpose. What it was meant to do is its intended effect. But once millions of people use something, it also does things nobody planned: these are its unintended effects. Unintended effects can be good or bad, and they reach both society (how people live, work and treat each other) and the environment (the land, water, air and living things).

The mobile phone in Nepal

When the first mobile network began in 1999, its purpose was simple: to let people make calls without a telephone line, which most villages did not have. It did that. It also did a great deal more that nobody planned:

• Families apart stay together. Many Nepalis work abroad. A cheap call or a video call each week keeps a father in the Gulf close to his children in Dang.
• Money moves by phone. Phones became wallets: people pay shops, fees and bills, and receive money, from a phone, without a bank branch nearby.
• Help arrives faster. After the 2015 earthquake, phones and the internet helped people tell their families they were safe and helped volunteers find who needed help. Flood warnings now reach people’s phones as text messages.
• Farmers and traders know prices in the market before they carry their goods there.
• New harms. Scams by phone and message (Unit 8), cyberbullying and hours lost to endless feeds (Unit 9), and road accidents caused by drivers looking at phones.
• The environment. Phones and their towers use electricity. Making a phone needs metals dug from mines, some of them in places where children work in dangerous mines. And old phones, chargers and batteries become e-waste (Unit 1), electronic waste, which harms soil, water and people when it is dumped or burned.

You have been learning ways of solving problems: breaking a job into parts in Grade 6, writing exact steps in algorithms and programs. These ways of thinking are not only for programmers. People in many jobs use them every day, often with a computer, sometimes on paper. Together they are called computational thinking: thinking about a problem so that its solution can be carried out step by step, by a person or by a computer. It has four parts, or habits:

• Decomposition: breaking a big problem into smaller parts (you met it in Grade 6).
• Patterns: spotting what repeats, or what is alike, so one solution can be used again.
• Abstraction: keeping only the details that matter for the problem and leaving out the rest. A bus route map is an abstraction: it shows the stops in order and leaves out every house and tree.
• Algorithms: writing the exact steps that solve the problem, so they can be followed the same way every time.

A flood forecaster

Every monsoon, rivers in Nepal can rise within hours. Forecasters at the Department of Hydrology and Meteorology in Kathmandu watch them day and night. Look at how their work uses all four habits.

• Decomposition. “Warn people about floods” is too big a job. Split it: measure the rivers; measure the rain upstream; work out how fast high water travels down the river; know who lives where; send the message.
• Patterns. From past floods, forecasters know how fast a river rises after heavy rain upstream, and how long the high water takes to reach the next town.
• Abstraction. A whole river, with its rocks, colors and fish, becomes a few numbers: the height of the water at each station, and two lines, a warning level and a danger level.
• Algorithm. If the water at a station passes the warning level, send a warning message to phones near the river downstream; if it passes the danger level, send another and alert the police and local officials. A computer can follow these steps every few minutes, without sleep.

Three more workers

Worker	The problem	Computational thinking in it
a dairy cooperative’s manager in Chitwan	pay each farmer fairly for milk	abstraction: each delivery becomes liters and how much fat the milk has; algorithm: price = liters × the rate for that fat level, the same for everyone; patterns: whose milk drops in the dry season
a nurse at a health post	make sure every child gets each vaccine on time	decomposition: one list per vaccine and age; algorithm: each week, find children who reach the age for a vaccine and haven’t had it; patterns: a ward where many miss their dose
a programmer making a bus ticket app	sell seats without selling the same seat twice	decomposition: search, choose a seat, pay, ticket; algorithm: hold a seat while one person pays, release it if they don’t; abstraction: a bus becomes a grid of seat numbers

When a machine does work by itself that people used to do, it is called automation. Automation is as old as Jacquard’s loom (Grade 6), and computers have made it spread to almost every job.

Automation does three different things to work:

1. It replaces some tasks, and sometimes whole jobs. The human computers of the 1940s did sums by hand; electronic computers took that job. Telephone operators once connected every call by plugging a cable into a board; automatic exchanges took over.
2. It changes most jobs. A bank still has people at the counter, but ATMs and phone banking count out the cash and move the money, so bank staff now spend more of their time on loans, problems and advice.
3. It creates new jobs that didn’t exist before: people who repair phones, write apps, run computer networks, deliver what people order on apps, or design websites.

Usually automation takes over tasks, the parts of a job that repeat in the same way every day, before whole jobs. Tasks that need judgment, care for people, or hands working in changing places, such as nursing, teaching, plumbing or farming on a hillside, are much harder to automate.

Who gains, and who pays?

New jobs don’t always go to the people who lost the old ones. A telephone operator didn’t become a network engineer overnight; she needed new skills, and many people were too old, too poor or too far from a training center to learn them. So the same automation can be good for a country and still hard on particular people. Whether it ends well depends on choices people make: whether workers can learn new skills, and whether the people who gain help the people who lose.

Access to computing means being able to use computers and the internet when you need them. It needs five things together: a device (a phone or computer); a connection (a signal, and electricity to charge); money for the device and for data; the skills to use it; and apps and websites that work in your language and for your body, whether or not you can see, hear or move easily. Take away any one, and access is gone.

The gap between people who have access and people who don’t is called the digital divide. In Nepal, as everywhere, it depends on:

• Place. A town with fiber cable and towers on every hill, or a valley in Karnali where the signal comes and goes.
• Money. A smartphone and a monthly data pack cost far more, compared with what a family earns, for a poor family than for a rich one; a computer even more.
• Age. Many grandparents never learned, and apps are rarely designed with them in mind.
• Being a woman or a man. In many families the man has his own phone and the woman shares it, or has none.
• Ability. A blind person, or someone who can’t use their hands well, can use a phone only if it and its apps are built so they can.
• Language and schooling. Most apps and websites are in English or Nepali; many people’s first language is Maithili, Bhojpuri, Tharu, Tamang or one of the more than a hundred other languages of Nepal.

Look at the edge of a footpath where it meets the road. In many cities, a small ramp has been cut into it. It was made for people in wheelchairs. But parents with prams use it, and so do porters with heavy loads, cyclists, and anyone pulling a suitcase. Something designed for people who need it most often turns out better for everyone.

Designing so that as many people as possible can use something, whatever their age, ability, language, money or place, is called designing for everyone (designers also call it inclusive design or universal design). For an app or website it means:

• Text that can grow and can be read: it gets bigger when the phone’s text size is set larger, and it is dark on light (or light on dark), never pale gray on white.
• Your language: Nepali as well as English, and more languages where the users speak them.
• Never color alone: “Open” in words as well as a green dot, because some people, most of them men, can’t easily tell red from green.
• Named buttons: every button and picture has a name that a screen reader (Grade 6) can read aloud, not just “button”.
• Big targets: buttons large enough to tap with a shaking finger.
• Light pages: pages that open on a weak signal and use little data.
• Another way in: a text message or a phone number for people with a basic phone, and a counter for people with none.
• Captions on videos, and plain words in short sentences.