It’s time to diversify the tech scene…

Anisah Osman Britton is challenging the concept that the tech industry is a man’s world. Since completing the IB Diploma Programme (DP) at Bilborough College, in Nottingham, UK, Anisah founded 23 Code Street – a coding school for women in the UK, where every paying student will fund a lesson for a disadvantaged student in the slums of Mumbai, India. The school, which is based in London, gives students the foundation that they need to become developers. While in India, it is working closely with a Mumbai-based non-profit, which concentrates on women’s health, to plan classes in the


The founder of 23 Code Street, and IB alumni, Anisah Osman Britton, tells IB World magazine how she is making coding more accessible to women in the UK and India

city from September 2018. 23 Code Street will focus on digital skills, which will provide the necessary skills to help women get data entry jobs and regain independence. In the future, 23 Code Street will also teach coding. Looking back, Anisah always thought she would go to university after school, as this seemed a natural pathway.

“I don’t think I had really considered anything else apart from going to university,” she says. “But the IB made me realize that university wasn’t my only option.  “The open-minded way of approaching education and the way you are encouraged to question everything you know, made me realize that I had other options open to me and that, actually, I had the skills and confidence to go down another route”.

Lack of women in tech

Anisah realized that she wanted to start her own company, and after graduating from Bilborough College, she interned in businesses around the world to gain an understanding of what was needed to start and grow a company.

“I didn’t have any knowledge of technology. But, as I fell into the tech industry, I realized how valuable it would have been to have had some of these skills at college, and have had conversations around the impact of technology, the lack of women in the industry and the change we could have been part of”.

Five years ago, when Anisah was working at The Bakery – a company that pairs brands with tech startups – she realized that the tech industry was male-dominated. Nine out of 10 people she worked with were men. “I heard views I disagreed with, I found people patronizing towards women who didn’t have technical skills, or, more importantly, who didn’t have tech jargon as part of their vocabulary. I saw that we worked with a majority of startups, which were led by men, and the female-founded companies had to prove themselves that little bit more.

“I saw men in Third World countries, especially the rising working class, who had doors opening for them because technology was accessible to them. And I saw products and services that seemed to forget that women existed”.

For example, Apple released a health app without a period tracker on it for women.

“23 Code Street was born out of a need to give women the skills to build the future, to be part of the conversation and to diversify the tech scene”, says Anisah.

She started pushing for more women on teams at The Bakery, and over time the gender split improved from 20 per cent female, to 40 per cent. Tom Salmon, founder of The Bakery, realized the value women bring to the industry and invested in Anisah’s idea of a coding school for females.

“We need women who are marginalized and often forgotten in certain societies to have the tools and knowledge to be able to even imagine a change they could create”, explains Anisah.

She credits the DP for the success of 23 Code Street, as it challenged her in ways that she’ll never forget. “The DP taught me how to ask for help and to be grateful for that help, and how to be a team player. I hire smarter people than me and don’t feel threatened. I challenge people to be better than me in my own company. I go to employees for advice”, says Anisah.

“The IB also taught me to be proud of being a feminist. Nobody had labelled me that in a positive way before. I came to understand feminism meant the fight to be equal. I debated history, literature and science, to understand the role (or lack of) of women in the world. The day I graduated, my English teacher gave us all a book as a parting gift. I was given A Vindication of the Rights of Woman: with Strictures on Political and Moral Subjects (1792), written by Mary Wollstonecraft. I read it and realized how far we’d come but also, how far we had to go”.

Expanding into Europe

To date, Anisah has won four awards and has been nominated for ‘We are Tech Women finalist 2017’ and ‘Forbes 30 under 30 nominee 2017’. In addition, many London students have successfully completed the course and gone on to work in the tech industry. But, it’s just the beginning for Anisah and 23 Code Street. She wants to create more courses in the UK and expand to other cities in Europe. “We are also bringing the courses online”, she says.

“In India, we want to create a sustainable model where our alumni begin training our new students. If we were to ever close, which is not the plan(!), we want to have left the infrastructure for the community”.

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World’s Best Girl Programmers


Did you know about Ada Lovelace? You Should Thank Every Time You Use a Computer. She worked on an early computer known as “Analytic Engine”. She wrote the first algorithm for this engine that is thought of as the first computer program in the world.

  • Grace Hopper – She was one of the first programmers of the Harvard Mark I computer. She made up the first compiler for a computer programming language.
  • Betty Holberton – She was one of the six original programmers of ENIAC, the first general-purpose electronic digital computer.
  • Audrey Tang – She is famous for Perl programming languages contribution. Tang started coding in Perl programming language at age 12.
  • Marissa Mayer – She started the career as a programmer and now CEO of Yahoo! She is a famous girl programmer in the world. She is known as employee number 20 for Google too.
  • Sheryl Sandberg – Sheryl is COO of Facebook company currently and founder of
  • Tracy Chou – She is famous for his coding skills and is a software engineer and advocate for diversity in her field.

See updates to this article here

Building A.I. That Can Build A.I.

SAN FRANCISCO — They are a dream of researchers but perhaps a nightmare for
highly skilled computer programmers: artificially intelligent machines that can build other artificially intelligent machines. With recent speeches in both Silicon Valley and China, Jeff Dean, one of Google’s leading engineers, spotlighted a Google project called AutoML. ML is short for machine learning, referring to computer algorithms that can learn to perform particular tasks on their own by analyzing data. AutoML, in turn, is a machine-learning algorithm that learns to build other machine-learning algorithms.
With it, Google may soon find a way to create A.I. technology that can partly take the humans out of building the A.I. systems that many believe are the future of the technology industry. The project is part of a much larger effort to bring the latest and greatest A.I. techniques to a wider collection of companies and software developers.
The tech industry is promising everything from smartphone apps that can recognize
faces to cars that can drive on their own. But by some estimates, only 10,000 people worldwide have the education, experience and talent needed to build the complex
and sometimes mysterious mathematical algorithms that will drive this new breed of artificial intelligence. The world’s largest tech businesses, including Google, Facebook and Microsoft, sometimes pay millions of dollars a year to A.I. experts, effectively cornering the market for this hard-to-find talent. The shortage isn’t going away anytime soon, just because mastering these skills takes years of work. The industry is not willing to wait. Companies are developing all sorts of tools that will make it easier for any operation to build its own A.I. software, including things like image and speech recognition services and online chatbots. “We are following the same path that computer science has followed with every new type of technology,” said Joseph Sirosh, a vice president at Microsoft, which recently unveiled a tool to help coders build deep neural networks, a type of computer algorithm that is driving much of the recent progress in the A.I. field. “We are eliminating a lot of the heavy lifting.” This is not altruism. Researchers like Mr. Dean believe that if more people and companies are working on artificial intelligence, it will propel their own research. At the same time, companies like Google, Amazon and Microsoft see serious money in the trend that Mr. Sirosh described. All of them are selling cloud-computing services that can help other businesses and developers build A.I. “There is real demand for this,” said Matt Scott, a co-founder and the chief technical officer of Malong, a start-up in China that offers similar services. “And the tools are not yet satisfying all the demand.” This is most likely what Google has in mind for AutoML, as the company continues to hail the project’s progress. Google’s chief executive, Sundar Pichai, boasted about AutoML last month while unveiling a new Android smartphone. Eventually, the Google project will help companies build systems with artificial intelligence even if they don’t have extensive expertise, Mr. Dean said. Today, he estimated, no more than a few thousand companies have the right talent for building A.I., but many more have the necessary data. “We want to go from thousands of organizations solving machine learning problems to millions,” he said. Google is investing heavily in cloud-computing services — services that help other businesses build and run software — which it expects to be one of its primary economic engines in the years to come. And after snapping up such a large portion of the worlds top A.I researchers, it has a means of jump-starting this engine. Neural networks are rapidly accelerating the development of A.I. Rather than building an image-recognition service or a language translation app by hand, one line of code at a time, engineers can much more quickly build an algorithm that learns tasks on its own. By analyzing the sounds in a vast collection of old technical support calls, for instance, a machine-learning algorithm can learn to recognize spoken words. But building a neural network is not like building a website or some run-of-the-mill smartphone app. It requires significant math skills, extreme trial and error, and a fair amount of intuition. Jean-Fransois Gagna, the chief executive of an independent machine-learning lab called Element AI, refers to the process as “a new kind of computer programming. In building a neural network, researchers run dozens or even hundreds of experiments across a vast network of machines, testing how well an algorithm can learn a task like recognizing an image or translating from one language to another. Then they adjust particular parts of the algorithm over and over again, until they settle on something that works. Some call it a “dark art,” just because researchers find it difficult to explain why they make particular adjustments. But with AutoML, Google is trying to automate this process. It is building algorithms that analyze the development of other algorithms, learning which methods are successful and which are not. Eventually, they learn to build more effective machine learning. Google said AutoML could now build algorithms that, in some cases, identified objects in photos more accurately than services built solely by human experts. Barret Zoph, one of the Google researchers behind the project, believes that the same method will eventually work well for other tasks, like speech recognition or machine translation. This is not always an easy thing to wrap your head around. But it is part of a significant trend in A.I. research. Experts call it “learning to learn” or “meta-learning.” Many believe such methods will significantly accelerate the progress of A.I. in both the online and physical worlds. At the University of California, Berkeley, researchers are building techniques that could allow robots to learn new tasks based on what they have learned in the past. “Computers are going to invent the algorithms for us, essentially,” said a Berkeley professor, Pieter Abbeel. “Algorithms invented by computers can solve many, many problems very quickly — at least that is the hope.” This is also a way of expanding the number of people and businesses that can build artificial intelligence. These methods will not replace A.I. researchers entirely. Experts, like those at Google, must still do much of the important design work. But the belief is that the work of a few experts can help many others build their own software. Renato Negrinho, a researcher at Carnegie Mellon University who is exploring technology similar to AutoML, said this was not a reality today but should be in the years to come. “It is just a matter of when,” he said.

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