Rosa Emerald Fox

Technology, career and lifestyle blogger

Category: diversity

AI and Machine Learning studies begin

I recently started on a learning program provided through work (Government Digital Service), to better understand emerging technologies, with a focus on Artificial Intelligence (AI) and Machine Learning.

AI and Machine Learning are huge subject areas and I am learning too fast to be able to write about it all. It can get deeply technical very quickly. I will aim to cover core concepts but realistically this blog will give more of an overview.

If you are interested in learning more then I will point you in the direction of the resources I have used, but essentially if you are interested in how to start scratching the surface in exploring these topics then you are in the right place 🙂

For 10 weeks I am studying for 2 days a week. The learning is fairly self directed, though once a week I meet with my brilliant mentor Ivan who is a lecturer in AI and Data Science at The University of Bristol. He is also a fellow at The Alan Turing Institute which is the national institute for data science and artificial intelligence and is based in the British Library.

First things first, a quick bibliography:

Algorithm

Think of an algorithm as a set of instructions… inputs that result in outputs… an example could be a cake recipe.

Artificial Intelligence

When a machine performs tasks that would usually require human brain power to accomplish.

Data Science

Turning data into useful information. The study of data science brings together researchers in computer science, mathematics, statistics, machine learning, engineering and the social sciences.

Machine Learning

A subset of Artificial Intelligence. It is based on writing computer algorithms (sets of instructions given to a computer) that can learn from information they have previously processed in order to generate an output.

Systems appear ‘intelligent’ because they can adapt to different situations based on what they have learnt/seen before.

I found the FAQ of the Alan Turing Institute gave a great introductions to these terms. Their website in general is great for gaining an understanding the different areas that AI encompasses.

Due to factors such as faster processing speeds and access to huge amounts of data, AI technologies are being implemented within a wide variety of areas. Last week I helped out on a stand at a popular recruitment event in London called Silicon Milk Roundabout. I spoke to different people for 2 hours non stop and over a third were looking for data science roles. I would be surprised if this was anywhere near as high even a year ago. The way people are thinking about how we develop technology feels like it is quickly shifting toward being much more data driven.

Example uses of AI and machine learning include:

  • The classic example: Fraud detection
  • Smart homes, where decisions can be made based upon factors such as energy consumption or perceived home safety
  • Connected and self driving cars
  • Sentiment analysis (for example analysing if a review is positive or negative)
  • Managing workloads of computer systems (Google Deepmind reduced energy used for their coding data centres by 40%)
  • Health care – helping doctors with diagnosis
  • Recruitment – sourcing candidates and interviews with chatbots
  • Predicting vulnerability exploitation in software
  • Financial market prediction 
  • Accounting and Fintech – automating data entry and reporting
  • Proposal review – reviewing contracts, cost, quality level
  • Voice assistants

Last week, I managed to catch a panel discussion on the subject ‘AI for Social Good’ at the AI and Big Data Expo. The Head of Programme, Digital Commission of the disability charity Scope gave some interesting examples of AI being used for social good.

She spoke about how in New York, screens that people can interact with using sign language are being trialled and installed on buses to improve accessibility. Microsoft are developing ‘Seeing AI’ used within a text recognition application designed with people that are blind. Really excitingly, The National Theatre and Accenture have developed Smart Caption Glasses. They are a way for people with hearing loss to see a transcript of the dialogue and descriptions of the sound from a performance displayed on the lenses of the glasses.

The panel also discussed how although the design focus of these AI applications may have been for people with specific disabilities, they will benefit many others. Somebody could be holding a baby and suddenly they wouldn’t be as mobile as they were before. It is a shame that a business argument to design accessibly would need to be made, but designing for people with specific needs shouldn’t be viewed as designing for a small subset of users as the benefits will cascade. Thus designing accessibly shouldn’t be an afterthought.

Of course it isn’t all good. Systems learn based on what they have seen before. Society is inherently bias against minority groups. If we let this (to name a few) racist, sexist, transphobic view of the world run through our systems and then rely on those systems to make predictions based on this information, then we are only going to amplify bias. The people developing the systems need to do so with this in mind. Machine learning models should be made transparent where possible.

There is growing concern that some job sectors will be replaced with AI. If you work in a job that involves solving lots of problems and a high level of human interaction then you will probably be less at risk. If you are a train driver, mortgage advisor or stock market trader then it is quite possible your job market could be affected one day.

Another concern is data privacy. As users, we want our data to be kept safe. It’s no secret that tech companies largely profit from selling on information. We take this as the trade off for not paying to use their platforms, but are not very confident in how our data will be used or what the limits of surveillance are. On the flip side, to train Machine Learning systems so that they can make accurate predictions, we want lots of data. This is ok if you work at a large corporation with a lot of access to user data, but if not there is a reliance on collecting it yourself of using open data sets.

In 2018 The Department of Culture, Media and Sport released The Data Ethics Framework. The framework sets out clear principles for how data should be used in the public sector. It will help us maximise the value of data whilst also setting the highest standards for transparency and accountability when building or buying new data technology. Many open data sets are available on https://data.gov.uk/.

Some examples of how public sector organisations are implementing emerging technologies, including AI and machine learning have been presented here by the Innovation Team at Government Digital Service. Projects range from anomalous ship detection to resource allocation of fire engines to predicting people in crisis. A visualisation of the research can be found here and can be filtered in various ways.

Emerging technologies present a lot of interesting technical challenges to the public sector which is greatly motivating to me. In terms of my studies, realistically there is only so much I can cover in 10 weeks (to put this in to perspective, a lot of the data scientists I have met at work have PHDs, in maths…) but I am interested to explore machine learning from the perspective of a software developer and seeing how much I can learn and practically implement (albeit it may rely on the help of some handy Python libraries that already implement the mathematical heavy lifting…).

This post has focused a lot on the application of the technologies rather than the technical implementation of machine learning, which is somewhat less glamorous and involves spending a lot of time cleaning up data and finding relationships between data points. I plan to write about both, so please keep reading if you are interested in following my journey and get in touch if you have any questions or resources.

 

Mentoring at ThinkNation – Young people creating tech solutions for big social challenges

On October 13th I was a mentor to a group of young people at @thinknat ‘s @brightondigitalfestival event. The aim of the event was for the young people (aged 14-24) to come up with tech solutions for big social challenges.

ThinkNation founder Lizzie kicked off the day with introductions and we broke out into our groups. Within our group, we spent the day discussing and researching our subject, creating ideas for a tech solution and then putting together a pitch style presentation. The young people then presented in the evening, live on stage to an audience of around 70 members of the public… busy day!

The subjects were selected as a result of a vote, in which young people chose the questions they felt had the most impact on their lives. They were:

  • How can tech can help mental health support for young people? (There were actually 2 groups on this as mental health was such a strong concern).

  • How can tech help the homeless in Brighton and Hove?

  • What tech would you invent to eliminate beach pollution?

  • How can Artificial Intelligence (AI) create a fairer housing solution in Brighton and Hove?

 

The focus was to think creatively about how technology can be applied to help to reduce these problems. Although the technology was important, because we were not actually building the products and didn’t have a budget (ah the dream!) the focus was much more on coming up with creative ideas, thinking big and not limiting the imagination. Despite this, after the presentations, as there were a lot of people from industry with the event being part of Brighton Digital Festival, all of the groups had companies keen to speak to them further about developing the ideas. Even if an idea would have been very big and difficult to implement, a descoped version could still have real impact. We were able to show the young people that often, other technologies are already in place that could be used to help build their idea faster.

My group consisted of five young people, three from Sweden and two from the UK, as well as two other mentors; Andy Cummings, Director or Product Development and Rebecca Willis, BDM International Education Marketing and Management.

Our question was ‘How can Artificial Intelligence (AI) create a fairer housing solution in Brighton and Hove?’. This focused more on poor quality of housing and expensive rent as opposed to homelessness. Though of course lack of housing, expensive rent, high deposits and private landlords being able to tell people to move at a months notice can easily equate to homelessness (a question focused on by another group) so there was some cross over in our initial discussions.

Poor housing conditions in Brighton is an issue that I had a lot of familiarity with, let me tell you! I lived in Brighton for 8 years and I absolutely loved the city, but there seemed to be a constant string of housing issues. Everywhere I lived there was an ongoing battle with mould. I once ended up in A&E due to allergies and the damp conditions. There was the ‘rat flat’, my friend Katie Jane’s dearly beloved ‘cockroach flat’, there was the mushroom crop growing shower in first year uni halls, meaning that whilst it was being replaced we had to have one shower for our twelve person (yep twelve person) flat for a while… I could go on and on.

Although the stories are sort of funny to recall looking back, and of course there are many people much worse off, that don’t have the privilege of living in exciting places like Brighton or London (where I am now… still enduring the less than great conditions!), feeling so unsettled and living in these conditions does take its toll on your happiness and well being, yet landlords are continuing to rake in more and more money whilst taking advantage of people’s desperation.

Young people at the event expressed a lot of concern over not being able to afford to move out. If their option (if they have the luxury of the option and can stay at home) is to spend most of their salary on a place that causes them discomfort, then they are not as likely to be out their learning how to be independent, confident and how to start building lives for themselves, which simply isn’t fair.

Anyway, back to the event… As a group we began by discussing these problems. Having the mix of UK and Swedish backgrounds in our group generated loads of interesting conversations. The Swedish young people expressed that they never really see homelessness so were shocked when they came to Brighton where homelessness is very apparent. Sweden has more space, less people, no ‘let to buy’ and thus high prices charged by private landlords, rental properties are generally let out by large private companies, often there are no deposits and generally low rent. They do however have to pay high taxes and things like food and drink are expensive (I was shocked at spending £13.50 on a single G&T in Stockholm!!).

We identified some of the main issues in Brighton as:

  • Lack of regulation, accountability and monitoring of housing conditions or rent prices for landlords.
  • Lack of space to build in Brighton and Hove due to being between the sea and the Sussex Downs.
  • Lack of affordable materials.
  • Lack of affordable housing.
  • Not enough social housing.
  • Renting from Private landlords.
  • Cost of land.
  • Spaces owned by private companies.
  • Ideologies from people in general. People want more money for themselves, this is destructive.
  • Bad conditions – dangerous, rats, mould, health impacts. Impacts other services such as hospitals.
  • Bad health can affect people going to work. 
  • Feeling unsafe and security.

The discussions covered a range of topics so we narrowed the ideas down to two themes which were ‘regulations’ and ‘physical space’. We moved onto talking about technology. We discussed what we thought Artificial Intelligence (AI) was and the young people were already very clued up on its practical applications.

AI can seem a bit futuristic and scary, despite being around for decades. What it boils down to is a computer performing a task based on information it has been given. The more information it is given, the more the computer ‘learns’ and is able to perform a better output based on being able to predict what usually happens given certain information. Of course there can be grave flaws in computational models (I have been reading ‘Weapons of Math Destruction’ by Cathy O’Neill which I would recommend to learn about ethics and computational modelling) as they can often lead to unfair bias towards vulnerable people… but like most technologies there are good and bad applications. Some examples of AI applications could be self driving cars or the software that is used for targeting the ads we see in Instagram.

In thinking about how we could apply technological solutions to our two themes, we hit a fork in the road. One route we could have gone down was our vision to build a ‘smart’ eco island of social housing out on the sea, utilising the energy from the tides and thus solving the problem of there being no space to build… it may sound a bit ‘out there’, but let us not forget the palm tree shaped islands built in Dubai due to lack of space, or the underground city built in Montreal due to above ground being just so cold (the young people thought us building and living underground Brighton might be a bit dark and depressing!).

The other route, and the one the group decided to take, was to focus on regulations and holding landlords accountable. We knew that apps like ‘Rate My Landlord’ already exist, but they depend on the user manually submitting the review through a form. The idea that the young people came up with was an application that connected tenants and their landlords.

Tenants could raise issues with landlords through sending messages, but alerts of potential problems occurring could also be predicted via AI using sensory data. All homes would have sensors hooked up to collect data on moisture levels, water pressure, temperature, air quality, noise, electricity use/if the power is working etc. An algorithm could then make predictions and if it looked like something was about to break it could send alerts to the tenants and landlords. It would also make it easy for landlords to dispatch the relevant handy people. It could tell you how long something was broken for.

Ultimately we thought it would be great if somehow laws could enforce that if the sensors showed that conditions were not up to a high level, then tenants would be able to have their rent capped until the problem was fixed. Realistically this in its entirety would be fairly tough to implement across every home. In steps towards ‘Smart Homes’, currently the Smart Energy GB ‘Smart Meter’ is being rolled out across all homes in the UK in order to put a stop to metered bills and to increase awareness of energy use. It is a very complex and expensive project, but it is happening. As network connections improve and people become more accepting of the permeation of technology in our daily lives, it could all be possible for monitoring through sensory data and AI based predictions to be effective one day at a large scale.  

The young people named the app ‘7th Sense’ and worked together to create a logo and the presentation for their pitch. They portrayed the problem, their solution, who it would benefit, potential issues and a call to action (invest!!). Two young women from the group presented in the evening and answered questions from the audience afterwards. They did an amazing job and I was really proud of them. At the end of the presentations, someone from a company that have developed a smart air brick for tracking indoor air quality/humidity and are installing them in social housing in Hackney, sat down and spoke with the group, so they were able to learn more about how related solutions are being applied in homes which was a great result.

The presentations from the rest of the groups were all really eye opening and the ideas were generally things that would make waves towards solving the problem they were focussed on. There were also screenings of short videos, both made separately by 14 year old boys. One was about homelessness and the other about mental health. To see a 14 year old boy get up on stage to introduce his video and speak openly and confidently about mental health was really inspiring, as it has been such a notoriously taboo subject for previous generations. These young people cared deeply about social issues and were using Youtube as a platform to educate others and themselves. They seemed a lot more clued up about the world around them than I remember ever being at that age and they are making their voices heard.

I hope that by attending the event the young people enjoyed thinking creatively and have confidence that they genuinely have brilliant ideas that could be applied to the real world. If they hadn’t considered working with technology before, I hope attending the event helped them see that it is an exciting avenue for them to potentially take. Personally I loved my experience of mentoring, it was a long day but it went so so fast and it helped me build my own confidence in that I could be some help to the young people in pulling together their fabulous ideas.

If you are ever interested in mentoring at a future ThinkNation event, please get in touch with them through their website. Lizzie the founder is truly inspiring and does an amazing job. It is so important to help raise the voices of young people and I look forward to see what ThinkNation do in 2019.

uncodebar

uncodebar is codebar‘s annual unconference. This year it was hosted at Twitter and gathered 86 developers from our codebar community.

At an unconference there is no specified agenda, meaning that speakers are not booked in advance to present. Instead, at the start of the day, people that attend the event pick up the microphone and pitch sessions that they want to run. There is a show of hands to determine which room size the session will require and it is added to a time slot on the schedule on the wall.

The sessions usually take the format either of a talk with Q&A, a hands on coding workshop or a group discussion around a set topic.

Schedule which took shape as a result of pitches:

I took part in a session about running community meet ups, saw a talk about ‘the art of saying no’, learnt about the highs and lows of @thisisjofrank’s project in which she created a tweet controlled LED wedding dress (it was AMAZING, find out more in Jo’s post here), saw a thought provoking talk about software and ethics by @richardwestenra and finally a talk about coaching software dev ?

Honestly, go along to an unconference if you can. You never know quite what you are going to get, but that is part of the fun. In this post about a Civil Service unconference, Claire writes about the value of moving away from having “speakers” and “listeners” as the collective knowledge of the audience is likely to be more than that of any one speaker.

Huge thanks to the codebar organisers that put this together (I can take no credit as I wasn’t involved in organising this… just attended!), they did a brilliant job. I left feeling very proud of the codebar community and look forward to next year.

Break Into Public Speaking

I had the pleasure of watching the final presentations from the ‘Break Into Public Speaking’ workshops that I co-organise at work for other people from minority backgrounds.

All the talks were brilliant and covered a range of subjects, some examples of which were the importance of including LGBTQ participants in user research, travelling alone with a a disability and using agile techniques to help to organise your home life.

Check out Lucy’s blog post to find out more about the workshop series.

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