Today I am back at the Data Fest Data Summit 2018, for the second day. I’m here with my EDINA colleagues James Reid and Adam Rusbridge and we are keen to meet people interested in working with us, so do say hello if you are here too!
I’m liveblogging the presentations so do keep an eye here for my notes, updated throughout the event. As usual these are genuinely live notes, so please let me know if you have any questions, comments, updates, additions or corrections and I’ll update them accordingly.
Intro to Data Summit Day 2 – Maggie Philbin
We’ve just opened with a video on Ecometrica and their Data Lab supported work on calculating water footprints.
I’d like to start by thanking our sponsors, who make this possible. And also I wanted to ask you about your highlights from yesterday. These include Eddie Copeland from Nesta’s talk, discussion of small data, etc.
Data Science for Societal Good — Who? What? Why? How? – Kirk Borne, Principal Data Scientist and Executive Advisor, Booz Allen Hamilton
Data science has a huge impact for the business world, but also for societal good. I wanted to talk about the 5 i’s of data science for social good:
- Interest
- Insight
- Inspiration
- Innovation
- Ignition
So, the number one, is the Interest. The data can attrat people to engage with a problem. Everything we do is digital now. And all this information is useful for something. No matter what your passion, you can follow this as a data scientist. I wanted to give an example here… My background is astrophysics and I love teaching people about the world, but my day job has always been other things. About 20 years ago I was working in data science at NASA and we saw an astronomical – and I mean it, we were NASA – growth in data. And we weren’t sure what to do with it, and a colleague told me about data mining. It seemed interesting but I just wasn’t getting what the deal was. We had a lunch talk from a professor at Stanford, and she came in and filled the board with equations… She was talking about the work they were doing at IBM in New York. And then she said “and now I’m going to tell you about our summer school” – where they take kids from inner city kids who aren’t interested in school, and teach them data science. Deafening silence from the audience… And she said “yes, we teach the staff data mining in the context of what means most for these students, what matters most. And she explained: street basketball. So IBM was working on a software called IBM Advanced Calc specifically predicting basketball strategy. And the kids loved basketball enough that they really wanted to work in math and science… And I loved that, but what she said next changed my life.
My PhD research was on colliding galaxy. It was so exciting… I loved teaching and I was so impressed with what she had done. These kids she was working with had peer pressure not to be academic, not to study. This school had a graduation rate of less than 50%. Their mark of success for their students was their graduation rate – of 98%. I was moved by that. I felt that if this data science has this much power to change lives, that’s what I want to do for the rest of my lives. So my life, and those of my peers, has been driven by passion. My career has been as much about promoting data literacy as anything else.
So, secondly, we have insight. Traditionally we collect some data points but we don’t share this data, we are not combining the signals… Insight comes from integrating all the different signals in the system. That’s another reason for applying data to societal good, to gain understanding. For example, at NASA, we looked at what could be combined to understand environmental science, and all the many applications, services and knowledge that could be delivered and drive insight from the data.
Number three on this list is Inspiration. Inspiration, passion, purpose, curiousity, these motivate people. Hackathons, when they are good, are all about that. When I was teaching the group projects where the team was all the same, did the worst and least interestingly. When the team is diverse in the widest sense – people who know nothing about Python, R, etc. can bring real insights. So, for example my company run the “Data Science Bowl” and we tackle topics like Ocean Health, Heart Health, Lung Cancer, drug discovery. There are prizes for the top ten teams, this year there is a huge computing prize as well as a cash prize. The winners of our Heart Health challenge were two Wall Street Quants – they knew math! Get involved!
Next, innovation. Discovering new solutions and new questions. Generating new questions is hugely exciting. Think about the art of the possible. The XYZ of Data Science Innovation is about precision data, precision for personalised medicine, etc.
And fifth, ignition. Be the spark. My career came out of looking through a telescope back when I lived in Yorkshire as a kid. My career has changed, but I’ve always been a scientist. That spark can create change, can change the world. And big data, IoT and data scientists are partners in sustainability. How can we use these approaches to address the 17 Sustainability Development Goals. And there are 229 Key Performers Indicators to measure performance – get involved. We can do this!
So, those are the five i’s. And I’d like to encapsulate this with the words of a poet…. Data scientists – and that’s you even if you don’t think you are one yet. You come out of the womb asking questions of the world. Humans do this, we are curious creatures… That’s why we have that data in the first place! We naturally do this!
“If you want to build a ship, don’t drum up people to gather wood adn don’t assign them tasks and work, but rather teach them to yearn for the vast and endless sea” – Antoine de Saint-Exupery.
This is what happened with those kids. Teach people to yearn for the vast and endless sea, then you’ll get the work done. Then we’ll do the hard work
Slides are available here: http://www.kirkborne.net/DataFest2018/
Q&A
Comment, Maggie Philbin) I run an organisations, Teen Tech, and that point that you are making of start where the passion actually is, is so important.
KB) People ask me about starting in data science, and I tell them that you need to think about your life, what you are passionate about and what will fuel and drive you for the rest of your life. And that is the most important thing.
Q1) You touched on a number of projects, which is most exciting?
A1) That’s really hard, but I think the Data Bowl is the most exciting thing. A few years back we had a challenge looking at how fast you can measure “heart ejection fraction – how fast the heart pumps blood out” but the way that is done, by specialists, could take weeks. Now that analysis is built into the MRI process and you can instantly re-scan if needed. Now I’m an astronomer but I get invited to weird places… And I was speaking to a conference of cardiac specialists. A few weeks before my doctor diagnosed me with a heart issue…. And that it would take a month to know for sure. I only got a text giving me the all clear just before I was about to give that talk. I just leapt onto that stage to give that presentation.
The Art Of The Practical: Making AI Real – Iain Brown, Lead Data Scientist, SAS
I want to talk about AI and how it can actually be useful – because it’s not the answer to everything. I work at SAS, and I’m also a lecturer at Southampton University, and in both roles look at how we can use machine learning, deep learning, AI in practical useful ways.
We have the potential for using AI tools for good, to improve our lives – many of us will have an Alexa for instance – but we have to feel comfortable sharing our data. We have smart machines. We have AI revolutionising how we interact with society. We have a new landscape which isn’t about one new system, but a whole network of systems to solve problems. Data is a selleble asset – there is a massive competitive advantage in storing data about customers. But especially with GDPR, how is our data going to be shared with organisations, and others. That matters for individuals, but also for organisations. As data scientists there is the “can” – how can the data be used; and the “should” – how should the data be used. We need to understand the reasons and value of using data, and how we might do that.
I’m going to talk about some exampes here, but I wanted to give an overview too. We’ve had neural networks for some time – AI isn’t new but dates back to the 1950s. .Machine learning came in in the 1980s, deep learning in the 2010s, and cognitive computing now. We’ve also had Moore’s Law changing what is theoretically possible but also what is practically feasible over that time. And that brings us to a definition “Artificial Intelligence is the science of training systems to emulate human tasks through learning and automation”. That’s my definition, you may have your own. But it’s about generating understanding from data, that’s how AI makes a difference. And they have to help the decision making process. That has to be something we can utilise.
Automation of process through AI is about listening and sensing, about understanding – that can be machine generated but it will have human involvement – and that leads to an action being made. For instance we are all familiar with taking a picture, and that can be looked at and understood. For instance with a bank you might take an image of paperwork and passports… Some large banks check validity of clients with a big book of pictures of blacklisted people… Wouldn’t it be better to use systems to achieve that. Or it could be a loan application or contract – they use application scorecards. The issue here is interpretability – if we make decisions we need to know why and the process has to be transparent so the client understands why they might have been rejected. You also see this in retail… Everything is about the segment of one. We all want to be treated as individuals… How does that work when you are one of millions of individuals. What is the next thing you want? What is the next thing you want to click on? Shop Directory, for instance, have huge ranges of products on their website. They have probably 500 pairs of jeans… Wouldn’t it be better to apply their knowledge of me to filter and tailor what I see? Another example is the customer complaint on webchat. You want to understand what has gone wrong. And you want to intervene – you may even want to do that before they complain at all. And then you can offer an apology.
There are lots of applications for AI across the board. So we are supporting our customers on the factors that will make them successful in AI, data, compute, skillset. And we embed AI in our own solutions, making them more effective and enhancing user experience. Doing that allows you to begin to predict what else might be looked at, based on what you are already seeing. We also provide our customers with extensible capabilities to help them meet their own AI goals. You’ll be aware of Alpha Go, it only works for one game, and that’s a key thing… AI has to be tailored to specific problems and questions.
For instance we are working on a system looking at optimising the experience of watching sports, eliminating the manual process of tagging in a game. This isn’t just in sport, we are also working in medicine and in lung cancer, applying AI in similar 3D imaging ways. When these images can be shared across organisations, you can start to drive insights and anomalies. It’s about collaborating, bringing data from different areas, places where an issue may exist. And that has social benefit of all of us. Another fun example – with something like wargaming you can understand the gamer, the improvements in gameplay, ways to improve the mechanics of how game play actually works. It has to be an intrinsic and extrinsic agreement to use that data to make that improvement.
If you look at a car insurer and the process and stream of that, that’s typically through a call centre. But what if you take a picture of the car as a way to quickly assess whether that claim will be worth making, and how best to handle that claim.
I value the application, the ways to bring AI into real life. How we make our experiences better. It’s been attributed to Voltaire, and also to Spiderman, that “with great power comes great responsibility”. I’d say “with great data power comes great responsibility” and that we should focus on the “should” not the “could”.
Q&A
Comment) A correction on Alpha Go: Alpha Zero plays Chess etc. It’s without any further human interaction or change.
Q1) There is this massive opportunity for collaboration in Scotland. What would SAS like to see happen, and how would you like to see people working together?
A1) I think collaboration through industry, alongside academia. Kirk made some great points about not focusing on the same perspectives but on the real needs and interest. Work can be siloed but we do need to collaborate. Hack events are great for that, and that’s where the true innovation can come from.
Q2) What about this conference in 5 years time?
A2) That’s a huge question. All sorts of things may happen, but that’s the excitement of data science.
Socially Minded Data Science And The Importance Of Public Benefits – Mhairi Aitken, Research Fellow, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh
I have been working in data science and public engagement around data and data science for about eight years and things have changed enormously in that time. People used to think about data as something very far from their everyday lives. But things have really changed, and people are aware and interested in data in their lives. And now when I hold public events around data, people are keen to come and they mention data before I do. They think about the data on their phones, the data they share, supermarket loyalty cards. These may sound trivial but I think they are really important. In my work I see how these changes are making real differences, and differences in expectations of data use – that it should be used ethically and appropriately but also that it will be used.
Public engagement with data and data science has always been important but it’s now much easier to do. And there is much more interest from funders for public engagement. That is partly reflecting the press coverage and public response to previous data projects, particularly NHS data work with the private sector. Public engagement helps address concerns and avoid negative coverage, and to understand their preferences. But we can be even more positive with our public engagement, using it to properly understand how people feel about their data and how it is used.
In 2016 myself and colleagues undertook a systematic review of public responses to sharing and linking of health data for research purposes (Aitken, M et al 2016 in BMC medical ethics, 17 (1)). That work found that people need to understand how data will be used, they particularly need to understand that there will be public benefit from their data. In addition to safeguards, secure handling, and a sense of control, they still have to be confident that their data will be used for public benefits. They are even supportive if the benefit is clear but those other factors are faulty. Trust is core to this. It is fundamental to think about how we earn public trust, and what trust in data science means.
Public trust is easy to define. But what about “public benefit”. Often when people call about data and benefits from data. People will talk about things like Tesco Clubcard when they think of benefit from data – there is a direct tangible benefit there in the form of vouchers. But what is the public benefit in a broader and less direct sense. When we ask about public benefit in the data science community we often talk about economic benefits to society through creating new data-driven innovation. But that’s not what the public think about. For the public it can be things like improvements to public services. In data-intensive health research there is an expectation of data learning to new cures or treatments. Or that there might be feedback to individuals about their own conditions or lifestyles. But there may be undefined or unpredictable potential benefits to the public – it’s important not to define the benefits too narrowly, but still to recognise that there will be some.
But who is the “public” that should benefit from data science? Is that everyone? Is it local? National? Global? It may be as many as possible but what is possible and practical? Everyone whose data is used? That may not be possible. Perhaps vulnerable or disadvantaged groups? Is it a small benefit for many, or a large benefit for a small group. Those who may benefit most? Those who may benefit the least? The answers will be different for different data science projects. That will vary for different members of the public. But if we only have these conversations within the data science community we’ll only see certain answers, we won’t hear from groups without a voice. We need to engage the public more with our data science projects.
So, closing throughts… We need to maintain a social license for data science practices and that means continual reflection on the conditions for public support. Trust is fundamental – we don’t need to make the public trust us, we have to actually be trustworthy and that means listening, understanding and responding to concerns, and being trustworthy in our use of data. Key to this is finding public benefits of data science projects. In particular we need to think about who benefits from data science and how benefits can be maximised across society. Data scientists are good at answering questions of what can be done but we need to be focusing on what should be done and what is beneficial to do.
Q&A
Q1) How does private industry make sure we don’t leave people behind?
A1) BE really proactive about engaging people, rather than waiting for an issue to occur. Finding ways to get people interested. Making it clear what the benefits are to peoples lives There can be cautiousness about opening up debate being a way to open up risk. But actually we have to have those conversations and open up the debate, and learn form that.
Q2) How do we put in enough safeguards that people understand what they consent to, without giving them too much information or scaring them off with 70 checkboxes.
A2) It is a really interesting question of consent. Public engagement can help us understand that, and guide us around how people want to consent, and what they want to know. We are trying to answer questions where we don’t always have the answers – we have to understand what people need by asking them and engaging them.
Q3) Many in the data community are keen to crack on but feel inhibited. How do we take the work you are doing and move sooner rather than later.
A3) It is about how we design data science projects. You do need to take the time first to engage with the public. It’s very practical and valuable to do at the beginning, rather than waiting until we are further down the line…
Q3) I would agree with that… We need to do that sooner rather than later rather than being delayed deciding what to do.
Q4) You talked about concerns and preferences – what are key concerns?
A4) Things you would expect on confidentiality, privacy, how they are informed. But also what is the outcome of the project – is it beneficial or could they be discriminatory, or have a negative impact on society? It comes back to causing public benefits – they want to see outcomes and impact of a piece of work.
Automated Machine learning Using H2O’s Driverless AI – Marios Michailidis, Research Data Scientist, H2O.ai
I wanted to start with some of my own background. And I wanted to talk a bit about Kaggle. It is the world’s biggest preictive modelling competition platform with more than a million members. Companies host data challenges and competitors from across the world compete to solve them for prizes. Prizes can be monetary, or participation in conferences, or you might be hired by companies. And it’s a bit like Tennis – you gain points and go up in the ranking. And I was able to be ranked #1 out of a half million members t here.
So, a typical problem is image classification. Can I tell a cat from a dog from an image. That’s very doable, you can get over 95% accuracy and you can do that with deep learning and neural net. And you differentiate and classify features to enable that decision. Similarly a typical problem may be classifying different bird song from a sound recording – also very solvable. You also see a lot of text classification problems… And you can identify texts from a particular writers by their style and vocabulary (e.g. Voltaire vs Moliere). And you see sentiment analysis problems – particularly for marketing or social media use.
To win these competitions you need to understand the problem, and the metric you are being tested on. For instance there was an insurance problem where most customers were renewing, so there was more value in splitting the problem into two – one for renewals, and then a model for others. You have to have a solid testing procedure – really strong validation environment that reflects what you are being tested on. So if you are being tested on predictions for 3 months in the future, you need to test with past data, or test that the prediction is working to have the confidence that what you do will be appropriately generalisable.
You need to handle the data well. Your preprocessing, your feature engineering, which will let you get the most out of your modelling. You also need to know the problem-specific elements and algorithms. You need to know what works well. But you can look back for information to inform that. You of course need access to the right tools – the updated and latest software for best accuracy. You have to think about the hours you put in and how you optimize them. When I was #1 I was working 60 hours on top of my day job!
Collaborate – data science is a team sport! It’s not just about splitting the work across specialisms, it’s about uncovering new insights by sharing different approaches. You gain experience over time, and that lets you focus your efforts on where you can focus your effort for the best gain. And then use ensembling – combine the methods optimally for the best performance. And you can automate that…
And that brings us to H2O’s diverless AI which automates AI. It’s an AI that creates AI. It is built by a group of leading machine learning engineers, academics, data scientists, and kaggle Grandmasters. It handles data cleaning and feature engineering. It uses cutting edge machine learning algorithms. And it optimises and combines them. And this is all through a hypothesis testing driven approach. And that is so important as if I try a new feature or a new algorithm, I need to test it… And you can exhaustively find the best transformations and algorithms for your data. This allows solving of many machine learning tasks, and it is all in parallel to make it very fast.
So, how does it work? Well you have some input data and you have a target variable. You set an objective or success metric. And then you need some allocated computing power (CPU or GPU). Then you press a button and H2O driverless AI will explore the data, it will try things out, it will provide some predictions and model interpretability. You get a lot of insight including most predictive insights. And the other thing is that you can do feature engineering, you can extract this pipeline, these feature transformations, then use with your own modelling.
Now, I have a minute long demo here…. where you upload data, and various features and algorithms are being tried, and you can see the most important features… Then you can export the scoring pipeline etc.
This work has been awarded Technology of the Year by InfoWorld, it has been featured in the Gartner report.
You can find out more on our website: https://www.h2o.ai/driverless-ai/ and there is lots of transparency about how this work, how the model performs etc. You can download a free trial for 3 weeks.
Q&A
Q1) Do you provide information on the machine learning models as well?
A1) Once we finish with the score, we build the second model which is simple to predict that score. The focus on that is to explain why we have shown this score. And you can see why you have this score with this model… That second interpretability model is slightly less automated. But I encourage others to look online for similar – this is one surrogate model.
Q2) Can I reproduce the results from H2O?
A2) Yes. You can download the scoring practice, it will generate the code and environment to replicate this, see all the models, the data generated, and you can run that script locally yourself – it’s mainly Python.
Q3) That’s stuff is insane – probably very dangerous in the hands of someone just learning about machine learning! I’d be tempted to throw data in… What’s the feedback that helps you learn?
A3) There is a lot of feedback and also a lot of warning – so if test data doesn’t look enough like training data for instance. But the software itself is not educational on it’s own – you’d need to see webinars, look at online materials but then you should be in a good position to learn what it is doing and how.
Q4) You talked about feature selection and feature engineering. How robust is that?
A4) It is all based on hypothesis testing. But you can’t test everything without huge compute power. But we have a genetic algorithm to generate combinations of features, tests them, and then tries something else if that isn’t working.
Q5) Can you output as a model as eg a deserialised JSON object? Or use as an API?
A5) We have various outputs but not JSON. Best to look on the website as we have various ways to do these things.
Coming up:
Innovation Showcase
Matt Jewell, R&D Engineer, Amiqus
Carlos Labra, CEO & Co-Founder, Particle Analytics
Martina Pugliese, Data Science Lead, Mallzee
Steven Revill, CEO & Co-Founder, Urbantide
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