Joined the Advisory Board for Imperial’s MSc in Business Analytics

Delighted to have officially joined the Advisory Board for Imperial College‘s MSc in Business Analytics at the Business School. This is the first year the MSc has been running and the progress so far has been phenomenal. The course is heavily subscribed by candidates from around the globe and the current class has a wonderful diversity of students and experience.

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I attended the most recent Board meeting in March and was impressed with the content and ambition of the course. We are working hard to improve the course for the next class and strongly believe the course will be a world leader in delivering data science and business analytics over the years to come. Excited to be a part of this!

We came, we saw, we hacked!

Last weekend I spent Saturday and Sunday hacking on government data at this year’s BayesImpact‘s hackathon – Bayes Hack 2016! Located at OpenDNS HQ, the event invited teams of Data Scientists, Engineers, Designers, and anyone who is interested in data to hack for 24 hours.

For those unfamiliar with ‘hacking’: the premise is basically to build something in a very very short amount of time. We call it ‘hacking’ because you have to cut corners and write some ugly code to get a product out quickly. It’s different to your normal job but very liberating!

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I teamed up with four other Data Scientists from Airbnb and an Engineer and we decided to look at the Department of Labour‘s database on jobs and associated skills, knowledge, education requirements. Our prompt was the following:

Economic landscapes change dramatically, often outpacing a workforce lagging in its adaptation to new opportunities and industries. How can data scientists leverage predictive modeling to close the gap?

What did we build? We broke into two teams and one team built a recommendation engine for users to enter their skills and abilities to get back job suggestions. The second team, which I worked on, built an interactive visualisation for these recommendations to enable users to explore related jobs.

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For each of the 954 jobs in the database, we computed the the coordinates of the job in the 35-dimensional space of skills. These skills are include: Reading Comprehension, Active Listening, Writing, etc. For each pair of jobs in this skills vector space, we computed the distance between them using the Kullback-Leibler Divergence to give us a value between 0 and 1. The smaller the distance (divergence), the more similar two jobs are in terms of the skills required to be competent in the jobs. The visualisation was made in Gephi and exported to SigmaJs.

We were one of the 8 finalists on the day but eventually lost out to the fantastic Go Bot Chat team working on the Department of Interior’s database. The project provides parks and recreations recommendations to people using a chatbot service built on top of Facebook’s Messenger.

The weekend was super fun and inspiring to see how much can be done so quickly on so much openly available data. You can see our full source code on Github and all the other projects from the competition there too.

 

Artificial Intelligence set to dominate Financial Services

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A recent article by a Foreign-Exchange Journalist suggests the ‘Skynet’ of Finance is not too far away.

The article points  to the huge improvements in Artificial Intelligence and the bullishness of Financial Services firms for takeover by technology in their industry. In particular, Transfer/Payments business expect to lose 28% of their business to FinTech in the next 5 years, and Banks expect to lose 24% of their business.

The silver lining to this takeover could however be, the article points out, the greater emphasis on the ‘human touch’ in key customer interfacing areas. For example, a human hand at the wheel to prevent another ‘flash crash’ or a human interpreter of the decisions of an Artificial Intelligence made lending / investment decision.

Whatever happens, we are likely to see more automation, lower costs for the customer, and smarter decision making – albeit in the near term.

Hedge funds are luring away Tech’s AI superstars

hedgefundsai An arms race has resumed amongst the world’s biggest hedge funds. Seeing the potential of the technologies produced at some of the most prolific Machine Learning groups in big tech companies such as Google and Facebook, a recent article notes that hedge funds are lifting lead Data Scientists to work on building better alpha strategies.

In the past, algorithmic trading prided itself on hiring highly skilled statisticians to sculpt informative signals and combine them in a state-of-the-art model to predict movements in prices. With the success of deep learning software, such as IBM’s Watson, hedge funds now see potential in throwing their financial big data at artificial intelligence at these artificial intelligence black boxes to predict alpha.

Bridgewater hired David Ferrucci, former lead engineer at IBM for developing Watson, Renaissance Technologies was founded by Bob Mercer and Peter Brown, former language recognition leads at IBM, and recently Blackrock hired Bill MacCartney, a former Google scientist.

For these robotics rockstars moving from Tech to Finance, one downside is that there work becomes a lot more secretive. The nature of algorithmic trading is very hush hush with all hedge funds in direct competition with each other. Compared to publishing research papers at IBM or Google, the traders at these funds will have to keep their advances to themselves – which is a loss for the rest of the scientific community.

Self driving cars must learn to kill

As we move towards more technological capability and deferring judgement and decision to artificial intelligence, some difficult ethical questions will come up.

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A recent article in TechnologyReview highlights how self-driving cars will be programmed to make tradeoffs in difficult situations. The use of the image to the left demonstrates the type of situation in which a self driving car may have to deliberately chose to kill one person to save many people.

It gets even more confusing when we think about one adult vs one child, a cyclist vs a car, a passenger vs a pedestrian. There will be a huge new body of research in practical ethics and applied philosophy that companies such as Google will be looking to for guidance.

Talking Trust with Kellogg’s MBA class

I had the pleasure of video-conferencing into Kellogg‘s MBA class in Social Media at Northwestern University yesterday. Brayden King kindly invited me to talk about how Airbnb thinks about Trust and the challenges facing sharing economies.

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We spoke about the role of Data Science at the company and how it has changed over the years. As the volume of data has grown, we have more often than not moved away from explanatory predictive models to Machine Learning algorithms.

One thing that stood out to me as top of mind for the students in the MBA class was the process of Trust development for first time users. How does a first time guest get accepted by a host on Airbnb? How does a first time host get selected by a guest?

At Airbnb we have a team of highly skilled Data Scientists and Engineers working on matching algorithms designed to help first time guests and hosts. And even more than this, the community are their own best resource. Experienced hosts help new hosts manage their listing and new guests book their first experience.

At the heart of everything data-related we work on at Airbnb is the community and enabling them to make more connections amongst themselves and new users.

CyberLaunch Accelerator launches Security ML challenge

I recently joined Cyberlaunch, the world’s leading accelerator for information security (Infosec) and machine learning (ML), as a Mentor for their startup companies.

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Last week they launched a Startup Challenge to find the brightest solutions to challenging Infosec and ML problems. There are two prizes, each worth over $150,000.

Its sure to be a very competitive field and I am looking forward to the entries!

Imperial in top 10 for Data Science

A recent survey by eFinancialCareers of their CV database has put Imperial College London at number 8 in the world wide rankings for best places to prepare for a financial career in Data Science. This is hot off the heels of the launch of their new Data Science Institute last year and the new MSc Business Analytics.

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The usual East Coast (CMU, Columbia, NYU) and West Coast (Stanford, Berkeley) are also in the top 10, as well as Cambridge and Oxford from the UK.

Airbnb launches first ever Kaggle competition!

In an exciting new partnership, Airbnb has teamed up with Kaggle to create an online Data Science data challenge. In this challenge we provide historical data on the first country guests book and then ask candidates to predict future first bookings.

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Try the challenge yourself! You have until February 11th 2016 to submit your entries. And if you have any questions you can use the forum and I will respond as soon as possible. Good luck and hope you have fun playing with our data!