Educating everyone to create value from data with machine learning

In the PAPIs team, we share the vision that a predictive world will be a much better world. We are beginning to enter this new world where we can do things such as predicting demand to make better usage of our resources, where we can anticipate breakdowns and other issues (including medical ones) so we can take action to prevent them. In this world, information systems also give us access to the right content at the right time, businesses serve customers better by predicting their needs, and tedious tasks that require intelligence are automated. For some people, this can lead to terrible things. For others like us, it also opens up amazing possibilities to transform our world for the better.

Learning from data and making predictions, at large scale

Currently, our world is mostly one where we measure everything we can — situations and outcomes — by collecting data. The Internet of Things is one example of that. Sometimes, it isn’t so clear why we collect this data for. It turns out that machines can learn from data and "understand" how to relate situations to outcomes. They can generalise from their learnings to predict future outcomes when encountering new situations (e.g. maintenance issues, demand, actions…). As such, predictive technology powered by machine learning techniques is the main way that we can create value from (big) data. This has been referred to as “Big Data 2.0” in the Data Science for Business book by Foster Provost and Tom Fawcett.

Bret Victor, a designer behind the 1st iPad, wrote in his 2006 essay Magic Ink: “Until machine learning is as accessible and effortless as typing the word “learn,” it will never become widespread”. 5 years later, the McKinsey Big Data report pointed at a shortage of talent necessary for organizations to take advantage of big data in the coming years. Today, there’s a new wave of data analysis tools that provide an important part of the solution by removing many barriers to entry to machine learning. They make it easier to create predictive APIs that will be consumed by applications to make them starter. Some of these tools were presented last year at PAPIs ’14, the 1st International Conference on Predictive APIs and Apps, and I believe that they are key to exploiting the value of data at large scale, in all domains. But for this technology to be useful, it has to be connected to the right data sources, and predictions should be used in ways that solve real-world problems.

Announcing PAPIs Connect...

There were two things that stood out from the original description of PAPIs: 1. providing a "forum for the presentation of new machine learning APIs, techniques, architectures, and tools to build predictive applications" and 2. bringing together the makers of these APIs and tools, their users, and practicioners from industry, government and academia. APIs in general — and Predictive APIs in particular — impact everybody, including non technical people. PAPIs ’14 was proof of that with 25% of its audience being non technical. We think it’s important to address this part of our audience more, in order to connect predictive technology to business and to domains where it will be beneficial.

Today, we are announcing PAPIs Connect, a new series of events that are applications and business-driven, aimed at increasing awareness of predictive technology. We want to educate decision makers and enable them to instil a data-oriented, predictive culture into their organizations — whether they are startups, SMBs or large enterprises. For that, we intend to keep the practical mindset of PAPIs '14. The first PAPIs Connect will take place in Paris on 21 May 2015 (don’t confuse PAPIs and Paris!) and we just opened registration! We’re also planning a PAPIs Connect in the US towards the end of the year.

David Gerster (BigML), Alexandre Vallette (ANTS), Keiran Thompson (Datagami), Andy Thurai (IBM), Shawn Scully (Dato) and Misha Bilenko (Microsoft) at PAPIs '14

David Gerster (BigML), Alexandre Vallette (ANTS), Keiran Thompson (Datagami), Andy Thurai (IBM), Shawn Scully (Dato) and Misha Bilenko (Microsoft) at PAPIs '14

Coming soon to a city near you!

Of course, the annual PAPIs conferences will continue to exist; they will keep a technical bias by featuring more technical talks and they will be more focused on the presentation of new use cases, latest advancements and challenges in building predictive APIs and applications. The next edition, PAPIs ’15, will take place on 6-7 August 2015 in Sydney (right before KDD). By the way, our Call for Proposals is still open until 3 April...

PAPIs Connect will travel around the world and events will take place more frequently, which will increase the chances that we come near where you live! They will complement PAPIs and help achieve its objective: to expand adoption of machine learning and predictive technology by showing everyone how to use them and how to benefit from them.

We hope you will be a part of our journey.

General Chair of

Louis Dorard

Author of Bootstrapping Machine Learning.