Machine Learning and decision making – Part 1
In the first of a two part blog post, Marko Balabanovic, Chief Technology Officer, at the Digital Catapult writes about Machine Learning (ML) and the challenges around systems that make decisions.
We recently participated in the The Royal Society’s investigation of machine learning. In this two part post I’ll give a little background to where machine learning is today, but then claim that we have a bigger set of challenges around systems that make decisions, regardless of whether they use machine learning algorithms or more conventional software.
Machine learning grew from the broader field of Artificial Intelligence, and gradually became a significant discipline in its own right. Machine Learning (ML) algorithms “learn” from examples and improve from experience, rather than being explicitly programmed. But why the sudden excitement? Because, over the last few years, ML has advanced to the point of making significant impact in our lives and in business.
The highest profile developments have been in “deep learning”, new architectures of neural networks that, when combined with the huge volumes of examples to learn from (“training” data) that are now more readily available, and large-scale cloud processing power, have made spectacular progress in many areas, ranging from recognition of images and natural language to more fun demonstrations like learning to play video games, or challenging the world’s top Go player.
It is no coincidence that the big five “GAFAM” internet companies (Google, Apple, Facebook, Amazon and Microsoft) are racing to acquire companies and scientists globally, including the $400m+ acquisition of DeepMind by Google and the recent $250m acquisition of SwiftKey by Microsoft, both in the UK. They all have the necessary huge sets of training data, and they all have immediate ways to monetise any new developments with improvements to their existing services (such as Google Translate translating text from smartphone cameras, Microsoft’s Skype Translator, Facebook’s face recognition or Twitter’s porn detection).
Stranglehold on large-scale data
These companies have a stranglehold on large-scale data in many areas particularly consumer behaviour on web and mobile, photographs and videos (there were 1.8B images uploaded every day even back in 2014). This strategic advantage is so great that they have been happy to open source sophisticated libraries and toolkits for ML projects such as TensorFlow from Google and Torch, contributed to by Facebook, and even hardware designs, while Amazon contributed to the $1B invested to found Elon Musk and Sam Altman’s new non-profit research company OpenAI.
The question was asked by the Royal Society whether certain sectors have particularly strong uses for machine learning. Our thought is that any sector where decisions are based on data can make use of all kinds of data science techniques including ML, and these days that will include most sectors! For instance a selection of recent examples include medical diagnosis, funny cartoons, image recognition, exam revision, financial trading, background checking and robot tasks.
Many of the really exciting opportunities for companies, and where machine learning will affect all of us, are with systems that become more autonomous, where an algorithm is making the decisions – often decisions about people. The move is from humans making decisions (perhaps using rules of thumb, gut instinct or behaviours handed down and learned), to humans making decisions based on data, with increasingly sophisticated analytics, and finally to machines making the decisions.
Impacting our lives
We’re particularly interested in the cases where these decisions affect our lives in significant ways. Bruce Schneier has called this development the “World-Sized Web” – imagining all the sensors globally as the eyes and ears of this “robot” and all of the actuators (that include autonomous drones, or car navigation systems that can re-route traffic) as its hands and feet.
We can see an analogue with how online businesses have deepened their use of data. We started with simple streams of raw information, like counts of visitors and page views, as a minor influence on human decision making, alongside existing experience from retail or marketing. Over time the data became more sophisticated, and we could track repeat visits, correlate with purchasing both on and offline, and tie in to channels for marketing, promotion and advertising.
However, the decision making, although now very data-driven, was still largely a human activity. Finally we reach a stage where the system can become fully automated. Systems run experiments comparing personalised variants of messages or ads, and optimising over time.
Many use variants of the “multi-armed bandit” algorithms that would be considered a form of ML, in particular “reinforcement” learning that adapts based on rewards or punishments. Complex recommender systems feed merchandising decisions on Amazon, sort posts in the Facebook news feed and tailor our media consumption on Netflix.
You can read the second part of Marko’s post tomorrow.