Machine Learning and Washing Machines

The best musing about machine learning of this week comes from Benedict Evans:

Washing machines are robots, but they’re not ‘intelligent’. They don’t know what water or clothes are. Moreover, they’re not general purpose even in the narrow domain of washing - you can’t put dishes in a washing machine, nor clothes in a dishwasher (or rather, you can, but you won’t get the result you want). They’re just another kind of automation, no different conceptually to a conveyor belt or a pick-and-place machine. Equally, machine learning lets us solve classes of problem that computers could not usefully address before, but each of those problems will require a different implementation, and different data, a different route to market, and often a different company. Each of them is a piece of automation. Each of them is a washing machine.

Hence, one of the challenges in talking about machine learning is to find the middle ground between a mechanistic explanation of the mathematics on one hand and fantasies about general AI on the other. Machine learning is not going to create HAL 9000 (at least, very few people in the field think that it will do so any time soon), but it’s also not useful to call it ‘just statistics’. Returning to the parallels with relational databases, this might be rather like talking about SQL in 1980 - how do you get from explaining table joins to thinking about Salesforce.com? It’s all very well to say ‘this lets you ask these new kinds of questions’, but it isn’t always very obvious what questions. You can do impressive demos of voice recognition and image recognition, but again, what would a normal company do with that? As a team at a major US media company said to me a while ago: ‘well, we know we can use ML to index ten years of video of our talent interviewing athletes - but what do we look for?’

What, then, are the washing machines of machine learning, for real companies? I think there are two sets of tools for thinking about this. The first is to think in terms of a procession of types of data and types of question:

1. Machine learning may well deliver better results for questions you’re already asking about data you already have, simply as an analytic or optimization technique. For example, our portfolio company Instacart built a system to optimize the routing of its personal shoppers through grocery stores that delivered a 50% improvement (this was built by just three engineers, using Google’s open-source tools Keras and Tensorflow).
2. Machine learning lets you ask new questions of the data you already have. For example, a lawyer doing discovery might search for ‘angry’ emails, or ‘anxious’ or anomalous threads or clusters of documents, as well as doing keyword searches,
3. Third, machine learning opens up new data types to analysis - computers could not really read audio, images or video before and now, increasingly, that will be possible.