The need for technology to catch up to our imaginations has been a constant feature of AI since the first spark of an idea flickered into existence. This is because no matter how far it comes, there will always be a new generation for whom it is still inadequate. Sam Zimmerman, CTO & Co-founder at Freebird and another one of our speakers, describes the immense amount of progress which has been made since he entered the machine learning world less than 8 years ago. "When I first entered the field in 2011, machine learning was just beginning to extend outside of advertising and finance into domains like sentiment analysis and computer vision. Largely this was a migration from quite clear optimizations of well-defined outcome variables (like click-through-rates and PnL) to much more abstract, subjective, and ill-defined outcome variables (like the "sentiment" of a sentence or the "setting" of a photo)."

There are, of course, limitations to what you can do at the edge. Today's machine learning algorithms are designed to run on powerful servers. Therefore, in the case of driverless cars, much of the heavy lifting still takes place in the cloud, with algorithms trained using millions of miles of recorded driving data before being deployed at the edge for inference. Increasingly, however, in other applications, we are starting to see algorithms trained locally too. This is far more cost-effective, requiring less ongoing bandwidth and storage cost. Swim, for example, is a streaming data analytics startup that uses a distributed network architecture to operate self-training machine learning at the edge in real-time.

Data is now being mined from a variety of sources that can help insurers build a fuller picture of their customers. Machine learning algorithms can analyze this wealth of information quickly and accurately, without being tainted by human bias, and help to offer more accurate prices. In health insurance, for example, data from wearable devices such as Fitbit can track a customer's activity, while analysis of their social media may give a more accurate idea of somebody's lifestyle choices than they are willing to share. This will likely punish those who are unhealthier than they say, but it will also reward those who live healthier lifestyles.

Data has been at the heart of its success since eHarmony's launch 16 years ago, and is a major differentiator from newer rivals such as Tinder and Happn. Carter notes that originally users didn't even use pictures, instead creating a dating profile and filling out a 450-item questionnaire that covered every facet of their personality. Since then, they have cut these questions down to a slightly more manageable, but still fairly large, 150 and they also now allow photos, but the amount of data they have managed to accrue during this time is still tremendous, enabling them to pinpoint a significant number of features that people tend to look for in a prospective partner. For example, they found that when it comes to height there is a strong correlation when it comes to the probability of communication, with women tending to go for men taller than them and vice versa. Food preference is also important.

Ultimately, AI is not going to render human social media analysts redundant any time soon. Marketers are still needed to audit sentiment for accuracy and to feed in data to help machines get better at building a comprehensive view of language so that it can analyzed. But language is such a fast moving target that it is likely people will never not be needed. Which leads to the question, have machines made social media better. In order to gain a nugget of value now, you have to sift through a pile of trash.