We live in an age of distributed consumer engagement, enabled by technology. Human intelligence is modelled into data algorithms which are creating a new generation of artificial intelligence solutions. I suspect we’re approaching a new tipping point in our history: the age of productizing intelligence and using predictive modeling to help us forecast events.
Back in 16th century London, the notion of ‘seeing a black swan’ was used as a metaphor for a statement of improbability (that is, before we discovered Australia where black swans are native).
In data science circles, ‘black-swan events’ are synonymous with the unexpected and extremely hard to predict; but we’re now predicting and modelling better than ever before. The eponymous Black Swan Data is a trend prediction agency that’s making waves across the research industry. By reading conversations across the internet, they’re able to help brands hear the things of value which they’re unaware of – new trends – and then test the principles of what brands want to release to their consumers. They do this by identifying the gaps and needs of audiences using a reverse version of their trend analysis tools.
These guys have also been putting their minds to how technology and analytics can improve health outcomes. They’ve launched a version of their data ingestion platform which they envisage will help accelerate the diagnosis of rare diseases by sending unidentified symptoms out to thousands of data points and connecting this information together.
In the future, expertise is likely to be more predictable, more measurable and more accessible than ever before
White Swan the company’s charitable trust, tells the story of the founder’s sister Julie, the first to benefit from their new approach to diagnosis. Her rare disease saw her health deteriorate over many years; she became wheelchair bound and, despite endless tests, doctors were at a loss to explain her condition. In response to the distribution of her symptoms using a data algorithm across over 30,000 medical data points, three returned suggestions she had a rare but treatable form of Parkinson’s disease.
Julie has just run her first triathlon and normal life with her family has been resumed. Heart-warming perhaps, but as White Swan starts to probe epidemiology, ankylosing spondylitis, disease diaspora mapping and social colloquial expressions of stage one pulmonary thrombosis, it poses questions about new collaborations between unlikely partners. Do commercial data science businesses and academic R&D have a greater role in shaping the future of pharma and healthcare?
What role therefore do data and AI play in the world of healthcare? I recently read with interest a piece on how Deep Mind’s AI could potentially have a huge impact in reducing instances of preventable sight loss as it can now match health experts at spotting eye disease. Indeed, an expert in any discipline is expert because they’ve defined the best sources of relevant knowledge along with the most effective way of combining these to create their ‘expertise’. In the future, expertise is likely to be more predictable, more measurable and more accessible than ever before.
What AI really does is to model this expertise and create democratised access unlike anything we’ve ever been able to offer humanity. That makes it something of a game changer. With the advent of AI and robotics there will potentially be less work for human beings to do, as machines can and will be trained to do things better with less fallibility. But freeing up time for other purposes is not necessarily a negative … and if we can provide better health, more simply, to greater numbers in need, then why wouldn’t we?