by Paul Eaton, Associate Director, Aon — read the whole report
Chicago, IL (Aug. 9, 2018) – Artificial intelligence is the new electricity. We hear it will fundamentally shift the balance of power between labor and capital, mostly by rendering labor obsolete. It will enable and empower transformative technologies that will rearrange the sociopolitical landscape and may lead to humanity’s transcendence (or extinction) within our lifetimes. As it changes the world it will necessarily rewrite the rules of insurance. That’s the myth, and the nature of the headlines.
Interestingly, insurance is heavy on intellectual property (think of proprietary underwriting models), technology, and data. And AI is hungry; hungry for data, of course. But also hungry for systems that can be automated and for proprietary classification problems that can be improved. That places insurance right in the appetite of artificial intelligence and its promise of transformation. If we want to act on artificial intelligence’s transformational potential, we need to understand what it actually is, separate the technologies from the hype, and develop a practical understanding of what is required to implement AI powered solutions in the insurance sector. This article will highlight these three steps and offers a realistic approach for carriers to take advantage of the opportunities.
Defining Artificial Intelligence
Unfortunately, our first step is also our hardest, as a working definition of artificial intelligence is difficult to assess. The scope of the term AI is broad and it requires careful consideration to avoid becoming hopelessly confounded with its own hype. It is also challenging to come to a clear definition of natural intelligence, which leaves us struggling for a definition of artificial intelligence because the latter is so often compared to the former.
AI tends to be discussed in two flavors. The first is general artificial intelligence (also, artificial general intelligence and strong AI). GAI is machinery capable of human level cognition, including a general problem-solving capability that is potentially self-directed and broadly applicable to many kinds of problems. GAI references are accessible through fictional works, such as C-3PO in Star Wars or Disney’s eponymous WALL-E. The most important feature of GAI is that it does not currently exist and there is deep debate about its potential to ever exist.
The second is usually referred to as narrow AI. Narrow AI is task specific and non-generalizable. Examples include facial recognition on Apple’s iPhone X and speech-to-text transliteration by Amazon’s Alexa. Narrow AI looks and feels a lot like software or, perhaps, predictive models . Narrow AI can be described as a class of modeling techniques that fall under the category of machine learning.
What is machine learning? Imagine a set of input data; this data has one or more potential features of interest. Machine learning is a technique for mapping the features of input data to a useful output. It is characterized by statistical inference, as advanced techniques often underlie machine learning predictive models. Through statistical modeling, software can infer a likely output given a set of input features. The predictive accuracy of machine learning methods increase as their training data sets increase in size. As the machine ingests more data, it is said to learn from that data. Hence, machine learning.
Perhaps most important of all, machine learning (as an implementation of narrow AI) is real and here today.
Read the full report on Aon’s website.
Aon plc (NYSE:AON) is a leading global professional services firm providing a broad range of risk, retirement and health solutions. Our 50,000 colleagues in 120 countries empower results for clients by using proprietary data and analytics to deliver insights that reduce volatility and improve performance.
Source: AonTags: Aon, Artificial Intelligence (AI), Machine Learning (ML), Transformation