How to Think About the Rise of the Machines

The first machine age, the Industrial Revolution, saw the automation of physical work. We live in the second machine age, where there is increasing augmentation and automation of manual and cognitive work. This second machine age has seen the rise of artificial intelligence (AI), or “intelligence” that is not the result of human cogitation. It is now ubiquitous in many commercial products, from search engines to virtual assistants. AI is the result of exponential growth in computing power, memory capacity, cloud computing, distributed and parallel processing, open-source solutions and global connectivity of both people and machines. The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made a necessity of speedily processing and of generating meaningful, actionable insights from it. Demystifying Artificial Intelligence The term “artificial intelligence” is often misused. To avoid any confusion over what AI means, it’s worth clarifying its scope and definition.

• AI and Machine Learning—Machine learning is just one area or sub-field of AI. It is the science and engineering of making machines “learn.” That said, intelligent machines need to do more than just learn—they need to plan, act, understand and reason.

• Machine Learning and Deep Learning—"Machine learning" and "deep learning" are often used interchangeably. Deep learning is actually a type of machine learning that uses multi-layered neural networks to learn. There are other approaches to machine learning, including Bayesian learning, evolutionary learning and symbolic learning.

• AI and Cognitive Computing—Cognitive computing does not have a clear definition. It can be viewed as a subset of AI that focuses on simulating human thought process based on how the brain works. It is also viewed as a “category of technologies that uses natural language processing and machine learning to enable people and machines to interact more naturally to extend and magnify human expertise and cognition.” Cognitive computing is a subset of AI, not an independent area of study.

• AI and Data Science—Data science refers to the interdisciplinary field that incorporates statistics, mathematics, computer science and business analysis to collect, organize and analyze large amounts of data to generate actionable insights. The types of data (e.g., text, audio, video) and the analytic techniques (e.g., decision trees, neural networks) that both data science and AI use are very similar.

Differences, if any, may be found in the purpose. Data science aims to generate actionable insights to businesses, irrespective of any claims about simulating human intelligence, while the pursuit of AI may be to simulate human intelligence. Self-Driving Cars When the U.S. Defense Advanced Research Projects Agency (DARPA) ran its 2004 Grand Challenge for automated vehicles, no car was able to complete the 150-mile challenge. In fact, the most successful entrant covered only 7.32 miles. The next year, five vehicles completed the course. Now, every major car manufacturer plans to have a self-driving car on the road within five to 10 years, and the Google Car has clocked more than 1.3 million autonomous miles. See Also: What You Must Know About Machine Learning AI techniques—especially machine learning and image processing— help create a real-time view of what happens around an autonomous vehicle and help it learn and act from past experience. Amazingly, most of these technologies didn’t even exist 10 years ago. Emerging risk identification through man-machine learning “People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.” —Pedro Domingos, author of The Master Algorithm Emerging Risks & New Product Innovation Identifying emerging risks (e.g., cyber, climate, nanotechnology), analyzing observable trends, determining if there is an appropriate insurance market for these risks and developing new coverage products in response historically have been creative human endeavors. However, collecting, organizing, cleansing, synthesizing and even generating insights from large volumes of structured and unstructured data are now typically machine learning tasks. In the medium term,  combining human and machine insights offers insurers complementary, value-generating capabilities. Man-Machine Learning Artificial general intelligence (AGI) that can perform any task a human can is still a long way off. In the meantime, combining human creativity with mechanical analysis and synthesis of large volumes of data—in other words, man-machine learning (MML)—can yield immediate results. For example, in MML, the machine learning component sifts through daily news from a variety of sources to identify trends and potentially significant signals. The human-learning component provides reinforcement and feedback to the ML component, which then refines its sources and weights to offer broader and deeper content. Using this type of MML, risk experts can identify emerging risks and monitor their significance and growth. MML can further help insurers identify potential customers, understand key features, tailor offers and incorporate feedback to refine product introduction. Computers That “See” In 2009, Fei-Fei Li and other AI scientists at Stanford AI Laboratory created ImageNet, a database of more than 15 million digital images, and launched the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). The ILSVRC awards substantial prizes to the best object detection and object localization algorithms. The competition has made major contributions to the development of “deep learning” systems, multilayered neural networks that can recognize human faces with more than 97% accuracy, as well as recognize arbitrary images and even moving videos. Deep learning systems can now process real-time video, interpret it and provide a natural language description. Artificial Intelligence: Implications for Insurers AI’s initial impact relates primarily to improving efficiencies and automating existing customer-facing, underwriting and claims processes. Over time, its impact will be more profound; it will identify, assess and underwrite emerging risks and identify new revenue sources.

• Improving Efficiencies—AI is already improving efficiencies in customer interaction and conversion ratios, reducing quote-to-bind and FNOL-to-claim resolution times and increasing speed to market for products. These efficiencies are the result of AI techniques speeding up decision-making (e.g., automating underwriting, auto-adjudicating claims, automating financial advice, etc.).

• Improving Effectiveness—Because of the increasing sophistication of its decision-making capabilities, AI will soon improve target prospects to convert them to customers, refine risk assessment and risk-based pricing, enhance claims adjustment and more. Over time, as AI systems learn from their interactions with the environment and with their human masters, they are likely to become more effective than humans, and the AI systems will replace them. Advisers, underwriters, call center representatives and claims adjusters will likely be most at risk.

• Improving Risk Selection and Assessment—AI’s most profound impact could well result from its ability to identify trends and emerging risks and assess risks for individuals, corporations and lines of business.

Its ability to help carriers develop new sources of revenue from risk- and non-risk-based information will also be significant. See Also: How Machine Learning Changes the Game Starting the Journey Most organizations already have a big data and analytics or data science group. (We have addressed elsewhere how organizations can create and manage these groups.) The following are specific steps for incorporating AI techniques within a broader data science group:

1. Start from business decisions—Catalogue the key strategic decisions that affect the business and the related metrics that need improvement (e.g., better customer targeting to increase conversion ratio, reducing claims processing time to improve satisfaction, etc.).

2. Identify appropriate AI areas—Solving any particular business problem will, very likely, involve more than one AI area. Ensure that you map all appropriate AI areas (e.g., NLP, machine learning, image analytics) to the problem you want to address.

3. Think big, start small—AI’s potential to influence decision making is huge, but companies will need to build the right data, techniques, skills and executive decision-making to exploit it. Have an evolutionary path toward more advanced capabilities. AI’s full power will become available when the AI platform continuously learns from both the environment and people (what we call the “dynamic insights platform”).

4. Build training data sets—Create your own proprietary data set for training staff and measuring the accuracy of your algorithms. For example, create your own proprietary database of “crash images” and benchmark the accuracy of your existing algorithms against them. You should consistently aim to improve the accuracy of the algorithms against comparable human decisions.

5. Pilot with parallel runs—Build a pilot of your AI solution using existing vendor solutions or open-source tools. Conduct parallel runs of the AI solution with human decision makers. Compare and iteratively improve the performance/accuracy of the AI solution.

6. Scale and manage change—Once the AI solution has proven itself, scale it with the appropriate software/hardware architecture and institute a broad change management program to change the internal decision-making mindset.