As market dynamics change, insurers are pivoting away from competing on price and renewing their focus on customer experience and product innovation. To get new products to market quickly and build seamless customer journeys, automation is vital. However, today's automation tools struggle to handle the complex, non-linear processes that core insurance workflows depend on, which is why insurers need to shift to a new paradigm: hyperautomation. Hyperautomation is the convergence of digital operating systems, workflows, robotic process automation (RPA), and artificial intelligence (AI), to deliver high-value autonomous processes through intelligent decisions. In this eBook, we'll explore how hyperautomation can help insurers deliver the digital enterprise through autonomous processes with intelligent decisioning at the core of every workflow.

While automation is definitely part of the goals of artificial intelligence, and in particular automating things that require human cognitive capabilities, simply automating things doesn't make them intelligent. Have a good conversation with your crock pot lately? Increasingly, customers are also becoming aware of the differences of automation and intelligence. This despite the fact that many vendors are selling their wares with a claim that they have AI capabilities, even though their products don't seem to provide much evidence of that. However, with the increasing requirement for cognitive forms of automation, vendors are listening and starting to add more aspects of intelligence to their suites, especially the so-called robotic process automation (RPA) vendors.

Tim O'Reilly is a kind of bard of technology, a lyrical poet of computing's past, present and future. Ask him a question and whole paragraphs of reflection bubble up. Is the present state of artificial intelligence, for example, bigger than the open-source software revolution, an epochal development chronicled in detail from the front lines by O'Reilly's publishing company? "That's an interesting question," says O'Reilly, before re-framing it, declaring that there is something bigger than AI itself. "I think in the long run, this transformation to machine autonomy, and to, basically, machines that are in a new kind of hybrid existence with humans -- they talk about AI as separate from us, but all interesting machines are hybrids of human and machine -- we have this machine that has been amplifying things we can do, and I think of the human-machine symbiosis as a trend that is probably bigger than the internet, and bigger than open source, and of which AI is one manifestation."

PDDL2.1 was designed to push the envelope of what planning algorithms can do, and it has succeeded. It adds two important features: durative actions, which take time (and may have continuous effects); and objective functions for measuring the quality of plans. The concept of durative actions is flawed; and the treatment of their semantics reveals too strong an attachment to the way many contemporary planners work. Future PDDL innovators should focus on producing a clean semantics for additions to the language, and let planner implementers worry about coupling their algorithms to problems expressed in the latest version of the language. All things considered, Fox and Long have done a terrific job producing PDDL2.1.

PDDL2.1 was designed to push the envelope of what planning algorithms can do, and it has succeeded. It adds two important features: durative actions, which take time (and may have continuous effects); and objective functions for measuring the quality of plans. The concept of durative actions is flawed; and the treatment of their semantics reveals too strong an attachment to the way many contemporary planners work. Future PDDL innovators should focus on producing a clean semantics for additions to the language, and let planner implementers worry about coupling their algorithms to problems expressed in the latest version of the language. All things considered, Fox and Long have done a terrific job producing PDDL2.1.