The AI Evolution and the Challenge of Decision Automation

In his latest post Prof. Warren Powell contrasts “using AI to automate decisions to Tesla’s aggressive use of robotics to automate their assembly lines. It is important to understand the most common use of AI today (machine learning) with the tools for making decisions. I also talk about “failures” of AI as primarily failures in setting expectations. AI is not a revolution – it is an evolutionary process, and we have to do more than just automate what people do, and how they do it.” Link

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ML & FSM help amputees walk more naturally

Researchers from the University of Utah designed a robotic leg that learns from the user’s motion how to help amputees walk more naturally.  It uses machine learning to generate a human-like stride. It also helps wearers step over obstacles in a natural way. Link   Continue reading

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Regressing Testing Decisions

Today Carole-Ann Berlioz (Sparkling Logic) led the first post-DecisionCAMP-2020 Zoom Session presenting “Best Practices for Regressing Testing Decisions”. Watch her presentation and Q&A at

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Decision analytics: What is AI?

Prof. Warren B Powell from Princeton University: “The most common types of AI come in three flavors: rules, machine learning, and making decisions (“optimization”). Rules are used for guiding computers to identify patterns or make recommendations, and have to be designed by people. This is how AI was done in the 1970’s. Learning involves using an external dataset to fit a statistical model.” Link Continue reading

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Giving GPT-3 a Turing Test

GPT-3 is a general language model, trained on a large amount of uncategorized text from the internet. It isn’t specific to a conversational format, and it isn’t trained to answer any specific type of question. The only thing it does is, given some text, guess what text comes next.
Ofer Razon wrote: GPT-3 discussions are all over the network now, and while we all see some mind-blowing use cases that tease our mind what can come next, we also get to see the pitfall of AI/ML systems. Put in simple words – it doesn’t know how to say “I don’t know”.  This article “Giving GPT-3 a Turing Test” is a must-readLink

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Implementing Rating Engines

Carole-Ann Berlioz from Sparkling Logic posted practical product-agnostic recommendations for  implementation of various decision engines aimed at calculating a fee or cost: rating engine, pricing engine, compensation calculation, fee calculation, claims calculation, etc. Link

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Teach Your Microservices to Dance

Jonathan Schabowsky from focuses on the choreography of stateless event-driven microservices: “Orchestration entails actively controlling all elements and interactions like a conductor directs the musicians of an orchestra, while Choreography entails establishing a pattern or routine that microservices follow as the music plays, without requiring supervision and instructions.” In the decision management world the role of a conductor is usually played by a BPM engine or some kind of ruleflow, while  choreography is implemented with as an event broker when “everything happens in an asynchronous manner, without waiting for a response or worrying about what happens next“. Link
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Blockchainizing Existing Databases

In his latest post Bozho wrote: “Blockchain has been a buzzword for the past several years and it hasn’t lived to its promises (yet). Blockchain is largely a shared database. Sharing data with other participants in a given business process in a secure way that doesn’t allow any of the participants to cheat.  There are dozens of blockchain projects, networks, protocols, “standards”, but deploying and integrating a separate blockchain solution is usually a large project in itself and especially in the COVID-19 crisis likely gets postponed because of the questionable return on investment.” From decision management perspective, “blockchainizing” existing databases will feed the perpetually running decision-making systems with new facts coming from various sources in real-time. Link

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“Why” of the BPMN+CMMN+DMN Triple Crown

Sandy Kemsley: “…agility often depends on the design decisions such as the split between process and decision logic – what do you model in as a decision, and what do you model as a process. Unsurprisingly, many DM practitioners don’t think first about processes, but suggest that you first focus on determining and modeling your business decisions, then build processes to gather the information and execute the steps required to make those decisions. Within those decisions, however, they often use rule flows, which are really processes that link together individual rules/decisions into larger decisions; this starts to muddy the waters.” Link

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Climbing Towards Natural Language Understanding

This paper “Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data” won the “Best Theme Award” at the ACL2020 AI conference.  “… in contrast to some current hype, meaning cannot be learned from form alone. This means that even large language models such as BERT do not learn “meaning”; they learn some reflection of meaning into the linguistic form which is very useful in applications.Link

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