- Follow on WordPress.com
Sponsors
-
Recent Posts
- Combining LLM with Traditional Coding
- What AI is, isn’t, and could be
- Miss Manners is back – Challenge June-2023
- An active repository of all human knowledge?
- OptaPlanner continues as Timefold
- Red Hat and IBM: Business Automation Update
- Consistency Checks of Large Language Models
- wAIting for Godot
- How do we define intelligence?
- Don Knuth takes ChatGPT for a ride
- MiniCP: A lightweight Java Constraint Programming Solver
- AI Decisioning Platforms
- Human-Machine Conversations: Dynamic Planning and Composition
- AI: Yann LeCun vs Yuval Noah Harari
Recent Comments
Categories
- Algortithms
- API
- Architecture
- Art
- Artificial Intelligence
- Authentication and Access Control
- AWS
- Blockchain
- Books
- BPM
- Business Analytics
- Business Processes
- Business Rules
- Case Management
- Case Studies
- CEP
- Challenges
- ChatGPT
- Cloud Platforms
- Constraint Programming
- Containers
- Coronavirus
- Customer Experience
- Customer service
- Data Science
- Database
- Decision Intelligence
- Decision Making
- Decision Modeling
- Decision Models
- Decision Monitoring
- Decision Optimization
- DecisionCAMP
- DevOps
- Digital Decisioning
- Digital Transformation
- Discoveries
- DMN
- Education
- Ethics
- Event-driven
- Events
- Excel
- Explanations
- Fraud Prevention
- Games
- GPT-4
- HR
- Human Intelligence
- Human-Machine Interaction
- Humor
- Innovation
- Insurance Industry
- Java
- Knowledge Representation
- Languages
- LLM
- Logic and AI
- Machine Learning
- Manufacturing
- Microservices
- Misc
- MISMO
- Most Influential
- Natural Language Processing
- Open Source
- Optimization
- PMML
- Process Mining
- Products
- Prolog
- QA
- Reactive Rules
- Reasoning
- Retail
- RPA
- Rule Engines and BRMS
- Rule Violations
- RuleML
- Scheduling and Resource Allocation
- Security
- Semantic Web
- Serverless
- Software Development
- Sponsors
- Spreadsheets
- Standards
- State Machines
- Testing
- Trends
- Uncategorized
- Vendors
Archives
- June 2023
- May 2023
- April 2023
- March 2023
- February 2023
- January 2023
- December 2022
- November 2022
- October 2022
- September 2022
- August 2022
- July 2022
- June 2022
- May 2022
- April 2022
- March 2022
- February 2022
- January 2022
- December 2021
- November 2021
- October 2021
- September 2021
- August 2021
- July 2021
- June 2021
- May 2021
- April 2021
- March 2021
- February 2021
- January 2021
- December 2020
- November 2020
- October 2020
- September 2020
- August 2020
- July 2020
- June 2020
- May 2020
- April 2020
- March 2020
- February 2020
- January 2020
- December 2019
- November 2019
- October 2019
- September 2019
- August 2019
- July 2019
- June 2019
- May 2019
- April 2019
- March 2019
- February 2019
- January 2019
- December 2018
- November 2018
- October 2018
- September 2018
- August 2018
- July 2018
- June 2018
- May 2018
- April 2018
- March 2018
- February 2018
- January 2018
- December 2017
- November 2017
- October 2017
- September 2017
- August 2017
- July 2017
- June 2017
- May 2017
- April 2017
- March 2017
- February 2017
- January 2017
- December 2016
- November 2016
- October 2016
- September 2016
- August 2016
- July 2016
- June 2016
- May 2016
- April 2016
- March 2016
- February 2016
- January 2016
- December 2015
- November 2015
- October 2015
- September 2015
- August 2015
- July 2015
- June 2015
- May 2015
- April 2015
- March 2015
- February 2015
- January 2015
- December 2014
- November 2014
- October 2014
- September 2014
- August 2014
- June 2014
- May 2014
Meta
Category Archives: Machine Learning
Demystifying ChatGPT
2 Millions people have already signed up to use ChatGPT that has been generating a lot of buzz in the AI community. What can it create, and where are the humans in the loop? How does this generalize? Cassie Kozyrkov, Chief … Continue reading
AAAI-23 Constraint Programming and Machine Learning Bridge
The AAAI-23 Constraint Programming and Machine Learning Bridge is part of the AAAI-23 Bridge Program. Bringing together CP (Constraint Programming) and ML (Machine Learning) is an important aspect of the larger goal of integrating Reasoning and Learning. Participants are not expected to … Continue reading
Posted in Constraint Programming, Events, Machine Learning
Leave a comment
On the Paradox of Learning to Reason from Data
Cornell University published this article on May-2022: “Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained end-to-end to solve logical reasoning problems presented in natural language? We attempt to answer this question … Continue reading
Forrester announces AI 2.0
As more businesses leverage artificial intelligence to drive transformative customer experiences and real-time business decisions, Forrester announces “a new era of AI development – one that addresses accuracy, speed, and security.” Link
Intelligent Business Automation (IBA)
The hype surrounding intelligent business automation is at all-time high. “Intelligent business process automation is the next evolution of BPM. BPM is a way to automate processes, which allows people and companies to be more efficient and effective when getting … Continue reading
Using Episodic Memories to Predict Upcoming Events
This paper addresses an important problem in control of episodic memory to be used to predict upcoming states in an environment where past situations sometimes reoccur. One of the key benefits is reducing the risk of retrieving irrelevant memories. Read more
Learning Jointly from Rules and Data
Today’s post in Google AI Blog “Controlling Neural Networks with Rule Representations” introduces a novel approach that does not require machine learning models retraining to adapt the rule strength. In real-world domains where incorporating rules is critical – such as physics … Continue reading
Semantic Rules & Machine Learning
Dr. Walid Saba discusses the limitations of the data-driven, statistical and machine learning (ML) approaches that are the currently dominant paradigm in the use of natural language processing (NLP) in text analytics. Using very simple examples, he argues that these … Continue reading
ML has a proof-of-concept-to-production gap
Andrew Ng: “All of AI, not just healthcare, has a proof-of-concept-to-production gap. The full cycle of a machine learning project is not just modeling. It is finding the right data, deploying it, monitoring it, feeding data back [into the model], … Continue reading
Posted in Decision Modeling, Machine Learning
Leave a comment
You must be logged in to post a comment.