A rule-based system may be viewed as consisting of three basic components: a set of rules [rule base], a data base [fact base], and an interpreter for the rules. In the simplest design, a rule … can be viewed as a simple conditional statement, and the invocation of rules as a sequence of actions chained by modus ponens.
– from The Origin of Rule-Based Systems in AI. Randall Davis and Jonathan J. King, reprinted as Ch. 2 of Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley Series in Artificial Intelligence). Bruce G. Buchanan and Edward H. Shortliffe (Eds.). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1984.
Building artificial intelligence into your products, services, and processes can make you smarter, faster, and better able to compete. But building smart systems using machine learning is not like buying an accounting package or an enterprise resource planning system. That's why executives need as much training as engineers when adopting AI, said Larry Pizette, the head of of data science at Amazon's Machine Learning Solutions Lab, in the latest edition of The AI Show from VentureBeat. It's also key to understanding the major mistakes companies make when they're kicking off AI projects. "The part that I think gets missed frequently is teaching the business folks, because people always think about the data scientists and the software developers learning about these skills," said Pizette.
Although there has been great progress in artificial intelligence (AI) over the past few years, many of us remember the AI winter in the 1990s, which resulted from overinflated promises by developers and unnaturally high expectations from end users. Now, industry insiders, such as Facebook head of AI Jerome Pesenti, are predicting that AI will soon hit another wall--this time due to the lack of semantic understanding. "Deep learning and current AI, if you are really honest, has a lot of limitations," said Pesenti. "We are very, very far from human intelligence, and there are some criticisms that are valid: It can propagate human biases, it's not easy to explain, it doesn't have common sense, it's more on the level of pattern matching than robust semantic understanding." Other computer scientists believe that AI is currently facing a "reproducibility crisis" because many complex machine-learning algorithms are a "black box" and cannot be easily reproduced.
When I try to introduce the concept of AI DApps, I often find that it is particularly difficult when people lack an accurate grasp of what machine learning is. There is an overwhelming amount of information online about machine learning targeted toward audiences with different levels of technical expertise. In this series, I introduce machine learning at different technical levels, with the aim of providing a basic framework that helps you understand machine learning, regardless of your background, starting at the highest level. In traditional programming, programmers write programs, which are made of lines of code that instruct computers to perform certain tasks. For example, a programmer can write a program to detect whether the word "book" exists in a news article.
We present the Multi-value Rule Set (MRS) for interpretable classification with feature efficient presentations. Compared to rule sets built from single-value rules, MRS adopts a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than classical single-value rules in capturing and describing patterns in data. Our formulation also pursues a higher efficiency of feature utilization, which reduces possible cost in data collection and storage. We propose a Bayesian framework for formulating an MRS model and develop an efficient inference method for learning a maximum a posteriori, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency.
This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification. An integer program is formulated to optimally trade classification accuracy for rule simplicity. Column generation (CG) is used to efficiently search over an exponential number of candidate clauses (conjunctions or disjunctions) without the need for heuristic rule mining. This approach also bounds the gap between the selected rule set and the best possible rule set on the training data. To handle large datasets, we propose an approximate CG algorithm using randomization.
Lifted inference rules exploit symmetries for fast reasoning in statistical rela-tional models. Computational complexity of these rules is highly dependent onthe choice of the constraint language they operate on and therefore coming upwith the right kind of representation is critical to the success of lifted inference.In this paper, we propose a new constraint language, called setineq, which allowssubset, equality and inequality constraints, to represent substitutions over the vari-ables in the theory. Our constraint formulation is strictly more expressive thanexisting representations, yet easy to operate on. We reformulate the three mainlifting rules: decomposer, generalized binomial and the recently proposed singleoccurrence for MAP inference, to work with our constraint representation. Exper-iments on benchmark MLNs for exact and sampling based inference demonstratethe effectiveness of our approach over several other existing techniques.
As digital transformation sweeps across the enterprise landscape, F&A processes continue to evolve. The decades-old manual process of entering data into a spreadsheet for reconciliation purposes has given way to digital reconciliation, with the advent of automation technology to make it faster and more efficient. However, even automated processes have evolved in the last few years with the advances made in machine learning and AI. What do these advances mean for F&A teams today?To illustrate the profound implications of AI and machine learning for F&A, consider the evolution of the transaction matching process in reconciliation. From the earliest days, F&A departments have largely relied on manual processes to reconcile accounts.
Prior to 2018, regulators resisted recommending the use of Machine Learning (ML) based Artificial Intelligence (AI) for AML compliance. There was a mindset shift in mid 2018 indicating that proceeding with caution in implementing AI approaches for AML is appropriate. Regulators realize the adoption of recent innovation, such as the use of AI-ML and robotic process automation (RPA) techniques, enables AML compliance improvements not otherwise attainable. A risk-based approach to compliance, underpinned by AI/Machine Learning, creates opportunities for governance and process refinement as well as identifying potential untapped revenues. Reliance on box-ticking approaches familiar to users of legacy rules-based compliance systems is no longer sufficient.
WELLINGTON – Mike Moore, who served as New Zealand's prime minister before leading the World Trade Organization during a tumultuous time when thousands protested in Seattle riots, died early Sunday. He died at his home in Auckland, his wife Yvonne Moore said. He had suffered a number of health complications since having a stroke five years ago. Moore was an advocate for both advancing the rights of blue-collar workers and for expanding international trade, a combination which, to some, seemed at odds with itself. Although he had a long political career in New Zealand, Moore's tenure as prime minister was brief: just two months in 1990 before he was defeated in an election.